Advances on Broad-Band Wireless Computing, Communication and Applications

Discovering and recommending points of interest are drawing more attention to meet the increasing demand from personalized tours. In this paper, we propose and evaluate a new fuzzy-based system for decision of sightseeing spots considering different conditions. In our system, we considered four input parameters: Ambient Temperature (AT), Air Quality (AQ), Noise Levle (NL) and Hot Spot Access (HSA) to decide the sightseeing spots Visit or Not Visit (VNV). We evaluate the proposed system by computer simulations. From the simulations results, we conclude that when the AT is normal, the VNN is the best. But when AQ and NL are increased, the VNV is decreased. Considering the effect of HSA parameter, we found that when HSA is increased, the VNV is increased. The simulation results have shown that the proposed system has a good performance and can choose good sightseeing spots.

According to the WHO(World Health Organization) and UNSD(United Nations Statistics Division) definition, when the percentage of elderly people (65 years of age or older) in the population exceeds 7%, it becomes an “aging society”, if it exceeds 14%, it becomes an “aged society”, and if it exceeds 21%, it becomes a “super-aged society”. Some developed countries are becoming super-aged societies. In a super-aged society, there are various problems in medical services for health management. To solve these problems, it is desirable for all generations, including the elderly, to take the initiative to maintain their own health. In this paper, we propose a system aimed at every one of them actively managing their health. The system always monitors and accumulates the biological information of the subject using various contact or non-contact sensors. By analyzing these data in an integrated manner, the subject can easily recognize changes in the physical condition. And also, it promotes the provision of information to remote healthcare professionals when people receive healthcare at home.

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 Hill Climbing (HC) for solving node placement problem in WMNs, called WMN-HC. Then, we implemented a hybrid simulation system based on PSO and HC, called WMN-PSOHC. In this paper, we analyze the performance of WMNs by using WMN-PSOHC considering Constriction Method (CM) and Linearly Decreasing Inertia Weight Method (LDIWM). Simulation results show that a good performance is achieved for CM compared with LDIWM.

In opportunistic networks the communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The enormous growth of nodes having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and nodes, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT nodes in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT node selection in opportunistic networks. For our proposed system, we use four input parameters: Node’s Distance from Task (NDT), Node’s Remaining Energy (NRE), Node’s Buffer Occupancy (NBO) and Node Inter Contact Time (NICT). The output parameter is Node Selection Decision (NSD). We also implemented a testbed with the same input and output parameters and compared its results with the simulation results. The results show that the proposed system makes a proper selection decision of IoT nodes in opportunistic networks. The IoT node selection is increased up to 40% and decreased 38% by decreasing NBO and increasing NICT, respectively.

The explosive development of Intelligent Vehicles (IVs) has led to a complex network, which is difficult to manage due to the extensive communication of vehicles and storage of vehicles’ data. Due to increase in number of vehicles, IVs come up with large difficulties. Huge data generated by IVs is very difficult to be handled due to limited storage and lack of intelligent management. Many security and privacy problems are also related to the IV networks. Traditional centralized approaches are used to deal with limited storage and security issues. Increasing number of vehicles expand the number of links in network and also leads to the intensive data. Lack of coordination of vehicles, reliability of the network and traffic among vehicles are some of the major issues. These issues hinder the performance of the vehicle industry. We propose a consensus based mechanism using blockchain technology to manage the intensive data and authenticate the data of vehicles in the EV industry. This mechanism also ensures data privacy, security and also promotes data immutability. The transactions are stored in distributed ledger to provide facility of transparency. In a nutshell, blockchain technology is incorporated in EVs sector to revolutionize the World.

Internet of vehicles (IoVs) are connected with each other through Internet. In recent years, IoV provides security mechanisms and quick information sharing schemes, etc. The rapid growth of IoV causes various challenges including data storage, intelligent transport system, selfishness of nodes, distrusted nodes and sensor’s data leakage of information. To overcome data storage and delay in services, a decentralized, distributed, secure, transparent and scalable management system is proposed using blockchain technology. Provable data possession (PDP) scheme is used to validate the new data blocks. Message transfer process argon2 (MTP-Argon2) technique is used for data filtration. Using this technique, the raw data are filtered to remove the duplicate and unnecessary data which are obtained from different nodes. Data storage, less delay in service request or response, more sensor’s data sharing and secure communication channel are achieved using proposed system.

During the process of charging, electric vehicle’s location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without restricting trading activities through $$(\epsilon , \delta )$$ -differential privacy. The proposed scheme does not only preserve the electric vehicle’s location; however, prevents semantic, linking and data mining based attacks. Simulation results show that as the privacy level increases, the risk revealing decreases as well.

In order to increase the performance of the IoT (Internet of Things), the FC (Fog Computing) model is proposed. Here, subprocesses of an application process to handle sensor data are performed on fog nodes in addition to servers. In this paper, we newly introduce join subprocesses with multiple input parameters. If a node gets faulty, the child nodes are disconnected and have to be reconnected to new parent nodes. In our previous studies, new parent nodes are selected at the same level as the faulty node. In this paper, we newly propose a GTBFC (General TBFC) algorithm by which disconnected nodes are reconnected to new parent nodes so that the data obtained from the output data of every disconnected node is processed by an ancestor join node of the faulty node. In the evaluation, we show the energy consumption and execution time of a new parent node selected by the GTBFC algorithm.

In the IoT (Internet of Things), devices have to be prevented from maliciously accessed. The CapBAC (Capability-Based Access Control) model is proposed to make IoT devices secure. In the CapBAC model, an owner of a device issues a capability token, i.e. a set of access rights to a subject. Here, the subject is allowed to manipulate the device according to the access rights authorized in the capability token. Suppose a subject $$sb_i$$ is allowed to get data from a device $$d_2$$ but not allowed to get data from a device $$d_1$$ . The subject $$sb_i$$ can get the data of the device $$d_1$$ in the device $$d_2$$ after another subject $$sb_j$$ brings the data from the device $$d_1$$ to the device $$d_2$$ . Here, the data in the device $$d_1$$ illegally flow to the subject $$sb_i$$ . In order to prevent illegal information flow, an OI (Operation Interruption) protocol is proposed in our previous studies, where illegal get operations are interrupted. However, in the OI protocol, a subject $$sb_i$$ can get data $$dt_1$$ of a device $$d_1$$ generated at time $$\tau $$ even if the subject $$sb_i$$ is not allowed to get the data $$dt_1$$ at time $$\tau $$ . In this case, the data $$dt_1$$ come to the subject $$sb_i$$ later than expected by the subject $$sb_i$$ to get the data $$dt_1$$ , i.e. the data $$dt_1$$ flow late to the subject $$sb_i$$ . In this paper, we newly propose a TBOI (Time-Based OI) protocol to prevent late information flow in addition to illegal information flow from occurring.

