Optimal Number Of Cluster Heads Research Articles

EDB‐CHS‐BOF: energy and distance‐based cluster head selection with balanced objective function protocol

In this research, two novel cluster head selection protocols are proposed, namely, Energy and Distance Based Cluster Head Selection (EDB-CHS) and EDB-CHS with Balanced Objective Function (EDB-CHS-BOF). They mainly aim at balancing energy consumption amongst all sensor nodes, prohibiting their precocious death, and extending the network lifetime. More specifically, in EDB-CHS protocol, a new cluster shape is proposed and consequently a tight closed-form expression for the optimal number of cluster heads (CHs) in the network is derived. Additionally, an efficacious CHS algorithm is presented, which is an expression of threshold probability that takes into account the residual energy of sensor nodes, how far they are located from the Base Station (BS), as well as the node's optimal probability of being a CH. On the other hand, the EDB-CHS-BOF protocol is proposed mainly to address the matter of having long-distance communications as a result of obtaining adjacent CHs. In EDB-CHS-BOF protocol, a new threshold probability for each sensor node to be a CH in any round is proposed. Not only to this extent, but rather a balanced objective function is proposed to ensure a well-proportioned distribution of CHs over the network. Simulation results demonstrate the superiority of the proposed protocols over other directly related works in terms of network lifetime and total data delivery.

Research on routing optimization of WSNs based on improved LEACH protocol

LEACH routing protocol equalizes the energy consumption of the network by randomly selecting cluster head nodes in a loop, which will lead to the defect of unstable network operation. Therefore, in order to solve this problem, it is necessary to reduce the energy consumption of data transmission in the routing protocol and increase the network life cycle. However, there is also a problem that cluster heads count with a wide range and the cluster head forwarding data consumed greatly power in the LEACH, which remains to be solved. In this paper, we put forward an approach to optimize the routing protocol. Firstly, the optimal number of cluster head is calculated according to the overall energy consumption per round to reduce the probability of excessive cluster head distribution. Then, the cluster head is used as the core to construct the Voronoi Diagram. The nodes in the same Voronoi diagram become a cluster, that the energy consumption communication in intra-cluster would be less. Finally, in order to optimize the multi-hop routing protocol, an ant colony algorithm is added using a cluster head near the BS to receive and forward it from a remote cluster head. According to the MATLAB simulation data, the protocol can significantly prolong the lifetime of WSNs compared with the LEACH protocol and increase the energy efficiency per unit node in per round. Energy consumption of the proposed approach is only. The approach improved the First Node Death (FND) time by 127%, 22.2%, and 14.5% over LEACH, LEACH-C, and SEP, respectively.

Genetic-fuzzy based load balanced protocol for WSNs

<p><span>Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.</span></p>

Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing

Energy is a precious resource in the sensors-enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly, which may interrupt the operations in the network. Further, a single artificial intelligence technique is not enough to solve the problem of load balancing and minimize energy consumption, because of the integration of ubiquitous smart-sensors-enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesized fuzzy logic and a neural network to counterbalance the selection of the optimal number of cluster heads and even distribution of load among the sensors. We developed fuzzy rules, sets, and membership functions of an adaptive neuro fuzzy inference system to decide whether a sensor can play the role of a cluster head based on the parameters of residual energy, node distance to the base station, and node density. The proposed ANFCA outperformed the state-of-the-art algorithms in terms of node death rate percentage, number of remaining functioning nodes, average energy consumption, and standard deviation of residual energy.

Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm

Past decades have witnessed the advancement of wireless sensor networks (WSNs) in both academic and industrial communities. Clustering is one of the most popular methods to increase the lifespan of WSNs. The optimal number of cluster heads and how to organize the clusters are the most important issues to be addressed in the clustering methods. In this paper, we proposed a novel user-independent and dynamical method to calculate the optimal number of clusters, organize the clusters, and determine the best cluster heads in each round. In this method, efficient energy consumption and link quality were considered to compute the optimal number of clusters. Then, the algorithm began to organize the compact clusters with high energy level cluster heads. We investigated a new fitness function in order to achieve these objectives. A new version of gravitational search algorithm (GSA) was used to solve this optimization problem. In this algorithm, the power distance sums scaling method was applied to calculate the mass values. Then, a fuzzy logic controller is employed to identify the parameter of this algorithm to control the exploitation and exploration abilities of the method during the computational process of the algorithm. Then, the novel version of GSA was applied to reach an appropriate solution for the fitness function, find the optimal number of clusters, and properly organize these clusters. To evaluate the effectiveness of the proposed method, several experiments were performed and the obtained results were compared with the results of other popular clustering methods. The simulation results revealed that the performance of the modified GSA was better than other state-of-the-art meta-heuristic optimization algorithms. Moreover, the proposed method for the clustering problem in WSNs outperformed other popular clustering methods and increased the lifetime of WSNs.

