Emerging Technologies in Computing

Both blockchain technologies and cloud computing are contemporary emerging technologies. While the application of Blockchain technologies is being spread beyond cryptocurrency, cloud computing is also seeing a paradigm shift to meet the needs of the 4^th industrial revolution (Industry 4.0). New technological advancement, especially by the fusion of these two, such as Blockchain-as-a-Service (BaaS), is considered to be able to significantly generate values to the enterprises. This article surveys the current status of BaaS in terms of technological development, applications, market potentials and so forth. An evaluative judgement, comparing amongst various BaaS platforms, has been presented, along with the trajectory of adoption, challenges and risk factors. Finally, the study suggests standardisation of available BaaS platforms.

The blockchain design pattern has many variations and is a concept that will continue to lead many implementations in the years to come. New design and implementation patterns are frequently being announced and the choices available continue to expand. The design patterns imply tradeoffs which are reviewed.

We begin by describing the components of a blockchain; network nodes, blocks and consensus in a concept. We further elaborate on the key characteristics of the various design areas that are available adding emphasis to those used in private blockchains.

The individual components can be designed in different ways and imply tradeoffs between such quality attributes as performance and security or availability. We conclude with an initial tradeoff matrix that identifies the quality attributes that one should look for in designing these software systems.

Bitcoin was launched in 2009, becoming the world’s first ever decentralized digital currency. It uses a publicly distributed ledger called the blockchain to record the transaction history of the network. The Bitcoin network is structured as a decentralized peer-to-peer network, where there are no central or supernodes, and all peers are seen as equal. Nodes in the network do not have a complete view of the entire network and are only aware of the nodes that they are directly connected to. In order to propagate information across the network, Bitcoin implements a gossip-based flooding protocol. However, the current flooding protocol is inefficient and wasteful, producing a number of redundant and duplicated messages. In this paper, we present an alternative approach to the current flooding protocol implemented by Bitcoin. We propose a novel protocol that changes the current flooding protocol to a probabilistic flooding approach. Our approach allows nodes to maintain certain probabilities of sending information to their neighbours, based on previous message exchanges between the nodes. Our experimental evaluation shows a reduction in the number of duplicated messages received by each node in the network and the total number of messages exchanged in the network, whilst ensuring that the reliability and resilience of the system were not negatively affected.

Exceptional level of research work has been carried in the field of cloud and distributed systems for understanding their performance and reliability. Simulators are becoming popular for designing and testing different types of quality of service (QoS) matrices e.g. energy, virtualisation, and networking. A large amount of resource is wasted when servers are sitting idle which puts a negative impact on the financial aspects of companies. A popular approach used to overcome this problem is turning them ON/OFF. However, it takes time when they are turned ON affecting different matrices of QoS like energy consumption, latency, consumption and cost. In this paper, we present different energy models and their comparison with each other based on workloads for efficient server management. We introduce a different type of energy saving techniques (DVFs, IQRMC) which help toward an improvement in service. Different energy models are used with the same configuration and possible solutions are proposed for big data centres that are placed globally by large companies like Amazon, Giaki, Onlive, and Google.

Offloading of all or part of any cloud service computation, when running processing-intensive Mobile Cloud Computing Services (MCCS), to servers in the cloud introduces time delay and communication overhead. Edge computing has emerged to resolve these issues, by shifting part of the service computation from the cloud to edge servers near the end-devices. An innovative Smart Cooperative Computation Offloading Framework (SCCOF), to leverage computation offloading to the cloud has been previously published by us [1]. This paper proposes SOSE; a solution to offload sub-tasks to nearby devices, on-the-go, that will form an “edge computing resource, we call SOSE_EDGE” so to enable the execution of the MCCS on any end-device. This is achieved by using short-range wireless connectivity to network between available cooperative end-devices. SOSE can partition the MCCS workload to execute among a pool of Offloadees (nearby end-devises; such as Smartphones, tablets, and PC’s), so to achieve minimum latency and improve performance while reducing battery power consumption of the Offloader (end-device that is running the MCCS). SOSE established the edge computing resource by: (1) profiling and partitioning the service workload to sub-tasks, based on a complexity relationship we developed. (2) Establishing peer2peer remote connection, with the available cooperative nearby Offloadees, based on SOSE assessment criteria. (3) Migrating the sub-tasks to the target edge devices in parallel and retrieve results. Scenarios and experiments to evaluate SOSE show that a significant improvement, in terms of processing time (>40%) and battery power consumption (>28%), has been achieved when compared with cloud offloading solutions.

