Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Network lifetime is one of the important metrics that indicate the performance of a sensor network. Different techniques are used to elongate network lifetime. Among them, clustering is one of the popular techniques. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most widely cited clustering solutions due to its simplicity and effectiveness. LEACH has several parameters that can be tuned to get better performance. Percentage of cluster heads is one such important parameter which affects the network lifetime significantly. At present it is hard to find the optimum value for the percentage of cluster head parameter due to the absence of a complete mathematical model on LEACH. A complete mathematical model on LEACH can be used to tune other LEACH parameters in order to get better performance. In this paper, we formulate a new and complete mathematical model on LEACH. From this new mathematical model, we compute the value for the optimal percentage of cluster heads in order to increase the network lifetime. Simulation results verify both the correctness of our mathematical model and the effectiveness of computing the optimal percentage of cluster heads to increase the network lifetime.

Stable Sensor Network (SSN): A Dynamic Clustering Technique for Maximizing Stability in Wireless Sensor Networks

Stability is one of the major concerns in advancement of Wireless Sensor Networks (WSN). A number of applications of WSN require guaranteed sensing, coverage and connectivity throughout its operational period. Death of the first node might cause instability in the network. Therefore, all of the sensor nodes in the network must be alive to achieve the goal during that period. One of the major obstacles to ensure these phenomena is unbalanced energy consumption rate. Different techniques have already been proposed to improve energy consumption rate such as clustering, efficient routing, and data aggregation. However, most of them do not consider the balanced energy consumption rate which is required to improve network stability. In this paper, we present a novel technique, Stable Sensor Network (SSN) to achieve balanced energy consumption rate using dynamic clustering to guarantee stability in WSN. Our technique is based on LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most widely deployed simple and effective clustering solutions for WSN. We present three heuristics to increase the time before the death of first sensor node in the network. We devise the algorithm of SSN based on those heuristics and also formulate its complete mathematical model. We verify the efficiency of SSN and correctness of the mathematical model by simulation results. Our simulation results show that SSN significantly improves network stability period compared to LEACH and its best variant.

Dynamic Clustering with Relay Nodes (DCRN): A Clustering Technique to Maximize Stability in Wireless Sensor Networks with Relay Nodes

With the growing popularity of wireless sensor networks, network stability has become a key area of current research. Different applications of wireless sensor networks demand stable sensing, coverage, and connectivity throughout their operational periods. In some cases, the death of just a single sensor node might disrupt the stability of the entire network. Therefore, a number of techniques have been proposed to improve the network stability. Clustering is one of the most commonly used techniques in this regard. Most clustering techniques assume the presence of high power sensor nodes called relay nodes and implicitly assume that these relay nodes serve as cluster heads in the network. This assumption may lead to faulty network behavior when any of the relay nodes becomes unavailable to its followers. Moreover, relay node based clustering techniques do not address the heterogeneity of sensor nodes in terms of their residual energies, which frequently occur during the operation of a network. To address these two issues, we present a novel clustering technique, Dynamic Clustering with Relay Nodes (DCRN), by considering the heterogeneity in residual battery capacity and by removing the assumption that relay nodes always serve as cluster-heads. We use an essence of the underlying mechanism of LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most popular clustering solutions for wireless sensor networks. In our work, we present four heuristics to increase network stability periods in terms of the time elapsed before the death of the first node in the network. Based on the proposed heuristics, we devise an algorithm for DCRN and formulate a mathematical model for its long-term rate of energy consumption. Further, we calculate the optimal percentage of relay nodes from our mathematical model. Finally, we verify the efficiency of DCRN and correctness of the mathematical model by exhaustive simulation results. Our simulation results reveal that DCRN enhances the network stability period by a significant margin in comparison to LEACH and its best-known variant.