Energy-based Clustering for Wireless Sensor Network Lifetime Optimization

International audienceClustering in wireless sensor networks is an efficient way to structure and organize the network. It aims to identify a subset of nodes within the network and bind it a leader (i.e. cluster-head). This latter becomes in charge of specific additional tasks like gathering data from all nodes in its cluster and sending them by using a longer range communication to a sink. As a consequence, a cluster-head exhausts its battery more quickly than regular nodes. In this paper, we present BLAC, a novel Battery-Level Aware Clustering family of schemes. BLAC considers the battery-level combined with another metric to elect the cluster-head. It comes in four variants. The cluster-head role is taken alternately by each node to balance energy consumption. Due to the local nature of the algorithms, keeping the network stable is easier. BLAC aims to maximize the time with all nodes alive to satisfy application requirements. Simulation results show that BLAC improves the full network lifetime 3-time more than traditional clustering schemes by balancing energy consumption over nodes and still delivering high data percentage

Energy-Efficient Hybrid K-Means Algorithm for Clustered Wireless Sensor Networks

Energy efficiency is the most critical challenge in wireless sensor network. The transmission energy is the most consuming task in sensor nodes, specifically in large distances. Clustered routing techniques are efficient approaches used to lower the transmission energy and maximize the network’s lifetime. In this paper, a hybrid clustered routing approach is proposed for energy optimization in WSN. This approach is based on K-Means clustering algorithm and LEACH protocol. The simulation results using MATLAB tool have shown that the proposed hybrid approach outperforms LEACH protocol and optimizes the nodes energy and the network lifetime

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

© The Author(s) 2020. Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases

Energy efficient clustering and routing optimization model for maximizing lifetime of wireless sensor network

Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significant-performance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs

Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs

[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches.This work has been partially supported by the 'Ministerio de Ciencia e Innovacion', through the 'Plan Nacional de I+D+i 2008-2011' in the 'Subprograma de Proyectos de Investigacion Fundamental', project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). 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Energy optimization for wireless sensor networks using hierarchical routing techniques

Philosophiae Doctor - PhDWireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These two models are complemented by a load balancing model structured to balance energy consumption on the network of cluster heads that forms the backbone for any cluster-based hierarchical sensor network. We present two novel approaches for clustering the nodes of a hierarchical sensor network: a) a distanceaware clustering where nodes are clustered based on their distance and the residual energy and b) a service-aware clustering where the nodes of a sensor network are clustered according to their service offered to the network and their residual energy. These approaches are implemented into a family of routing protocols referred to as EOCIT (Energy Optimization using Clustering Techniques) which combines sensor node energy location and service awareness to achieve good network performance. Finally, building upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed as a novel multipath routing protocol that finds energy efficient routing paths for sensor readings dissemination from the cluster heads to the sink/base station of a hierarchical sensor network. Our simulation results reveal the relative efficiency of the newly proposed approaches compared to selected related routing protocols in terms of sensor network lifetime maximization

Cooperative Hyper-Scheduling based improving Energy Aware Life Time Maximization in Wireless Body Sensor Network Using Topology Driven Clustering Approach

The Wireless Body Sensor Network (WBSN) is an incredible developing data transmission network for modern day communication especially in Biosensor device networks. Due to energy consumption in biomedical data transfer have impacts of sink nodes get loss information on each duty cycle because of Traffic interruptions. The reason behind the popularity of WBSN characteristics contains number of sensor nodes to transmit data in various dense regions. Due to increasing more traffic, delay, bandwidth consumption, the energy losses be occurred to reduce the lifetime of the WBSN transmission. So, the sensor nodes are having limited energy or power, by listening to the incoming signals, it loses certain amount of energy to make data losses because of improper route selection. To improve the energy aware lifetime maximization through Traffic Aware Routing (TAR) based on scheduling. Because the performance of scheduling is greatly depending on the energy of nodes and lifetime of the network. To resolve this problem, we propose a Cooperative Hyper-scheduling (CHS) based improving energy aware life time maximization (EALTM) in Wireless Body sensor network using Topology Driven Clustering Approach (TDCA).Initially the method maintains the traces of transmission performed by different Bio-sensor nodes in different duty cycle. The method considers the energy of different nodes and history of earlier transmission from the Route Table (RT) whether the transmission behind the Sink node. Based on the RT information route discovery was performed using Traffic Aware Neighbors Discovery (TAND) to estimate Data Transmission Support Measure (DTSM) on each Bio-sensor node which its covers sink node. These nodes are grouped into topology driven clustering approach for route optimization. Then the priority is allocated based on The Max-Min DTSM, the Cooperative Hyper-scheduling was implemented to schedule the transmission with support of DTSM to reduce the energy losses in WBSN. This improves the energy level to maximization the life time of data transmission in WBSN than other methods to produce best performance in throughput energy level

Lifetime improved WSN using enhanced-LEACH and angle sector-based energy-aware TDMA scheduling

This research article published by Cogent Engineering, 2020Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is nonoptimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node’s energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an EnhancedLow-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigne