Energy efficient data collection through hybrid unequal clustering for wireless sensor networks

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

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

Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. The main goal of clustering protocols is to minimize the energy consumption and enhance the network lifetime of wireless sensor networks. We propose DEBC protocol, which is distributed energy balanced clustering for wireless sensor networks. DEBC forms clusters, selects cluster heads, selects cluster senders and determines appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. DEBC compared to well known protocol with two metrics network lifetime and energy dissipation. The simulation results show that DEBC minimizes energy consumption of sensor nodes and enhance the network lifetime.

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

Wireless sensor networks consist of large number of sensor nodes that have an ability to interact with the surrounding environment. The importance of finding the suitable protocol to estimate the trajectory for any moving target in a certain environment is a case of study for researchers. In this paper, new and accurate protocol for target tracking with minimum energy consumption is proposed. The proposed approach uses the static cluster and dynamic cluster head mechanism (SCDCH) to collect data from the active nodes, and then forwards it to the cluster head (CH), which in turn will send the collected data to the base station, without changing cluster border during the network's lifetime. Simulation results show that the proposed protocol has higher accuracy and save more energy compared with static clustering protocols which in turn increases the 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.

Adequate clustering provides an effective way for prolonging the lifetime of a wireless sensor network (WSN). Most proposed clustering algorithms do not consider the location of the base station. This will lead to the hot spots problem in multi-hop WSNs. In this paper, a fuzzy clustering topology is employed to prolong the lifetime of WSNs. Considering the residual energy, the distances to the base station and influence of neighboring parameters of the sensor nodes, the cluster-head radius are adjusted by fuzzy clustering topology. This helps to decrease the intra-cluster traffic load of sensor nodes closer to the base station or having lower battery level. The uncertainties in the estimation of cluster-head radius are handled by fuzzy logic. Our approach is compared with some popular algorithms in literature, including LEACH, Gupta and CHEF. Our approach performs in various performance metrics, such as First Node Dies (FND), Half of the Nodes Alive (HNA), Last Node Dies (LND) and energy-efficiency metrics. Simulation results show that the proposed method performs better than the other algorithms, up to 54% in certain cases. Therefore, this method is a stable and energy-efficient clustering algorithm can be applied to any real-world WSN applications.

Energy efficiency is an active research area in WSNs technology. There is an extensive research work going towards this direction and existing mechanisms fail to address the problem completely due to its limited specificity. This work focuses on the idea of enhancing the network lifetime on the basis of cluster-heads (CHs) remaining energy levels. Thus, instead of changing CHs at every round for dynamic clustering, we propose two sorts of CH threshold function as energy threshold function and optimal CH range threshold function for maximizing the network lifetime. Simulation results show that our protocol maximizes the network lifetime and captures the data three times more than the other known energy-efficient protocols in the literature.

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 clarification of wireless sensor network (WSN) clustering process at household and overseas, an energy efficient cluster head formation technique for WSN are set forth with the purpose of solving the problem of arbitrary cluster head election which may cause overlying coverage and uneven energy utilization in cluster communique. Proposed algorithm selects the cluster head in two stages. In first stage selection of cluster head is done by professed probability and in second stage selection of cluster head is done by endurance time approximation. Simulation outcomes exhibit proposed scheme steadiness network energy in two stages in contrast with existing scheme. The network lifetime is enlarged by significant amount compared existing LEACH protocol. Furthermore, endurance time of network is elongated in contrast with EBDC algorithm as well as AEOC algorithm accomplishing the helpfulness of network energy utilization which has larger network lifespan.

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

In order to solve the problem of dynamic clustering for wireless sensor networks(WSN) triggered by target event, a dynamic clustering protocol, focusing on cluster head selection, cluster member solicitation and cluster reorganization, is proposed. In cluster head selection mechanism, the proposed selection approach takes the detected signal strength, residual energy of node and distance between the cluster head and the sink node into consideration. In cluster member solicitation, an adaptive method based on task requirement is proposed in terms of cluster range setting, through this method, the cluster size is constrained and the energy consumption of intra-cluster communication is reduced. The simulation results show that the proposed protocol can efficiently conserve energy and prolong the lifetime of WSN, especially when the sink node is located far from the network.

Wireless sensor network (WSN) consists of thousands of sensor nodes with low power, low memory, and low computational capabilities. Therefore, energy issues have to be taken into considerations while designing WSN applications and protocols. Routing protocols such as LEACH and LEACH-C have been proposed for energy conservation in WSN. However, these typical protocols have drawback of unequal energy depletion in cluster heads (CHs) due to the different transmission distance from each CH to the base station (BS). In order to minimize the energy consumption caused by large distance of CHs from the BS, we propose dynamic clustering and distance aware routing protocol (DDAR) for WSN which takes into account of node distance for selecting CH, dynamic approach of selecting the CH nodes, and two level hierarchy of clustering with super cluster head (SCH) node near to the BS. Simulation results show that the proposed DDAR protocol consumes about 15.5% less energy than conventional LEACH protocol.

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.

Clustering techniques in wireless sensor networks (WSNs) compare to random selection techniques is less costly due to the saving of time in journeys, reduction in number of transmissions and receptions at each node, identification, contacts, etc., which are valuable for increasing the overall network life, scalability of WSNs. Clustering sensor nodes is an effective and efficient technique for achieving the requirement. The maximizing lifetime of network by minimizing energy consumption poses a challenge in design of protocols. Therefore, proper organization of clustering and orientation of nodes within the cluster becomes one of the important issues to extend the lifetime of the whole sensor network through Cluster Head (CH). We investigate the problem of energy consumption in CH rotation in WSNs. In this paper, CH selection algorithm has been proposed from an unequal cluster. The total energy and expected number of packet retransmissions in delivering a packet from the sensor node to other nodes have been mathematically derived. In this paper, we applied the approach for producing energy-aware unequal clusters with optimal selection of CH and discussed several aspects of the network mathematically and statistically. The simulation results demonstrate that our approach of re-clustering in terms of energy consumption and lifetime parameters.