A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

Networked control system with MANET communication and AODV routing

The industries are presently exploring the use of wired and wireless systems for control, automation, and monitoring. The primary benefit of wireless technology is that it reduces the installation cost, in both money and labor terms, as companies already have a significant investment in wiring. The research article presents the work on the analysis of Mobile Ad Hoc Network (MANET) in a wireless real-time communication medium for a Networked Control System (NCS), and determining whether the simulated behavior is significant for a plant or not. The behavior of the MANET is analyzed for Ad-hoc on-demand distance vector routing (AODV) that maintenances communication among 150 nodes for NCS. The simulation is carried out in Network Simulator (NS2) software with different nodes cluster to estimate the network throughput, end-to-end delay, packet delivery ratio (PDR), and control overhead. The benefit of MANET is that it has a fixed topology, which permits flexibility since mobile devices may be used to construct ad-hoc networks anywhere, scalability because more nodes can be added to the network, and minimal operating expenses in that no original infrastructure needs to be developed. AODV routing is a flat routing system that does not require central routing nodes. As the network grows in size, the network can be scaled to meet the network design and configuration requirements. AODV is flexible to support different configurations and topological nodes in dynamic networks because of its versatility. The advantage of such network simulation and routing behavior provides the future direction for the researchers who are working towards the embedded hardware solutions for NCS, as the hardware complexity depends on the delay, throughput, and PDR

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio