Multipath energy balancing for clustered wireless sensor networks

Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

Wireless Sensor Network (WSN) is a promising approach for a variety of applications. Because of limitation of energy resource, memory space and processing capability of sensor nodes, it is very difficult to implement IP-based routing protocols in WSN. Recently, many research focus on developing special routing protocols for WSNs with the main design criteria: energy efficiency, load balance and reliability. The most well known routing protocol class is hierarchical which divides network into many clusters. Each cluster is represented by a cluster head (CH) that is responsible for receiving data from all non-CH members, aggregating these data and sending to the base station (BS). In order to balance energy consumption of the entire network, CHs are not fixed but rotated. Therefore, the hierarchical protocols can decrease and balance the energy consumption and prolong network lifetime. LEACH is one of the first cluster-based for WSN, which includes distributed cluster formation. In this paper, we propose a new hierarchical routing protocol (called EL-LEACH: Energy and Load balance LEACH) which achieves energy efficiency and load balance. Our simulation result shows that our new scheme EL-LEACH achieves better energy consumption, load balance and network life time than other well known LEACH protocols.

A wireless sensor network with a large number of small sensor nodes can be used as an effective tool for gathering data in various situations. The paradigm of data aggregation enables wireless sensor networks to accurately estimate the different parameters of an environment. The aim of this paper is to introduce a new fault tolerant protocol striving to reduce overall power consumption, prolong network lifetime, and support mobile nodes in a wireless sensor network. Our protocol, FTPASC (fault tolerant power aware protocol with static clustering), partitions the network into static clusters and distributes the energy load among high-power sensor nodes. Mobility management is another attractive feature of this protocol that guarantees reliable communications between mobile and sensor nodes. Simulation results show that FTPASC outperforms conventional protocols in terms of network lifetime and power consumption minimization

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.

It is important to prolong the network lifetime in Wireless Sensor Networks. There are two approaches to increase the network lifetime, which are balancing energy consumption and reducing the number of non-working nodes. This paper presents two Cluster head Selection algorithms, one is based on Ensuring target Area and the other is based on Affinity Propagation. We propose a method which makes the energy uniformly distributed throughout the network nodes. In particular, we analyse the effect of the powerful nodes on the network lifetime and cluster characteristics. Simulation results show that the two proposed algorithms distribute non-working nodes evenly, such that the target area is well covered by the remaining nodes. Our proposed algorithms balance the traffic load and energy distributions, and prolong the network lifetime effectively compared with the existing schemes.

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Recently, wireless sensor networks (WSNs) have had many real-world applications; they have thus become one of the most interesting areas of research. The network lifetime is a major challenge researched on this topic with clustering protocols being the most popular method used to deal with this problem. Determination of the cluster heads is the main issue in this method. Cognitively inspired swarm intelligence algorithms have attracted wide attention in the researh area of clustering since it can give machines the ability to self-learn and achieve better performance. This paper presents a novel nature-inspired optimization algorithm based on the gravitational search algorithm (GSA) and uses this algorithm to determine the best cluster heads. First, the authors propose a rank-based definition for mass calculation in GSA. They also introduce a fuzzy logic controller (FLC) to compute the parameter of this method automatically. Accordingly, this algorithm is user independent. Then, the proposed algorithm is used in an energy efficient clustering protocol for WSNs. The proposed search algorithm is evaluated in terms of some standard test functions. The results suggest that this method has a better performance than other state-of-the-art optimization algorithms. In addition, simulation results indicate that the proposed clustering method outperforms other popular clustering method for WSNs. The proposed method is a novel way to control the exploration and exploitation abilities of the algorithm with simplicity in implementation; therefore, it has a good performance in some real-world applications such as energy efficient clustering in WSNs.

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster‐based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three‐level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH‐WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

Energy consumption is the main issue that researchers have dealt with to design any routing application. To design comprehensive reliable wireless sensors networks (WSNs), it is essential to consider node failure and energy constraint as inevitable phenomena. In this paper, a new method for optimal clustering is proposed, that accurately localizes sensors while minimizing their power consumption. It also splits the task of Cluster head between sensor nodes to send data to the base station (BS). The proposed scheme is based on K-means method to selected Cluster-Head. The selection of CH starts with the closest nodes to the centroid point. In the second step, we selected nodes with high power compared to its neighbors, after repeating this phenomenon until the energy of all the nodes is drained. This method can equilibrate the role of Cluster-Head between all the network sensors. Simulation results showed that our clustering mechanism seemed to effectively improve the network lifetime over Low Energy Adaptive Clustering Hierarchy (LEACH) and Energy efficient clustering algorithm for maximizing the lifetime of wireless sensor networks.

In past few years the potential use of wireless sensor network has increased tremendously in a wide range of applications and it has become a hot research area. WSN has two types of routing protocols: flat routing protocols and clustering routing protocols. Low Energy Adaptive Clustering Hierarchy (LEACH) is first major improvements in clustering routing protocols in WSN. This paper presents two modified architectures of LEACH: LEACH-Balanced (LEACH-B) and Two-Level Hierarchy LEACH (TL-LEACH). The main objective of these protocols is to improve the energy efficiency of the sensor nodes and to prolong the lifetime of the wireless sensor network. The performance of LEACH-B and TL-LEACH are compared in terms of Throughput, Packet Delivery Ratio (PDR), Residual Energy and Life-Time of the network.

In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications.Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station.But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime.In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network.The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station.GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value.Also, GCR uses a greedy route finding strategy for balancing energy consumption.Experimental results show that GCR achieves significant energy savings and prolong network lifetime.

Ensuring stability and extending network lifetime in Wireless Sensor Networks (WSNs) achieved through significantly reduced energy consumption is considered as a potential challenge. The selection of Cluster Head (CH) during the process of clustering is determined to be highly complicated in spite of its role in facilitating efficient and balanced energy consumption in the network. In this paper, Hybrid Stochastic Ranking and Opposite Differential Evolution enhanced Firefly Algorithm (HSRODE-FFA)-based clustering protocol is proposed for handling the issues of location-based CH selection approaches that select duplicate nodes with increased computation and poor selection accuracy. This HSRODE-FFA clustering scheme includes the process of sampling for selecting the CHs from among the sensor nodes that exist in the sample population and address the problems introduced by different locations of nodes and CHs. It is proposed as an attempt to improve stability and lifetime of WSNs based on the merits of Stochastic Firefly Ranking (SFR) that enhances the exploration capability of Firefly Algorithm (FFA). The hybridization of the enhanced FFA with Opposition Differential Evolution (ODE) aids in speeding and ensuring optimal exploitation in the selection of CHs. The proposed HSRODE-FFA thereby maintains a balance between the rate of exploitation and exploration for deriving mutual benefit of rapid and potential selection of CHs from the sampling population. The experimental results of the proposed HSRODE-FFA scheme confirm an enhanced stability period and network lifetime of 16.21% and 13.86% respectively in contrast to the benchmarked Harmony Search and Firefly Algorithm-based Cluster Head Selection (HSFFA-CHS), Krill Herd Optimization and Genetic Algorithm-based Cluster Head Selection (KHOGA-CHS), Particle Swarm Optimization with Energy Centers Searching-based Cluster Head Selection (PSO-ECS-CHS) and Spider Monkey Optimization-based Cluster Head Selection (SMO-CHS) schemes.

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.