Advances in Internet, Data & Web Technologies

The IEEE 802.11e standard for Wireless Local Area Networks (WLANs) is an important extension of the IEEE 802.11 standard focusing on QoS that works with any PHY implementation. The IEEE 802.11e standard introduces EDCF and HCCA. Both these schemes are useful for QoS provisioning to support delay-sensitive voice and video applications. EDCF uses the contention window to differentiate high priority and low priority services. However, it does not consider the priority of users. In order to deal with this problem, in our previous work, we proposed a Fuzzy-based Admission Control System (FACS), which is used in a WLAN triage testbed. In this paper, we present a new function for preventing short reconnection in a WLAN Triage system. These experimental results show that in previous system, all clients reconnect to AP between 12 and 24 s. In the proposed system, when UP is 100, the clients reconnect to AP after 22 s. However, when UP is 0, the clients are reconnected to AP after 166 s. We found that if UP is higher, the reconnected time is shorter compared with the case when UP is low.

The rapid progress of globalization has been increasing our opportunity to have English communication with various people living in different countries. In this study, we focus on ‘local-pronunciation English’, which is English that has characteristics in pronunciation and accent depending on countries and areas, and propose an English listening system for learning different local-pronunciation English in various countries and areas. The feature of our system is that our system can extract countries and areas where English speech is relatively easy to understand to a learner by the sound characteristics, based on the listening learning history of local-pronunciation English. This function allows each learner to learn English listening step by step according to the learner’s listening capability. In this study, we evaluate the feasibility of our system using a Web-based prototype.

Treatment and management of the increasing complexity in medical conditions experienced by an ageing demographic requires increased use of medical resources and patient management. Effective management may be achieved using autonomic health monitoring systems, a topic much discussed in the literature, however such monitoring has generally been limited to the ‘smart-home’ environment. In this paper we consider extending patient monitoring from only the ‘smart-home’ to a wider ‘smart-environment’ which conflates ‘smart-homes’ with the ‘smart-city’ based on the FOG computing paradigm. FOG is a term created by Cisco systems and is also known as edge computing or ‘fogging’. We introduce a model which incorporates FOG and cloud-based computing for a low latency healthcare monitoring system which enables comprehensive ‘real-time’ monitoring with situational awareness and data analytic solutions. Two illustrative scenarios are presented predicated on the monitoring of patients with dementia, however, the posited approach will generalise to other medical conditions where monitoring is required.

Ambient Intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In our previous work, we presented the implementation and evaluation of actor node for AmI testbed. In this paper, we introduce the implementation of the AmI testbed. We present the simulation results of the proposed Fuzzy-based Sleeping Condition System (FSCS) considering four parameters: room lighting, humidity, temperature and noise. The simulation results show that different parameters have different effects on human sleeping condition.

Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system for node placement in WMNs, called WMN-PSO. Also, we implemented a simulation system based on Simulated Annealing (SA) for solving node placement problem in WMNs, called WMN-SA. In this paper, we implement a hybrid simulation system based on PSO and SA, called WMN-PSOSA. We evaluate the performance of WMN-PSOSA by conducting computer simulations considering four different replacement methods. The simulation results show that LDIWM have better performance than CM, RIWM and LDVM replacement methods.

In this work, we present a distributed event-based awareness approach for P2P groupware systems. Unlike centralized approaches, several issues arise and need to be addressed for awareness in P2P groupware systems, due to their large-scale, dynamic and heterogenous nature. In our approach, the awareness of collaboration will be achieved by using primitive operations and services that are integrated into the P2P middleware. We propose an abstract model for achieving these requirements and we discuss how this model can support awareness of collaboration in mobile teams. We present a fuzzy-based model, in which every member has the task accomplishment according to four parameters: state of workflow, number of exchanged messages, available resources and sustained communication time. This model will be implemented in MobilePeerDroid system to give more realistic view of the collaborative activity and better decisions for the groupwork, while encouraging peers to increase their reliability in order to support awareness of collaboration in MobilePeerDroid Mobile System.

Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO, and a simulation system based on Genetic Algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. We evaluate WMN-PSODGA by computer simulations. The simulation results show that the WMN-PSODGA has good performance when the number of GA islands is 64.

The opportunistic networks are a subclass of delay-tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The Internet of Things (IoT) present the notion of large networks of connected devices, sharing data about their environments and creating a diverse ecosystem of sensors, actuators, and computing nodes. IoT networks are a departure from traditional enterprise networks in terms of their scale and consist of heterogeneous collections of resource constrained nodes that closely interact with their environment. There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use three input parameters: IoT Device Storage (IDST), IoT Device Waiting Time (IDWT) and IoT Device Security (IDSC). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decision of IoT-devices in opportunistic networks.

Wireless Sensor and Actor Network (WSAN) is formed by the collaboration of micro-sensor and actor nodes. Whenever there is any special event i.e., fire, earthquake, flood or enemy attack in the network, sensor nodes have responsibility to sense it and send information towards an actor node. The actor node is responsible to take prompt decision and react accordingly. In order to provide effective sensing and acting, a distributed local coordination mechanism is necessary among sensors and actors. In this work, we consider the actor node selection problem and propose a fuzzy-based system that based on data provided by sensors and actors selects an appropriate actor node. We use 4 input parameters: Job Type (JT), Distance to Event (DE), Remaining Energy (RE) and different from our previous work we consider the Failure of Assigned Task (FAT) parameter. The output parameter is Actor Selection Decision (ASD). Based on these parameters, the simulation results show that the proposed system makes a proper selection of actor nodes.

We consider the peer-to-peer (P2P) type of topic-based publish/subscribe (P2PPS) model where each process (peer) can publish and subscribe event messages with no centralized coordinator. An event message $$e_1$$e1 may carry information of another event message $$e_2$$e2 causally preceding $$e_1$$e1, which are denoted by topics. If a peer receiving $$e_1$$e1 is not allowed to subscribe the topics, illegal information flow to the peer occur. In our previous studies, the subscription-based synchronization (SBS), topic-based synchronization (TBS), and flexible synchronization for hidden topics (FS-H) protocols are proposed to prevent illegal information flow. In this paper, we newly consider malicious information flow among peers where a source peer does not give related topics or gives unrelated topics to event messages. If a source peer $$p_i$$pi publishes an event message e without including some related topic or with including some unrelated topic, the peer $$p_i$$pi is malicious. In this paper, we newly define types of malicious information flow among peers based on a set of topics which every peer can publish and subscribe.

It is critical to reduce the electric energy consumption of servers in a cluster. In this paper, we discuss a migration approach to reducing the electric energy consumption where a virtual machine with application processes migrates to a more energy-efficient server. We newly propose an ISEAM2 algorithm to select a pair of a virtual machine on a host server to perform a process issued by an application and a guest server to which the virtual machine migrates so that the electric energy consumption of the host and guest servers can be minimized. In the evaluation, we show the total electric energy consumption and active time of the servers and the average execution time of processes can be reduced in the ISEAM2 algorithm.

In recent years, the labor shortage accompanying with the declining birthrate and aging of population is becoming a big social problem. Therefore, it is important to consider other labor resources. To deal with this problem, there are many research work that consider application of robots. The robots can play an active role in factories or plants. It can be used also in hotels, offices or medical institutions. In this paper, we present the implementation of an omnidirectional automated transportation robot. We describe a method for collision avoidance considering entropy approach for ensuring safety of robots.

