Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Distributed Clustering Based on Node Density and Distance in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are special type of network with sensing and monitoring the physical parameters with the property of autonomous in nature. To implement this autonomy and network management the common method used is hierarchical clustering. Hierarchical clustering helps for ease access to data collection and forwarding the same to the base station. The proposed Distributed Self-organizing Load Balancing Clustering Algorithm (DSLBCA) for WSNs designed considering the parameters of neighbor distance, residual energy, and node density.  The validity of the DSLBCA has been shown by comparing the network lifetime and energy dissipation with Low Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy Efficient Distributed Clustering (HEED). The proposed algorithm shows improved result in enhancing the life time of the network in both stationary and mobile environment

An Improved Energy-Aware Distributed Unequal Clustering Protocol using BBO Algorithm for Heterogeneous Load Balancing

With the rapid extension of IoT-based applications various distinct challenges are emerging in this area Among these concerns the node s energy efficiency has a special importance since it can directly affect the functionality of IoT-Based applications By considering data transmission as the most energy-consuming task in IoT networks clustering has been proposed to reduce the communication distance and ultimately overcome node energy wastage However cluster head selection as a non-deterministic polynomial-time hard problem will be challenging notably by considering node s heterogeneity and real-world IoT network constraints which usually have conflicts with each other Due to the existence of conflict among the main system parameters various solutions have been proposed in recent years that each of which only considered a few real-world limitations and parameter

Hierarchical routing protocols for wireless sensor network: a compressive survey

Wireless Sensor Networks (WSNs) are one of the key enabling technologies for the Internet of Things (IoT). WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource-constrained WSNs. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location-based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state-of-the-art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocols and a comparison of the different versions presented in the literature

Design Methods for the Cluster Head Selection in WSNs based on Node Residual Status to Enhance the Lifetime and Performance of the Network

This study introduces a novel approach to enhance Wireless Sensor Networks (WSNs) by proposing an energy-balancing algorithm and a unique Cluster Head (CH) selection strategy based on nodes' residual energy states. Unlike conventional methods, our algorithm dynamically distributes energy across nodes, mitigating localized energy depletion and extending the overall network lifetime. The Knapsack method optimizes resource allocation considering energy constraints. Performance evaluations, conducted using NS2.34/2.35, compare our approach with a widely used energy-balancing technique in WSNs. Our results showcase significant improvements. Notably, our algorithm achieves a remarkable 20% increase in network lifetime and a 25% enhancement in data throughput, demonstrating its effectiveness in optimizing resource usage. Furthermore, a 30% reduction in latency ensures faster and more responsive data transmission. The CH selection method positively impacts network coverage, resulting in a substantial 20% expansion of monitored areas. Compared to the existing technique, our proposed strategy consistently outperforms in key metrics, indicating its superior efficacy. This research contributes to advancing WSN sustainability and efficiency, particularly in scenarios prioritizing energy efficiency. The proposed algorithm emerges as a promising solution, demonstrating its potential to optimize WSN performance and longevity. Furthermore, the network simulator (NS) is used to examine the performance of the network with the NS2.34/2.35 version. The results exhibit the dominance of the projected method compared to existing techniques. Additionally, the method shows adaptability to dynamic network conditions, ensuring effective CH reselection in the presence of node failures or energy fluctuations