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

SummaryNon‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.

Improvement of Hierarchical Routing Algorithm in Wireless Sensor Network

To prolong the life cycle of the network and increase the effective data transmission quantity of the network, several classic network layer routing algorithms are analyzed and they are implemented with programming language, which verifies their real advantages and disadvantages. In the model of ring networking, the optimal number of cluster heads in the annular region is deduced with the goal of minimizing the average energy consumption of cluster heads in each ring,and the formation mechanism of cluster heads is proposed for the purpose of preferentially using nodes with more residual energy as cluster heads.To further optimize energy consumption performance, a dynamic self-organizing multi-hop routing selection mode is proposed.Finally, the network simulation software is applied to construct the model based on ring network, and the optimization algorithm is adopted to obtain the flow chart of actual programming and some programming methods.The results show that the proposed algorithm achieves the ultimate optimization goal, which increases the number of effective monitoring data packets.

An Improved Clustering Protocol for Energy Heterogeneous WSN

This paper studies on the problem that the unreasonable choice of clustered routing protocol cluster head of energy heterogeneous WSN and the overload of some cluster head node which are far away from the base station in remote communication. And an improved clustering routing protocol based on energy and density for heterogeneous wireless sensor networks is proposed. Firstly, the optimal number of cluster head is got according to the network energy consumption model, and the node density factor is modified. Then, the new threshold is established by considering the residual energy of the node, the node density and the distance from the node to the BS. The cluster head communicates with the BS by using a single hop and a hybrid way through a relay node to reduce the cluster head load of the remote base station. Finally, the simulation results based on OMNet++ and Matlab show that the improved protocol has better performance in terms of network lifetime and network energy consumption compared with existing protocols.

An optimal clustering mechanism based on Fuzzy-C means for wireless sensor networks

In order to balance the node’s energy consumption and extend the lifetime under energy-constrained wireless sensor networks, an energy-efficient clustering algorithm based on Fuzzy-C means for wireless sensor networks is proposed. Taking into account the uneven distribution of the sensor nodes and the uncertainty of the radio channel, the cluster formation process of nodes is modeled as a fuzzy partition of sample space in this paper. Firstly, the overall energy consumption of the networks is analyzed, and the optimal number of cluster heads is estimated based on node’s density. Secondly, in the design of the objective function, the distance from the node to the cluster head and the weight of the membership values are considered. Then, the improved Fuzzy-C means clustering algorithm is proposed to divide the sensor nodes into a specified number of clusters. Finally, a single hop communication mode is used for intra cluster communication, and inter cluster communication adopts a multi-hop communication mode. The simulation results show that the proposed algorithm can obtain uniform spatial distribution of cluster heads and balance the energy consumption of network effectively.

IRPL: An energy efficient routing protocol for wireless sensor networks

Abstract This study aims to overcome the disadvantages of the original RPL (IPv6 Routing Protocol for Low power and Lossy networks) routing protocol (RPL including problems with energy consumption and energy load balance). We developed a relatively balanced RPL – the improved protocol (IRPL). This protocol is based on an efficient clustering algorithm and an effective topology control model of the loop domain communication route. The clustering algorithm can be used to calculate the optimal number of cluster heads by assumption of the network model. Combined with the clustering probability model and the node competition mechanism, the cluster head node in the wireless sensor network was used to complete the clustering process. In the topology control model, the wireless sensor network was divided into concentric rings with equal areas. Nodes determined the best network route, depending on different levels of ring domain and the optimal forwarding communication area defined in this study. Simulation results indicate that the IRPL routing protocol can reduce overall network energy consumption, balance network energy consumption, and prolong network lifetime.

Cutting Down Energy Usage in Wireless Sensor Networks Using Duty Cycle Technique and Multi-Hop Routing

A wireless sensor network is composed of many sensor nodes that have been given out in a specific zone and each of them had an ability of collecting information from the environment and sending collected data to the sink. The most significant issues in wireless sensor networks despite the recent progress is the trouble of the severe limitations of energy resources. Since that in different applications of sensor nets we could throw a static or mobile sink then all aspects of such networks should be planned with an awareness of energy. One of the most significant topics related to these networks is routing. One of the most widely used and efficient methods of routing is a hierarchy (based on clustering) method. In The present study with the objective of cutting down energy consumption and persistence of network coverage we have offered a novel algorithm based on clustering algorithms and multi hop routing. To achieve this goal first we layer the network environment based on the size of the network. We will identify the optimal number of cluster heads and every cluster head based on the mechanism of topology control will start to accept members. Likewise we set the first layer as gate layer and subsequently identifying the gate nodes we would turn away half of the sensors and then stop using energy and the remaining nodes in this layer will join the gate nodes because they hold a critical part in bettering the functioning of the system. Cluster heads off following layers send the information to cluster heads in the above layer until sent data will be sent to gate nodes and finally will be sent to sink. We have tested the proposed algorithm in two situations 1) when the sink is off and 2) when a sink is on and simulation data shows that proposed algorithm has better performance in terms of the life span of a network than LEACH and E LEACH protocols.