In this paper, we enhanced the Binary Spray and Wait (B-S&W) routing protocol and create two versions of Spray and Wait (S&W-V1 and S&W-V2) and evaluate and compare their performance in a Delay Tolerant Network (DTN). The network is created from pedestrians, cars and buses of public transport, equipped with smart devices that move and exchange information in an urban area in Tirana city, Albania. Different simulations are conducted to evaluate the performance of the enhanced protocols. Simulations are done using the Opportunistic Network Environment (ONE) simulator. We use the delivery probability and average latency as evaluation metrics. Based on simulation results, we found that our proposed versions S&W-V1 and S&W-V2 improve the delivery probability and average latency.

In the TBFC (Tree-Based Fog Computing) and TWTBFC (Two-Way TBFC) models the electric energy consumed by fog nodes and servers can be reduced in the fog computing (FC) model. Here, fog nodes are hierarchically structured in a height-balanced tree, where a root node is a cloud of servers, leaf nodes are edge nodes which communicate with devices, and each node receives data from child nodes and sends the processed data to a parent node. In the TWTBFC model, nodes send processed data to not only a parent node but also each child node. In order to reduce the network traffic in the TWTBFC model, only aggregate nodes at some level collect the output data of every other aggregate node, i.e. aggregate data. Since only target actuators are to be activated, the aggregate data has to be only delivered to target actuators. Nodes whose descendant actuators are target ones are relay nodes. On receipt of aggregate data, only relay nodes forward the aggregate data to the child nodes. We evaluate the new TWTBFC model in terms of energy consumption of nodes and number of messages transmitted to deliver aggregate data to edge nodes.

Decision making has been an essential aspect of the life of both individuals or organizations. We all have to face situations where we need to decide whether a daily one such as having coffee or tea in the breakfast or selecting a graduate school for Ph.D. The importance of a decision increases as the duration of the impact of its results and the number of people that are affected increases. Food-Energy-Water (FEW) is one of the fields where the impacts can stay for a long time and affect many people and areas. In this paper, we proposed a game theory-based approach for decision making among FEW actors sharing a finite amount of continuous resource where actors have different weights on their trust and the amount of share that they receive in their payoff functions. Then, we run simulations on scenarios utilizing a more realistic discrete solution set for actors. Results have shown that when actors place more weight on trust in their payoff function, they tend to propose fairer solutions that are closer to the consensus point. Also, they move towards that point faster compared to actors with low trust weight.

Distributed applications are composed of multiple objects. An object is an unit of computation resource. Conflicting transactions have to be serialized to keep objects mutually consistent. In this paper, the energy-efficient purpose ordering (EEPO) scheduler is proposed to not only serialize multiple conflicting transactions in the significant order of purposes assigned to the transactions but also reduce the total electric energy consumption of servers by omitting meaningless methods.

Improving commissioning mechanisms of field devices is a necessity under the massively increasing number of these devices today and in the future 5G-enabled industrial automation systems. Hence, we propose a simplified commissioning for an edge computing-based plug & use functionality using NFC tags with information models. This approach is expected to increase the efficiency in commissioning the field devices as it reduces the required manual user input. We also present two application scenarios for 5G-enabled field devices, which benefit from our proposed approach.

To take advantage of IPv6 stack in IoT technologies, an efficient header compression scheme is required. Since 2004, many IPv6 header compression schemes have been proposed and some of them have been standardized by the IETF. In [9], Static Context Header Compression (SCHC) mechanism has been designed for Low Power Wide Area Networks (LPWAN). SCHC compression is based on a common static context stored in both the IoT device and the network side. This static context defines the compression and decompression rules of the headers. The SCHC framework is compatible with LoRaWAN v1.0 [2] but not with LoRaWAN v1.1 that supports roaming of devices during mobility between different LoRaWAN operators. During roaming, the header values of the protocol stack change and are no longer static. In this paper, we propose a solution based on SCHC to support roaming of devices during mobility between different LoRaWAN operators. We define a server to manage the context between operators. In addition, the LoRaWAN frame route and the communication scheme are updated. A testbed has been setup to show the time differences between current LoRaWAN network and our proposal. The results shows that our proposal improves the communication process and decreases the time delay to handle the transmitted messages “uplink” before the registration.

Internet of Things (IoT) is growing exponentially and bringing revolution in today’s modern society. IoT based smart devices are source of convenience to human life and producing huge amount of data on daily basis. This data is useful for consumers like industries, marketplaces, and researchers to extract valuable and functional data from raw data generated by these devices. This data is used by industries and developers to provide more efficient devices and services to users. Owner of the IoT device can generate revenue by selling IoT device data to interested consumers. However, on the other hand consumers do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional systems for data trading have many limitations, such as they are centralized, lack reputation system, security and involve third party. Therefore in this paper, we have leveraged the IoT with blockchain technology to provide a trustful trading through automatic review system for monetizing IoT data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. All transactions are secure and payments are automated without any human intervention. Data quality is ensured to consumer through reviews and ratings about the data. Additionally, Ethereum blockchain system requires gas for every transaction. We have used 2 parameters: gas consumption, string input length and in terms of time and cost, and examined our model.

The growing number of Renewable Energy Sources (RES) in the energy system provides new market approaches according to price and decentralized generation of electricity. Local market, in which consumers and prosumers can trade locally by generation of electricity through RES directly within their community. This approach creates a balance between generation and consumption in a decentralized manner. In this paper, a distributed technology of Blockchain is used, which highlights the decentralized nature of local market. It provides a decentralized market platform for trading locally without the need of central intermediary through Periodic Double Auction (PDA) mechanism. With the introduction of Smart Grid (SG) systems, there have been improvements in how utility companies interact with customers with regards of electricity usage. However, there is a tendency for the data of users to be compromised in SG. In this proposed system, users are able to do trading through PDA and get access of their own previous history. The blockchain provides transparency, traceability and is utilized to mitigate the above mentioned problems. Smart contacts, are used to exclude the third party to provide a transparent system between users in the network.

Mobile ad-hoc network consists of a collection of mobile devices that are interconnected each other. It has attracted attention in various areas such as information sharing and intelligent transportation. In particular, information sharing on MANET in an emergency is crucial for victims. In this situation, information on relief supplies and safety confirmation should be replicated to keep it on the network as long as possible. On the other hand, the cost of replicated information should be taken into account since the storage of each mobile device is limited. In this paper, we proposed a data replication protocol based on the cuckoo search that is a meta-heuristic algorithm inspired by the egg-laying habits of cuckoos to improve the availability of low demand data in MANET. It realizes the space-efficient data replication without a significant impact on data availability.

Decentralized M2M service platforms enable the integration of end-user-based M2M applications and end-user-located M2M resources without the use of central entities or components in the system architecture. Sharing end-user-based M2M applications with other users’ part of an M2M community allows the creation of new and complex M2M applications. However, a fully decentralized system often leads to several trust issues regarding the behavior of end-users and M2M applications. A powerful measure to overcome possible limitations of decentralized M2M service platforms and to replace the missing control authority are trust relationships among the nodes. Therefore, this publication proposes a novel concept for trusted M2M application service provision. Moreover, it introduces the integration of blockchain elements and trust evaluation techniques to optimize the M2M application service provision. A trust consensus protocol is integrated in order to secure the decision-making process among the stakeholders which optimizes several aspects, such as peer joining, service registration and application configuration.

