Residual Energy Of Cluster Head Research Articles

Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization

Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a “hot zone” problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.

Lifetime Maximization of Heterogeneous WSN Using Fuzzy-based Clustering

Background: Wireless Sensor Network (WSN) is an arising field for research and development. It has various applications ranging from environmental monitoring to battlefield surveillance and more. WSN is a collection of multiple sensor nodes used for sensing the environment. But these sensing nodes are deployed in such areas where it is not that easy to reach, so battery used in these nodes becomes quite impossible to change, so we need to utilize this energy to get the maximum sensing for a long time. Objective: To use the Fuzzy approach in the clustering algorithm. Clustering is a key approach to prolong the network lifetime with minimum energy utilization. In this paper, our main concern is on the Cluster Head (CH) selection. So, we are proposing a clustering algorithm which is based on some of the attributes: Average Residual Energy of CHs, Average Distance from nodes to CHs, Standard Deviation of member nodes, and Average Distance from CH to Base Station(BS). Methods: Initially, some of the nodes are found having greater residual energy than the average network energy, and fifteen populations are made each having an optimum number of CHs. The final and best CHs set is chosen by determining the maximum fitness value using a fuzzy approach. Result: The result positively supports the energy-efficient utilization with lifetime maximization, which is compared with the Base algorithm [1] and LEACH [2] protocol based on residual energy and the number of nodes that die after performing some rounds. Conclusion: The proposed algorithm determines a fuzzy-based fitness value, provides load-balancing among all the networking nodes, and performs a selection of best Cluster Heads, resulting in prolonged network lifetime and maximized efficiency.

Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network

The nodes in the wireless sensor network are furnished with restricted and irreplaceable battery power. The continuous sensing, computation, and communication drain out the energy of sensors very quickly. The optimal utilization of the sensor energy has always been a key issue for all the applications in the wireless sensor network. To manage the energy issue of nodes, various approaches were proposed in the past which focused on designing the proper energy management methods. The clustering of sensors is one of the most popular techniques used to manage the energy-related concerns of networks. In this paper, a particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance. The proposed protocol uses the particle swarm optimization technique to select the cluster head and relay nodes for the network. The cluster head is selected by employing the particle swarm optimization based fitness function which considers the energy ratio (initial energy and residual energy) of nodes, distance between nodes and cluster head, and node degree to appoint the most optimal node for the cluster head job. For the data transfer to base station, the proposed scheme uses the fitness value based on residual energy of cluster head and distance to base station parameters to nominate the relay nodes for the multi-hop data transfer to the base station. The performance of the proposed protocol is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness. The proposed scheme has improved the lifetime of the network by 238%, 136%, 106%, and 71% as compared to the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO techniques used in the simulation results for the comparison purpose. The stability period of the network in proposed scheme is approximately 396%, 321%, 246%, and 126% more than the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO protocol .

Design and Improvement of Routing Protocol for Field Observation Instrument Networking Based on LEACH Protocol

Field observation instruments in cold and arid areas are deployed with many difficulties caused by the harsh natural environment, which leads to the lag of information acquisition ability and severely restricts the geoscience research in these areas. Therefore, it is urgent to study the suitable routing technology of observation instrument networks according to the characteristics of cold and arid areas. In this paper, we have studied and designed an improved routing protocol for the field observation instruments network based on the LEACH protocol (FOI-LEACH). Firstly, the FOI-LEACH was proposed to mainly improve the LEACH protocol in three aspects: (1) the network nodes are heterogeneous and combined with the characteristics of field observation instrument networking. The residual energy and the rechargeable energy of nodes are added in the process of cluster head (CH) election to reduce the risk of premature death of CHs and shortened network life cycle caused by the selection of nodes with less energy as CH. (2) In the process of cluster forming, the distance from CH to the base station (BS) and the residual energy of CH is considered when setting the cluster radius, to reasonably plan the cluster size and alleviate the “hot spot” problem. The nonuniform distribution of clusters in the network is enhanced to balance the total network energy consumption. (3) The autonomous zone-based multihop routing mechanism is adopted to solve the low reliability of data transmission caused by the poor quality of intercluster communication links and premature death of nodes in long-distance transmission. Then, MATLAB was used to compare the network routing protocol model of the observation instrument from four aspects, network life, energy consumption rate, stability, and throughput. The results showed that the improved algorithm FOI-LEACH balances the network energy consumption and alleviates the “hot spot” problem, to extend the lifetime of network nodes.

APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm

APTEEN routing protocol exists the problems of uneven network energy consumption, premature death of some nodes, consume too much unnecessary energy and low effective coverage of the whole network. To solve these problems, this paper optimizes the APTEEN routing protocol by combining genetic algorithm with fruit fly optimization algorithm. By adding residual energy, distance from node to base station, distance from node to geometric center of the whole network, node degree and other selection factors to cluster heads selection, the genetic algorithm and fruit fly optimization algorithm is used to select cluster heads for the first time, and the second time of cluster heads selection based on density adaptive algorithm. Some nodes are selected to sleep according to the position and degree of nodes. The residual energy of cluster head, the distance between node and cluster head, and the number of cluster members are taken into account when nodes join clusters. When energy is transmitted from cluster heads to base station, the Dijkstra algorithm is used to find the optimal path. Add the rule of rotating cluster heads when the energy consumption of data transmission is too high, and the GA-APTEEN routing protocol is obtained through the above optimization. The simulation results show that the GA-APTEEN improves the 50% lifetime, 10% coverage and robustness of the network, reduces the energy consumption of the overall network system and avoids the phenomenon of the hot zone of energy.

Hybrid Cluster Head Election for WSN Based on Firefly and Harmony Search Algorithms

Design of energy efficient routing protocols for Wireless Sensor Network (WSN) is a great challenge for researchers. Recently, WSNs have gained lot of popularity and many energy efficient routing solutions are proposed. Most of the existing routing protocols focus on cluster head election and ignoring other important aspects of routing such cluster formation, data aggregation, etc. This research article presents a hybrid cluster head election for WSN based on firefly and harmony search algorithms. The contributions of the proposed protocols are (1) two level cluster head election strategy. In the first stage harmony search algorithm is used to determine initial set of energy efficient cluster head nodes that are sufficiently separated from on another by certain optimal distance. Then tentatively elected cluster head nodes are refined by firefly algorithm by considering the parameters such as node density, cluster compactness and energy to be consumed. Sometimes nature inspired optimization techniques may end up in early convergence and to avoid such problems, cluster head election scheme is divided at two levels. (2) a refined cluster formation strategy is designed where a normal node has privilege of joining to cluster head node either based on distance based metric or based on residual energy of cluster heads. This process of cluster formation helps in reduced energy consumption. The presented protocol is compared with some of the well-known clustering protocols such as LEACH, LEACH-C, EOICHD, and simple firefly based routing protocol based on the evaluation metrics such as number of alive nodes, energy consumption of network, number of packets received by Base Station, First Node Dead, Half Node Dead and Last Node Dead. Implementation is carried out using Network Simulator (NS 2.34) and results show that proposed hybrid cluster head election scheme outperforms the mentioned routing protocols.

MPF-LEACH: modified probability function for cluster head election in LEACH protocol

In this research, we enhance the LEACH protocol by updating Cluster Head (CH) election probability function (thresholds). More probability was given to an out-of-service CH to be elected again. The idea is to get benefit from CH residual energy in order to extend the network lifetime. A new threshold was introduced which guarantees a non-zero probability value of a CH. We proposed a newly developed research technique to enhance the original LEACH protocol. The enhancement focuses on extending a WSN's lifetime, and increasing its throughput. It is achieved by giving more probability to re-elect the expired CH that has been removed from CHs list because of its insufficient residual energy. Several experiments were conducted to evaluate the efficiency of our proposed MPF-LEACH approach. From the experimental results, a remarkable enhancement in the network lifetime and throughput is achieved. We have improved the election probability threshold for the original LEACH protocol by benefiting from the CH residual energy. As a result, the whole network lifetime was increased due to the extra chance that is given to a CH to be elected again.