In our previous work, it was presented an interactive learning process in order to increase the students learning motivation and the self-learning time. We proposed an Active Learning System (ALS) for student’s self-learning. For low and middle level class, we showed that the students could keep high concentration by using traditional ALS. However, for high level class, many student could not keep their concentration, because of they have a lot of question that they did not understand. In this paper, to solve this problem, we propose a method of group discussion to deal with students’ questions for high level class. We also present the performance evaluation of ALS for high level class. The evaluation results show that when the lecture use proposed ALS for high level class, the average dropping out was half compared with the conventional ALS. Also, the method of group discussion increases the students’ concentration for high level class.

In recent years, inter-vehicle communication has attracted attention because can be applicable not only to alternative networks but also to various communication systems. In our previous work, we proposed a method to reduce the duplicated bundle messages in Vehicular Ad-hoc Networks (VANETs). In this paper, we evaluate the performance of our proposed message suppression controller with delayed ack using different road traffic conditions. From the simulation results, we found that our proposed method with delayed ack has good performance for these traffic conditions.

Distributed applications are composed of multiple objects and each object is replicated in order to increase reliability, availability, and performance. On the other hand, the larger amount of electric energy is consumed in a system since multiple replicas of each object are manipulated on multiple servers. In our previous studies, the energy efficient quorum selection (EEQS) algorithm is proposed to construct a quorum for each method in the quorum based locking protocol so that the total electric energy of servers to perform methods can be reduced. In this paper, the improved energy efficient quorum selection (IEEQS) algorithm is proposed to furthermore reduce the total electric energy of servers by omitting meaningless methods. Evaluation results show the total electric energy of servers, the average execution time of each transaction, and the number of aborted transactions can be reduced in the IEEQS algorithm than the EEQS algorithm.

The financial institutions provide the resources to protect their sensitive data and information by trying to prevent unauthorized leakage. They approve policies and realize technical restrictions to block the loss and revelation of sensitive data and information by external attackers as well as careless insiders. One example of Data Loss Prevention (DLP) restrictions consists of endpoint protection solutions to block data transmissions to USB storage devices. Nevertheless, financial institutions approve exceptions to these policies, based on the business need for the specific user, in order to be able to fulfill their job-related tasks. But from these exceptions derive the following questions: How an approval for an exception can create impact over the risk of data leakage for the financial institution? What is the particular risk for according an individual user a confident exception? This paper introduces a new concept to risk depending on exception management, which will provide the financial institution to assign exceptions derived from on basic DLP. Initially, the paper presents an approach for evaluating and classification users based on their access to sensitive data and information, and afterward, a standard of rights is decided for assigning exceptions to derive from the classification of users, which allows specific approvers to prepare knowledgeable decisions concerning exception requests.

Environmental monitoring departments in China adopt multiple air quality prediction models, each behaving differently depending on the scenario. Integrated methods are needed to obtain an integrated model with higher accuracy and adaptability. In relation to this, the most similar interpolation algorithm is often used to deal with missing data. We assigned different weights to six existing models based on the most similar value and Pearson coefficient. Then, we used the bootstrap algorithm for data augmentation. Next, we used multiple air quality prediction models for the proposed integrated model called the SIM-PB-Wavelet model. The experiment results show that the mean square root error and the Theil inequality coefficient of the model are lower than those of the six other models. Specifically, the RMSE is reduced by 36% and the TIC is reduced by 33.3% compared with the best results of the six models, thus indicating the ability of the integrated model to improve prediction accuracy.

In order to mitigate the extra cost and to reduce the energy consumption, distributive power system are widely accepted in recent years. The reason of adaptation of distributive power system is the scalability of power supply and demand which helps in reliable power supply and optimizes the annual expenditures. Moreover, the integration of power distributive systems with renewable energy sources enabled the optimal utilization of photovoltaic arrays for effective and cost efficient power supply. To exploit the integration of distributive power and renewable sources, we solve the power dispatch problem with heuristic optimization techniques. We have performed scheduling for supply side management. For this purpose, we have formulate our problem using chance constrained optimization and transformed the problem into mixed integer linear programming. Finally, simulation results demonstrate that the proposed scheduling method for microgrid performs efficiently and effectively.

Smart grid (SG) provides a prodigious opportunity to turn traditional energy infrastructure into a new era of reliability, sustainability and robustness. The outcome of new infrastructure contributes to technology improvements, environmental health, grid stability, energy saving programs and optimal economy as well. One of the most significant aspects of SG is home energy management system (HEMS). It encourages utilities to participate in demand side management programs to enhance efficiency of power generation system and residential consumers to execute demand response programs in reducing electricity cost. This paper presents HEMS on consumer side and formulates an optimization problem to reduce energy consumption, electricity payment, peak load demand, and maximize user comfort. For efficient scheduling of household appliances, we classify appliances into two types on the basis of their energy consumption pattern. In this paper, a meta-heuristic firefly algorithm is deployed to solve our optimization problem under real time pricing environment. Simulation results signify the proposed system in reducing electricity cost and alleviating peak to average ratio.

Underwater Wireless Sensor Networks (UWSNs) facilitate an extensive variety of aquatic applications such as military defense, monitoring aquatic environment, disaster prevention, etc. However UWSNs routing protocols face many challenges due to adverse underwater environment such as high propagation and transmission delays, high deployment cost, nodes movement, energy constraints, expensive manufacture, etc. Due to random deployment of nodes void holes may occur that results in the failure of forwarding data packet. In this research work we propose two schemes, Geographic and Opportunistic Routing using Backward Transmission (GEBTR) and Geographic and Opportunistic Routing using Collision Avoidance (GECAR) for UWSNs. In aforesaid scheme fall back recovery mechanism is used to find an alternative route to deliver the data when void occurs. In later, fall along with nomination of forwarder node which has minimum number of neighbor nodes is selected. Simulation results show that our techniques outperform compared with baseline solution in terms of packet delivery ratio by 5% in GEBTR and 45% in GECAR, fraction of void nodes by 8% and 11% in GECAR and energy consumption by 8% in GEBTR and 10% in GECAR.

In smart grid, Demand Side Management (DSM) plays a vital role in dealing with consumer’s demand and making communication efficient. DSM not only reduces electricity cost but also increases the stability of the grid. In this regard, we introduce an energy management system model for a home and office, then propose efficient scheduling techniques for power usage in both. This system schedule the appliances on the basis of four different optimization techniques to achieve objectives that are electricity cost minimization, reduction in Peak to Average Ratio and energy consumption management. Moreover, we use Real Time Pricing because it is highly flexible and provides an understanding to consumer about price signal variations. Simulation results show that the proposed model for energy management work efficiently to achieve the objectives and provide cost-effective solution to increase the stability of smart grid.

Underwater wireless sensor networks (UWSNs) find applications in various aspect of life like the tsunami and earthquake monitoring, pollution monitoring, ocean surveillance for defense strategies, seismic monitoring, equipment monitoring etc. The sensor node consumes more energy and load distribution suffer from imbalance at long distance. In this paper, we present an energy balanced load distribution through energy gradation (EBLOAD-EG) technique to minimize the energy consumption in direct transmission. The proposed scheme aims to balance the load distribution among different coronas of network field. In this scheme, the numbers of sensor nodes are uniformly deployed in a circular network field and the sink is located at the center of network field. In EBLOAD-EG, the accumulated data is partitioned into data fractions like small, medium and large. Simulation results show that our scheme outperforms the existing scheme in terms of energy efficiency, balanced load distribution, stability period and network lifetime.