Current network systems destined for collecting disaster information can be categorized into two types: (1) centralized systems collecting all information to a center server, and (2) distributed systems collecting information to several local servers at geographically distributed area. By the system (1), we can understand the disaster condition from a higher perspective. However, the system requires reliable connections. On the other hand, the system (2) can easier to obtain disaster information although it is limited by geographically. Both systems of (1) and (2) are useful for disaster relief activities even though these systems contribute to different perspective. In this paper, we discuss a new disaster information sharing system based on mobile agent. The proposed system implemented on Raspberry pi are evaluated on our test bed.

In order to solve the problem that the existing neural collaborative filtering methods are not comprehensive to mine the latent information of embedded vectors, a deep hybrid collaborative filtering based on multi-dimension analysis is proposed. The idea is to use different feature fusion methods for the embedded vectors of users and items to obtain multiple dimensional fusion features, so that the information explored by different methods can complement each other, and the model can better discover the interaction between users and items. Experimental results show that, compared with the single-method of dimension analysis, the multi-dimension analysis can effectively improve the model’s ability to mine the interaction between users and items, and improve the performance of the recommender system.

In current cellular networks, cell association is heavily based on the Downlink signal power and all devices are associated with the same Base Station in Downlink and Uplink. While as of now this technique has been proved adequate in homogeneous networks where all BSs have similar transmission levels, in increasingly dense heterogeneous networks rate is heavily dependent on the load, which can significantly vary from Base Station to Base Station. Due to increased demands for usage over several devices in heterogenous networks, large disparities in the Downlink pose a threat to the quality of services rendered by the network and this technique seems obsolete. Uplink and Downlink decoupling is the proposed solution, where the Downlink cell association is not necessarily based on the same criteria as Uplink. We propose using SINR and Path Loss with Range Extension as factors for choosing the appropriate Base Station for connection in Downlink and Uplink respectively, taking into consideration the Base Stations’ Resource Block availability, to avoid overloading Base Stations and we will use simulations to test our theory.

The transmission power limitations in telecommunication systems are tackled by using low code rates and high order modulation schemes, thus achieving high spectral efficiency with lower costs per bit. This paper evaluates the User-Centric model for Fifth Generation wireless telecommunication systems, attempting to efficiently improve user terminal-Base Station communication aspects. We propose a resource-aware method of improving network coverage across all layers by choosing to decouple the overall network into two separate uplink and downlink networks. The algorithm fully respects each user’s throughput demands and solves the User Equipment – Base Station association problem efficiently by choosing the appropriate Modulation and Coding Scheme that maximizes spectral efficiency inside each macro cell coverage area. Results are evaluated for both acceptable frequency ranges defined in 5G New Radio protocol interfaces (namely, Frequency Range 1 and Frequency Range 2) and show that the proposed Modulation and Coding Scheme-based mechanism offers perfect Quality of Service preservation and augmented data rates in favor of ultimate user coverage, in both scenarios. Additionally, due to the extended resources Frequency Range 2 provides, the equivalent simulation not only offers increased data rates compared to Frequency Range 1, but also a lower number of devices unsupported by the mechanism.

The mobile edge computing (MEC) is a new paradigm for providing computing at the edge of networks to support wireless devices to offload computational intensive tasks to MEC server for execution. In mobile environment, different users have different sizes of computation tasks with different target latency for smooth running of applications. Moreover, tasks will arrive at the MEC server for execution at different rate depending upon the time of the day or users density. In such varying environment, it is necessary to consider probabilistic approach to offload tasks for successful mobile edge computing. In this paper, we derive successful computation probability, successful communication probability and successful edge computing probability. We then simulate how the successful probabilities change for different sizes of task, target latency and task arrival rate.

Opportunistic Networks are composed of wireless nodes opportunistically communicating with each other following the store, carry and forward mechanism. These networks are designed to operate in an environment characterized by high delay, intermittent connectivity and non-guarantee of the end-to-end path between the sender and the destination. Opportunistic networks can play a crucial role, when cellular networks are heavily stressed and where infrastructure is unavailable due to terrorist attacks, wars, or natural disasters and censorship. Geocasting, where messages are scheduled to specific regions instead of individual devices, has a large potential in real-world communication systems. In this paper, we propose a fuzzy geocasting mechanism in opportunistic networks, termed as F-GSAF. The proposed protocol employs fuzzy attributes, that are very much likely to affect a network in the real world, to determine the next hop for the message. These attributes are Movement (direction and speed), remaining energy, and remaining buffer space. Mamdani is the fuzzy controller, which has been used in this work. Obtained simulation results confirm that the proposed F-GSAF protocol is more efficient than traditional routing protocols for opportunistic networks.

The concept of refining digital content downloaded over a network is introduced. If a content item consists of a lot of preliminarily defined attributes, then content refinement can be executed by collecting a number of partly unreliable releases of the same content item from different information sources. In the refinement process, each release of the content item is divided into attributes of signifieds or signifié and weighted majority voting is applied to determine the most probable signifier or significant of the attributes. In addition, results of a simple experiment are presented to show the advantages of content refinement.

This paper treats a 3D graphics software development system called IntelligentBox and its web version called WebIB. Originally, IntelligentBox was implemented as a development system for desktop 3D graphics applications. It provides various 3D software components called boxes each of which has a unique functionality and a 3D visible shape. IntelligentBox also provides a dynamic data linkage mechanism called slot connection that allows users to develop interactive 3D graphics applications only by combining already existing boxes through direct manipulations on a computer screen. Ten years ago, the author extended IntelligentBox system to make possible the development of web-based 3D graphics applications. This extended version of IntelligentBox is called WebIB. Furthermore, this time, the author extended WebIB to make possible the development of web-based VR (Virtual Reality) applications. This version of IntelligentBox called WebIBVR. In this paper, the author explains several new functionalities of WebIBVR and introduces use cases of web-based VR applications.

Enemy management in the game is one of the research hotspots in the field of game AI. Enemy attack management algorithms are especially important in action role-playing games, of which the main body is combat interaction. The paper proposes an enemy attack management algorithm suitable for action role-playing games. Traditional enemy attack management algorithms have many limitations in the process of game implementation with poor adaptability, which affects the player experience. The algorithm has high adaptability, which can avoid the problems above effectively. The goal of the algorithm is to provide an efficient and complete solution for the game designers to implement the enemy attack management system, shorten the development cycle, and then complete the game with appropriate game mechanism, art and music design. Finally, the extension, implementation and application effects of the algorithm are prospected.

This study applied a Lagrange Interpolation based access control mechanism in the cloud Personal Health Record (PHR) system. We proposed an access control mechanism to make sure that health care information sharing is secure through the public key cryptosystem and Lagrange interpolation. Additionally, the access authority and the privacy setting have to be confined severely when we consider personal health records and private information. The PHR ought to prevent unauthorized users on the top of providing users with the access authority. As a result, we analyzed the mechanism’s security of the network attackers. On the basis of our analysis consequent, the access control and key management mechanism that we proposed can protect the health care information which can be shared among medical institutions efficiently and effectively.

