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

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems

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