Cluster Head Rotation Research Articles

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Optimal vs rotation heuristics in the role of cluster-head for routing in IoT constrained devices

Wireless Sensor Networks (WSNs) are crucial for implementing the Internet of Things (IoT) vision. A WSN can include distributed devices that can be battery powered, have a small amount of memory, have limited CPU processing capabilities and limited transmission range. WSNs are categorised into two main types that are homogeneous and heterogeneous. Homogeneous WSNs are composed of nodes that are all equal while heterogeneous ones are composed of nodes that can have different transmission rate and different initial energy. Collecting data in an energy efficient manner is one of the main goals when building wireless sensor networks since devices are battery powered and battery replacement can be difficult or impossible to be performed. Clustering protocols are one of the main approaches that have been used in order to collect data in an energy efficient way. A clustering protocol groups nodes into a set of clusters. Each cluster has a representative node that is referred to as Cluster Head (CH). This collects data from its cluster members and forwards them to an external Base Station, possibly in a multi-hop way among cluster heads. A rotation strategy for electing the cluster head is often used together with clustering in order to prolong the network lifetime. Rotation eliminates the overhead traffic that is needed for leader election and cluster formation since the old cluster heads directly designate new ones. In this work, we describe an Optimum Rotation Scheduling (ORS) that uses Integer Linear Programming in order to find the optimum rotation strategy. ORS assumes the WSN clusters are already formed by using some clustering protocol. A novel integer linear programming formulation is then used in order to define a cluster head rotation that produces the optimum cluster lifetime. Comparisons with existing heuristics and the provided optimum are then shown and discussed by means of extended simulations involving both homogeneous and heterogeneous WSNs.

Cluster Head Selection Algorithms for Enhanced Energy Efficiency in Wireless Sensor Networks: A Systematic Literature Review

The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network. Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes, and this has significant effects on the network's energy consumption. The objective of this paper is to analyze existing cluster head selection algorithms and the parameters they implement to enhance energy efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library, Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more parameters that can be used to elect cluster heads to improve on energy consumption.

Collecting data in smart cities using energy harvesting technology

<p>This work investigates the problems of extending the sensors network lifetime in smart cities. The limited capacity of the sensors’ batteries, and the difficulty of replacing the sensors’ batteries in hard-to-reach areas are some of the main challenges that contribute in reducing the lifetimes of the networks. The direction of this study is to use renewable energy as an energy source for collecting data from various infrastructures that are distributed throughout these cities. We present a model for data collection based on combining energy harvesting (EH) with the cluster head rotation feature, which results in flexible and sustainable networks that can be used in smart cities. Simulation results depict the performance of the proposed model with and without EH technology. The metrics used to compare the performance of the proposed model with and without EH technology include the consumed energy by sensors, number of live and dead sensors, and energy variance. The results show that the network lifetime increases when EH technology is used.</p>

An energy-balanced unequal clustering approach for circular wireless sensor networks

The network lifetime of the Wireless Sensor Networks (WSNs) is a critical factor for the relevant applications due to the limited energy supply. However, the imbalanced energy consumption of the whole network always leads to the “Hot Spot Problem” and the decline of network lifetime inevitably. To this end, the schemes of energy-balanced unequal clustering and energy-efficient cluster head rotation are considered in this paper. To be specific, we conduct detailed theoretical derivation and mathematical calculation based on the concept of gradient to obtain the optimal number of CHs, with the aim of balancing the energy consumption for the whole network topology. In addition, a fuzzy logic-based mechanism for CHs rotation is also proposed to balance the energy distribution among different cluster heads. Subsequently, a novel Energy-Balanced unequal Clustering Approach (EBCA) for circular wireless sensor networks is proposed and detailed. Finally, we conduct extensive simulations to verify its performance. The experimental results indicate that EBCA is able to balance the energy consumption of cluster heads in different gradient, reduce the energy consumption and prolong the network lifetime effectively compared with the classic and latest clustering algorithms.

