Distributed Cluster Head Research Articles

A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks

Aiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumption. Through effective transformation and theoretical analysis, cluster radius of each ring is derived by minimizing the network energy consumption and balancing the residual energy among nodes in different rings. Distributed cluster heads (CHs) selection and cluster formation are carried out within this range to control the cluster size and the corresponding energy cost. Expected times for being CHs metric is defined to measure nodes’ energy and spectral potential and help select powerful CHs. Simulation results show that ECMUC protocol is superior to most clustering protocols designed for CRSNs in terms of network surveillance capability and network lifetime, and it is also demonstrated that taking control overhead into consideration is beneficial for improving the network performance.

Code-based encryption techniques with distributed cluster head and energy consumption routing protocol

Fog computing and the Internet of Things (IoT) played a crucial role in storing data in the third-party server. Fog computing provides various resources to collect data by managing data security. However, intermediate attacks and data sharing create enormous security challenges like data privacy, confidentiality, authentication, and integrity issues. Various researchers introduce several cryptographic techniques; security is still significant while sharing data in the distributed environment. Therefore, in this paper, Code-Based Encryption with the Energy Consumption Routing Protocol (CBE-ECR) has been proposed for managing data security and data transmission protocols using keyed-hash message authentication. Initially, the data have been analyzed, and the distributed cluster head is selected, and the stochastically distributed energy clustering protocol is utilized for making the data transmission. Code-driven cryptography relies on the severity of code theory issues such as disorder demodulation and vibration required to learn equivalence. These crypto-systems are based on error codes to build a single-way function. The encryption technique minimizes intermediate attacks, and the data have protected all means of transmission. In addition to data security management, the introduced CBE-ECR reduces unauthorized access and manages the network lifetime successfully, leading to the effective data management of 96.17% and less energy consumption of 21.11% than other popular methods.The effectiveness of the system is compared to the traditional clustering techniques.

Energy-efficient cluster head selection algorithm for IoT using modified glow-worm swarm optimization

Internet of Things (IoT) is a new age technology that connects virtually every IP-enabled things in the world. Because of the constrained nature of the resources, energy-efficient clustering plays a very important role in successful implementation of these networks and also in increasing its lifetime. The proposed methodology provides an adaptive cluster head selection algorithm based on glow-worm swarm optimization algorithm. Most of the existing approaches groups nodes into clusters containing a fixed number of nodes. This is not applicable in certain cases, such as, when many of the nodes are dead. In the proposed approach, the number of nodes in each cluster is not fixed, and it automatically changes according to the number of alive nodes in the network, which increases the lifetime of the network. The proposed approach also ensures the minimum overlapping of the clusters by selecting geographically distributed cluster heads which increases the energy efficiency of the network by minimising the communication overhead. Repeated selection of cluster head also helps in load balancing in the network. We have implemented the proposed algorithm in OMNET++ simulator and demonstrated how it behaves with different densities of sensor deployment and communication ranges. The proposed algorithm is found to work notably well as compared to state-of-the-art IoT clustering protocol even when more than 20% nodes run out of energy. As time progresses, the improvement is even more apt.

Distributed genetic algorithm for lifetime coverage optimisation in wireless sensor networks

In this paper, a protocol called distributed genetic algorithm for lifetime coverage optimisation (DiGALCO) is suggested to preserve the coverage and enhance the lifetime of a wireless sensor network (WSN). DiGALCO combines three energy-efficient schemes: virtual network subdivision into subfields, distributed cluster head selection in each subfield, followed by sensor activity scheduling-based genetic algorithm (GA) optimisation performed by each cluster head. DiGALCO works into rounds. Each round consists of three phases: discovery, cluster head selection and GA decision and sensing. The decision process, which results in an activity scheduling vector, is achieved by a cluster head node through executing the GA to pick out a set of sensors staying active for monitoring through the current sensing round. In comparison with other protocols, several experimental results were done by using OMNeT++ network simulator show that DiGALCO is capable of prolonging the lifetime of WSN and gives enhanced coverage performance.

