Energy Aware Optimized Hierarchical Routing Technique for Wireless Sensor Networks

Wireless sensor networks are battery-powered ad hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. Since these networks consist of sensors that are battery operated, care has to be taken so that these sensors use energy efficiently. This paper proposes an optimized hierarchical routing technique which aims to reduce the energy consumption and prolong network lifetime. In this technique, the selection of optimal cluster head (CHs) locations is based on artificial fish swarm algorithm that applies various behaviors such as preying, swarming, and following to the formulated clusters and then uses a fitness function to compare the outputs of these behaviors to select the best CHs locations. To prove the efficiency of the proposed technique, its performance is analyzed and compared to two other well-known energy efficient routing techniques: low-energy adaptive clustering hierarchy (LEACH) technique and particle swarm optimized (PSO) routing technique. Simulation results show the stability and efficiency of the proposed technique. Simulation results show that the proposed method outperforms both LEACH and PSO in terms of energy consumption, number of alive nodes, first node die, network lifetime, and total data packets received by the base station. This may be due to considering residual energies of nodes and their distance from base station , and alternating the CH role among cluster's members. Alternating the CH role balances energy consumption and saves more energy in nodes.

Wireless Sensor Networks (WSN) is an ever growing field owing to their extensive variety of applications. WSN have limited battery energy, consequently, an energy efficient routing protocol is of major concern. LEACH (Low Energy Adaptive Clustering Hierarchy) is one of the fundamental clustering protocols in WSN that provides high energy efficiency. Many protocols have been derived from LEACH which mostly address a single inherent challenge. Instead of focusing on a single parameter, combining independent techniques can compound gains in energy efficiency. This paper proposes Yet Another LEACH (YA-LEACH) which uses centralised cluster formation to ensure optimal clusters and allow Cluster Heads (CH) to extend operation into multiple rounds to achieve energy savings. Also, the proposed protocol will have an alternative (vice) CH that takes over when CH residual energy reaches a critically low level. The extended protocol was simulated on NS2 with results showing an improvement in network lifetime and overall data throughput.

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Wireless Sensor Networks (WSNs) is the vast area and developing very speedily now-a-days. This paper gives some of the major applications and critical limitations of WSNs like power and lifespan, communication bandwidth, memory size. Sensor nodes are working by node energy so the energy consumption is the essential issue in WSN. Routing in WSN consumes more energy than any other phase. Various routing techniques in WSNs are discussed and different routing protocols are illustrated. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the popular and feasible hierarchical clustering routing protocols in WSN. Working of LEACH is explained in brief. Furthermore, the descendants of LEACH e.g. LEACH-C (Centralized LEACH), LEACH-B (Balanced LEACH), LEACH-F, Multihop LEACH, MS-LEACH (Multihop and Single hop LEACH), MH-LEACH etc. are discussed in terms of the energy utilization. By analyzing the variants of LEACH, it is observed that the energy requirement in the cluster set up phase or data transmission phase is minimized to minimize the total energy consumption of the WSN. To elect the proper cluster head node, centralized approach is followed. Afterwards, data transmission will use the multihop communication technique. This will help to save some energy when base station is located far away from the cluster head node. Proposed protocol aims to decrease the energy overhead of the sensor nodes. The new approach is proposed entitled PV-LEACH which will save the energy consumption in cluster setup phase and also in data transmission phase of the conventional LEACH protocol. So, this will enhance the performance ratio in terms of energy consumption and life time of the sensor nodes.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Energy efficient routing is the paramount point of significance in any wireless sensor network (WSN) so that the network sustains without any human intervention. Various energy efficient protocols have been proposed, multi-level Low Energy Adaptive Clustering Hierarchy (LEACH) being one among these. The multi-level LEACH protocol involves election of cluster heads at various hierarchical levels. If the area of deployment is large, the farther cluster heads cannot reach the base station directly. In such cases, it is needed to make use of an energy efficient routing technique for communication among cluster heads and forward the data towards the base station. Energy consumption is reduced by adopting routing mechanisms based on multi-hop and inverted tree in this multi-level LEACH protocol. This work deals with multi-level LEACH comprising of 1L-LEACH and 2L-LEACH. Directed diffusion carried out in 1L-LEACH and flat topology are compared with flooding. In directed diffusion, routing is done between a source and a sink optimizing the path taken. NS-3 simulation platform has been used and the results are presented.

The main objective of this paper is to achieve prolonged network lifetime with overall energy efficiency in ad hoc sensor networks through controlled utilization of limited energy. In wireless sensor networks, most of the energy spent is consumed in routing the sensed data from cluster head to the base station. We optimize the clustering and routing techniques. We introduce: (1) Backup heads (to avoid frequent re-clustering) in each cluster, (2) Secondary membership heads (to eliminate multiple/redundant transmissions), (3) Optimum distance hoping to achieve overall energy efficiency of the network. A modification to existing clustering technique, in combination with our energy-efficient routing technique (using secondary memberships), results in large increase in the overall network lifetime. Use of secondary memberships (to route the data through shortest path) conserves more energy than that of the extra energy spent (for backup heads) in clustering. This results in overall energy efficiency with prolonged network lifetime.

