The Internet of Vehicles (IoV) is a subset of cyber-physical systems (CPS) that interconnects vehicles and infrastructure through networking to provide safety, convenience, and improved traffic management. IoV promises to mitigate congestion, lower fuel consumption, reduce pollution, and enhance travel efficiency, all while extending internet access to vehicles. Clustering is a technique shown to address challenges in IoV, and cluster head selection plays a significant role in the stability of the network as the cluster head orchestrates intra and inter-cluster communication. When a cluster head possesses a weak link among its members, the risk of link breakage rises, diminishing information sharing. Previous research has offered a number of solutions for cluster head selection in IoV, but current solutions rely on centralized infrastructure and data to determine the optimal cluster head. As such, these solutions suffer from latency and are not applicable in regions where there is limited infrastructure. In addition, the centralization of data raises privacy and security concerns. We propose a novel decentralized approach for cluster head selection enabled by Federated Deep Reinforcement Learning (FDRL). Our solution offers a low latency, distributed learning approach that preserves the private data of each vehicle. The performance of our proposal is examined using the Veins framework that is based on the two simulators OMNET++ and SUMO. Our results achieved low latency communication with high neighbor connectivity, packet delivery, and cluster stability.

In this vision paper, we analyze the protocols used in the Internet of Things (IoT), encryption methods, and their combination for exploitation. The Internet of Things (IoT) is an important paradigm of modern technology that connects physical objects and devices into a single network where they can exchange data and interact without direct human intervention. The Internet of Things is used in a variety of areas, from controlling household appliances to monitoring the condition of objects in industry and agriculture. The MQTT (Message Queuing Telemetry Transport) protocol was used in this work, which is a lightweight protocol for transmitting messages in IoT networks. It allows for efficient data exchange between devices, ensuring low energy consumption and minimizing bandwidth. The following ciphers were used to ensure the security of information in IoT networks: ASCON and Grain128-AEAD. ASCON is used to encrypt and authenticate data, ensuring its confidentiality and integrity. Grain128-AEAD is also used for data protection, providing a high level of security and encryption. This research work simulates an IoT environment based on the MQTT communication protocol and tests the performance of lightweight cryptographic algorithms. As evident from the results, encryption is an essential part of security for IoT -based communication systems and such lightweight algorithms could help in boosting the overall performance with low to none cases of failure. This paper looks to envision the suitability of these lightweight cryptographic (LWC) security and privacy solutions for IoT and Cyber-Physical Systems (CPS).

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. Rogue nodes in VANETs cause a Sybil attack to create an illusion of fake traffic congestion by broadcasting malicious information leading to catastrophic consequences, such as the collision of vehicles. Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose a fog computing-based Sybil attack detection for VANETs (FSDV), which utilizes onboard units (OBUs) of all the vehicles in the region to create a dynamic fog for rogue nodes detection using statistical techniques. We aim to reduce the data processing delays, overhead, and FPR in detecting rogue nodes causing Sybil attacks at high vehicle densities. The performance of our framework was carried out with simulations using OMNET++ and SUMO simulators. Results show that our framework ensures 43% lower processing delays, 13% lower overhead, and 35% lower FPR at high vehicle densities compared to existing Sybil attack detection schemes.

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region for rogue nodes detection. The novelty of F-RouND lies in providing low processing delays and FPR at high vehicle densities. The performance of our F-RouND framework was carried out with simulations using OMNET ++ and SUMO simulators. Results show that F-RouND ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at high vehicle densities compared to existing rogue nodes detection schemes.

Selecting secure and dynamic watchdogs for detecting attacks using a type of intrusion detection system (IDS). The selection procedure of watchdogs in the random ad‐hoc wireless sensor network is a load creation job in the absence of a centralised controller. In this type of network, the data processing transmission for the routing process and secure watchdog selection process create overhead in each node. It drains the energy of an individual node easily. Founded on these issues, this work concentrates on the secure selection of concealed watchdogs and maintenance of optimal watchdog availability ratio. In the random ad‐hoc wireless sensor network, the secure and authorised watchdogs are selected from the neighbour list of each node on‐demand basis to provide security for the network. In addition to this work concentrates on dynamic uncertain conditions to build a secure and authenticated multi‐watchdog system in the distributed scenario. The proposed system uses the combination of both customised layer masking techniques and secure routing and monitoring techniques for the protection of random ad‐hoc wireless sensor networks.

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due to obstacle shadowing regions with high vehicle densities, which leads to frequent disconnection problems and blocks radio wave propagation between vehicles. Previous researchers used multi-hop, vehicular cloud or roadside infrastructures to solve the routing issue among the vehicles, but they suffer from significant packet delays and frequent packet losses arising from obstacle shadowing. We proposed a vehicular cloud based hybrid technique called Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. The novelty of our approach lies in the fact that our proposed technique dynamically adapts between obstacle shadowing and non-obstacle shadowing regions. Simulation based performance analysis of Hybrid-Vehcloud showed improved performance over Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high vehicle densities.

An approach is presented that permits the configuration of application specific hardware, with arbitrary hardware redundancy, to match the signal flow graph of arbitrary applications. The hardware is mapped to the signal flow graph of an application. An inventory of heterogeneous processors, specialized to perform a predefined set of functions, enables rapid prototyping of systems with arbitrary topologies. Application specific systems that match the signal flow graph of applications outperform general purpose systems in speed and throughput. This research focuses on solving the problems associated with the interconnection of the heterogeneous building blocks. A communication architecture is proposed that allows the interconnection of processors with varying speed and functionalities. Standardization of the interface control unit (ICU) greatly reduces the development cost by removing the need to design custom interfaces. The ICU permits the introduction of varying degree of hardware redundancy into the topology at the system, cluster or processor level.

An approach is presented for the implementation of high-performance heterogeneous digital signal processors. This approach facilitates tailoring the configuration of the hardware to the signal flow graph of the application. The hardware system comprises heterogeneous processors which are specialized for performing a predefined set of functions. The focus of this research is to solve the communication problems associated with the interconnection of the heterogeneous building blocks to implement application-specific systems. An interface control unit (ICU) architecture is proposed that allows the interconnection of processors with varying speeds and functionalities. A protocol has been developed that facilitates transfer of large data blocks in sparse networks. The hardware design and implementation of the ICU is based on this protocol. The digital design of the ICU is suitable for fabrication as a single VLSI circuit.>

A major difficulty in rapid prototyping is the interconnection of processors with tailored networks to implement systems. This difficulty may be alleviated by utilizing a standardized processor-to-processor interface. This paper describes an interface control unit (ICU) VLSI circuit which uses a 'standard' communication interface to implement signal processing systems. The ICU comprises queuing structures, communication channel interfaces, a processor interface, and a control interface. Communication protocols are described for interfaces between the ICU and communication channels, the processor, and the control unit. Based on these protocols, a digital design has been completed which is described. The chip will be fabricated using 0.6 micron CMOS technology.>