Distributed Genetic Algorithm for Lifetime Coverage Optimization in Wireless Sensor Networks

In this paper, a protocol called distributed genetic algorithm for lifetime coverage optimisation (DiGALCO) is suggested to preserve the coverage and enhance the lifetime of a wireless sensor network (WSN). DiGALCO combines three energy-efficient schemes: virtual network subdivision into subfields, distributed cluster head selection in each subfield, followed by sensor activity scheduling-based genetic algorithm (GA) optimisation performed by each cluster head. DiGALCO works into rounds. Each round consists of three phases: discovery, cluster head selection and GA decision and sensing. The decision process, which results in an activity scheduling vector, is achieved by a cluster head node through executing the GA to pick out a set of sensors staying active for monitoring through the current sensing round. In comparison with other protocols, several experimental results were done by using OMNeT++ network simulator show that DiGALCO is capable of prolonging the lifetime of WSN and gives enhanced coverage performance.

Energy Balanced Clustering Protocol for Wireless Sensor Networks

Wireless sensor network is composed of a large number of sensor nodes by self-organization in the monitoring area. The energy, computing and communication capabilities of sensor nodes are very limited, and therefore a good routing protocol is very important to prolong the lifetime of the network. An energy-balanced clustering protocol is proposed to solve the inequality problem of consumption in routing protocol. Clustering protocol considers the residual energy of nodes and the distance between sink nodes in the selection of cluster head node, which can be more evenly distributed cluster head. On the basis of estimating the optimal cluster number, the clustering method is improved, the number of clustering is reduced, and the network energy consumption is saved. The simulation experiment shows that the Clustering protocol makes the whole network energy consumption more balanced and increases the life cycle of the wireless sensor network.

Combining Solar Energy Harvesting with Wireless Charging for Hybrid Wireless Sensor Networks

The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1:61(1+ϵ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles' moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network.

Clustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS), which gives rise to the long distance communications problem, and in multi-hop routing, the CHs near the BS have to forward data from other nodes that lead those CHs to die prematurely, creating the hot zones problem. Unequal clustering has been proposed to solve these problems. Most of the current algorithms elect CH only by considering their competition radius, leading to unevenly distributed cluster heads. Furthermore, global distances values are needed when calculating the competition radius, which is a tedious task in large networks. To face these problems, we propose a flooding tree uneven clustering protocol (FTUC) suited for large networks. Based on the construction of a tree type sub-network to calculate the minimum and maximum distances values of the network, we then apply the unequal cluster theory. We also introduce referenced position circles to evenly elect cluster heads. Therefore, cluster heads are elected depending on the node’s residual energy and their distance to a referenced circle. FTUC builds the best inter-cluster communications route by evaluating a cluster head cost function to find the best next hop to the BS. The simulation results show that the FTUC algorithm decreases the energy consumption of the nodes and balances the global energy consumption effectively, thus extending the lifetime of the network.

Quick Evidence Synopsis

Wireless Sensor Network (WSN) provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS) algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI) and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios.

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.

DECH: Equally Distributed Cluster Heads Technique for Clustering Protocols in WSNs

Recent research works focus on the development of wireless sensor networks, leading to the arrival of new protocols. One of the goals of a routing protocol is reducing the power consumption. Clustering techniques are used in routing protocols to increase network lifetime and reduce network overhead. Major clustering protocols like LEACH, TEEN, DEEC and DSBCA randomly select nodes or cluster heads to initiate the clustering process. Here we propose a new pre-clustering technique that improves the life time of WSN and also increases the throughput. The proposed technique has been implemented on the above said protocols, which equally distributes the randomly triggered nodes or Cluster Heads based on link cost. The simulation results show that the stability period, network lifetime and throughput of the selected routing protocols have been improved using DECH technique.

Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network

Wireless Sensor Network (WSN) provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS) algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI) and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios.

