Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

Novel Clustering Techniques in Wireless Sensor Networks – A Survey

A study of Wireless Sensor Networks has been growing tremendously these days. Wireless Sensor Networks play a major role in various fields ranging from smart homes to health care. WSN’s operate independently in remote places. Because of tiny size of the nodes in such type of networks, they have a limited number of resources in terms of energy and power. Basically, sensor networks can be classified into flat and cluster based Wireless Sensor Networks. But, Clustering based Sensor Networks play a major role in reducing the energy consumption in Wireless Sensor Networks. Clustering also focuses on solving the No.s that arise during transmission of data. Clustering will group nodes into clusters and elects Cluster Heads for all clusters in the network. Then the nodes sense data and send that data to cluster head where the aggregation of data will take place. This paper focuses on various novel clustering techniques that improve the network’s lifetime

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

Mitigating Hotspot Problem Using Chaotic Salp Swarm Algorithm for Energy Efficient IoT Assisted Wireless Sensor Networks

Wireless Sensor Networks (WSN) and Internet of Things (IoT) continued to be pro-active study due to their far reaching applications and also a crucial technology for ubiquitous living. In WSN, energy awareness becomes a significant design problem. Clustering can be defined as a renowned energy-efficient method and renders a lot of benefits like energy competence, less delay, scalability, and lifetime; but it resulted in hot spot problems. To sort out this problem a method called unequal clustering is designed. In unequal clustering, the cluster size differs with the Base Station (BS) distance. In this study, a new Chaotic Salp Swarm Algorithm Based Unequal Clustering Approach (CSSA-UCA) methodology to resolve hot spot issues in IoT-assisted WSN is proposed. The major objective of the CSSA-UCA methodology lies in the effectual identification of CHs and unequal cluster sizes. To accomplish this, the CSSA-UCA technique initially derives the CSSA by the incorporation of chaotic notions into the conventional SSA. At the same time, a fitness function incorporating multiple input parameters was considered for unequal cluster construction. A wide range of experimental result analyses is performed to exhibit the supremacy of the CSSA-UCA technique. The experimental results stated that the CSSA-UCA technique proficiently balances energy accretion and improves the network lifetime

Routing Algorithm with Uneven Clustering for Energy Heterogeneous Wireless Sensor Networks

Aiming at the “hotspots” problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor network with multilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is fully reflected in the mechanism of cluster-heads’ election. It optimizes the competition radius of the cluster-heads according to the residual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies or near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing election fully takes account of the factors of residual energies and the distances among the nodes. The simulation results show that the introduction of an uneven clustering mechanism and the optimization of competition radius of the cluster-heads significantly prolonged the lifetime of the network and improved the efficiency of data transmission