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incorporate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use cryptographic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.

MANET are self-configurable wireless network where the nodes do not have fixed infrastructure, no centralized mechanism, nodes are fully cooperative, highly mobile and dynamic. There is no inherent security between the nodes for secure communication and data exchange. One of the huge security challenges is authentication of nodes in such environment in general and peer communicating nodes in particular where nodes are communicating for the first time.

The proposed scheme presents a novel solution to authenticate peer nodes (source and destination) with no prior trust and security associations. As no pre-established trust exists before the MANET is initialized therefore, in MANET, nodes present a huge challenge of authenticating communicating peer nodes. The proposed scheme provides a solution to authenticate the sending and receiving nodes using trust based scheme as the sender and receiver doesn’t have first-hand information about these trust values as they could be at the opposite end. Thus, the trust is calculated by nodes for all their neighbours and is send to peer communicating nodes when requested before peer nodes initiate communication. We refer to this process as authentication through trust. Lastly, to ensure end to end data encryption, the mutual trust scheme is combined with Diffie-Hellman Elliptic Curve DHEC Key Exchange. This allows nodes pair to exchange data securely by using shared secret keys to encrypt data.

Smartphones have changed the world from a primitive to a high-tech standpoint. However, there have been many incidents where third parties have used confidential data of the users without their consent. Thus, it causes people to be paranoid and distrustful of their smartphones, never knowing which application threatens to expose them. In this paper, we have conducted an in-depth review of the significance of smartphones in human life, and we have discussed the methods used by various authorities to collect and exploit users’ data for enigmatic benefits. Moreover, we surveyed the smartphone users to identify the vulnerabilities leading to privacy violation, and to examine their knowledge about the protection mechanisms. We determined that Technology and Human are the two major vulnerabilities that are exploited to invade users’ privacy. It is the necessity of the moment for the researchers and developers to formulate solutions that could be used to educate and protect smartphone users from potential threats and exploitation of data.

Phishing attack has been considered as a major security challenge facing online community due to the different sophisticated strategies that is being deployed by attackers. One of the reasons for creating phishing website by attackers is to employ social engineering technique that steal sensitive information from legitimate users, such as the user’s account details. Therefore, detecting phishing website has become an important task worthy of investigation. The most widely used blacklist-based approach has proven inefficient. Although, different models have been proposed in the literature by deploying a number of intelligent-based algorithms, however, considering hybrid intelligent approach based on rule induction for phishing website detection is still an open research issue. In this paper, a hybrid rule induction algorithm capable of separating phishing websites from genuine ones is proposed. The proposed hybrid algorithm leverages the strengths of both JRip and Projective Adaptive Resonance Theory (PART) algorithm to generate rule sets. Based on the experiments conducted on two publicly available datasets for phishing detection, the proposed algorithm demonstrates promising results achieving accuracy of 0.9453 and 0.9908 respectively on the two datasets. These results outperformed the results obtained with JRip and PART. Therefore, the rules generated from the hybrid algorithm are capable of identifying phishing links in real-time with reduction in false alarm.

Airport exercises and action plans have significantly improved in the last two decades. They help in the development of the worldwide carrier industry. The amendment of the tenets, controls and the deregulation of the new aeronautics period in North America, Europe, Asia, and creative nations have given movement development, enhancement and noteworthy decisions for carrier travelers. In the course of the last few decades, airports have turned out to be more required with more unpredictable tasks. In doing such, they have fortified their capacity to center around and effect instead of productivity. Various factors that can be considered for developing smart airports have been studied so as to address the lackings. The main objectives of this study are to improve the experience of travelers, to make new income streams and to increase operational excellence and enhance security. This research identifies the areas for different sectors supporting the airport management to provide better smart services to the travellers leading to smart world.