Metaheuristic Load-Balancing-Based Clustering Technique in Wireless Sensor Networks

The resource-constrained nature of wireless sensor networks engenders the development of energy-efficient network operations. To mitigate the prime concern of developing an energy-efficient network, clustering of the nodes has emerged as a very effective tool. If executed intelligently, clustering can not only help in obtaining even load distribution among the network nodes but also help in having the enhanced network lifetime and scalability. In this work, a Metaheuristic Load-Balancing-Based Clustering Technique (MLBCT) in wireless sensor networks has been proposed which formulates the energy-balanced clusters based on the differential evolution technique to improve the network lifetime. To ensure the formation of balanced clusters, several metrics like nodes’ proximity, nodes’ distribution, and energy distribution across the sensing field have been considered. Moreover, to facilitate the even load distribution among the cluster members, a randomized rotation of cluster head is implemented. The supremacy of the proposed scheme is confirmed through an extensive set of simulations against the state-of-art schemes. Simulation results reflect an average gain of 51.85% in network lifetime under the variable network configurations in an ideal environment. Moreover, a thorough statistical analysis is performed to prove the efficacy of the proposed fitness function by obtaining confidence intervals under two different network scenarios with variable node counts.

Hierarchical Clustering Nodes Collaborative Scheduling in Wireless Sensor Network

Network lifetime and coverage are the key evaluation metrics of Wireless Sensor Networks (WSNs). Aiming at network lifetime, Hierarchical Clustering-task Scheduling Policy (HCSP) breaks the traditional clustering framework, but it doesnt fully consider the network coverage problem. We propose a Hierarchical Clustering Node Collaborative Scheduling (HCNCS) protocol that optimizes HCSP to extend the network lifetime and improves the coverage performance of WSNs by nodes collaborative sensing. According to the deployment location, HCNCS classifies nodes into Node Collaborative Sensing Disks(NCSDs), which as units for data sensing and transmission. Nodes are rotated in WSNs to balance the energy consumption and through collaborative sensing to improve the coverage of NCSDs. In addition, HCNCS constrains the number of clusters by NCSDs classification based on K-means to reduce the communication energy consumption of clustering. Based on the HCNCS, this paper further proposes an Enhanced Hierarchical Clustering Node Collaborative Scheduling (EHCNCS) protocol. Compared with HCNCS, EHCNCS selects NCSDs according to Node Collaborative Scheduling (NCS) algorithm after clustering, which improves the coverage capability of NCSDs at the cost of increasing a small amount of the communication energy consumption. EHCNCS also rotates Cluster Heads (CHs) by the CH rotation candidate table to balance network energy consumption. The extensive simulations show that HCNCS has an obvious advantage over HCSP in terms of network lifetime and network coverage. Compared with HCNCS, EHCNCS significantly prolongs the network lifetime of WSNs at the expense of a bit of coverage performance.

Clustered chain founded on ant colony optimization energy efficient routing scheme for under-water wireless sensor networks

One challenge in under-water wireless sensor networks (UWSN) is to find ways to improve the life duration of networks, since it is difficult to replace or recharge batteries in sensors by the solar energy. Thus, designing an energy-efficient protocol remains as a critical task. Many cluster-based routing protocols have been suggested with the goal of reducing overall energy consumption through data aggregation and balancing energy through cluster-head rotation. However, the majority of current protocols are concerned with load balancing within each cluster. In this paper we propose a clustered chain-based energy efficient routing algorithm called CCRA that can combine fuzzy c-means (FCM) and ant colony optimization (ACO) create and manage the data transmission in the network. Our analysis and results of simulations show a better energy management in the network.

Improved energy efficient hierarchical cluster tree based routing to prolong network lifetime

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Residual Energy-Aware Clustering Transformation for LEACH Protocol

Energy is one of the crucial performance parameters in wireless sensor networks (WSNs). Enhancement of network lifetime is an important consideration as well. Low energy-aware clustering hierarchy (LEACH) is one of the protocols proposed for WSNs. In this paper, a residual energy-aware clustering transformation protocol for LEACH (REACT-LEACH), enhancing performance of LEACH by introducing a clustering mechanism, is proposed. The proposed cluster head (CH) rotation and cluster reformation processes are more effective in REACT-LEACH, as residual energy is considered to be one of the metrics. Performance of REACT-LEACH is validated based on simulations