A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions.

This study proposes a novel power-efficient and anti-fading clustering based on a cross-layer that is specific to the time-varying fading characteristics of channels in the monitoring of coal mine faces with wireless sensor networks. The number of active sensor nodes and a sliding window are set up such that the optimal number of cluster heads (CHs) is selected in each round. Based on a stable expected number of CHs, we explore the channel efficiency between nodes and the base station by using a probe frame and the joint surplus energy in assessing the CH selection. Moreover, the sending power of a node in different periods is regulated by the signal fade margin method. The simulation results demonstrate that compared with several common algorithms, the power-efficient and fading-aware clustering with a cross-layer (PEAFC-CL) protocol features a stable network topology and adaptability under signal time-varying fading, which effectively prolongs the lifetime of the network and reduces network packet loss, thus making it more applicable to the complex and variable environment characteristic of a coal mine face.

Data collection using miniature aerial vehicles in wireless sensor networks

Energy constraints of sensor nodes in wireless sensor networks (WSNs) is a major challenge and minimising the overall data transmitted across a network using data aggregation, distributed source coding, and compressive sensing have been proposed as mechanisms for energy saving. Similarly, use of mobile nodes capable of relocating within the network has been widely explored for energy saving. In this study, the authors propose a novel method for using miniature aerial vehicles for data collection instead of actively sensing from a deployed network. The proposed mechanism is referred as data collection fly (DCFly). It is suitable for data collection from WSNs deployed in harsh- undulating terrain with the base station located far from the sensing region. The DCFly is compared with data collection based on multi-hop data aggregation and data collection with mobile sinks. The numerical results justify that the proposed data collection mechanism is effective and efficient for use in WSNs. 1 Introduction Wireless sensor networks (WSNs) are deployed in an area to be monitored, referred to in this paper as the sensing region, for diverse applications such as habitat and environmental monitoring, battlefield surveillance, and industrial monitoring. Using active node mobility in the network, overall communication in the network can be reduced and specifically nodes close to the base station (BS) could be prevented from draining out. Various utilities that are possible using active node mobility in the network are: mobile relay, mobile data mule, and mobile BS. Alternatively, in-network data processing mechanisms such as data aggregation, network coding, and compressive sensing could be utilised for minimising the overall communication of the network. These two approaches, that is, node mobility, and in-network processing have been extensively explored individually and independently. This paper presents a mechanism for data collection using a data collection fly (DCFly). The DCFly would aerially collect data from the network. This is the first work that explores the potential of using miniature aerial vehicles (MAVs) for collecting data, collectively involving node mobility and in-network data processing. The underlying framework adopted in this paper has been presented in our preliminary work (1). The remaining paper has been structured as follows: in Section 2, previous work relating to node mobility and in-networking data processing has been presented. Section 3 provides an insight into factors governing optimal number of cluster heads (CHs) and inter-relation with position of BS, data collection operation by the DCFly; these aspects influence the network architecture. The factors governing the DCFly's possible use in the sensing region and the network operational conditions considered for evaluation are presented in Section 4. The DCFly's governing factors formulated as an optimisation problem to determine the feasible operational utility are presented in Section 5. The optimum number of clusters that can be covered by a single DCFly are presented in Section 6, along with the comparative evaluation of the proposed DCFly with a multi-hop data aggregation mechanism (referred as DA mechanism). The DCFly-based data collection is also compared with data collection from the network relying on mobile elements (MEs) in Sections 6.1 and 6.2. Finally, this paper is concluded in Section 7. 2 Related work As stated earlier, the methods for minimising the energy consumption in the network can be broadly categorised into node mobility and in-network data processing-based mechanisms.

