Vehicle Positioning System Based on Cubic Spline Interpolation Using Statistical Analysis

Vehicle monitoring and positioning become an essential factor in road management to secure and safeguard the vehicular network, which influences the coupling of reliability on the advanced automobile technologies. Furthermore, to predict the exact location of a car in a given time is challenging, because it depends on a myriad number of elements. Moreover, knowing the position of a vehicle helps passengers as well as increase vehicle network security. In this paper, we propose a mathematical model to predict the position of a car from a prepopulated dataset using spline interpolation. More interestingly, the prediction point of a mobile vehicle will be presented without any help from real-time monitoring devices. Simulation of vehicle positioning is done using bus trajectory data in a university environment in the University of Malaya to verify the feasibility and benefit of the proposed approach. Accordingly, a process of evaluation has been performed based on a plethora of components and existing works to show the effectiveness of the proposed method

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms, such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study’s core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavors

Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB)

A hot spot problem is a problem where cluster nodes near to the base station (BS) tend to drain their energy much faster than other nodes due to the need to perform more communication. Unequal clustering methods such as unequal clustering routing (UDCH) and energy-efficient fuzzy logic for unequal clustering (EEFUC) have been proposed to address this problem. However, these methods only concentrate on utilizing residual energy and the distance of sensor nodes to the base station, while limited attention is given to enhancing the data transmission process. Therefore, this paper proposes an energy-efficient unequal clustering scheme based on a balanced energy method (EEUCB) that utilizes minimum and maximum distance to reduce energy wastage. Apart from that, the proposed EEUCB also utilizes the maximum capacity of node energy and double cluster head technique with a sleep-awake mechanism. Furthermore, EEUCB has devised a clustering rotation strategy based on two sub-phases, namely intra- and inter-clustering techniques, that considers the average energy threshold, average distance threshold, and BS layering node. The performance of the proposed EEUCB protocol is then compared with various prior techniques. From the result, it can be observed that the proposed EEUCB protocol shows lifetime improvements of 57.75%, 19.63%, 14.7%, and 13.06% against low-energy adaptive clustering hierarchy (LEACH), factor-based LEACH FLEACH, EEFUC, and UDCH, respectively