Research Challenges of Improved Cluster Chain Power-Efficient Routing Using Natural Computing Methods for Wireless Sensor Network

Wireless Sensor Networks (WSNs) primarily operate on batteries, making energy conservation crucial, especially in routing processes. Efficient routing in WSNs is challenging due to the network's distinct attributes. Among various routing protocols, CCPAR is noteworthy as it utilizes a chain between cluster heads to relay data to the base station. This research delves into nature-inspired techniques for energy-efficient routing in WSNs. It introduces the Moth-Dolphin Optimization Algorithm, capitalizing on the communication between moths to enhance routing performance. This innovative method combines the navigational skills of moths and the communicative attributes of dolphins. When benchmarked against other optimization methods, the Moth-Dolphin algorithm offers favorable results. The study then applies this algorithm to improve CCPAR routing, aiming for reduced energy consumption. The modified routing's effectiveness is evaluated against other top-tier algorithms, considering factors like energy consumption, throughput, network longevity, and delay

A novel bio-inspired routing algorithm based on ACO for WSNs

The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission

Comparison and Analysis on AI Based Data Aggregation Techniques in Wireless Networks

In modern era WSN, data aggregation technique is the challenging area for researchers from long time. Numbers of researchers have proposed neural network (NN) and fuzzy logic based data aggregation methods in Wireless Environment. The main objective of this paper is to analyse the existing work on artificial intelligence (AI) based data aggregation techniques in WSNs. An attempt has been made to identify the strength and weakness of AI based techniques.In addition to this, a modified protocol is designed and developed.And its implementation also compared with other existing approaches ACO and PSO. Proposed approach is better in terms of network lifetime and throughput of the networks. In future an attempt can be made to overcome the existing challenges during data aggregation in WSN using different AI and Meta heuristic based techniques

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

A Novel Approach for Enhancing Routing in Wireless Sensor Networks using ACO Algorithm

Wireless Sensors Network (WSN) is an emergent technology that aims to offer innovative capacities. In the last decade, the use of these networks increased in various fields like military, science, and health due to their fast and inexpressive deployment and installation. However, the limited sensor battery lifetime poses many technical challenges and affects essential services like routing. This issue is a hot topic of search, many researchers have proposed various routing protocols aimed at reducing the energy consumption in WSNs. The focus of this work is to investigate the effectiveness of integrating ACO algorithm with routing protocols in WSNs. Moreover, it presents a novel approach inspired by ant colony optimization (ACO) to be deployed as a new routing protocol that addresses key challenges in wireless sensor networks. The proposed protocol can significantly minimize nodes energy consumption, enhance the network lifetime, reduce latency, and expect performance in various scenarios

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

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms

Improved LEACH Protocol based on Moth Flame Optimization Algorithm for Wireless Sensor Networks

Wireless sensor nodes are made up of small electronic devices designed for detecting, determining, and sending data under severe physical conditions. These sensor nodes rely heavily on batteries for energy, which drain at a quicker pace due to the extensive communication and processing tasks they must carry out. Managing this battery resource is the major challenge in wireless sensor networks (WSNs). This work aims at developing an improved performance and energy-efficient low-energy adaptive clustering hierarchy (IPE-LEACH) that can extend the lifespan of networks. This paper proposes a novel LEACH protocol that uses the moth flame optimization (MFO) algorithm for clustering and routing to increase the longevity of the sensor network. IPE-LEACH proved to have a better cluster-head (CH) selection technique by eliminating redundant data, thereby extending the network lifetime. IPE-LEACH was compared with four other existing algorithms, and it performed better than: original LEACH by 60%, EiP-LEACH by 45%, LEACH-GA by 58%, and LEACH-PSO by 13.8%. It can therefore be concluded that IPE-LEACH is a promising clustering algorithm that has the potential to realize high flexibility in WSNs in case the CH fails.     

Swarm Intelligence-Optimized Energy Management for Prolonging the Lifetime of Wireless Sensor Networks

 Recent technological and industrial progress has enabled the development of small, high-performing, energy-saving, affordable sensor nodes that possess the potential to adapt, be self-aware, and self-organize. These nodes are designed for versatile communications applications. Sensor networks for sustainable development focus on the ways in which sensor network technology can enhance social development and improve living standards without causing harm to the environment or depleting natural resources. Wireless sensor networks (WSNs) offer undeniable benefits in various fields, including the military, healthcare, traffic monitoring, and remote image sensing. Given the constraints of sensor networks, varying degrees of security are necessary for these critical applications, posing difficulties in the implementation of conventional algorithms. The issue of security has emerged as a primary concern in the context of IoT and smart city applications. Sensor networks are often regarded as the fundamental building blocks of IoTs and smart cities. The WSN encompasses a routing algorithm, network strength, packet loss, energy loss, and various other intricate considerations. The WSN also addresses intricate matters such as energy usage, a proficient approach for picking cluster heads, and various other concerns. The recent growth of Wireless Sensor Networks (WSNs) has made it increasingly difficult to ensure the trustworthiness and reliability of data due to the distinct features and limitations of nodes. Hostile nodes can easily damage the integrity of the network by inserting fake and malicious data, as well as launching internal attacks. Trust-based security is employed to detect and identify rogue nodes, providing a robust and adaptable protection mechanism. Trust evaluation models are crucial security-enhancement mechanisms that enhance the reliability and collaboration of sensor nodes in wireless sensor networks. This study recommends the use of DFA UTrust, a unique trust technique, to effectively satisfy the security requirements of WSNs