A Centralized Energy Efficient Distance Based Routing Protocol for Wireless Sensor Networks

Wireless sensor network (WSN) typically consists of a large number of low cost wireless sensor nodes which collect and send various messages to a base station (BS). WSN nodes are small battery powered devices having limited energy resources. Replacement of such energy resources is not easy for thousands of nodes as they are inaccessible to users after their deployment. This generates a requirement of energy efficient routing protocol for increasing network lifetime while minimizing energy consumption. Low Energy Adaptive Clustering Hierarchy (LEACH) is a widely used classic clustering algorithm in WSNs. In this paper, we propose a Centralized Energy Efficient Distance (CEED) based routing protocol to evenly distribute energy dissipation among all sensor nodes. We calculate optimum number of cluster heads based on LEACH’s energy dissipation model. We propose a distributed cluster head selection algorithm based on dissipated energy of a node and its distance to BS. Moreover, we extend our protocol by multihop routing scheme to reduce energy dissipated by nodes located far away from base station. The performance of CEED is compared with other protocols such as LEACH and LEACH with Distance Based Thresholds (LEACH-DT). Simulation results show that CEED is more energy efficient as compared to other protocols. Also it improves the network lifetime and stability period over the other protocols.

Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network

Abstract Wireless Sensor Network (WSN) has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS) aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC) algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC).

Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks

Energy hole problem is a critical issue for data gathering in wireless sensor networks. Sensors near the static sink act as relays for far sensors and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to alleviate this problem. However, determining an optimal moving trajectory for a mobile sink is a non-deterministic polynomial-time hard problem. Thus, this paper proposed a mobile sink-based adaptive immune energy-efficient clustering protocol (MSIEEP) to alleviate the energy holes. A MSIEEP uses the adaptive immune algorithm (AIA) to guide the mobile sink-based on minimizing the total dissipated energy in communication and overhead control packets. Moreover, AIA is used to find the optimum number of cluster heads (CHs) to improve the lifetime and stability period of the network. The performance of MSIEEP is compared with the previously published protocols; namely, low-energy adaptive clustering hierarchy (LEACH), genetic algorithm-based LEACH, amend LEACH, rendezvous, and mobile sink improved energy-efficient PEGASIS-based routing protocol using MATLAB. Simulation results show that MSIEEP is more reliable and energy efficient as compared with other protocols. Furthermore, it improves the lifetime, the stability, and the instability periods over the previous protocols, because it always selects CHs from high-energy nodes. Moreover, the mobile sink increases the ability of the proposed protocol to deliver packets to the destination.

Research on Optimizing Region-based Clustering for LEACH Routing Protocol of WSN

This paper intends to improve the current LEACH Hierarchical Routing Protocol of WSN, in order to solve the problems of uneven distribution of cluster heads and the imbalanced energy consumption caused by randomly selecting the cluster heads. This paper proposes an algorithm of optimizing the number of the cluster heads, which divides the clus- ter regions evenly according to the minimum distance of the cluster heads which are determined by the optimal number of cluster heads. Furthermore, our novel approach restricts the cluster heads rotation within the cluster region, with our opti- mized algorithm, so it can guarantee an optimal balanced point for each interval of the cluster rotations, which saves the network energy consumption, and prolongs the generation period of the network, and improves the load balance of the network as well. Therefore, our approach improves the network performance of WSN significantly.

Improvement of Clustering Algorithm for WSNs

Energy consumption is a hot issue in WSNs (Wireless Sensor Networks). In this paper, we present an improved clustering algorithm. By changing the order of traditional WSNs clustering algorithm, this algorithm uses k-means clustering firstly base on optimal number of cluster head is determined; Then selects cluster head by an improved LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm; Finally, Our experimental results demonstrate that this approach can reduces energy consumption and increases the lifetime of the WSNs.

A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks

This paper presents a new Genetic Algorithm-based Energy-Efficient adaptive clustering hierarchy Protocol (GAEEP) to efficiently maximize the lifetime and to improve the stable period of Wireless Sensor Networks (WSNs). The new protocol is aimed at prolonging the lifetime of WSNs by finding the optimum number of cluster heads (CHs) and their locations based on minimizing the energy consumption of the sensor nodes using genetic algorithm. The operation of the GAEEP is broken up into rounds, where each round begins with a set-up phase, when the base station finds the optimum number of CHs and assigns members nodes of each CH, followed by a steady-state phase, when the sensed data are transferred to CHs and collected in frames; then these frames are transferred to the base station. The performance of the GAEEP is compared with previous protocols using Matlab simulation. Simulation results show that GAEEP protocol improves the network lifetime and stability period over previous protocols in both homogeneous and heterogeneous cases. Moreover, GAEEP protocol increases the reliability of clustering process because it expands the stability period and compresses the instability period.

Research on Improvement of LEACH Protocol with Heterogeneous Nodes

It is necessary to consider the energy efficiency of nodes and load balance in wireless sensor networks. This paper combines with the network of the actual heterogeneous nodes, the improved algorithm of LEACH is proposed, which is called LEACH-HA. The optimal number of cluster heads is derived with improved in the network which increases energy of nodes become cluster probability. A hybrid communication mode that cluster heads alternate between single hop and multi-hop mode is proposed. After analysis LEACH-HA improves the energy efficiency among nodes and expands the coverage of network.