Energy-Balanced On Demand Clustering Algorithm Based On Leach-C

As the use of Wireless Sensor Networks (WSNs) has grown enormously, the need for energy-efficient management has also risen. With advances in ubiquitous computing environment, WSNs have been broadly studied and many energy-efficient routing protocols had been proposed. LEACH (Low Energy Adaptive Clustering Hierarchy) is a popular cluster-based protocol, which provides distributed adaptive clustering and periodic cluster head (CH) selection rotation. As extension to LEACH, LEACH-C (LEACH Centralized) was proposed, in which the energy is utilized to select CH. However, both can’t guarantee cluster head distribution, in addition to considerable periodic clustering overhead. Furthermore, network topology change is a critical characteristic that has influence on communication path and load distribution among nodes. To resolve such problems, Energy-Balance on Demand Clustering Algorithm Based on LEACH-C is proposed. The algorithm adopts centralized cluster formation and distributed CH selection methods. Minimum energy clustering is used to divide the network into clusters, while energy and total communication distance are considered as secondary criteria to select optimal CH. From simulation results the proposed algorithm outperforms LEACH-C in life time, stability period and performance efficiency.

Moving target tracking through distributed clustering in directional sensor networks.

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

TEA-CBRP: Distributed cluster head election in MANET by using AHP

Mobile Ad hoc Network consists of a set of mobile nodes that are communicating in a wireless channel. In this network, the number of nodes and their mobility have an impact on the routing performance. In order to improve the routing performance of large scale Mobile Ad Hoc Networks, clustering is one of the solution. When clustering is implemented, an unconditional cooperation among the intra cluster and inter cluster nodes is necessary. In the event where a malicious or selfish node is elected as a cluster head, the routing performance gets significantly affected. In this paper, the key decision factors such as the trust value, remaining energy, and the time of availability of the mobile node is explored to elect a cluster head. Further, these three decision factors are incorporated into the Analytical Hierarchy Process technique in order to elect the most cooperative node as the cluster head. An enhancement to the existing Cluster based Routing Protocol, is proposed in this paper and then enhanced work, is termed as Trust Energy Availability based Cluster Based Routing Protocol. A network based on the proposed protocol is simulated. The important routing performance parameters such as packet delivery ratio, end to end latency, routing packet overhead, and the number of times cluster head changes are discussed for the simulated network and the results are compared with AODV and CBRP routing protocols. The simulation results have shown that the proposed cluster based routing protocol improves the network performance by eliminating malicious and selfish nodes from being elected as cluster head.

A distributed clustering scheme with self nomination: proposal and application to critical monitoring

Clustering is a well known methodology to optimize the use of the resources, to lower the congestion and to improve the reliability in self-organized networks as the wireless sensor networks. This paper deals with the proposal of a novel clustering approach based on a low complexity distributed cluster head election based on a two-stage process. In particular, a suitable objective function is introduced in order to take into account the number of 1-hop neighbours (i.e., node degree) and the residual node energy. It is shown in the paper that the proposed protocol achieves remarkable performance improvements with respect to different alternatives, especially in the case of unpredictable scenarios. Moreover, the proposed protocol exhibits self-organize capabilities that are of special interest for critical monitoring applications, in particular when the effect of nodes mobility is significant.

Multilayer cluster designing algorithm for lifetime improvement of wireless sensor networks

Cluster-based network is a proven architecture for energy-aware routing, but more attention is required to ameliorate the energy consumption aspect of its cluster designing process. In this research work, we introduce a novel design of clustered network architecture. The proposed design technique is innovative in its idea. The general trend in this scene is either centralized decision at base station for cluster head selection and its members or distributed decision by exchanging information between neighboring nodes until the cluster head and its members are selected. Both the techniques drastically create mess in energy consumption due to too much broadcasting, especially in large networks as well as message exchange until some final decision is made. Our novel layer-based hybrid algorithm for cluster head and cluster member selection comes up to novel communication architecture. Since its substantial constituent is cluster designing, we named it Multilayer Cluster Designing Algorithm (MCDA). The proposed design not only has effect on lessening blind broadcasting, but also on decreasing the message exchange in a passionate way. It also encapsulates the beauty of efficient centralized decision making for cluster designing and energy-aware distributed cluster head selection and cluster member allocation process. Comprehensive experimentations have been performed on the comparative analysis of MCDA with state-of-the-art centralized and distributed cluster designing approaches present in published literature. Calculation of energy consumption in various operational parametric values, number of clusters designed and the number of packets broadcasted during cluster designing are the main performance evaluation parameters. It has been found that MCDA outperforms compared to its three competing algorithms with respect to the aforementioned parameters due to its multilayered synergistic mating approach.