The damage from targeted email attacks continues to be an acute issue for Internet users. Several recent studies have demonstrated that psychological manipulation techniques (e.g. Cialdini’s principles) are used effectively in phishing mails, the susceptibility to Cialdini’s principles correlates with the personality factors (so-called Big Five), and the use of AI can serve to facilitate the assessment of the personality factors based on social media information. Based on the results outlined in the aforementioned studies, this paper considers the possibility of a new type of attack that uses open source intelligence (OSINT) tools to obtain social media information about the target and then misuse personality estimation tools and psychological manipulation techniques to create malicious emails with a highly effective level of psychological manipulation for each specific target. In this paper, to estimate the possibility of such attack, investigation and analysis in relation to such questions as whether Cialdini’s principles work in targeted emails, and whether the effectiveness of Cialdini’s principles in targeted email correlates to personality factors was performed through conducting a user experiment.

Currently, authentication in Smart locks is performed by fingerprint or face authentication. However, these authentications are inconvenient for smart locks because they require the user to stop for several seconds in front of the door and remove certain accessories (e.g., gloves, sunglasses). In this paper, we propose a user authentication method based on gait features. We propose a system model of gait-based authentication method using accelerometers in a smartphone and a wearable device (i.e., smartwatch), that is robust for unknown data using anomaly detection by machine learning. In addition, we conduct experiment to confirm the authentication rate of the proposed gait-based authentication. As a result, when using Isolation Forest as the anomaly detection algorithm, the average FAR (False Acceptance Rate) was 8.3%, the average FRR (False Rejection Rate) was 9.5%. Furthermore, we found that the better algorithm of anomaly detection of FAR and FRR is different depending on the subjects.

Minimizing the energy consumption of Internet of Things (IoT) devices is one of the biggest challenges and crucial issues for the future of a sustainable IoT vision. In order to estimate the remaining device lifetime and optimize its energy consumption, it is necessary to have an accurate online view on the consumed energy with minimal overhead. This is non-trivial, as many factors influence energy consumption, therefore requiring a generic measurement methodology. For example, the Medium Access Control (MAC) protocols have a very important influence on the energy consumption. This paper presents an accurate method for estimating the energy consumption of IoT devices using Energest. Our method combines a device-specific offline profiling phase, with a device and protocol-agnostic online energy estimation methodology. Energy measurements have been performed for different scenarios, using measured values and values from the datasheet, for Carrier Sense Multiple Access (CSMA) and Time Slotted Channel Hopping (TSCH) protocols. Results show that the accuracy of our method is very high, more than 96% for CSMA and more than 82% for TSCH, with very small overhead of 0.11%.

Internet of Things (IoT) is used more and more in our everyday life, connecting different wireless devices, and their distance can vary from some centimeters to many kilometers. New IoT technologies such as Long Range (LoRa) are emerging enabling power efficient wireless communication over very long distances. Simulation of LoRa networks is quite important, because can be used for the design and the evaluation of LoRa based application without the need of costly implementations or before to proceed to the actual implementation of the system. Choosing the right parameters of the systems like spreading factor can improve the energy consumption of the wireless devices. This paper presents the most important LoRa simulation environments available in the literature and after that, we present a comparative evaluation of LoRa simulation environments. The benefits, the disadvantages and the highlights of each LoRa simulation environment is presented.

Software Defined Networks (SDN), Network Function Virtualization (NFV) and Network Slicing are the key technologies for future network implementation. Their aggregation allows more flexibility for the networks by provisioning network slices according to specific use cases requirements. However, in order to ensure these requirements during all the slice execution time, a management module has to be implemented. In this paper, we present our considered architecture for the management of network slices. We detail especially the network controller components. Moreover, we propose a proactive dynamic approach which forecasts the future workload behavior of network slices. Based on the actual and predicted load state, the management algorithm, which is based on a fuzzy logic system (FLS), will determine the adequate management decision for the deployed slices. Based on real network traces, an evaluation of the efficiency of our algorithm is presented.

With the rapid development of tourism, the demand for travel is becoming increasingly personalized. Travelers are increasingly traveling to places that they have not visited previously. When travelers decide to visit unfamiliar scenic spots, they need to spend a great deal of time making relevant travel plans. Therefore, we consider a system that is specifically designed to make travel plans for travelers when they visit a country or city for the first time. Simultaneously, the optimal path result is obtained using a genetic algorithm. This system can provide travelers with highly satisfying travel paths that only require the traveler to enter the degree of destination and time constraints.

This paper proposes the artificial intelligence technique based on hybrid optimization phasor particle swarm optimization and a gravitational search algorithm, called PPSO-GSA for optimal allocation of renewable energy-based distributed generators (OA-RE-DGs), particularly wind and solar power generators, in distribution networks. The main objective is to maximize the techno-economic benefits in the distribution system by optimal allocation and integration of RE-DGs into distribution system. The proposed PPSO-GSA is implemented and validated on 94-bus practical distribution system located in Portuguese considering single and multiple scenarios of RE-DGs installation. The results reveal that optimizing the location and size of RE-DGs results in a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile and stability. Moreover, the convergence characteristics, computational efficiency and applicability of the proposed artificial intelligence technique is evaluated by comparative analysis and comparison with other optimization techniques.

In this paper will be presented a classification of sharing algorithms dedicated to secure services management in Cloud Computing. Especially will be described an impact of sharing processes in service protocols, realised in the Cloud. Service management procedures can be used at different levels, so the impact of service management processes will be analysed at all of them. Also will be presented the impact of service management and cloud data sharing protocols, on decision-making processes in management-support systems, using linguistic and biometric threshold schemes.

Transformative computing is a new computational paradigm, which join wireless technologies with artificial intelligence, and edge computing. In this paper we describe possible applications of different cognitive protocols for such computational areas, especially focused on security technologies, and considering personal characteristics, and environmental features. Also will be presented the impact of cognitive approaches and cryptographic algorithms for securing distributed systems and distributed infrastructures.

This paper analyzes three mobile bike applications that compare different measurements in this sport. For cyclists, it is crucial to know the power of pedaling, several computer systems estimate or calculate this variable instead of measuring. There are power meters, but several models give different measurements. This paper tries to show that some mobile applications for cycling supply different measurements to each other, as well as the power obtained by estimation. We showed by means three experimental rides that sometimes the power measurements are not proportional to the speed produced by the cyclist, so we propose to build a mobile bike application that integrates data from power meters, speedometers, and wireless sensor network to synchronize power and speed for delivering it to the cyclist in real time.

In this paper, in order to keep safe and secure driving, a new generation wide area road surface state information platform based on crowd sensing and V2X Technologies is introduced. In crowd sensing, various environmental sensors including accelerator, gyro sensor, infrared temperature sensor, quasi electrical static sensor, camera and GPS are integrated to precisely detect the various road surface states and determine the dangerous locations on GIS. Those road information are transmitted the neighbor vehicles and road side server in realtime using V2X communication network. In V2X communication on the actual road, both the length of communication distance and the total size of data transmission must be maximized at the same time when vehicle are running on the road. The conventional single wireless communication such as Wi-Fi, IEEE802.11p, LPWA, cannot satisfy those conditions at the same time. In order to resolve such problems, N-wavelength cognitive wireless communication method is newly introduced in our research. Multiple next generation wireless LANS including IEEE802.11ac/ad/ah/in addition to the current popular LANs with different wavelengths are integrated to organize a cognitive wireless communication. The best link of the cognitive wireless is determined by SDN. Driver can receive the road surface status information from the vehicle in opposite direction or road side server and eventually pay attentions to his/her driving before encountering the danger location. This technology can also apply for automatic driving car.