A green clustering protocol for mobile sensor network using particle swarm optimization

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods.

Wireless sensor networks (WSNs) are widely applied in monitoring and communication but remain constrained by limited energy resources. Efficient routing protocols are critical to prolong network lifetime and ensure balanced energy consumption among sensor nodes. Traditional cluster-based routing often fails to achieve optimal energy balance, leading to premature node failures and degraded performance. Intelligent optimization algorithms, including meta heuristics such as particle swarm optimization (PSO) and emperor penguin optimization (EPO), have been applied to improve routing efficiency. These methods enhance convergence and adaptability but typically operate as standalone approaches. Limited attention has been given to integrating multi criteria decision making (MCDM) methods with meta heuristics. Without this integration, assigning precise weights to multiple criteria and balancing energy consumption across nodes remains difficult. This paper proposes a novel uneven cluster-based routing protocol that integrates fuzzy constrained nonlinear programming- variable weight analysis-technique for order preference by similarity to ideal solution (FCNP-VWA-TOPSIS) with an improved EPO. The protocol first assigns accurate weights to seven multi criteria using FCNP VWA and selects cluster heads (CHs) with TOPSIS. It then constructs the routing tree using improved EPO guided by the weighted fitness function. Extensive simulations show that the proposed protocol achieves superior energy balance, extending network lifetime by 158.0%, 119.3%, and 113.7% compared to uneven clustering routing (UCR), unequal clustering fuzzy logic intelligent algorithm (UCFIA), and fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB ER), respectively. By combining MCDM and meta heuristic optimization, the protocol advances cluster-based routing in WSNs, significantly enhancing energy efficiency and reliability for real world applications.

Restricted battery life is a crucial constraint in wireless sensor networks (WSNs). Many routing protocols have been proposed to reduce the energy consumption by extending the network lifetime. So, a new design of energy efficient routing using clustering is proposed where we use heterogeneous nodes for data transmission through the optimal path between cluster heads (CHs) and the base station (BS). This paper discusses the algorithm called Advanced-Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with heterogeneous nodes. Multi-hop LEACH protocol changes the mode of communication to prolong the network lifetime from single hop to multi-hop between the CH and BS by reducing energy consumption. The paper tries to evaluate the performance of the network in terms of lifetime and stability by introducing the concept of optimum number of clusters and also considers the optimum number of hops in the network with heterogeneous nodes. This paper also provides the algorithm for the avoidance of the overlapping of the clusters and collisions in the network. For the evaluation and comparison of the performance of advanced single-hop and multi-hop energy efficient LEACH protocols with heterogeneous nodes, MATLAB simulations are carried out. Results show that advanced-multi-hop LEACH protocol outperforms the advanced single-hop LEACH protocol regarding energy efficiency, stability and network lifetime.

The paper presents a novel algorithm called Modified End-to-End Secure Low Energy Adaptive Clustering Hierarchy (ME-LEACH) for extending the lifetime of Wireless Sensor Networks (WSNs).Energy constraints are a significant challenge in WSNs, and thus, efficient energy usage is essential for all activities within a WSN.Existing protocols have been introduced to address energy dissipation issues, such as the End-to-End Secure Low Energy Adaptive Clustering Hierarchy (E-LEACH) protocol, which is a hierarchical routing algorithm.However, E-LEACH suffers from high energy consumption as each Cluster Head (CH) directly sends data to the base station.In the proposed ME-LEACH method, a CH identifies the nearest CH and uses it as the next hop, forming a chain of CHs that serve as a path to the base station or sink.This approach aims to improve energy efficiency in WSNs and reduce energy dissipation during data transmission.The widespread applications of WSNs in various fields of science and technology highlight the significance of this research.

The research explores advanced routing protocols and swarm intelligence algorithms to enhance energy efficiency in Wireless Sensor Networks (WSNs), focusing on optimizing network performance. It examines the performance of Hierarchical Energy-Efficient Distributed (HEED), Low-Energy Adaptive Clustering Hierarchy (LEACH), and Threshold-sensitive Energy Efficient sensor Network (TEEN) protocols, alongside swarm intelligence technique Chaotic Based Firefly Algorithm (CFA). Swarm Intelligence algorithms enhance WSNs by addressing routing challenges and cluster head selection. The study introduces a novel method for mitigating malicious routes and optimizing cluster heads using these algorithms. Evaluation measures include Latency, Packet Delivery Ratio, End-to-End Delay, Throughput, Energy Consumption, and Network Lifetime, aiming to identify strategies for enhancing network performance and sustainability