Dynamic and hierarchical IPv6 address configuration for a mobile ad hoc network

SUMMARYThe paper proposes a dynamic and hierarchical IPv6 address configuration scheme for a mobile ad hoc network (MANET). The scheme proposes the hierarchical architecture and combines the distributed and centralized address configuration approaches. In the architecture, a central node assigns IPv6 addresses for cluster heads that are distributed around a MANET, and distributed cluster heads assign IPv6 addresses for cluster members. The generation algorithm of a cluster is proposed, and it uses the number of potential cluster members as a measurement unit and minimizes the number of cluster heads. Therefore, the address configuration cost for cluster heads is reduced. A central node/cluster head uses the unicast communication mode to achieve the real‐time address recovery in order to ensure that it has enough address resources for assignment. The paper also proposes the low‐cost MANET merging/partitioning algorithm that guarantees that no address collision happens during the MANET merging/partitioning process. This paper analyzes the performance parameters of the proposed scheme, including the address configuration cost, the address configuration delay, and the number of MANET merging. The analytical results show that the proposed scheme effectively reduces the address configuration cost, shortens the address configuration delay, and decreases the number of MANET merging. Copyright © 2013 John Wiley &amp; Sons, Ltd.

EFFICIENT ENERGY SAVING AND DATA COLLECTION IN WIRELESS SENSOR NETWORKS

Energy efficiency operations are essential in increasing the lifetime of Wireless Sensor Networks. Clustering sensor nodes is an energy saving based interesting technique for achieving these goals. The main challenge in wireless sensor network is to optimize energy consumption when collecting data from sensor nodes. Efficiently collecting the data in wireless sensor networks plays a key role in power conservation. In this paper, a new energy-efficient approach for clustering nodes in adhoc sensor networks is proposed to collect data from sensor nodes and to reduce energy consumption using modified tabu search method, which enables with low communication cost. Dynamic Tabu search uses a local or neighborhood search procedure to iteratively move from a solution x to a solution x' in the neighborhood of x, until some stopping criterion has been satisfied. Communication between the distributed cluster heads is to be achieved by low cost. This approach is suitable to avoid the energy wastage during the transmission and to prolong the lifetime of sensor networks. The MNL (Maximising Network Lifetime) algorithm is being added to prolong the life time of the distributed network. NS-2 is used to perform the progress. The performance of distributed approach is to be compared with the centralized clustering approach using Dynamic modified tabu search.

Recovery properties of distributed cluster head election using reaction–diffusion

Chemical reaction–diffusion is a basic component of morphogenesis, and can be used to obtain interesting and unconventional self-organizing algorithms for swarms of autonomous agents, using natural or artificial chemistries. However, the performance of these algorithms in the face of disruptions has not been sufficiently studied. In this paper we evaluate the use of reaction–diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system. We consider variants of reaction–diffusion systems around the activator–inhibitor model, able to produce spot patterns. We evaluate the robustness of these models against the deletion of activator peaks that signal the location of cluster heads, and the destruction of large patches of chemicals. Three models are selected for evaluation: the Gierer–Meinhardt Activator–Inhibitor model, the Activator–Substrate Depleted model, and the Gray–Scott model. Our results reveal a trade-off between these models. The Gierer–Meinhardt model is more stable, with rare failures, but is slower to recover from disruptions. The Gray–Scott model is able to recover more quickly, at the expense of more frequent failures. The Activator–Substrate model lies somewhere in the middle.