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Residual Energy Based Cluster-head Selection in WSNs for IoT Application
Trupti Mayee Behera, Sushanta Kumar Mohapatra, Umesh Chandra Samal, Mohammad. S. Khan, Mahmoud Daneshmand, Amir H. Gandomi
TL;DR
Energy-constrained WSNs need routing and cluster-head selection that conserve power because deployed sensor batteries are difficult to replace. The paper modifies LEACH to favor higher-residual-energy nodes while considering initial energy and an optimal cluster count. Simulations report higher throughput for R-LEACH than LEACH, including a 60% throughput increase.
Problem
WSN routing must conserve power because sensor batteries are difficult to replace, while LEACH can randomly elect low-energy cluster heads and shorten network span.
Method
The modified LEACH scheme uses initial energy, residual energy, and an optimal number of clusters to assign higher cluster-head selection probability to higher-energy nodes.
Results
60% throughput increase for R-LEACH over LEACH is reported from the modified cluster-head selection threshold.
Takeaways & Limitations
R-LEACH extends network operation by selecting cluster heads according to residual energy and optimal cluster count, with reported benefits for homogeneous IoT-oriented networks.
Abstract
from arXiv · showhide
Wireless sensor networks (WSN) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. The paper focuses on an efficient cluster head election scheme that rotates the cluster head position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy and an optimum value of cluster heads to elect the next group of cluster heads for the network that suits for IoT applications such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the LEACH protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.
I. INTRODUCTION
WSNs connect sensing nodes to IoT systems, but limited battery power makes energy-efficient routing and clustering essential. LEACH improves lifetime yet random cluster-head election can select low-energy nodes, motivating residual-energy-aware selection.
- IoT and WSN context: WSNs monitor physical and environmental parameters while transferring sensed data to the Internet through energy-constrained sensor nodes.Recharging deployed sensor batteries is practically impossible, making energy-efficient routing necessary.
- Clustering and LEACH: Clustering groups sensing nodes, and each cluster head aggregates data before sending it to the sink node or base station.LEACH uses member-node communication, TDMA schedules, and data aggregation within clusters.
- LEACH limitations: Cluster-head selection must consider multiple factors, including node distance, residual energy, mobility, and throughput.The selection process is therefore presented as a multifactor decision rather than a purely random choice.
- LEACH limitations: LEACH randomly selects cluster heads each round, giving low- and high-energy nodes equal election priority.A low-residual-energy cluster head may die quickly, shortening the network span.
- Proposed direction: The proposed modification considers initial energy, residual energy, and the optimal number of cluster heads when selecting cluster heads.After each round, higher-energy non-cluster-head nodes receive greater selection probability for the current round.
II. RELATED WORK
Related work addresses WSN power management through clustering, residual-energy-aware selection, optimization, and modified LEACH protocols. The paper identifies a gap in jointly using initial energy, residual energy, and an optimal cluster count in the election threshold.
- Motivation: Replacing batteries in deployed sensors is difficult, making power management a major IoT challenge.This challenge is especially relevant when sensors operate in remote or difficult-to-access environments.
- Clustering approaches: Clustering reduces unnecessary power consumption by grouping nodes and assigning each group a cluster head to send data to the base station.The election process should distribute energy uniformly across sensor nodes.
- Existing approaches: Prior work modifies LEACH using residual energy, multi-hop communication, data aggregation, particle swarm optimization, and multicriteria selection.These methods use combinations of residual energy, distances, node degree, optimal cluster counts, or decision tools.
- Motivation: Energy conservation remains challenging in IoT because wireless communication drains more power than receiving or remaining idle.Researchers therefore focus on energy-efficient sensor-network deployment and cluster-head selection.
- Research gap: The paper reports that prior threshold modifications had not, to its knowledge, jointly considered residual energy, initial energy, and an optimum cluster count.This combination defines the stated gap motivating the proposed modification.
III. SYSTEM MODEL
The system model applies IoT environmental monitoring with clustered sensor nodes and a central sink. It describes LEACH and its radio-energy assumptions, while motivating energy-aware cluster-head selection.
- IoT environment monitoring: Environmental monitoring groups sensors into four room-based clusters, with one cluster head per room forwarding fused data to the end user.Each room contains eight sensor nodes, and the sink collects data from the room cluster heads.
- System assumptions: The model assumes static, homogeneous nodes initialized with 0.5J, random deployment, periodic transmission, and a fixed sink at the network center.Each room has a cluster head that communicates with the base station through single-hop or multi-hop communication.