Remote sensor networks are a flexible innovation that deals the capacity to observe thorough actual occurrences as well as a wide-range environment where physical frameworks are considered unsuitable and costly. This study presents the context-aware based remote sensing network (remote or wireless sensor networks or WSN uses alternatively in this paper) for indoor ecological observing at home. Indoor environs atmosphere as well as stability among occupant’s well-being and predicting plants are the principles of this proposed framework. The introduced framework comprises of various sensor gadgets simultaneously evaluating temperature, relative humidity via mobile sensors, illumination, carbon dioxide CO₂, oxygen O₂ and benzene C₆H₆ levels in separate spaces. This study also exhibits the framework structure, the context-aware lifecycle and the context modeling and reasoning architectures for observing the environment.

Wireless Networks are error-prone due to multiple physical changes including fading, noise, path loss and interferences. As a result, the channel efficiency can be severely degraded. In addition, in saturated multihop wireless networks, nodes with multiple hops away from the destination suffer additional throughput degradation signified by high collisions resulting in high packet loss. It has been shown that packets fragmentation and buffer size play an important role in improving performance. In this work, we propose a technique to dynamically estimate appropriate buffer size and fragmentation threshold for individual nodes across the network in reference of their locality from the gateway and on their traffic load. The results show that nodes far from the gateway incur significantly higher throughput. The technique also results in better fairness across all nodes. Furthermore, it enhances the total network throughput while lowering the end to end and MAC delays.

The journey of data from the state of being valueless to valuable has been possible due to powerful analytics tools and processing platforms. Organizations have realized the potential of data, and they are looking far ahead from the traditional relational databases to unstructured as well as semi-structured data generated from heterogeneous sources. With the numerous devices and sensors surrounding our ecosystem, IoT has become a reality, and with the use of data science, IoT analytics has become a tremendous opportunity to perceive incredible insights. However, despite the various benefits of IoT analytics, organizations are apprehensive with the dark side of IoT such as security and privacy concerns. In this research, we discuss the opportunities and concerns of IoT analytics. Moreover, we propose a generic data science methodology for IoT data analytics named as Plan, Collect and Analytics for Internet-of-Things (PCA-IoT). The proposed methodology could be applied in IoT scenarios to perform data analytics for effective and efficient decision-making.

Recent research has established that the risk perceived by users is one of the main reasons why, despite offering numerous benefits, the worldwide adoption of mobile payment remains surprisingly low. This pilot study aims to establish more specifically what types of risk have a negative effect on the adoption of mobile payment by proposing a new research model solely focused on the risk dimension. The model is composed of 6 types of risk that were extracted from the existing literature investigating mobile payment adoption. A 5-point likert scale-based questionnaire was used to collect sample data to test the model. The data was subsequently analysed by conducting a reliability analysis of the scale and a regression analysis aiming to quantify the effect of the variables on the users’ intention to use mobile payment. The results of the study suggest that Security Risk is the highest deterrent, followed by Financial Risk, Social Risk, Privacy Risk and Time Risk while Psychological Risk was not found to have any negative effect on the users’ Intention of Use. These findings potentially have implications for stakeholders such as mobile phone manufacturers and banking organisations as testing the research model on a larger sample of data would identify more precisely what aspects of mobile payment should be improved to increase its appeal to consumers. Furthermore, the proposed model can assist further research aiming to identify what features could reduce the risk perceived by potential mobile payment users.