An enhanced energy optimization routing protocol for WSNs

Wireless sensor networks (WSNs) have received increasing attention due to their broad application prospects. However, the nature of sensor nodes, i.e., limited battery life and inefficient protocols, greatly reduces the sensor networks’ lifetime. Therefore, determining how to extend their lifetime has become an important issue for WSNs. This paper focuses on the technique of extending the networks’ lifetime by reducing and balancing energy consumption and proposes an enhanced energy optimization routing protocol (EEORP) for WSNs. EEORP proposes a grid-based cluster head (CH) election algorithm and introduces the energy weight and declaration number order weight factors to reduce the energy consumption in the rotation of CHs. EEORP adopts the dynamic clustering algorithm to reduce the energy consumption in intra-cluster data collection. The hop-count gradient field and grid distance are also introduced in EEORP to minimize the energy consumption in inter-cluster forwarding of the data. Our proposed protocol (EEORP) shows better performance than the existing protocols (LEACH, IEE-LEACH and EAMR) in terms of the networks’ lifetime and data throughput in WSNs, as has been demonstrated experimentally.

SNR based Energy Efficient Communication Protocol for Emergency Applications in WBAN

Continuous remote monitoring of a patient’s health condition in dynamic environment imposes many challenges. Challenges further get multiplied based on the size of body area sensor network. One such challenge is energy efficiency of sensors. Maintaining longer life of all nodes, especially who participate in communicating vital signals from one network to another towards the base station is very important. In this work, an energy efficient communication protocol for the wireless body area network (WBAN) is proposed. The essential characteristics of the protocol are: random deployment of nodes, formation of clusters, node with high signal to noise ratio (SNR) as cluster head (CH), random rotation of CHs within each cluster, and so on. The developed algorithm is simulated in MATLAB by varying the number of nodes and networks. Obtained results are compared with some of the recent and most relevant existing works. It is found that there is an enhancement in the network lifetime by 19.5%, throughput by 12.61% and average remaining energy by 57.21%.

Optimized Energy – Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

Analysis of Energy Optimized Routing Algorithms for Improved Ant Colony in WSN

Level routing LEACH protocol has uneven cluster head distribution, and each cluster head rotation takes place throughout the network, which will cause excessive energy consumption. However, the application of ant colony algorithm in multi-hop routing still has the problem of unbalanced energy consumption. By dividing the network nodes into regions, we can use the residual energy of the nodes as a reference factor to improve the method. By introducing node energy into transfer probability, this paper improves ant colony routing algorithm. The simulation results show that the improved algorithm can overcome the shortcomings of classical ant colony algorithm, which will achieve efficient and real-time optimal routing.

An Energy Efficient Clustering Algorithm Based on Annulus Division Applied in Wireless Sensor Networks

Energy efficiency and energy balance are two important performance indexes in wireless sensor networks which determine the lifetime of networks. This paper proposes a protocol, termed as annulus division based energy-efficiency clustering protocol (ADEC), in which the monitor area is divided into several annuluses with different width. By considering the distance between sensor nodes and the base station, sensor nodes are firstly classified into different levels. Then, cluster heads selection and clusters formation are conducted independently in each annulus. Moreover, data transmission pathes are established among annuluses, and a strategy of cluster heads rotation and cluster adjustment is proposed. A key feature of this proposed algorithm is that the clustering is conducted in each annulus independently, such that a large amount of energy can be saved. Finally, the simulation results illustrate the effectiveness of ADEC in terms of energy efficiency and energy balance.

CS-CGMP: Clustering Scheme Using Canada Geese Migration Principle for Routing in Wireless Sensor Networks

Recently, researchers focus on wireless sensor networks (WSNs) as it plays a vital role in numerous applications. Node energy is one of the resource constraints of WSN. Hence, it is significantly important to design an energy efficient routing. In this paper, a Clustering Scheme using Canada Geese Migration Principle (CS-CGMP) has been proposed for routing in WSNs. In this approach, the network field is partitioned into zones and number of Cluster Heads (CHs) are selected based on the density of each zone. CGMP is applied for CH rotation. If residual energy of a CH is below threshold value, it has to be rotated to a member node which has high energy. Further, the optimal number of CHs required is assured in every round. CS-CGMP based approach was simulated and the performance parameters with respect to network lifetime, number of alive nodes, number of packets transferred to the BS and CH were analysed by comparing with LEACH-C, (ACH) $$^2$$ , and DEEC.