Dempster-Shafer evidence theory (DSET) is an important tool to combine uncertain and imprecise information from multiple sources. However, when combining information with highly conflict, it will lead counterintuitive results. A lot of research has been done to resolve the problem. In this paper, we focus on the approach to revise the basic probability assignment of information (evidence) through discount factors. Two methods are proposed to computer discount factors by multi-criteria of reliability measurement. Then we combine multi-source information in the improved Dempster’s rule. Finally, some numerical examples are used to illustrate the efficiency of our proposed methods.

The P2PPS (peer-to-peer type of a topic-based PS (publish/ subscribe)) model is a distributed model where each peer subscribes interesting topics and publishes messages with topics. Messages which have some common publication topic are related with one another. In the P2PPS model, only messages related with respect to topics are required to be causally delivered to peers. In our previous studies, each message is broadcast to every peer and each peer only receives messages whose publication topics include some subscription topic. In scalable systems, it is not easy, maybe difficult to broadcast messages. The two-layered P2PPS (2P2PPS) model is proposed in our previous papers in order to efficiently publish and receive messages and causally deliver related messages in a scalable system. The 2P2PPS model is a group of peers which is composed of subgroups. Peers of each subgroup are interconnected in a local area network (LAN) and the subgroups are interconnected in a wide area network (WAN). Here, messages are broadcast to every member peer in each subgroup and messages are unicast among subgroups. We propose a TLCO (Two-Layered Causally Ordering) protocol which uses the linear time vector to causally deliver related messages in a 2P2PPS model in this paper. In the evaluation, we show the number of pairs of unnecessarily ordered messages can be reduced in the TLCO protocol than the linear time (LT) protocol.

A system can be fault-tolerant by replicating each application process on multiple servers. Each replica is performed on a host server. According to the advances of hardware and architecture technologies of servers, each server can be considered to be free of fault, i.e. always proper. On the other hand, replicas of application processes easily suffer from faults, e.g. infected with virus. A faulty replica may send a proper reply, e.g. wiretapped reply. A replica which sends a proper reply but does faulty computation is implicitly faulty. Implicitly faulty replicas cannot be detected by checking the replies. It takes a longer or shorter time and a server supporting a faulty replica consumes more or smaller electric energy since the faulty replica does computation different from a proper replica. In this paper, we propose an algorithm to detect implicitly faulty replicas of a process by using the power consumption and computation models of a server in addition to checking replies in a cluster.

The fog computing (FC) model is proposed to efficiently realize the IoT (Internet of Things). In this paper, we consider the mobile FC (MFC) model including mobile fog nodes which communicate with other fog nodes in wireless networks. Here, each fog node is equipped with some process by which output data obtained by processing the input data from other nodes and devices is sent to other fog nodes in the opportunistic way. In the opportunistic protocols, each fog node exchanges only data with other fog nodes in the communication range. In this paper, we newly discuss the MFC model by which fog nodes exchange not only data but also processes with one another in the communication range. A node sends a process to another node which holds data to be handled by the process so that the electric energy to be consumed by the nodes can be reduced. Here, even if there are multiple target nodes in the communication range, a source node sends data to one of the target nodes. In this paper, we newly propose a parallel data transmission (PDT) algorithm where a fog node in parallel sends segments of data to multiple target nodes.

Although the LoRa private network has started to be used in various applications due to its characteristics, there are only arrangements for carrier sense time, transmittable time, and post-transmission standby time on the LoRa standard and Radio Law. It is necessary for the user side to independently deal with behaviors related to retransmission of data lost at the time of collision and error correction function. In addition, since it is a relatively new wireless communication method, it can be said that it is almost in a disorderly state without unified guidelines and tacit agreements that have been accumulated in the usual way. This situation is particularly problematic when various services start utilizing LoRa with their own protocols, so a uniformly available LoRa platform is necessary. We demonstrate a field-trial experiment of a low-speed/latency/loss tolerable SDN control/management-plane which can take advantage of the widely available IoT resources and easy-to-create wireless mesh for the timely recovery of the C/M-plane after disaster.

In the process of medicine innovation, pharmaceutical enterprises tend to seize the intellectual property highland actively. They engage in research and development independently, apply for patents for core technologies, or take the initiative to acquire patents from others. Before applying for patents by their own efforts or purchasing patents from others, pharmaceutical companies need to search for related patents in the patent pool and make a comparative analysis of them, in order to find technology blank areas as R&D objectives, or find valuable patents as potential acquisition targets. In this paper, we use deep learning technology and propose a semantic-based clustering algorithm for Traditional Chinese Medicine (TCM) patents, discarding the traditional literal–based text clustering method. We also give a visualization method for TCM patents, so as to facilitate pharmaceutical enterprises to intuitively understand the relevant patents.

With rapid increase in the use of technology, the world is now moving towards smart cities which require the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled carefully. Different works have already been proposed to provide a communication in a network for the IoT devices; however, nothing has been founded more effective in terms of resource utilization. Hybrid network architecture is the combination of a centralized and distributed network architectures. The centralized network is used for the communication of IoT devices with edge nodes and distributed network for communicating miner nodes with edge nodes. In this way, the network utilize a lot of resources. In this paper, we are proposing a single network which is the combination of both edge nodes and miner nodes. Blockchain is also implemented in this network to provide secure communication between the devices. The evaluation of the proposed model is done using different performance parameters such as time and cost against the number of devices. Limited number of devices are used to perform this evaluation. Furthermore, the results are obtained by utilizing Proof-of-Work consensus mechanism.

With the exponential growth of digital resource from Internet, search engines and recommendation systems have become the effective way to find relevant information in a short period of time. In recent years, advances in deep learning have received great attention in the fields of speech recognition, image processing, and natural language processing. The recommendation system is an important technology to alleviate information overload. How to integrate deep learning into the recommendation system, use the advantages of deep learning to learn the inherent essential characteristics of users and items from various complex multi-dimensional data, and build a model that more closely matches the user’s interest needs has become a hotpot in the research field. This paper reviews the research and application status of recommendation algorithms based on deep learning, and tries to discusses and forecasts the research trends of deep learning approaches applied to recommendation systems. proceedings.

In recent years, temporary network construction using Delay Tolerant Networking (DTN) has attracted attention as a preparation for communication infrastructure failures that may occur due to disasters. DTN is a technology that allows data packets to be delivered late due to temporarily failure of networking. We have proposed an aggregated message ferry method to improve performance of data transfer with the message ferry method which is the routing method of DTN. In our proposed method, each data are aggregated in advance to a node with high probability to communicate with the ferry node. However, our proposed method has a problem that data cannot be transferred to a destination node if the number of hops on a route is large when data are aggregated. In this paper, we propose a new message ferry method enables each node to establish a route in order to increase the number of data arrivals by shortening paths. In the proposed method, each node attempt to transfer data to a node that is included in the communication route to a ferry node. To show the effectiveness of our proposed method, we compare our proposed method with a conventional method in terms of hop count and data arrivals. Simulation results showed that our proposed method achieved higher data arrival number and hop count reduction than the conventional method.

Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure because it has many advantages such as low cost and increased high-speed wireless Internet connectivity. 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. Then, we implemented a hybrid simulation system based on PSO and distributed GA (DGA), called WMN-PSODGA. Moreover, we added in the fitness function a new parameter for the load balancing of the mesh routers called NCMCpR (Number of Covered Mesh Clients per Router). In this paper, we consider Exponential and Normal distributions of mesh clients and carry out a comparison study. The simulation results show that the performance of the Exponential and Normal distribution was improved by considering load balancing when using WMN-PSODGA. Moreover, for the same number of mesh clients, the Normal distribution has better behavior than the Exponential distribution, because all mesh clients are covered by a smaller number of mesh routers.

Through utilizing sensing and computing capabilities of mobile devices, mobile crowdsourcing network (MCN) can collect and analyze data in a cost-effective way. However, due to the selfishness of mobile devices, they may be reluctant to participate in crowdsourcing without additional incentives. In this paper, the incentive mechanism to encourage mobile devices’ participation in the multi-tasks of crowdsourcing is designed. By modelling MCN as a labour market, the crowdsourcing incentive mechanism is regarded as a moral hazard model under contract-based asymmetric information scenarios. Moreover, considering the interaction among crowdsourcing tasks, a multi-dimensional contract model is proposed. By evaluating mobile users’ performance, the service provider will reward and stimulate them to participate in crowdsourcing and work harder. Results demonstrate that the proposed contract has excellent performance in crowdsourcing incentives.

Vehicular communication systems are computer networks in which vehicles and RoadSide Units (RSUs) are the communicating nodes, providing each other with information, such as safety warnings and traffic information. Despite the advanced development in self-driving technologies, intervehicular communication is still an emerging field, with different applications, such as, platooning, road safety and so on. Platooning is an application where vehicles create a formation and travel together and other vehicles follow a physical leader (front car) or a virtual leader, depending on the implementation. Energy saving, labor saving and improvement of safety are expected outcomes of this technology. In this work, a comprehensive simulation called VENTOS is used to evaluate and compare ACC, Krauss and CACC car following models. The results show that, the following cars that use CACC have a better efficiency regarding CO2 emission and fuel consumption.

Due to the miniaturization, diversification, and massification of devices, the amount of installation of wireless AP (Access Point)s has increased, and the users can utilize the Internet by connecting their devices such as laptops, mobile phones, and tablet PCs to wireless APs when desired. However, there has been a big concern about the threat to steal user’s personal information from the devices connected to the wireless APs. People think this can be prevented by using the encrypted channel of the wireless AP, but the passwords of the wireless APs used in coffee shops, restaurants, or public places are null or a shared-single, so that the malicious user can also obtain such a password and the user’s personal information may be easily exposed. In this paper, we propose a protection scheme for user’s personal information delivered within the domain of a wireless AP and we introduce a method using a POS (Point of Sale) system for the scheme. A random SSID and password are generated through the POS system, and a user’s dedicated AP is generated based on this. Users can register their SSID and password with the device to protect them from the threat of personal information leakage by malicious users.

Continued decline in fertility rates and increased life expectancy cause serious population ageing in the world. As the older population increases, the demand of caregivers increases also, long-term care (LTC) is now to become a serious topic for each country who is facing this problem undoubtedly. In Taiwan, caregivers who need to work 16–24 h a day are maybe in the majority. Under the influence of prolonged over-exertion, high repetition or bad posture probably make caregivers in the risk of waist injuries. In this article, a LTC monitoring system for caregivers based on wireless sensing is designed. Through the change of waist bending rate, the system can remind caregivers to keep in a correct posture. In addition to this, caregivers can also track their own personal visualization chart via a mobile app at any time.

This paper provides a simulation method which can apply the Dijkstra’s algorithm (DA) based ray tracing to a large size of random rough surface (RRS). Since the RRS and path concatenations are performed, we can deal with this difficult problem even with a small size of personal computer (PC). By using the convolution method to generate 3D RRSs, concatenation of two adjacent RRSs is possible by keeping the random variables with respect to the 2D conjunction area between them. Concatenation of traced rays can also be executed by keeping the path data at the 2D conjunction area. First we start ray tracing for the first RRS with a source node, and next, keeping the 2D path data at the conjunction area, we move to the second RRS to execute ray tracing. We repeat this procedure until ray tracing for the last RRS is finished. All paths computed by the present method constitute shortest paths. However, the shortest paths thus obtained are different from the optical rays, and consequently three path modifications, path-linearization, path-selection and line of sight (LOS)-check, are required. Numerical examples reveal that the proposed concatenation method is an effective tool for a small size of PC to execute ray tracing along a large scale of RRS.

Square lattice two-dimensional photonic crystal waveguide with nonlinearity and linear dispersion is numerically analyzed for optical wavelength division multiplexing (WDM). In steady state for Gaussian beam incidence, the distribution ratio is calculated from the electric field profile of the three output ports. As a result, it is possible to switch the output port by changing the wavelength of the input signal. This result shows that passive all-optical switching is possible.

Cooperative communication is considered as a prospective technique to increase spectral efficiency. Because of the selfishness of relay nodes, it is necessary to offer relay nodes long-term incentives in the dynamic network. Two-stage dynamic contract with dynamic asymmetric information scenarios is studied in this paper. The type of each relay node is considered to be independent of the two stages with the same continuous probability distribution. By deriving the enough and essential conditions, a contract model is developed to identify the type of information on relay nodes. Moreover, to achieve the source’s maximum expected utility, this paper proposes an optimal sequential method to acquire the optimal relay-reward policy. Experimental results demonstrate that the system performance of cooperative communication is improved by the design of the optimal two-stage dynamic contract.

In recently developed unmaned vehicles technologies, a lot of interest from the research community is focused on the decision making of autopilot agents, which are programmed to safely drive or pilot an unmaned vehicle through road traffic or aerial traffic. A lot of work has to be done in connecting those agent-driven vehicles, in order to create possibilities for new applications, business models and communication paradigms. A lot of research has been done on the communication channel (i.e. mm wave, tdma techniques). However, vehicle communication applications and infrastructure require more attention and development. In this paper we investigate the performance of AODV routing protocol, and simulate different scenarios of environmental settings, vehicle movements, AODV operation and so on. We then discuss the findings based on the simulation results.

RFID is a very important automatic recognition tool for management of things. Recently, RFID technique is widespread in various situations for improving user’s convenience. However, RFID is affected by the environment in the vicinity, which effect the communication performance. In the previous work, we have shown the possibility to expand the communication distance when we put a parasitic element on the middle position between the table type RFID reader and the target tag. In this paper, when a parasitic element is put on the fixed position on the table type RFID reader we evaluate the communication performance of RFID system and show its usefulness.