Underwater Acoustic Sensor Networks (UASNs) have gained interest of many researches due to its challenges like long propagation delay, high bit error rate, limited battery power and bandwidth. Node mobility and the uneven load distribution of sensor nodes results in creation of void holes in UASNs. Avoiding void holes benefits better coverage over an area, less energy consumption and high throughput. Therefore, in our proposed scheme, the sleep awake scheduling of corona nodes is done in order to minimize the data traffic load as well as balance the energy consumption in each corona. After network initialization, nodes in the even numbered coronas are set to sleep mode whereas nodes in odd coronas are in active mode. When the nodes in a corona deplete certain amount of energy, the nodes in sleep mode are switched to active operation mode. Thus, by scheduling data traffic load on each corona node is minimized also the energy of corona nodes is balanced. Simulation results verify the effectiveness of our proposed scheme in terms of traffic load distribution and energy consumption in sparse network.

Underwater Wireless Sensor Networks (UWSNs) have captured interest of many researchers with the desire to control the large portion of the world overspread by water. Energy efficiency is one of the major concerns in UWSNs due to the limited energy of the underwater sensor nodes. In order to enhance the network lifetime, efficient and reliable protocols must be presented while considering the underwater acoustic communication challenges like low bandwidth, longer propagation delays and limited battery life of sensor nodes. In this paper, we present Modified Geographic and Opportunistic Depth Adjustment based Routing (MGEDAR) protocol to minimize the energy hole problem in UWSNs. Our protocol works by adaptively adjusting the transmission range of sensor nodes in case of void holes. Each node selects its forwarder on the basis of a cost function. Simulation results showed that our proposed scheme improves network performance in terms of maximum throughput, minimum energy consumption and reduced void holes.

A power system with different types of micro-sources are very popular in recent years. The aim of the paper is to make the environment green by reducing green house gases and meet the load demand in an efficient way. However, we propose a grid-connected microgrid system which meets the load demand in an efficient manner to achieve our objectives. The objective of this work is to find the optimal set points of controllable micro-sources in terms of cost minimization. The grid-connected microgrid also helps to exchange power with utility during different intervals of a day to meet the load demand. The significance and performance of the proposed strategy is proved through performing simulations in MATLAB. However, the overall cost of MG is less, while in schedulable microsources the cost of FC is less as compared to MT and DE.

Smart Grid (SG) plays vital role to utilize electric power with high optimization through Demand Side Management (DSM). Demand Response (DR) is a key program of DSM which assist SG for optimization. Smart Home (SH) is equipped with smart appliances and communicate bidirectional with SG using Smart Meter (SM). Usually, appliances considered as working for specific time-slot and scheduler schedule them according to tariff. If actual run and power consumption of appliances are observed closely, appliances may run in phases, major tasks, sub-tasks and run continuously. In the paper, these phases have been considered to schedule the appliances using three optimization algorithms. In one way, appliances were scheduled to reduce the cost considering continuous run for given time slot according to their power load given by company’s manual. In other way, actual running of appliances with major and sub-tasks were paternalized and observed the actual consumption of load by the appliances to evaluate true cost. Simulation showed, Binary Particle Swarm Optimization (BPSO) scheduled more optimizing scheduling compared to Fire Fly Algorithm (FA) and Bacterial Frogging Algorithm (BFA). A hybrid technique of FA and GA have also been proposed. Simulation results showed that the technique performed better than GA and FA.

The energy crisis and greenhouse gas emission are increasing around the world. In order to overcome these problems, distributed energy resources are integrated which introduce the concept of microgrid (MG). The microgrid exchanges power with utility to meet load demand with the help of common coupling point. An energy management strategy is proposed in this work, which helps to minimize the operating cost of MG while considering all constraints of the system. For this purpose, a firefly algorithm is used to schedule generators of MG to fulfill the consumer demand considering the desired objectives. The proposed scheme employs FA to minimize the operating cost of a MG. In both grid-connected and islanded modes of MG, proposed scheme is applied for scheduling of distributed generators. The Significance of the proposed strategy is verified through simulations and results.

The continuous development of mobile devices and the huge number of mobile application users is implicating that somewhere in near future many mobile applications will be focusing on maximizing the usage of possibilities offered by the mobile devices. In that manner, effective usage of the data from the sensors embedded in the mobile devices is crucial. Today this data has more meaning for the mobile devices behavior than for the mobile applications. In this paper we are proposing a model based on the existing frameworks or content management systems to implement a service as a backend for lightweight mobile application. This does not mean that we might abandon the development of web applications as we know them, but in contrary, they could help in development of the mobile applications as decoupled (headless) web services focused on the data processing from the mobile devices sensors.

LEOSAR (Low Earth Orbit Search and Rescue) is an international satellite system which operates continuously, detecting transmissions from emergency beacons carried by ships, aircrafts and individuals, providing location information related to worldwide distress events. LEOSAR based on Low Earth Orbits it is still limited on instantaneous alert and coverage. To improve the performance, this system is migrating towards MEOSAR (Medium Earth Orbit Search and Rescue), restructuring the capability to Medium Earth Orbit satellites. By real time 24-h satellite tracking and the simulation of the Almanac YUMA file using Trimble’s Planning Software, a methodology of comparison is provided. For the MEOSAR system advantages in global coverage and instantaneous alert are evidenced. From the obtained results it is shown the limitation of the LEOSAR capability, but also the efficiency improvement of the search and rescue operations provided by MEOSAR.

In this paper, we integrate a Wireless Local Area Network (WLAN) with a Delay Tolerant Network (DTN) to improve the network performance when the network is congested or communication link problems occur. We evaluate the performance under different scenarios for three routing protocols. Simulations are conducted with the Opportunistic Network Environment (ONE) simulator. The simulation results shows that with the increase of the simulation time DTN hosts are in movement for longer time and the probability of the DTN hosts to meet and exchange the data is increased. The usage of DTN improves the performance of the network by sending information with high delivery probability.

The lifetime of the Wireless Multimedia Sensor Network (WMSN), located along the green borderline, depends directly on their battery. Conversion of the captured image by sensor nodes in the black and white image directly affects in energy saving. However, in this case, there will be a loss of details of the objects in the image, respectively the loss of the structure of objects in the image. This will create problems in identifying various criminal groups during the illegal crossing of the state border from different animals which can be very active across the borderline. Therefore, in this paper, we will present an efficient algorithm, which will restore corrupted image pixels from various noises and will retain the image structure captured by WMSN, without the application of an algorithm for detection of the image edges.

The future of the Internet is to be found in Internet of Things (IoT) where every device communicates with others by making simple but intelligent decisions. To leverage the power of IoT, smart devices and objects need to be endowed with intelligent capabilities. Thus, the purpose of this paper is to investigate a selected Artificial Intelligence (AI) techniques/methods for use in the Internet of Things (IoT) concept. The main assumption is the use of a mobile device, typically mobile phone, as an intelligent object inside of IoT. To investigate the above issue, an IT system based on the concept of Internet of Things was built, and certain AI methods were implemented into this system. The paper covers also the issues of software engineering for building IT systems based on the IoT concept, using mobile devices and AI methods.

Big Data has become an enabling technology for many of the today’s innovations. Given the exponential rate at which the data is produced there is a clear necessity for scalable solutions to control the overwhelming flow of new streams of information and extract information out of DaaS Clouds. In this paper we review and analyze some VM deployment methods and their suitability for Data as a Service (DaaS) model in Clouds. Then we approach some novel aspects of VM deployment, including VM migration.

This paper presents a new formal approach to the modelling of information reuse and integration for innovation. Not all information is useful for innovation, and many ideas do not become profitable. We believe that information resources should not only be available, but also should be capable and compatible with the required information/needs. Use of relevant tools for information management should improve the capacity for effective decision making for innovation. Use of data mining technologies for the extraction of potentially useful information may not always produce the required information. Hidden or previously unknown information may be found in datasets, but the required information for innovation may not be in the datasets. There is a need for the development of techniques to ensure that decision makers are provided with capable and compatible information. Profile Theory is used for the analysis and modelling of reuse and integration of available information.