MPF-LEACH: modified probability function for cluster head election in LEACH protocol

In this research, we enhance the LEACH protocol by updating Cluster Head (CH) election probability function (thresholds). More probability was given to an out-of-service CH to be elected again. The idea is to get benefit from CH residual energy in order to extend the network lifetime. A new threshold was introduced which guarantees a non-zero probability value of a CH. We proposed a newly developed research technique to enhance the original LEACH protocol. The enhancement focuses on extending a WSN's lifetime, and increasing its throughput. It is achieved by giving more probability to re-elect the expired CH that has been removed from CHs list because of its insufficient residual energy. Several experiments were conducted to evaluate the efficiency of our proposed MPF-LEACH approach. From the experimental results, a remarkable enhancement in the network lifetime and throughput is achieved. We have improved the election probability threshold for the original LEACH protocol by benefiting from the CH residual energy. As a result, the whole network lifetime was increased due to the extra chance that is given to a CH to be elected again.

MLBC: Multi-objective Load Balancing Clustering technique in Wireless Sensor Networks

In Wireless Sensor Networks (WSN) there are scenarios with conflicting objectives. This needs to be modeled as multi-objective optimization formulation. The optimization problem changes with the nature of application, network scenario and input/output parameters. Depending upon the underlying application requirements, there is a need of an appropriate Multi-Objective Optimization technique to manage these multiple conflicting objectives simultaneously and to yield an overall optimal solution. In this paper, a novel Multi-objective Load Balancing Clustering (MLBC) technique is proposed by utilizing Multi Objective Particle Swarm Optimization (MOPSO). Two objective functions are defined: Energy Efficiency and Reliability. The energy efficiency is based on the average residual energy of Cluster Heads (CHs), whereas the reliability is based on the communication cost of inter-cluster routing. The nodes transmit their information to Cluster Head or Base Station in single-hop or multi-hop manner. A healing function is utilized to avoid loops in the generated path. The load balancing is performed by shuffling the roles of next hop node and Cluster Head in each iteration. The best compromise solution is selected through the fuzzy decision-based approach. The performance of the proposed and existing approaches has been evaluated in terms of energy efficiency, network lifetime, packet delivery ratio, data accuracy and number of active nodes. Apart from these parameters, coverage and scalability are also considered to evaluate the quality of solutions provided by multi-objective optimization approach.

An Election Energy Threshold Based Multi-Hop Routing Protocol in a Grid-Clustered Wireless Sensor Network

Owing to the limited energy of sensor nodes (SNs) in a wireless sensor network (WSN), it is important to reduce and balance the energy consumption of the SNs in order to extend the WSN lifetime. Clustering mechanism is a highly efficient and effective mechanism for minimizing the amount of energy that SNs consume during the transmission of data packets. In this paper, an election energy threshold based multi-hop routing protocol (mEEMRP) is presented. In order to minimize energy consumption, this routing protocol uses grid clustering, where the network field is divided into grid clusters. SNs in each grid cluster select a cluster head (CH) based on a weight factor that takes the node location, node’s residual energy (RE) as well as the node’s distance from the base station into consideration. An energy efficient multi-hop routing algorithm is adopted during the transmission of data packets from the cluster heads (CHs) to the base station (BS). This multi-hop routing algorithm uses an election energy threshold value, T­nhCH that takes into consideration the RE of CHs as well as the distance between CHs. Simulation results show a 1.77% and 10.65% improvement in terms of network lifetime for two network field scenarios over Energy Efficient Multi-hop Routing Protocol (EEMRP).

Energy‐efficient medium access control and routing protocol for multihop wireless sensor networks

This work presents a combined energy-efficient medium access control (MAC) and routing protocol for large-scale wireless sensor networks that aims to minimise energy consumption and prolong the network lifetime. The proposed communication framework employs the following measures to enhance the network energy efficiency. Firstly, it provides an adaptive intra-cluster schedule to arbitrate media access of sensor nodes within a cluster, minimising idle listening on sensor nodes, leading to improved energy performance. Secondly, it proposes an on-demand source cross-layer routing protocol ensuring selection of best routes based on energy level and channel quality indicator for the multihop inter-cluster data transmission. Lastly, an unequal cluster size technique based on cluster head residual energy and distance away from the base station is utilised. This technique balances the energy among clusters and avoids early network partitioning. This work further presents the analytical performance model for energy consumption and delay of the proposed communication framework. The performance measures used for evaluation are energy consumption, delay, and network lifetime. The results indicate that combining routing and MAC schemes conserves energy better than utilising MAC scheme alone.