- Radio energy model: Short-distance node-to-cluster-head transmission uses the free-space model, whereas longer cluster-head-to-sink transmission uses multi-path fading.Transmission energy depends on packet size k and distance d.
- LEACH baseline: LEACH forms dynamic clusters in rounds and elects cluster heads randomly, giving every node an equal selection probability regardless of residual energy.Nodes transmit sensed data to their cluster head, which aggregates it before forwarding it to the sink.
- R-LEACH motivation: R-LEACH is introduced as a round-based algorithm with cluster-formation and steady-state stages to address energy imbalance in cluster-head selection.Its motivation is the possibility that randomly selected, low-energy cluster heads die quickly.
IV. PROPOSED WORK
The proposed R-LEACH protocol retains hierarchical clustering and data aggregation while modifying cluster-head election after the first round. Subsequent selection incorporates node energy and the optimal cluster count.
- Protocol structure: R-LEACH uses setup and steady-state stages: nodes form clusters, cluster heads collect and fuse data, and then forward it to the base station.Data fusion removes redundant bits before routing during the steady-state stage.
- Initial round: The first round uses normal LEACH cluster and cluster-head formation, after which each node has spent a distance-dependent amount of energy.The next round therefore uses a modified cluster-head election equation.
- Energy-aware election: Subsequent cluster-head selection uses residual energy relative to initial energy and incorporates the optimal number of cluster heads.E_residual denotes remaining node energy, while E_initial denotes initially assigned energy.
- Cluster operation: After cluster heads are selected, member nodes join according to received signal strength and receive TDMA schedules for collision-free transmission.The round process continues until all network nodes exhaust their energy.
- Data transmission: During steady-state operation, inactive member-node radios turn off, cluster heads aggregate data, and fused information reaches the sink through single-hop or multi-hop communication.Aggregation compresses information and removes redundancy before forwarding.
V. SIMULATIONS
The simulation model uses randomly deployed sensor nodes, a centrally positioned base station, and fixed packet sizing. These parameters define the MATLAB network configuration.
- Network deployment: The MATLAB simulation deploys 100 nodes randomly and places the base station at the center of the network area.The deployment is illustrated in Fig. 5.
- Simulation parameters: Each simulated packet contains 4000 bits.This packet size is part of the network parameters used for simulation.
A. Network Analysis
R-LEACH improves network lifetime, packet delivery, residual-energy depletion, and throughput relative to LEACH by selecting cluster heads using residual energy and an optimal cluster count.
- Network Lifetime: First-node death occurs at 1092 rounds for LEACH and 1382 rounds for R-LEACH.
- Network Lifetime: Last-node death occurs at 1510 rounds for LEACH and 2474 rounds for R-LEACH.
- Energy and Packets: R-LEACH depletes residual energy more slowly than LEACH, extending network operation across more rounds.The comparison is described alongside average network energy expenditure and packet transmission.
- Energy and Packets: R-LEACH transmits more packets successfully to the BS because cluster heads are selected according to nodes’ remaining energy.
- Throughput: 60% higher throughput is reported for R-LEACH than LEACH after modifying the cluster-head selection threshold.
B. Variation of energy and its effect on Network metrics
The evaluation compares stability and lifetime across initial-energy settings, finding that R-LEACH improves these metrics over LEACH and other routing protocols by combining energy awareness with cluster-count optimization.
- Evaluation Metrics: Network stability is measured by first-node death, while network lifetime is measured from first-node death to last-node death.The analysis also considers half-node death and initial energies of 0.25, 0.5, and 1 J.
- Network Stability: R-LEACH produces an improved stable network across the evaluated energy values by considering residual energy and the optimum number of clusters.
- Comparative Results: R-LEACH outperforms LEACH, CBDAS, GHND, and IGHND on half-node death and first-node death for all evaluated energy values.
- Comparative Results: LEACH’s randomized cluster-head selection contributes to a shorter network span, while other compared methods omit the optimum cluster or zone count.
VI. CONCLUSION
The paper proposes a modified cluster-head selection algorithm to control energy dissipation and extend network lifetime in homogeneous WSN-based IoT scenarios.
- Conclusion: The modified routing process improves residual energy, packets sent to the BS, throughput, and network lifetime compared with LEACH.
- Conclusion: The approach is presented as suitable for homogeneous-network scenarios such as environmental monitoring using IoT.
- Future Work: Future work could add more cluster-head selection parameters, mobile-node settings, and realistic WSN-based IoT scenarios.