This study describes an application based on Arduino applied for Smart Driver as part of IoT. The purpose of such system comes as result of an evaluation for the payment of the vehicle insurance based on the way you drive the car. The technologies involved in our system are: Arduino, different sensors, Proteus simulation environment for verifying the circuit functionality, and a smart phone. The mobile technology will be used for the transmission of wireless data, also to complete the entire cycle for the application based on Android. The discussion about different use cases about the system functioning holds an important issue in the paper, here in this work are brought as well the common errors that might happen during the application of such project and those are the conclusions of this work.

Software as a Service (SaaS) is a type of cloud service that runs and operates over the Platform as a Service (PaaS), which in turn works on the Infrastructure as a Service (IaaS). In the past few years, there has been an enormous growth in the number of SaaS services. It is estimated that the revenue of SaaS services will reach US$ 112.8 billion in 2019. This growth in the number of SaaS services makes the selection process difficult for a consumer who is looking to select the best service among the many services that have similar functionalities. In this article, we propose a Find SaaS framework to select a service based on measuring the shortest distance to the consumer’s preferences. In order to explain how the Find SaaS framework works, a case study based on selecting a computer repair shop’s SaaS application for the consumer has been presented.

This paper proposes an efficient content sharing strategy using file splitting and difference between versions in hybrid Peer-to-Peer (P2P) networks. In this strategy, when a user requests a content item, he/she can get it from the network by retrieving the other version of the content item and the difference from the requested version, if the obtaining cost of the requested version is expensive. This way of content sharing can be expected to accomplish effective and flexible operation. Furthermore, efficient utilization of a peer’s storage capacity is achieved by splitting each replica of a content item into several small blocks and storing them separately in the plural peers.

Cloud Computing, SDN and virtual networking technologies have completely modified the relationship between the applications and the hardware resources that are used to execute them. They are no more tightly coupled to each other in a static context. However, elastic on-demand provisioning, auto-scaling and migration provided by cloud resources to address fluctuations in workload demands or available resource pools also bring with them new issues in managing security. In this paper, the authors propose a novel security system based on the concept of cognitive control overlay to proactively manage the security of service transactions. In particular, when the application components move, their configuration changes and the conventional intrusion detection systems (IDS) not aware of the mobility will fail. The cognitive overlay makes the IDS become aware of the mobility and take appropriate action. The solution addresses application security independent of server and network based security management systems.

Considering the existence of competition in the process of social network communication, and the change of sensitivity in the process of communication, this paper proposes a new relative influence weight function that combining with the existing linear threshold model, the sensitivity of information, and the threshold characteristic of the node, namely, URLT model. Which can measure the information communication ability. By simulating the spread of different networks, different sensitivity information and different node thresholds, comparing the final propagation situation, the experimental results show that the final influence range is consistent with the real spread situation. Therefore, the model has some reference value for the discovery and suppression of the law of information dissemination.

The temperature monitoring node based on MSP430F149 and CC1101 is designed, which has the low power consumption. The demo machine has been built and passed live test. The temperature monitoring node consists of MCU module, power module, CC1101 interface module and temperature acquisition module. The passive communication protocol is designed which can wake the CC1101 up on radio by polling. In order to collect temperature inside of the freezing truck at low-power consumption, the technology of function macro definition optimization and energy management based on context awareness is adopted. And the aim of monitoring temperature is realized by radio communication module sending temperature data to the sink node. The current is measured when the node run at different mode. When the node run at receiving mode, the measured current is 25 mA. When the node run at sending mode, the measured current is 9 mA. When the node run at sleeping mode, the measured current is 3 mA. The test results indicate that the temperature monitoring node runs stably, which lasts at least 90 days and achieves its objects.

With the advent of the big data era, the amount of data computation is getting larger and larger, and the computational load of clients is also increasing day by day. The advent of clouds allows clients to outsource their data to the cloud for computing services. Outsourced computation has greatly reduced the computational burden of clients, but also brings the issue of trust. Because the cloud is not trustworthy, clients need to verify the correctness of the remote computation results. In this paper, we mainly study the common norm operations, and propose two publicly verifiable schemes for 1-norm and 2-norm operations respectively, any client can publicly verify these two common norm operations under single-key setting by using our schemes.

With the rapid development of technology, now it can not only be aimed a correct running of systems but also a high level of technical performance. In this paper, we will evaluate the database performance in terms of optimization and also the security it provides. We will present briefly the potential causes of an unsatisfactory performance of the database and also the solution for each determined issue. We will give in detail the optimization of TempDB, Cache and Index Fragmentation and how does those affect the overall DB performance. The optimization is intended to be achieved by simultaneous implementation of these optimization techniques. Also, we will determine some of the best security techniques in the DB, such as encryption or recognizing User Roles, and High Availability, which provides security in several database hierarchies. The combinations of these techniques provide overall database security.

This paper treats one of the activities of a research project about IoT (Internet of Things) security education. In this project, the authors plan to provide SPOC (Small Private Online Course), MOOC (Massive Open Online Course) and education games (serious games) about IoT security. One of the key challenges is the development of educational materials that attract students to IoT security. As a first step, a database will be built that contains the IoT security information for educational materials. So, the authors have already been building a comprehensive database regarding the various kinds of IoT threats based on Linked Data. The other research agenda is to provide e-learning materials themselves using the database that attract students to IoT security. The use of VR (Virtual Reality) and AR (Augmented Reality) technologies enables e-learning materials to attract the students. In this paper, the authors propose e-learning material development framework supporting VA/AR based on Linked Data.

Emerging technologies driven by Network Function Virtualization (NFV) and Software Defined Network (SDN) should be implemented with high quality software. Lower service prices are necessary for carrier networks that provide conventional telephone services on an internet service network. However, the development and maintenance costs tend to remain high because the telephone services must be reliable and secure as they are valuable social infrastructure. Although the communication facilities that provided the backbone of communication infrastructure have become dramatically cheaper to run as hardware has been commoditized and virtualized, the software used in these facilities must guarantee sufficient quality and security. Thus the software development process requires various quality improvement measures, so the development cost remains high. To automate the software development process to solve this problem, we propose a method for automatically generating homogeneous test cases of the system testing phase that does not depend on the skills or know-how of engineers who interpret requirements specification documents written in natural language. We also implement a trial system for automatic test case generation to evaluate the effectiveness of the proposed method.

Academia, industry and government as well, are involved in big data projects. Many researches on big data applications and technologies are actively being conducted. This paper presents a literature review of recent researches on key technologies and open issues for big data management via cloud computing. Its goal is to identify and evaluate the main technology components and their impacts on cloud-based big data implementations. This is achieved by reviewing 40 publications published in the latest four years, 2014–2017. We classified the results based on the main technical aspects: frameworks, databases and data processing techniques, and programming languages. This paper also provides a reference source for researchers and developers, to determine the best emerging technologies for big data project implementation.

The volume of unstructured data is constantly growing, drawing the attention of the research community toward Natural Language Processing tasks. Recent advances in Information Extraction have led to the implementation of different systems and tools for Question Answering. These approaches are mainly language dependent as they need information about the language structure and syntax to perform well. This paper proposes an approach to extracting answers of factoid questions for a given text in Albanian Language. As far as we know, this is the first attempt of a Question Answering system for Albanian language. Experiments show that this is an effective solution for single domain documents.

