An Enhanced Cluster-Based Routing Model for Energy-Efficient Wireless Sensor Networks

Energy efficiency is a crucial consideration in wireless sensor networks since the sensor nodes are resource-constrained, and this limited resource, if not optimally utilized, may disrupt the entire network's operations. The network must ensure that the limited energy resources are used as effectively as possible to allow for longer-term operation. The study designed and simulated an improved Genetic Algorithm-Based Energy-Efficient Routing (GABEER) algorithm to combat the issue of energy depletion in wireless sensor networks. The GABEER algorithm was designed using the Free Space Path Loss Model to determine each node's location in the sensor field according to its proximity to the base station (sink) and the First-Order Radio Energy Model to measure the energy depletion of each node to obtain the residual energy. The GABEER algorithm was coded in the C++ programming language, and the wireless sensor network was simulated using Network Simulator 3 (NS-3). The outcomes of the simulation revealed that the GABEER algorithm has the capability of increasing the performance of sensor network operations with respect to lifetime and stability period

Trust And Energy-Aware Routing Protocol for Wireless Sensor Networks Based on Secure Routing

Wireless Sensor Network (WSN) is a network area that includes a large number of nodes and the ability of wireless transmission. WSNs are frequently employed for vital applications in which security and dependability are of utmost concern. The main objective of the proposed method is to design a WSN to maximize network longevity while minimizing power usage. In a WSN, trust management is employed to encourage node collaboration, which is crucial for achieving dependable transmission. In this research, a novel Trust and Energy Aware Routing Protocol (TEARP) in wireless sensors networks is proposed, which use blockchain technology to maintain the identity of the Sensor Nodes (SNs) and Aggregator Nodes (ANs). The proposed TEARP technique provides a thorough trust value for nodes based on their direct trust values and the filtering mechanisms generate the indirect trust values. Further, an enhanced threshold technique is employed to identify the most appropriate clustering heads based on dynamic changes in the extensive trust values and residual energy of the networks. Lastly, cluster heads should be routed in a secure manner using a Sand Cat Swarm Optimization Algorithm (SCSOA). The proposed method has been evaluated using specific parameters such as Network Lifetime, Residual Energy, Throughpu,t Packet Delivery Ratio, and Detection Accuracy respectively. The proposed TEARP method improves the network lifetime by 39.64%, 33.05%, and 27.16%, compared with Energy-efficient and Secure Routing (ESR), Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (MOSFA) , and Optimal Support Vector Machine (OSVM)

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

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

A Novel Method of Enhancing Security Solutions and Energy Efficiency of IoT Protocols

Mobile Ad-hoc Networks (MANET’s) are wireless networks that are capable of operating without any fixed infrastructure. MANET routing protocols must adhere to strict secrecy, integrity, availability and non-repudiation criteria. In MANETs, attacks are roughly categorised into two types: active and passive. An active attack attempts to modify or remove data being transferred across a network. On the other hand, passive attack does not modify or erase the data being sent over the network. The majority of routing protocols for MANETs were built with little regard for security and are therefore susceptible to a variety of assaults. Routing technologies such as AODV and dynamic source routing are quite common. Both however are susceptible to a variety of network layer attacks, including black holes, wormholes, rushing, byzantine, information disclosure. The mobility of the nodes and the open architecture in which the nodes are free to join or leave the network keep changing the topology of the network. The routing in such scenarios becomes a challenging task since it has to take into account the constraints of resources of mobile devices. In this an analysis of these protocols indicates that, though proactive routing protocols maintain a route to every destination and have low latency, they suffer from high routing overheads and inability to keep up with the dynamic topology in a large sized network. The reactive routing protocols in contrast have low routing overheads, better throughput and higher packet delivery ratio. AODVACO-PSO-DHKE Methodology boosts throughput by 10% while reducing routing overhead by 7%, latency by 8% and energy consumption by 5%. To avoid nodes always being on, a duty cycle procedure that's also paired with the hybrid method is used ACO-FDR PSO is applied to a 100-node network and NS-3 is used to measure various metrics such as throughput, latency, overhead, energy consumption and packet delivery ratio

A Novel Method of Enhancing Security Solutions and Energy Efficiency of IoT Protocols