Unknown network protocol’s stealth attack behavior is becoming a new type of attack, which greatly harms the cyber space security. The stealth behaviors are not easy to be detected by existing security measures. Starting with the implementation of the instructions of the protocol programs, the normal behavior instruction sequences are captured by dynamic binary analysis. The algorithm of instruction clustering and feature distance computation is designed to mine the potential stealth attack behavior instruction sequences. The mined stealth attack behavior instruction sequences (for inline assembly) are loaded into the general executing framework. A virtual protocol behavior analysis platform HiddenDisc has been developed, and the Dynamic analysis is implemented on the platform. Then the protocol execution security evaluation scheme is proposed and implemented. Using the stealth transformation method designed by ourselves, the stealth attack behaviors are transformed. We successfully attacked the virtual target machine by using the transformed stealth attack behaviors, but the stealth behaviors were not captured. The experimental results show that the present method can accurately and efficiently perception mining unknown protocol’s stealth attack behaviors, transform and use of stealth attack behavior can also enhance our information offensive and defensive capabilities.

There are many trademarks and logos around the world. Every trademark or logo represents its own brand or intellectual property. However, some people who are interested will benefit from counterfeiting trademarks or logos. As the number of counterfeit cases has increased, the technology of anti-counterfeiting has gradually been taken. Watermark is the protection method of today’s trademarks. Since the naked eye cannot guarantee the recognition rate, this study uses scientific and technical testing methods to distinguish the authenticity of the trademark. Through the method of this study combined with watermarking technology, not only the problem of misjudgment is improved, but also the location and area where the image has been tampered with can be accurately determined. It also enhances image recovery and image quality in tamper areas.

In this study, we deal with two topics. The first is that a machine, named Mediator, is added to a 5G system for managing and keeping track of UE’s authentication. The purpose is that when an AUSF fails, other AUSFs can successfully take over its authentication process on UEs. The second is that the proposed mechanism can detect this failure immediately and response properly, aiming to increase the QoS that an UE can receive from 5G networks. Experimental results show that the proposed system is better than those compared systems.

With the fast development of news media and freedom of speech in Taiwan, some news is not objectively reported. In fact, in order to attract people’s attention and increase the click rates of news, many journalists did not convey the exact meanings of news, even distorting news meanings or adding some subjective criticisms or opinions. As a result, news confusions come out one after the other. Based on the analysis of political opinion news, this study would like to analyze certain political characters, such as candidates during a certain period of time, for example, the election period. Last year (2018), Kaohsiung-city-mayor election was held in December.We develop a news gathering and analytical scheme, named Focused News Collection and analytical System (FNCaS), which predicts which candidate might be the winner. By analyzing the possible outcomes for readers through big data analysis techniques and deep learning approaches after some amount of news were gathered. The purpose is to reduce the time for readers to absorb news essentials, and to conclude the possible results of the analyses immediately, aiming to improve the efficiency that people access to news contents and understand the implications behind it. Our conclusion is that the FNCaS has capability in collecting news immediately and analyzing some amount of news of focused domains efficiently.

In this study, we propose a Mobile Physiological Sensor Cloud System for Long-term Care (MPCLC), the main functions of which are collecting carereceiver’s physiological data by using sensors and analyzing and reporting the carereceiver’s healthy condition. With the features of small in size, high convenience for use, and good immediacy of request response, the MPCLC can partially solve the problems of long-term care which are insufficiency of required labors and hard for tracking the results of traditional medical treatment. After the data measured by using sensors is collected, the data is sent to the cloud for storage. The health reports will be generated by the cloud, and delivered to caregivers and medical staffs for reference under their requests. This can save the time that people go and come between hospitals and the places where carereceivers stay for measuring patients’ psysilogical data. One of the other key functions is to prevent the carereceivers from disease in advance.

In recent years, the store-and-carry scheme has focused on recovery method which can be effectively applied to sparse and dense networks. In this paper, we propose a message relaying method with enhanced dynamic timer considering decrease rate of neighboring vehicles for Vehicular Delay/Disruption/Disconnection Tolerant Networking (DTN). From the simulation results, we found that the proposed method can reduce storage usage while maintaining high delivery rate.

The wheelchair with good performance for the aged and disabled people is attracting attention from the society. Also, the wheelchair can provide the user with many benefits, such as maintaining mobility, continuing or broadening community social activities, conserving energy and enhancing quality of life. The wheelchair body must be compact enough and should be able to make different movements in order to have many applications. In our previous work, we presented the design and implementation of an omnidirectional wheelchair. In this paper, we propose a position detecting system for improving the performance of omnidirectional wheelchair tennis. This is achieved by predicting RSSI value using Scikit-learn. The proposed system can find correctly the wheelchair position for avoiding the collision.

Nowadays, technologies like Autonomous Vehicles (AVs) are influencing the ways of our traveling. This paper inspects closely the development of a decentralized blockchain-based mechanism for providing secure, reliable and real-time availability of AVs for the customers who want to do the ride. The AVs have many advanced control systems and sensors to detect a number of hurdles (unsafe design of vehicles, negligence of civilians, etc.) in the environment. Blockchain is a decentralized temper proof business protocol used to facilitate the users with transparent, reliable, secure and cost-effective solutions. The consensus mechanisms are used in blockchain for validation purposes. This paper uses the Proof of Work consensus algorithm for the validation of Demand Response (DR) events. It provides the mechanism for real-time monitoring and real-time supervision to the ride of the end-user. Furthermore, it briefly specifies that the AVs working with blockchain mechanisms provides real-time traffic information to the end-user. The blockchain-based mechanism provides secure services to the end-user. It also provides the mechanism of Peer to Peer (P2P) car-sharing that removes the need for any bank or any reliable authority. The proposed system is proved in the Ethereum environment by DR events in the network. The simulations portray that our system is much cost-effective, efficient and reliable to meet the demands of customers.

In this paper, we propose an intelligent approach for resource management in Vehicular Ad Hoc Networks (VANETs) using Software Defined Networking (SDN) and Fuzzy Logic (FL) approaches. We introduce a layered Cloud-Fog-Edge computing architecture in SDN-VANETs which is coordinated by the SDN Controller (SDNC). A fuzzy based system implemented in SDNC is used to make decisions on the processing layer of the VANETs application data. The decision is made by prioritizing the application requirements and by considering the available connections. We demonstrate in simulation the feasibility of our proposed system to improve the management of the network resources.

In recent years, network service users have requested various services along with the rapid development of network services. Agent-oriented computing methods provide a flexible system that responds to various service requests. Moreover, agents can study the best action from results of past actions through “Learning” skill. Nevertheless, training agent system developers to have the development skill of “Learning” agent takes a long time and great effort. As a solution to this difficulty, we propose a tutorial for training agent system developers to have the development skill of “Learning” agent. Then we verify the effectiveness of the tutorial through experimentation.