Nowadays, analysis of opinions in online media such as newspapers, social media, forums, blogs, product review sites, has a key role in the human life. In this context, opinion mining is one of the fastest growing research areas in natural language processing that aims to extract and organize opinions from users. Machine Learning techniques represent a powerful instrument to analyze and understand correctly text data. In this paper we present a thorough experimental evaluation of machine learning algorithms used for opinion mining in Albanian language. The experimental results are interpreted with respect to various evaluation criteria for the different algorithms showing interesting features on the performance of each algorithm.

Text mining and natural language processing are gaining significant role in our daily life as information volumes increase steadily. Most of the digital information is unstructured in the form of raw text. While for several languages there is extensive research on mining and language processing, much less work has been performed for other languages. In this paper we aim to evaluate the performance of some of the most important text classification algorithms over a corpus composed of Albanian texts. After applying natural language preprocessing steps, we apply several algorithms such as Simple Logistics, Naïve Bayes, k-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines and Neural Networks. The experiments show that Naïve Bayes and Support Vector Machines perform best in classifying Albanian corpuses. Furthermore, Simple Logistics algorithm also shows good results.

The use of renewable energy is a major trend to meet datacenters energy needs. However, its intermittent nature requires energy storage devices to store the over produced energy when not being used. Thus, the green coverage value, representing the fraction of total energy consumption covered by renewable energy, is increased. In this paper, we analyze the impact of using different battery sizes to optimize renewable energy usage. We have built a battery simulation tool able to provide the battery state, track the amount of stored and used energy by the battery as a function of the energy consumed by a datacenter and the energy produced by solar panels. We show the impact of battery size on the green coverage percentage, green energy loss, and brown energy taken from the traditional grid. A comparison of these metrics is made for three different geographical locations at 10$$^{\circ }$$∘, 35$$^{\circ }$$∘, and 60$$^{\circ }$$∘ latitude. We discuss the competitiveness of constructing datacenters in different geographical locations based on the results.

Security and privacy are key issues in modern cloud computing. This paper focuses on data fragmentation techniques and it shows how these techniques can impact the global performance of a cloud service. Based on Amazon AWS, different fragmentation techniques are implemented and compared for several different types of file; the comparison is extended to AES encryption techniques in order to better evaluate the system performance and better understand potential effects on the overall security of the system. The presented results can be of great importance in the development of multi cloud and Fragmentation as a Service (FaaS) systems, targeting applications like large scale data mining and management.

Dynamic virtual machine resource allocation in cloud computing infrastructures is important in order to achieve optimal energy costs and minimal Service Level Agreement (SLA) violations. On the other hand, evaluating resource allocation approaches in real large-scale cloud infrastructures in repeatable and controlled manner, is difficult to achieve. For this reason, simulation and modeling tools are very important, especially in initial stages of development. Although existing state of art cloud computing simulation tools offer the possibility for virtual machine resource allocation evaluation, they are nonetheless complex to work with. In order to help speed up the process of evaluating new resource allocation approaches, we designed and implemented a new cloud computing simulator called SimpleCloud, that is simple to understand and efficient. Experimental evaluation through a resource allocation case study, show its efficiency and suitability for simulating large-scale cloud computing infrastructures.

Visual conversation has recently emerged as a research area in the visually-grounded language modeling domain. It requires an intelligent agent to maintain a natural language conversation with humans about visual content. Its main difference from traditional visual question answering is that the agent must infer the answer not only by grounding the question in the image, but also from the context of the conversation history. In this paper we propose a novel multimodal attention architecture that enables the conversation agent to focus on parts of the conversation history and specific image regions to infer the answer based on the conversation context. We evaluate our model on the VisDial dataset and demonstrate that it performs better than current state of the art.

Intelligent Transportations Systems (ITS) are complex information and communication technologies platforms aimed for a better and cost effectiveness public transport organization. Our aim is to extend the basic of ITS platform for real time environmental data monitoring and processing. Mobile environmental sensing is becoming one of the best options to monitor our environment due to its high flexibility. In this context, the proposed system utilizes public transportation vehicle intelligence and sensing to monitor a set of environmental parameters over a large area by “filling in the gaps” where people go but environmental monitoring sensor infrastructure has not yet been installed. In this article, we show as well how to extract useful data from low cost and uncalibrated environmental sensor nodes. The presented architecture is focused on low cost and highly scalable architecture in term of type and number of sensors that can be installed and processed. This work presents some data from a testing deployed infrastructure in Albania and the measured data indicate the usefulness of the proposed architecture.

Increased vehicular traffic in dense urban areas is an issue of major concern. With more cars hitting the road, the inherent results are higher pollution levels and car accidents, shrinking of parking areas and severe traffic congestion. Attempts must be made to encourage the use of fewer cars. Switching to public transport and walking suggests an improvement. In this paper, we present a method to estimate the rate of daily vehicle usage in the city of Tirana. With the use of SUMO (Simulator of Urban Mobility) micro-traffic simulator we simulate the city traffic and evaluate the average traveling distances. Given a distance/time threshold, destinations within less-than-threshold range are considered of ease of reach. The existence of a direct bus line and by-foot traveling time are considered when setting the threshold value. Tracking data are used to provide average daily driving information. Statistic results demonstrate the effectiveness of the proposed method.

Sequence mining is one of the most investigated tasks in data mining and it has been studied under several perspectives. With the rise of Big Data technologies, the perspective of efficiency becomes prominent especially when mining massive sequences. In this paper, we perform a thorough experimental evaluation of several algorithms for sequential pattern mining and we provide an analysis of the results focusing on the different algorithmic choices and how these affect the performance of each algorithm. Experiments performed on real-world and synthetic datasets highlight relevant differences between existing algorithms and provide indications for Big Data scenarios.

XXI century is full of smart devices which amount is still substantially growing. Therefore there is a big need of context-aware platform that uses all of these devices and connect them together. However, clearing this way to accomplishing that problem requires a lot of research and development. Internet of Things (IoT) is the one of approaches which help in finding the solutions for present and future directions. Global idea of treating the cars like many other devices and try to interconnect each of them may cause in solving the serious problems that could lead to decrease the number of accidents and improve the traffic transportation. Addressing these tendencies the paper presents the developed solution which could provide a big network for all cars with the use of IoT and cloud computing concepts.

Driver Monitoring System (DMS) is a promising IoT application in Intelligent Transport Systems (ITS) research field. DMS assists car drivers by monitoring their driving activities, sensing incidents to cause possible dangers, and alerting the drivers to prevent accidents. We aim to realize a new DMS that is inexpensive and highly effective. This paper proposes a method for detecting any incidents based on machine learning. The proposed method firstly configures a detector by training in-car environment data and driver’s vital signs gathered from multiple sensors. Then, the detector is embedded in a self-contained edge computer for monitoring a driver in a car. The device is always connected to the information communication network by radio waves. Those data obtained by monitoring are stored in the cloud server. The server learns and analyzes the stored data using processing such as machine learning. As a result, we acquire knowledge leading to safe driving. The edge computer uses these knowledge to process the sensor data in real time, observe the driver, sense the danger, and call attention. These mechanisms prevent occurrence of troubles such as traffic accidents. The paper describes the proposed system overview, implementation method, and initial evaluations.

As a performance index of wireless network, a heat map function for visualizing the distribution of signal strengths received from APs (Access Points) is a useful tool. Existing technologies, however, have some problems such as insufficient automation for acquiring signal information to visualize and inflexibility for dealing with on-demand monitoring requests issued by administrators on site. We propose a practical method for stably monitoring signal conditions in a managed site and efficiently visualizing the observation results. We implement the proposed method based on a light-weight publisher-subscriber communication framework applicable for various IoT applications. It can handle the on-demand monitoring requests for immediately visualizing the latest condition while it is constantly monitoring whole area as a background process. In this paper, we describe background and purpose, implementation detail, and preliminary evaluations of the proposed system.