Novel Cluster Rotating and Routing Strategy for software defined Wireless Sensor Networks

The biggest problems faced by the software defined wireless sensor network are energy conservation and load balancing techniques which impose a high level of constraint. In general clustering is used in a network in order to decrease the use of energy thereby enhancing lifetime of the network. Hot spot problems are a common issue caused due to drainage of battery when there are more multi-hop networks that are close to the base station. In order to overcome this restriction, we have proposed the use of multilayer clustering architecture that is used to choose the intra and inter-cluster communication, rotation of cluster head and forwarding node. Using the routing table, the proposed methodology will be able to efficiently handle the rotation of the forwarder node. The rotation takes place based on the residual energy’s threshold levels and also exploits the non-forwarder node, backup forwarder node, forwarder node and decision maker node to enhance the routing strategy of the WSN. Testing and evaluation of the proposed work is done using C programming language and results show that this methodology has better results than EADUC and TLPER as far as hop count, communication and energy consumption are taken into consideration in the cluster formation.

NCHR: A Nonthreshold-Based Cluster-Head Rotation Scheme for IEEE 802.15.4 Cluster-Tree Networks

The IEEE 802.15.4 standard specifies two network topologies: 1) star and 2) cluster tree. A cluster-tree network comprises of multiple clusters that allow the network to scale by connecting devices over multiple wireless hops. The role of a cluster head (CH) is to aggregate data from all devices in the cluster and then transmit it to the overall personal area network (PAN) coordinator. This specific role of CH needs to be rotated among multiple coordinators in the cluster to prevent it from energy drain out. Prior works on CH rotation are either based on threshold energy levels or rely on periodic rotation. Both approaches have their respective limitations and, at times, result in unnecessary CH rotations or nonoptimal selection of CH. To address this, we propose a nonthreshold CH rotation scheme (NCHR), which incurs minimal rotation overhead. It supports topological changes, node heterogeneity, and can also handle CH failures. Through simulations and hardware implementation, the performance of the proposed NCHR scheme is analyzed in terms of network lifetime, CH rotation overhead, and the number of CH rotations. It is shown that the proposed scheme boosts network lifetime, incurs less rotation overhead, and needs fewer CH rotations compared to other related schemes.

Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks

The problem of energy efficiency in cognitive radio sensor networks (CRSN) is mainly caused by the limited energy of sensor nodes and other channel-related operations for data transmission. The unequal clustering method should be considered for balancing the energy consumption among the cluster heads (CHs) for prolonging the network lifetime. The CH selection should consider the number of accessible free channels for efficient channel assignment. To improve fairness, the channel assignment problem should consider energy consumption among the cluster members. Furthermore, the relay metric for the selection of the best next-hop should consider the stability of the link for improving the transmission time. The CH rotation for cluster maintenance should be energy and spectrum aware. With regard to the above objectives, this paper proposes an energy and spectrum aware unequal clustering (ESAUC) protocol that jointly overcomes the limitations of energy and spectrum for maximizing the lifetime of CRSN. Our proposed ESAUC protocol improves fairness by achieving residual energy balance among the sensor nodes and enhances the network lifetime by reducing the overall energy consumption. Deep Belief Networks algorithm is exploited to predict the spectrum holes. ESAUC improves the stability of the cluster by optimally adjusting the number of common channels. ESAUC uses a CogAODV based routing mechanism to perform inter-cluster forwarding. Simulation results show that the proposed scheme outperforms the existing CRSN clustering algorithms in terms of residual energy, Network Lifetime, secondary user–primary user Interference Ratio, Route Discovery Frequency, throughput, Packet Delivery Ratio, and end-to-end delay.