Mobile Ad-hoc Networks (MANET’s) are wireless networks that are capable of operating without any fixed infrastructure. MANET routing protocols must adhere to strict secrecy, integrity, availability and non-repudiation criteria. In MANETs, attacks are roughly categorised into two types: active and passive. An active attack attempts to modify or remove data being transferred across a network. On the other hand, passive attack does not modify or erase the data being sent over the network. The majority of routing protocols for MANETs were built with little regard for security and are therefore susceptible to a variety of assaults. Routing technologies such as AODV and dynamic source routing are quite common. Both however are susceptible to a variety of network layer attacks, including black holes, wormholes, rushing, byzantine, information disclosure. The mobility of the nodes and the open architecture in which the nodes are free to join or leave the network keep changing the topology of the network. The routing in such scenarios becomes a challenging task since it has to take into account the constraints of resources of mobile devices. In this  an analysis of these protocols indicates that, though proactive routing protocols maintain a route to every destination and have low latency, they suffer from high routing overheads and inability to keep up with the dynamic topology in a large sized network. The reactive routing protocols in contrast have low routing overheads, better throughput and higher packet delivery ratio. AODVACO-PSO-DHKE Methodology boosts throughput by 10% while reducing routing overhead by 7%, latency by 8% and energy consumption by 5%. To avoid nodes always being on, a duty cycle procedure that's also paired with the hybrid method is used ACO-FDR PSO is applied to a 100-node network and NS-3 is used to measure various metrics such as throughput, latency, overhead, energy consumption and packet delivery ratio

Configurable Secured Adaptive Routing Protocol for Mobile Wireless Sensor Networks

This paper aims at designing, building, and simulating a secured routing protocol to defend against packet dropping attacks in mobile WSNs (MWSNs). This research addresses the gap in the literature by proposing Configurable Secured Adaptive Routing Protocol (CSARP). CSARP has four levels of protection to allow suitability for different types of network applications. The protocol allows the network admin to configure the required protection level and the ratio of cluster heads to all nodes. The protocol has an adaptive feature, which allows for better protection and preventing the spread of the threats in the network. The conducted CSARP simulations with different conditions showed the ability of CSARP to identify all malicious nodes and remove them from the network. CSARP provided more than 99.97% packets delivery rate with 0% data packet loss in the existence of 3 malicious nodes in comparison with 3.17% data packet loss without using CSARP. When compared with LEACH, CSARP showed an improvement in extending the lifetime of the network by up to 39.5%. The proposed protocol has proven to be better than the available security solutions in terms of configurability, adaptability, optimization for MWSNs, energy consumption optimization, and the suitability for different MWSNs applications and conditions

An Energy Efficient and Cost Reduction based Hybridization Scheme for Mobile Ad-hoc Networks (MANET) over the Internet of Things (IoT)

Wireless networks are viewed as the best-used network and specifically Portable Specially Appointed Organizations (MANETs) have tracked down numerous applications for its information transmission progressively. The plan issues in this organization are to confine the utilization of energy while communicating data and give security to the hubs. Soa protocol needs to be energy efficient to avoid network failures. Thereby this paper brings an effective energy efficient to optimize LEAR and make it energy efficient. The energy-mindfulness element is added to the LEAR guiding convention in this work using the Binary Particle Swarm Optimization method (BPSO). The recommended method selects programmes taking into account course length in addition to the programme level of energy when predicting the future. To get good results, the steered challenge is first designed using LEAR. The next step is to choose a route that enhances the weighting capability of the study hours and programming power used.This MANET has been secured using the cryptographic method known as AES.According to experimental findings, the proposed hybrid version outperformed other cutting-edge models

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Maximizing Network Lifetime using Fuzzy Based Secure Data Aggregation Protocol (FSDAP) in a Wireless Sensor Networks

Secure Data Aggregation in Wireless Senor Networks (WSNs) is a challenging issue. The various protocols has been suggested in the recent past such as EDIT[13], ADA[8], TSDA[9], SEDAN[10]. These protocols effectively meet the constraints of WSNs. In this paper, we have proposed a Fuzzy Based Secure Data Aggregation protocol (FSDAP) which is an efficient localized protocol. The FSDAP protocol is designed with three phases. The first phase selects Aggregator Node using ANS algorithm. An ANS algorithm involves two steps to elect an Aggregator Node in the clustered network. In first step, the cluster head is selected based on the Euclidean distance and in second step, the cluster head is selected based on the fuzzy product and fuzzy value (α). Then, in second phase, a selected AN eliminates data redundancy sensed by all sensor nodes within the cluster. Finally, in third phase, the FSDAP protocol effectively detects malicious node and provides secure data transmission path. Thus, the proposed protocol, FSDAP utilizes the node’s resource parameter uniformly, which in turn improves Network Lifetime, maximizes Throughput Rate, maximizes Packet Delivery Ratio and minimizes End-to-End Delay. The FSDAP is simulated using the NS2 simulator and compared with centroid algorithms Fuzzy C-Means and K-Means algorithm and a secure aggregation protocol implemented using SAR (Secure Aware Ad hoc Routing). The time complexity of FSDAP protocol is O(m2n)