C++ language has the characteristics of flexible programming, high execution efficiency, but there are also a large number of undefined behavior, which is easy to cause security risks. In this paper, focus on the memory-use error in C++ program, designed and implemented a memory check tools named MemDetect, based on dynamic instrumentation platform, which is platform-cross, efficiency and accuracy. MemDetect can detect memory leaks, cross-border access memory and memory does not match the release problems effectively, the validity and efficiency of MemDetect are proved by comparing with other detection tools.

The main objective of this paper is to improve the efficiency of vegetation fire emergency interventions by using MP-OLSR routing protocol for data transmission in Flying Ad Hoc NETwork (FANET) applications. The presented conceptual system design could potentially increase the rescuing chances of people caught up in natural disaster environments, the final goal being to provide public safety services to interested parties. The proposed system architecture model relies on emerging technologies (Internet of Things & Fog, Smart Cities, Mobile Ad Hoc Networks) and actual concepts available in the scientific literature. The two main components of the system consist in a FANET, capable of collecting fire detection data from GPS and video enabled drones, and a Fog/Edge node that allows data collection and analysis, but also provides public safety services for interested parties. The sensing nodes forward data packets through multiple mobile hops until they reach the central management system. A proof of concept based on MP-OLSR routing protocol for efficient data transmission in FANET scenarios and possible public safety rescuing services is given.

The Java Programming Learning Assistant System (JPLAS) has been studied to enhance Java programming educations by offering advanced self-learning environments. As one problem type in JPLAS, the code writing problem asks a student to write a source code to satisfy the specifications described in a test code that verifies the correctness of the code on JUnit. Previously, we proposed an informative test code approach to help a novice student to complete a complex source code using concepts in the object-oriented programming. It describes the necessary information to implement the code, such as names, access modifiers, and data types of classes, methods, and variables, in addition to behaviors. Unfortunately, it has drawbacks in handling input/output files for an assignment. In this paper, we propose an improved informative test code approach by adopting the standard input/output to solve them. For evaluations, we generated improved informative test codes for five graph algorithms and requested three students in our group to write the source codes, where all of them completed the source codes with high software metrics.

This paper treats tourism support systems and the authors propose a tourism support system for tourists’ migratory behaviors using AR (Augmented Reality). The target users of this system are mainly young tourists who frequently use one of the mobile devices like smartphones and tablets. The users of this system can receive tourism information about shops and restaurants in his/her sightseeing spots as push-typed data, sometimes called passive information, that are automatically sent to the target users by Bluetooth and Beacon. The sent information will be displayed using AR (Augmented Reality). The purpose of this system is to activate users’ migratory behaviors during their tours. In this paper, the authors also show experimental results to evaluate the usefulness of the proposed system. From the results, it can be said that the propose system will be useful.

Many of today’s applications use Wireless Sensor Networks (WSNs) to collect data from a particular phenomenon. Studying WSNs includes many aspects such as routing, security, evaluation of energy etc. Since data transmission is the operation that causes the biggest consumption of the node residual energy, our work is focused on the problem of data management on WSN in order to reduce data traffic. For this we have chosen to study the VCP (Virtual Cord Protocol) because it is an efficient routing scheme that also provides data management methods such as identifying, storing, and retrieving data. Our goal has been to improve VCP in order to reduce data traffic. During the analysis of the VCP we noticed some problems and proposed the respective solutions. The simulation results of the new algorithm have shown that routing is optimized and data traffic is reduced, facilitating data lookup process.

This paper describes development of our self-localization method in indoor environment based on LED optical frequency modulation. Full color LEDs are used as markers for position estimation. The characteristic of this system is that red, green and blue led’s optical patterns are frequency modulated independently and used them for including some kinds of information. By using the information of these optical patterns, the system can acquire all positions of the markers before the calibration. Then the time and labor for the calibration will be eliminated. In this paper, we conduct basic experiments which confirm the method to acquire the information which is provided from LED optical patterns in an actual environment.

Data as a Service (DaaS) is the next emerging technology in cloud computing research. Small clouds operating as a group may exploit the DaaS efficiently to perform substantial amount of work. In this paper an auction framework is studied when the small clouds are strategic in nature. We present the system model and formal definition of the problem. Several auction DaaS-based mechanisms are proposed and their correctness and computational complexity analysed. To the best of our knowledge, this is the first and realistic attempt to study the DaaS in strategic setting.

Fingerprinting is one form of biometrics, which people’s physical characteristics to identify them. Fingerprints are ideal for this purpose because they’re inexpensive to be collected and analysed. They never change, even as people grow old. The performance of a fingerprint image-matching algorithm depends heavily on the quality of the input fingerprint images. The acquired fingerprint images from the scanner are often with low contrast, noisy and the ridges are blurred. The enhancement is an essential step required to improve the quality of the fingerprint image. In this paper, we propose an enhancement method in spatial and wavelet domain. The fingerprint image contrast is increased, the histogram is equalized and ridges are deblurred. The image is then filtered by Gabor filters and denoised in wavelet domain. Experimental results show that this method increases the number of true minutiae extracted.

During the last decade, mobile communications and smartphone technology increasingly became part of people’s daily routine. Nowadays, Android smartphones and iPhone are more and more pervasive and widely used even from people with less incomes. Lots of new applications are daily introduced or updated. Such high usage brings new challenges regarding devices’ battery lifetime. For smartphone mobile devices and embedded system device, Power Management (PM) it’s getting more and more importance because of very limited battery power. There are more and more sensors, I/O and OS SW updates introduced recently in these mobile devices that can be used to improve the effectiveness of PM. In this paper, we do not want to show only the Android smartphones standards regarding power management the system uses, but also to compare the design of Android PM with possibilities that OS middleware libraries/kernel stack offers to reduce it. So, our aim in this paper is to show how kernel level solution can be realized and how. They solutions seems to be CPU less intensive interactions than any other user space or API ones.

A design of all-optical signal routing circuit for the internet or telecommunication system are proposed by photonic crystal waveguide, which is composed of line of defects in periodic structure by dielectric pillars. Cavities are introduced in or by the waveguide for filtering and switching by making use of Fabry-Perot resonance for a typical carrier signal. Experimental results for the model waveguide in microwave frequency are shown to demonstrate the filtering and switching characteristics depending on length of the cavity. Proposed circuit design is applicable for high speed network switches, which is free from electronic-optical (E/O) or O/E conversion with time delay and is also free from signal labeling by substitutive use of the carrier frequency.

Image semantic segmentation is a challenging task, influenced by high segmentation complexity, increased feature space sparseness and the semantic expression inaccurate. This paper proposes a stacked deconvolution neural network (SDN) based on adaptive super-pixel feature extraction to degrade computational cost and improve segmentation effectiveness. Firstly, the super-pixel segmentation is accomplished by simple linear iterative cluster (SLIC). Secondly, we add texture information as an optimization information to the evaluation function to guide the super-pixel segmentation and ensure the integrity of the super-pixel segmentation. Finally, we train a Stacked Deconvolution Neural Network (SDN) on the ISPRS Potsdam and the NZAM/ONERA Christchurch datasets and learn the sample data with weak annotation information to realize the accurate and fast super-pixel segmentation. Segmentation tests show that the proposed method can achieve the accurate segmentation of image semantics.

A real-time indoor position algorithm based on deep learning theory for many complicated situations is proposed to satisfy the current demands for collection of position information efficiently. Firstly, the video images captured by the camera in real time are input into the network, ZCA (Zero-phase Component Analysis) whitening preprocessing is used to reduce the feature correlation and reduce the network training complexity. Secondly, deep network feature extractor is constructed based on convolution, pooling, multi-layer sparse auto-encoder. Then, the extracted features are classified by the Softmax regression model. Finally, the collected feature is accurately identified by the face recognition module. The algorithm is evaluated on the Indoor Multi-Camera data set, the experimental results are expected to improve the positioning accuracy greatly and implement indoor precise positioning.

The limited storage capacity of small devices, such as mobile phone, has become a bottleneck for the development of many application, especially for security applications. The Ciphertext Policy Attributes Based-Encryption (CP-ABE) is a promising cryptographic scheme that allows encryption to choose an access structure that protects sensitive data. However, one of the problems with current CP-ABE scheme is the length of the key, whose size increases linearly with the number of attributes. In this paper, we propose a CP-ABE scheme for a constant size key. By modifying the modulus index in the key generation algorithm, the computational cost is reduced to a constant. Compared with other schemes for the literature, the private key is independent of the number of attributes.

Noise exposure has adverse effects on the physiology and psychology of the human body. This paper selects three typical noise sources in life according to the loudness level of different frequency pure tone of subjective annoyance common relative size, feature extraction from the frequency noise spectrum, pure tone synthesis of several noise samples will have the characteristic frequency, of subjective annoyance research on actual noise and synthetic noise by using the paired comparison method, for improving the environmental noise in the future.

At present, violent incidents occur frequently in the society and show a trend of getting younger and younger. Lead to explicit offensive aggressive behavior has many obvious incentives to study the relationship between the two in order to effectively prevent the occurrence of aggressive behavior. Noise as a common source of pollution in life, noise stimulation will affect people’s cardiovascular, endocrine, etc., but also have a great impact on human emotions, easy to produce anxiety, irritability and other emotions. Noise stimulation easily lead to negative emotions, such as anger, frustration, etc., these emotions are generated with the typical occurrence of aggressive behavior. Therefore, the study of the relationship between aggressive behavior and noise provides a theoretical basis for the effective prevention of such events as violence.

In this article, expanded mixed finite element method is used to approximate the Forchheimer model. This method extends the traditional mixed element technique. Existence and uniqueness are proved. Optimal $$L^2$$L2-error analysis is obtained. Numerical simulations are given to validate the theoretical derivation.

Since the outdoor positioning technology has matured, people pay more attention to the development of indoor positioning technology in recent years. The use of existing WLAN signal for indoor positioning is a convenient way to realize. Aiming at the location fingerprint location algorithm based on WLAN signal, an in-depth study has been carried out in this paper. The K-means clustering algorithm and fuzzy logic are used to optimize the traditional algorithms in off-line database creation and on-line location phrase, which is expected to reduce the positioning error while improving the positioning efficiency. In the field simulation experiment, the actual effect of several similar algorithms is analyzed and compared, which proves that the research of this paper is effective for the optimization of indoor location algorithm.

Under the settings of cloud storage, user’s private data is distributed and sent to different servers for storage service, thus authentication systems are required to ensure data integrity. In this paper, combining the idea of Dario Catalanno’s arithmetic circuit with Rank-based Merkle Hash Tree structure, a novel homomorphic authentication scheme is proposed. The main advantage of the proposed scheme is that the integrity of data transmission can be validated between different servers.

In this paper will be described several possible applications of cognitive and behavioral approaches in Fog and Cloud computing. In particular will be presented the ways of using selected behavioral characteristics for creation of security and cryptographic protocols, dedicated to secure distribution and management of strategic data in Fog and Cloud environment. Some possible applications of using movement and gesture features will be also presented.

Nowadays, many mobile applications in the telecommunication market provide Voice over IP (VoIP), video and data services. This paper presents the performance evaluation of a 4G mobile network in the city of Pristina. This was accomplished by testing two of the most popular VoIP applications, Skype and Viber. Stationary and dynamic scenarios have been considered to evaluate the voice quality and the Quality of Experience (QoE) of both applications under the same mobile network radio conditions, throughput capacity and coverage requirements. The Mean Opinion Score (MOS) method was used to evaluate the recorded speech files based on the feedback perceptions of different users. These results were found to be very useful for the continuously increasing demand on VoIP services and applications.

In this paper will be presented a description of service management protocols for Cloud infrastructure. Especially, presented solutions will be dedicated to Cloud and Fog Computing. The main idea will be described with application of semantic aspects, dedicated to service management application. Services management protocols in the cloud and in the fog can be realized with application of secure and strategic methods.

CAPTCHA is a type of challenge response test used to distinguish human users from malicious computer programs such as bots, and is used to protect email, blogs, and other web services from bot attacks. So far, research on enhance of CAPTCHA’s resistance to bot attacks has been proceeded to counter advanced automated attacks method. However, an attack technique known as a relay attack has been devised to circumvent CAPTCHA. In this attack, since human solves CAPTCHA, the existing measures assuming bots have no effect on this attack. We designed a new CAPTCHA scheme for relay attacks tolerance and automated attacks tolerance. In this paper, we tested the robustness of the proposed method against several types of automated attacks. We constructed an experimental environment in which a relay attack can be simulated, and designed a series of experiments to evaluate the performance of the proposed method. As a result, we found that the proposed CAPTCHA scheme offers some of level of resistance to automated attacks and relay attacks.

This paper proposes an information sharing method for a web-based virtual world. Our system uses computing resources on Web browsers and does not use server resources. The Web browsers construct a ring-type peer-to-peer network using WebRTC and share the information using this network. The WebRTC allows the browsers the communication with the other browsers without any backend servers. Therefore, the browsers can collect the information among browsers. We measure the average transfer time and show the characteristic of the topology.

At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching.The s-EMG signals, which are detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we introduced dynamic time warping (DTW) for improvement of the method of identifying gestures.

Software defined network (SDN) is an emerging technology that decouples the control plane from data plane in its network architecture. This architecture exposes new threats that do not appear in the traditional IP network. And probably One of the main serious attacks is man-at-the-end (MATE) attack. This paper addresses the existing preventing methods based on selected criteria and analyzes the Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege (STRIDE) Model to determine the related attacks to MATE and which one can be the entry point to MATE. Furthermore, a new method to solve the MATE problem is proposed.

Random walks play an important role in computer science, spreading a wide range of topics in theory and practice, including networking, distributed systems, and optimization. Lévy walk is a family of random walks whose the distance of a walk is chosen from the power law distribution. There are lots of works of Lévy walk in the context of target detection in swarm robotics, analyzing human walk patterns, and modeling the behavior of animal foraging in recent years. According to these results, it is known as an efficient method to search in a two-dimensional plane. However, all these works assume a continuous plane, so far. In this paper, we show the comparison of Lévy walk with various scaling parameters on the message dissemination problem. Our simulation results indicate that the smaller scaling parameter of Lévy walk diffuses a message efficiently compared to the larger one.

The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Intelligent traffic management systems needs to obtain information about traffic with different sensors to control the traffic flow properly. Traffic surveillance videos are very actively used for this purpose. In this paper, we firstly create a vehicle dataset from an uncalibrated camera. Then, we test Tiny-YOLO real-time object detection and classification system and SVM classifier on our dataset and well-known public BIT-Vehicle dataset in terms of recall, precision, and intersection over union performance metrics. Experimental results show that two methods can be used to classify real time streaming traffic video data.

