International Journal of Sensors Wireless Communications and Control - Volume 12, Issue 3, 2022

Background: In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter. Methods: Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter. This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiverbased defense mechanism is developed against jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK when the transmission is not successful and vice versa, to poison the training process of the attacker. Results: We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter by about 25% when compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the medium. Conclusion: A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.

Vehicular ad hoc network makes use of Roadside Unit to boost the communication ability and provide internet access to vehicles. Unfortunately, in big cities with dense road network topology, wireless propagation is disrupted due to several obstacles. Hence, a crowded RSU deployment is required for full coverage of the urban environment. However, the installation cost of RSU is immense; researchers thought of solving this issue as an optimization problem whose objective is to cover all parts of the roads with a minimum number of RSUs, in return reducing the cost of the entire VANET deployment. In this article, a brief account has been given on VANET and its challenges, followed by a survey of different methods introduced to solve the RSU deployment problem. It will help the reader to get a quick glance at the progress made in the field over the years.

Connected vehicles are making intelligent transportation system (ITS) a reality, and because of the increase in the onboard computing capability of vehicles, demand for new applications is growing. Vehicular fog computing (VFC) represents a collaborative multitude of onboard vehicular resources to augment the capacity of fog computing. It is an important concept to address numerous issues related to fog computing like, high installation cost, sub-optimal utilization of resources, and developing many novel applications, etc. Vehicular fog computing research has witnessed different architectural, applications, and implementation challenges, studied by various authors. In this paper, we study the architecture of vehicular fog computing and present various unique characteristics of vehicular fog computing along with providing a detailed comparison with conventional fog computing. We analyze VFC implications in ITS with respect to architecture, applications, and challenges. We discuss and analyze the existing applications and research challenges in them. We also explain a novel application use-case of vehicular fog computing, bridging cellular network coverage holes using vehicular fog computing. Finally, we present new and promising applications and open research challenges therein.

Background: Nowadays, there is an immense increase in the demand for high power computation of real-time workloads and the trend towards multi-core and multiprocessor CPUs. The realtime system needs to be implemented upon multiprocessor platforms. Introduction: The nature of processors in an embedded real-time system is changing day by day. The two most significant challenges in a multiprocessor environment are scheduling and synchronization. The popularity of real-time multi-core systems has exploded in recent years, driving the rapid development of a variety of methods for multiprocessor scheduling of essential tasks; on the other hand; these systems have constraints when it comes to maintaining synchronization in order to access shared resources. Methods: This research work presents a systematic review of different existing scheduling algorithms and synchronization protocols for shared resources in a real-time multiprocessor environment. The manuscript also presents a study based on various metrics of resource scheduling and comparison among different resource scheduling techniques. Conclusion: The survey classifies open issues, key challenges, and likely useful research directions. Finally, we accept that there is still a lot of capacity in developing better resource management and further maintaining the overall quality. The paper considers such a future path of research in this field.

Background: Graph analytics is one of the foremost established and unique strategies utilized in taking care of present-day designing issues. In this study, this procedure was applied to networks. The connectivity of gadgets is one of the intense issues distinguished in wireless systems. To deal with this issue, a unique Fibonacci-based TGO was proposed for a superior network. Methods: The proposed model attempts to construct a trimet graph based on the Fibonacci arrangement, implying that a cluster is formed with 3, 5, 8, 13, 21... hubs. To frame Fibonacci-based TGO, each of these hubs is recursively connected with a trimet diagram. For the random regular graph, the practical mesh is invariant. Edges, diameter, average degree, average clustering, density, and average shortest path are currently being compared for both meshes. Results: Fibonacci TGO has approximately 50 edges at 100 nodes and a constant diameter of 4. The average degree of Fibonacci TGO is less, which is approximately 3, having 0.7 high average clustering over random regular. As the number of nodes increases, the density decreases.TGO is having a better path than the random regular model. Finally, Fibonacci TGO mesh has better performance and connectivity over real-time meshes in wireless networks. Conclusion: We have proposed Fibonacci-based TGO mesh in the following steps. This formation is split into two stages. Fibonacci-dependent trimets based on the input nodes are created in the first stage, with 3, 5, 8, 13, and 21... nodes. These trimets will be connected in the second step to create a Fibonacci-based TGO. Both meshes are now being studied using network science parameters. In any scenario, Fibonacci-based TGO has better connectivity over the real-time random mesh. The NetworkX package in the Python language is used to produce the results automatically.

Aims: To model molecular signal propagation in confined environment. Background: Molecular communication (MC) is rooted in the concepts of understanding, modeling, and engineering information exchange among naturally and artificially synthesized nanosystems. To develop or analyze an MC system, there is the need to model the communication channel through which the molecular signal diffuse, from the transmitter to the receiver. Many models for the diffusion- based MC channel have been proposed in the literature for evaluating the performance of MC systems. Most of the contemporary works assume, and rightly so for some scenarios, that the MC channels under consideration have infinite boundaries. However, this assumption becomes invalid in bounded domains such as the interiors of natural cells and artificially synthesized nanosystems. Objective: In this paper, the model of molecular propagation in a confined. microenvironment is employ to explore the effect of such an environment on the MC system. Methods: The mutual information of the channel and specifically the closed-form expression of the channel capacity of the molecular signaling in the confined geometry is derive. Result: Numerical results showing the variation in the channel capacity as the function of the channel dimension are presented. Conclusion: Results showed that the channel capacity increases with the decrease in the channel dimension. Subsequently, as the dimension of the channel tends to the nanoscale range typical of many artificially synthesized nanosystems, the effect of the channel width on the capacity and by induction on many other system metrics increases.

Background: Survivability is one of the key issues of wireless optical broadband (WOBAN) access networks. Survivability means providing continuous services to users if a device/link failure occurs in the network. The component /link failure can occur in the network due to any reason. In this condition, huge data loss will occur in the network. Methods: In this paper, we consider the front-end survivability in WOBAN. We propose a novel maximum protection minimum link cost routing algorithm (MPMLC) that provides path protection with a minimum link cost to the front-end of the WOBAN. The proposed MPMLC algorithm assigns weight/cost to a wireless link using link-state prediction (LSP). The path with minimum cost is selected to route the traffic (actual/affected due to failure) of the front-end network. Results: The proposed algorithm outperforms in terms of reduced network delay and wireless link cost. It is also observed from simulation results that when multiple link failures occur in the network, the proposed MPMLC algorithm exhibit better results in reduced wireless link cost than the existing algorithm. Conclusion: The proposed MPMLC offers better candidature than the existing algorithm for the frontend survivability of the WOBAN.