Energy efficient in cluster head and relay node selection for wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way to sense and control the surrounding environment. However, nodes contain limited energy which is the key limiting factor of the sensor network operation. In WSN architecture, the nodes are typically grouped into clusters where one node from each cluster is selected as the Cluster Head (CH) and relays utilisation to minimise energy consumption. Currently, the selection of CH based on a different combination of input variables. Example of these variables includes residual energy, communication cost, node density, mobility, cluster size and many others. Improper selection of sensor node (i.e. weak signal strength) as CH can cause an increase in energy consumption. Additionally, a direct transmission in dual-hop communication between sensor nodes (e.g. CH) with the base station (BS) uses high energy consumption. A proper selection of the relay node can assist in communication while minimising energy consumption. Therefore, the research aim is to prolong the network lifetime (i.e. reduce energy consumption) by improving the selection of CHs and relay nodes through a new combination of input variables and distance threshold approach. In CH selection, the Received Signal Strength Indicator (RSSI) scheme, residual energy, and centrality variable were proposed. Fuzzy logic was utilized in selecting the appropriate CHs based on these variables in the MATLAB. In relay node selection, the selection is based on the distance threshold according to the nearest distance with the BS. The selection of the optimal number of relay nodes is performed using K-Optimal and K-Means techniques. This ensures that all CHs are connected to at least one corresponding relay node (i.e. a 2-tier network) to execute the routing process and send the data to BS. To evaluate the proposal, the performance of Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) was compared based on 100, 200, and 800 nodes with 1 J and random energy. The simulation results showed that our proposed approach, refer to as Energy Efficient Cluster Heads and Relay Nodes (EECR) selection approach, extended the network lifetime of the wireless sensor network by 43% and 33% longer than SEP and MAP, respectively. This thesis concluded that with effective combinations of variables for CHs and relay nodes selection in static environment for data routing, EECR can effectively improve the energy efficiency of WSNs

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

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