Customer satisfaction influences the profitability of organizations and can keep competitive advantages. One of the critical factors in customer satisfaction is the availability of a quality scale that measures the service. The service quality aims to ensure that the service delivered meets customer expectations. However, with the popularity of using mobile devices, there are many electronic businesses shifted to mobile platforms. Mobile platforms have unique features that differ from Personal computers, such as mobility, portable, wireless. Mobile business is a category of business development refers to new business platforms that enabled by using the technology of wireless and mobile devices. In this case, measuring of service quality of the mobile business is necessary nowadays to ensure the delivered services with the best quality. Due to the lack of a comprehensive framework to evaluate service quality at the mobile business, the business sector uses electronic service quality measurement to evaluate mobile business services, which results in difficulties in identifying accurate results. Using the theoretical base model of offline service quality “SERVQUAL” and the online service quality model “E-S-QUAL,” the researchers were able to propose a framework of service quality to evaluate the services provided through mobile commerce. The proposed service quality framework is consisting of six dimensions that are application design, reliability, responsiveness, trust, efficiency, and system availability. The proposed service quality framework helps business providers for better development of business strategies and leads for best customers’ expectations due to the compatibility of the proposed model with the unique features of mobile devices with considerations of the environment of business sectors.

The usefulness and importance of sentiment analysis task is a widely discussed and effective technique in e-commerce. E-commerce is a very convenient way to buy things online. It saves a lot of time that is usually spent traveling and buying by visiting the shops. E-commerce provides an efficient and effective way to shop sitting right in front of one’s computer/mobile at home. For a given product, sentiment analysis captures the users views; their feelings and opinion related to that product. The reviews are categorized into three basic classes i.e. negative, positive, and neutral. This paper focuses on Urdu Roman reviews that are obtained by one of the most famous and accessed e-commerce website of Pakistan–Daraz.​pk. There are total 20.286 K reviews which are annotated into three classes by three different experts. Vector space model, a.k.a bag of word model is applied for feature extraction which are later passed to Support Vector Machines (SVM) for sentiment classification. Experiments are conducted on MATLAB Linux server. The dataset is kept public for future use and experiments.

The modern portfolio theory targets to achieve a safe investment while extracting maximum profit. Its use in exploring export opportunities is undocumented. Traditionally, the gravity model of trade is widely used to calculate trade flows while the prediction of trade flow was based on application of time-series prediction algorithms on historical trade data. The proposed research introduced the risk involved in the trade opportunity as a quantitative factor determined by product complexity and gravity model of trade, while predicting the optimal export commodities to maximize profit and minimize risk. Improvement in trade prediction accuracy using portfolio optimization methods as compared to other previously documented methods is also reported. The results indicate MSE of 0.161 and 0.239 using Black Litterman model and CAPM against 1.226 and 1.026 using the traditional Holt and Grey models respectively. The results are supplemented by the level of risk attached to each commodity, to classify the optimal products for export investment.

In this research, the application of automatic speech recognition system in taxi call services is investigated. In comparison with traditional query handling systems such as live agents, Interactive Voice Response systems, type-base websites and mobile applications, the newest trend of artificial intelligence - speech recognition can be applied to make conversations in more natural way. For developing, training and testing of the system, Kaldi and CMUSphinx open-source speech recognition tools were utilized. Approximately 4 h of speech data in Azerbaijani have been processed for both tools. Testing has been accomplished in two ways; one of which is recognizing dataset from unknown speakers, and the other one is recognizing shuffled dataset. During these tests, variance and speed were investigated, along with accuracy. Kaldi showed accuracy between 97.3 and 99.6 with variance changing between 0.03 and 4.8. On the other hand, CMUSphinx attained accuracy between 95.6 and 97.8 with variance values of 0.2 and 3.8 in relatively less training time. Accomplished results were compared and used to define appropriate parameters for investigated models.

In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analytics in higher education. The proposed experiment is based on a combination of classic machine learning algorithms such as Naive Bayes and Random Forest with various ensemble methods such as Stochastic, Linear Discriminant Analysis (LDA), Tree model (C5.0), Bagged CART (treebag) and K Nearest Neighbors (KNN). We applied traditional classification methods to classify the students’ performance and to determine the independent variables that offer the highest accuracy. Our results depict that the data with the 11 features using random forest generated the best accuracy value of 0.7333. However, we revised the experiment with ensemble algorithms to reduce the variance (bagging), bias (boosting) and to improve the prediction accuracy (stacking). Consequently, the bagging random forest outperformed other methods with the accuracy value of 0.7959.