With an increase in the development of the Internet of Things (IoT), people have started using medical sensors for health monitoring purpose. The huge amount of health data generated by these sensors must be recorded and conveyed in a secure manner in order to take appropriate measures in critical conditions of patients. Additionally, privacy of the personal information of users must be preserved and the health records must be stored in a secure manner. Possession details of IoT devices must be stored electronically for eradication of counterfeited actions. The emerging blockchain is a distributed and transparent technology that provides a trusted and unalterable log of transactions. We have made a healthcare system using blockchain-based smart contracts which support enrollments of patients and doctors in a health center thereby increasing user participation in remote patient monitoring. Our system monitors the patients at distant places and generates alerts in case of emergency. We have used smart contracts for authorization of its devices and provided a legalized and secure way of using medical sensors. Using the blockchain technology, forgery and privacy hack in healthcare settings is reduced, thereby increasing the trust of people in remote monitoring. We have provided a graphical comparison of costs that verifies the successful deployment of contracts.

As an interface between human and computers, human interface device is supported by most computer systems. On behalf of user, HID devices can complete many operations including many sensitive operations with high authority. Exploiting this feature, attackers have designed and produced many malicious HID devices, imitating the user’s control. Meanwhile, most systems neglect to consider this security issue, posing great challenges to information security. In this regard, this paper reviews the development of malicious HID devices with analysis of technologies used. According to the technical characteristics, these devices are classified into three categories: pure HID devices, composite devices with HID interface, malicious devices with wireless communication capabilities. Furthermore, this paper discusses the challenges and opportunities of related research.

Accompanied with the status quo and problems that the low efficiency in the traditional methods of principal component analysis (PCA) when we face the problems of correlated power attack with large amount of data, we presents an improved method to reduce the noise of power data by wavelet packet transform (WPT) and then reduce the dimension by traditional principal component analysis, based the conclusion we have arrived about the advantage of wavelet packet transform in signal processing. It is more productive than common methods in the data processing phase of the related power attack, especially on the occasion that we own high dimensional data with low signal to Noise Ratio (SNR). Just to show you where we can optimize, the middle position of SM4 encryption algorithm was selected to measure the power consumption, and compared with the results of traditional principal component analysis. The results show that not only is the number of curves has been significantly reduced, but the computational complexity has been decreased easily, by all means, the computational time is less than the original required time so that the attack efficiency is significantly improved. Aiming at the goal with a highly targeted way to reduce the amount of data which are needed to crack the key especially for course of power analysis, the proposal submitted by us have the certain advantages under this circumstance when we face the high latitude data with low SNR within the process of correlated power attack.

When a website authenticates users, it does so for a so-called social login in electronic commerce (EC) site. A social login is used for a social media account, such as Facebook, Google, and Twitter. In such a case, the website uses OAuth and OpenID Connect. However, the implementation of a website might involve privacy concerns or be vulnerable to the attacks. In this paper, by crawling the login pages of 500 Japanese EC sites and tracing the authentication flows, we investigate the implementation status of social logins and their security against cross-site request forgery. We observed 28 websites that acquired more user permissions from SNS than necessary, or were vulnerable as a result of improper implementation.

In healthcare, interoperability has been focused recently, in which, Electronic Health Record (EHR) is patient-centric. However, patient-centered interoperability brings new challenges and requirements, like security and privacy, advance technology, immutability, transparency and trust among applications. Data related to healthcare is an asset of a patient that must be controlled and owned by patient. In this paper, we have proposed a blockchain based patient-driven interoperability and discussed how we can leverage blockchain. Blockchain facilitates us in data liquidity, data immutability, data aggregation, patient identity, digital access rules, incentives and clinical data volume. Our system provides patients an immutable log and easy access to their health data across the healthcare organizations. Furthermore, patient authorize healthcare organizations to access their health data. Stakeholders (patients and healthcare organization) of EHRs are also incentivized if any organization wants to access their health data.

Marine protection is closely related to the sustainable development of mankind. At present, CHINA COAST GUARD, an important functional department in the business of marine protection in China, mainly relies on manual work in the process of marine protection and law enforcement, which is inefficient and risky. In order to solve this problem, this paper presents a design framework of an intelligent decision-making system for marine protection and law enforcement. By using Internet of Things, Artificial Intelligence, Natural Language Processing and other advanced technologies, the decision-making system can automatically push intelligent punishment measures for marine protection and law enforcement through automatic matching with maritime laws and regulations. The design idea of this paper can also be extended to the assistant decision-making in other domains.

The rise of Electronic Commerce Industry makes O2O become one development tendency. As the largest classified information website, 58 city website contains most information in O2O area. However, the network environment is not as simple as we thought, fraud is ubiquitous in the market of intermediaries and information networks. There are vast amounts of information in 58, so that people have to firstly extract the information, and then make profitable decisions. This paper crawls Lianjia and 58 website data, uses BP neural network to predict the best quotation and transaction price of second-hand housing, provides the basis for the seller and intermediary to quote, and provides the transaction price expectation for the buyer and seller, so as to improve the market efficiency of the second-hand housing online trading platform. To measure the validity of the model, we propose three indicators: the Seller Market Efficiency (SEM), the Intermediary Market Efficiency (IME) and the Buyer’s Market Efficiency (BME). The results show that the model can effectively improve market efficiency.

3D reconstruction technology is an important branch in the field of computer vision. Due to the large proportion of face information when people browsing pictures and the rapid development of virtual reality applications, face reconstruction technology has attracted more attention from researchers over many years. In terms of the special structure of human face, 3D faces reconstruction is quite different from ordinary object 3D reconstruction. This paper summaries the approaches of obtaining face data and the expression of 3d face. The reconstruction effect of each method is analyzed, including the advantages and disadvantages, the issues that can be improved, as well as the prospect of the future work.

Wrist and hand rehabilitation are common as people suffer injuries during work and exercise. Typically, the rehabilitation involves the patient and the therapist, which is both time consuming and cost burdening. It is desirable to use advanced telemedicine technologies such that the patient is able to enjoy the freedom of performing the required exercise at their own time and pace, while the healthcare system can operate more efficiently. The Leap Motion Controller (LMC), an inexpensive motion detection device, seems to be a good candidate for remote wrist rehabilitation. In this paper, the functionality and capability of the LMC are examined. Experiments are carried out with a total of twelve people performing twelve different movements. From the experimental results, the feasibility of using the LMC as a rehabilitation device is discussed.

Aiming at the existing problems of high labor cost, huge training data and long detection period for Identification technology of cotton flax fiber, which is based on textural feature and convolutional neural network (CNN) method. In this paper, it proposed a cotton and flax fiber detection method based on transfer learning. According to sharing the weight parameters of the convolutional layer and the pooling layer, the model hyperparameters can be adjusted for the new network to achieve high detection accuracy. The experimental results show that the detection accuracy of cotton flax fiber obtained by transfer learning is up to 97.3%, the sensitivity is 96.7%, and the specificity is 98.2%. Compared with traditional machines, transfer learning method have large increase in the three indicators. Furthermore, the transfer learning method has shorter training time and fewer data sets.