During the past recent years there is an increasing interests in Cloud Services Orchestration. Efficient and even optimal allocation of Cloud resources is one of the main problems on which the scientific and development community has focused their effort. Some proposals for standards and middleware are now available for Cloud users and designers. However, the need for advancing on composition techniques is still requiring major efforts due to the new features, namely, composition of services at any layer of Cloud architecture, not only orchestration of resources. To that end, there have been proposed some Cloud patterns in order to describe composition of services. In a real setting, the composition is really complex and challenging, leading to Orchestration of Cloud Service, whose aim is to deal with both pattern-based composition and resource orchestration. In this paper, we show how the framework Orchestrator for Complex Services (OrCS) enables the use of pattern-based composition and resource orchestration. We also discuss its integration with the OpenStack Orchestrator (Heat).

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and they are primary to support cyber-security. Detectives use a many techniques and proprietary forensic software to analyze (copies of) digital devices, in order to discover hidden, deleted, encrypted, and damaged files or folders. Any evidence found is carefully analysed and documented in “finding reports” that are used during lawsuits. Forensics aim at discovering and analysing patterns of fraudulent activities. In this work, we propose a methodology that supports detectives in correlating evidences found by different forensic tools and we apply it to a framework able to semantically annotate data generated by forensics tools. Annotations enable more effective access to relevant information and enhanced retrieval and reasoning.

Settling down an efficient and reliable procedure for the evaluation of the EMF exposure, from the Base Station Antennas, is important for mobile communications. In this work a calculation method of the exposure under radiofrequency, due to the presence of some antennas of the cellular Base Stations is introduced. The model of wave diffusion in free space, under ideal conditions gives ground for a convenient calculation of the exposure, even in cases of a considerable distance from the antenna, whose covering area is considerably larger, thus resulting in overestimation of the exposure. The calculation of the electrical intensity of the radiation is possible when the technical specifications of the given antenna (provided by the manufacturer) are known and by defining the position of the given point in relation to the antenna.

Currently, typing character strings on a keyboard is used for personal authentication for PC login and unlocking. Although some graphical and biometric-based methods have been developed, most of them have weak authentication strength or weak shoulder surfing resistance. In this paper, we propose a personal authentication method that the user specifies positions on a N × N matrix using mouse clicks. The user can hide the mouse during authentication easily, so the method has shoulder surfing resistance and can be used in public places. We developed the proposed method (pattern method), and two variations (number and color, and combination method), and performed user testing and shoulder surfing experiment to validate the proposed method. The proposed method was tested by 20 subjects in pattern method, 23 subjects in the number and color, and the combination method. The authentication success rate is 63.1%, 56.3% and 87.5% on each method. The specific rate by shoulder surfing is 1.4% in pattern method.

In order to effectively detect phishing attacks, this paper proposes a method of combining Particle Swarm Optimization with BP neural network to build a new phishing website detection system. PSO optimizes neural network parameters to improve the convergence performance of neural network detection model. Experimental results show that this algorithm can improve the prediction speed and the accuracy of detecting phishing websites by 3.7% compared with the conventional BP neural network algorithm.

In recent years, P2P file-sharing networks are used all over the world. This has led to social problems such as the illegal distribution of copyrighted material and the leakage of personal information through computer viruses because P2P does not have a control function for file distribution. To address these issues, a control method called index poisoning has been studied. However, problems such as pollution in the network index and a need to increment control traffic have been reported when index poisoning is applied to a P2P file-sharing network. We propose a method that implements dynamic clustering to limit the range of index poisoning as a solution for these problems that also maintains the effectiveness of the control function. In this study, we implement index poisoning on the large-scale Winny network and evaluate the system performance.

Demand side management (DSM) and demand response (DR) are the key functions in smart grids (SGs). DR provides an opportunity to a consumer in making decisions and shifting load from on-peak hours to off-peak hours. The number of incentive base pricing tariffs are established by a utility for the consumers to reduce electricity consumption and manage consumers load in order to minimize the peak to average ratio (PAR). Throughout the world, these different pricing approaches are in use. Time of use tariff (ToU) is considered in this paper, to comparatively evaluate the performance of the heuristic algorithms; bacterial foraging algorithm (BFA), and harmony search algorithm (HSA). A hybridization of BFA and HSA (HBH) is also proposed to evaluate the performance parameters; such as electricity consumption cost and PAR. Furthermore, consumer satisfaction level in terms of waiting time is also evaluated in this research work. Simulation results validate that proposed scheme effectively accomplish desired objectives while considering the user comfort.

The behavior of grounding systems excited by high-current show great differs from that at low-frequency and low-current. Simulation of high-current draining to earth require accurate modeling of tower grounding system. The current model cannot meet this requirement. This paper proposed an accurate time-varying nonlinearly model of grounding systems established by ATP-EMTP, aiming at simulating the impulse characteristic of grounding systems. The results indicated that the strong agreement between the model and experimental values. At the end of this paper come to the calculation that the active length of the grounding bodies ray is 40 m in the grounding grid. And increasing the ray length infinitely is not conducive to reducing the impulse grounding resistance. The results above can be used for optimizing the grounding grid and selecting the length of grounding bodies ray.

Aiming at the problems of regional congestion and scramble for parking spaces in the current large parking lot, in this paper, a new parking area dynamic information guidance system is designed based on the new parking area division and parking guidance strategy; and the improved Dijkstra algorithm is applied in path planning. The model and algorithm have achieved good guidance effect in the installation and application of an underground parking lot. And this model improves the user’s satisfaction; alleviates the congestion phenomenon of parking lot inside area; improves the operation efficiency and intelligent management level of the parking lot.

The distinction between the three service models in cloud computing IaaS, PaaS and SaaS, especially between the last two, has diminished and will continue to do so with new cloud technology innovation happening every day. As the Albanian government has made progress on building the government Cloud, more challenges are faced, when it comes to the needs for more clouds services and resources by public institutions. The purpose of the paper is to estimate the impact of IaaS in the Government Cloud of Albania, and evaluate the advantages of moving services to the SaaS model in the overall IT applications of public organizations. We try to analyze the impact of moving to SaaS model in accordance with one of the most important usability attributes which is security, trying to set a stable equilibrium between the use and management of the resources available in the Cloud platform.

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based e-learning testbed. We carried out some experiments considering meditation parameter with a student of our laboratory. We used Mind Wave Mobile (MWM) to get the data and considered four situations: Playing Game, Watching Movie, Listening Music and Reading Book. The evaluation results show that our testbed can judge the student situation by meditation parameter.

Higher Education Massive Open Online Courses (MOOCs) introduce a way of transcending formal higher education by realizing technology-enhanced formats of learning and instruction and by granting access to an audience way beyond students enrolled in any one Higher Education Institution. However, although MOOCs have been reported as an efficient and important educational tool, there is a number of issues and problems related to their educational impact. More specifically, there is an important number of drop outs during a course, little participation, and lack of students’ motivation and engagement overall. This may be due to one-size-fits-all instructional approaches and very limited commitment to student-student and teacher-student collaboration. This paper introduces the development agenda of a newly started European project called “colMOOC” that aims to enhance the MOOCs experience by integrating collaborative settings based on Conversational Agents and screening methods based on Learning Analytics, to support both students and teachers during a MOOC course. Conversational pedagogical agents guide and support student dialogue using natural language both in individual and collaborative settings. Integrating this type of conversational agents into MOOCs to trigger peer interaction in discussion groups can considerably increase the engagement and the commitment of online students and, consequently, reduce MOOCs dropout rate. Moreover, Learning Analytics techniques can support teachers’ orchestration and students’ learning during MOOCs by evaluating students’ interaction and participation. The research reported in this paper is currently undertaken within the research project colMOOC funded by the European Commission.