Learning technology was used as standalone software to install in a particular system, which needs to buy learning software of a particular subject. It was costly and difficult to search CD/DVD of the particular program in the market. Nowadays the trend of learning is changed and people are learning via the internet and it is known as Electronic Learning (E-learning). Several e-learning web applications are available which are providing more stuff about students and it fulfills requirements. The aim of this paper is to present a well-structured, user-friendly framework with the web application for e-learning, which does not need any subscription. The experiment was conducted with 691 students and teachers, the result shows 91.98% of participants were satisfied with the proposed E-learning system.

An investigation into the variables that have a bearing on the acceptance of D-learning (Digital-learning) services such as Electronic-learning and Mobile-learning, in two universities of Jordan is presented along with a discussion on modernizing in particular m-learning with emerging technologies. The study fuses the Unified Theory of Acceptance and Use of Technology (UTAUT) model with the cultural paradigm. 100 valid questionnaires distributed to random Jordanian students in two cities were used to collect the primary data. The IBM SPSS^® (Statistical Package for the Social Sciences) software platform was used to analyze the data. The validity of the overall model was proven statistically with an acceptable data match with the measurement model. The findings show that the factor with the greatest bearing on “Intention to use M-learning” is the “Attitude toward using M-learning”. Whilst the influence with the greatest indirect bearing on “Intention to use M-learning” is “Compatibility”. The conclusions are that the: cultural factor has a significant and positive impact on the “perceived usefulness” and “perceived ease of use”. “Perceived usefulness” and “perceived ease of use” have the greater impact on the “customers’ attitude”, which consequently influences the students’ “intention to use M-learning services”. Emerging technologies such as the Cloud, AI (Artificial Intelligence) and the Blockchain and how they may be utilized to enhance the delivery of M-learning is discussed throughout the paper.

The detection and tracking of object in a video is an important problem in many applications. In surveillance and in robotic vision tracking and recognition of objects and it’s size is desired. In this paper, an algorithm to obtain size of an object in image or video is presented based on pixel relationship to actual size. The object is mainly tracked by the Kalman filter and Log Polar Phase Correlation method is used to more precisely recognize objects in a video. The tracking of objects is performed from frame to frame. As the image of an object gets deformed in a video due to motion of either the camera or the motion of an object a dynamic template for matching is proposed to minimize the error. Simulation results are presented showing the errors in determining the size of objects in an image.

The Solar Diameter Imager and Surface Mapper’s (SODISM) recording of data on the PICARD satellite in five different wavelengths has increased the need to extract features such as Sunspots. This paper analyses the overall sunspot detection performance, examines the correlation between the filling factor of different SODISM wavelengths and the Solar and Heliospheric Observatory (SOHO) filling factor, and compares them with the USAF/NOAA catalogue. Four months of data from SODISM and SOHO, obtained for the period Aug–Dec 2010, are analysed and compared. This comparison identifies the best wavelength for sunspot detection in SODISM, and compares the overall detection performance of three wavelengths; 535.7 nm, 607.1 nm and 782.2 nm. Furthermore, the study proposes a novel SODISM catalogue summarising SODISM data details including the Filling Factor, area, and the number of sunspots.

In this research, a platform is proposed based on optimization algorithms for Energy Management System for buildings. Building energy consumption can be minimized based on Artificial Intelligence and user requirements of power supplied therefore allowing comfort to consumer with efficient operation and functioning of the building. A prototype using SMART devices with a microcontroller is implemented and tested. It is observed with proper management of the operation of devices efficiency increases and therefore consumer costs reduced. A master controller communicating with multiple apartment controllers is proposed with massage passing interface.