Load-Aware Cluster-Head Rotation in Wireless Sensor Network

In a wireless sensor network (WSN), few to thousands of sensors monitor information about nearby events and cooperatively transmit it to the base station for further processing. The sensors do their duty with a finite energy source, and in extreme conditions, the replacement or recharge of this energy is not possible always. Therefore, it is extremely important for every sensor to efficiently use its finite energy to make the network durable. Clustering based protocols are widely used in the literature as an energy saving scheme. In a cluster, the head node consumes more energy than regular member nodes. The head node not only senses the data from its surroundings but also receives the data from associated members, aggregates the received data with its own data, and transmits the aggregated data to the base station. The existing clustering protocols use cluster-head rotation and re-clustering approaches to evenly distribute the energy-intensive load of cluster-head among all the member nodes and all the nodes of the network, respectively. This paper proposes a mechanism that considers the current energy load of head node to determine the dynamic threshold for cluster-head rotation to minimize the premature death of cluster-heads. The proposed cluster-head rotation mechanism is compared with the other version of the proposed protocol where cluster-head is rotated based on a static threshold on residual energy of cluster-head. The results show that the proposed rotation mechanism outperforms its comparatives.

Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks

WSNs have many applications in modern life. Thus, optimization of the network operation is required to maximize its lifetime. The energy is a major issue in order to increase the lifetime of WSNs. The clustering algorithm is one of the proposed algorithms to enhance the lifetime of WSNs. The operation of the clustering algorithm is divided into cluster heads (CHs) selection and cluster formation. However, most of the previous works have focused on CHs selection, and have not considered the cluster formation process, which is the important issue in clustering algorithm based routing schemes, and it can drastically affect the lifetime of WSNs. In this paper, a Fuzzy Logic based Clustering Algorithm for WSN (CAFL) has been proposed to improve the lifetime of WSNs. This approach uses fuzzy logic for CHs selection and clusters formation processes by using residual energy and closeness to the sink as fuzzy inputs in terms of CH selection, and residual energy of CH and closeness to CHs as fuzzy inputs in terms of clusters formation. Simulation results justify its efficiency.

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Power control strategy for clustering wireless sensor networks based on multi‐packet reception

For the requirements of energy saving in wireless sensor networks (WSNs), this study proposes a transmission power control strategy for cluster heads (CHs) based on multi-packet reception in clustering WSNs. First, this study proposes a new CHs selection algorithm for clustering WSNs. Second, transmission power levels of each CH are determined according to the number of CHs in monitoring area. Third, with restrictions of successive interference cancellation algorithm in sink, transmission power control strategy for CHs are determined by distance from CHs to sink and residual energy of CHs. For whole network, single-hop communication mode is designed to send data from sensor nodes to CHs, at the same time; it is also applied to send data from CHs to sink. Simulation results show that this power control strategy can reduce the energy consumption of sensor nodes and increase network throughput, which can greatly improve network lifetime. The authors believe that their findings will be useful guidelines for moving forward with successive interference cancellation research in WSNs.

Distributed Energy Balance Clustering Algorithm in Wireless Sensor Networks

Abstract The traditional clustering algorithm of Low-Energy Adaptive Clustering Hierarchy (LEACH) does not care about the residual energy of the cluster heads and the distance relationship of each node in the wireless sensor networks (WSNs). Each new round the cluster head is changed without any concern about the residual energy of the current cluster head, and the nodes join into a new cluster head without concern about the distance factor and the residual energy factor of all nodes in WSNs. To solve these problems in LEACH, this paper puts forward a distributed energy balance clustering algorithm, which takes into full consideration the residual energy of the cluster heads and the distance relationship between the base station and the cluster heads. The experimental results indicate that the proposed distributed energy balance clustering algorithm can prolong the lifetime of the networks more than LEACH, and the number of messages for selecting a new cluster head at the same time can be greatly reduced, which proves it as more suitable for practical applications.