Power Control In Sensor Networks Research Articles

HEEL: A new clustering method to improve wireless sensor network lifetime

In wireless sensor networks, some resources such as memory and energy are limited. In recent years, there has been an increasing interest in improving network lifetime. Node energy plays an important role in the network lifetime. Along with this remarkable growth in wireless sensor networks, however, there is an increasing concern over network lifetime. The principal purpose of this study is to develop an understanding of the effects of other parameters on selecting a cluster head. The methodological approach taken in this study is a mixed methodology typically based on the node's energy. The authors have operated four parameters to select the cluster head: Node energy, the energy of the node's neighbours, number of hops and number of links to neighbours. Each of these parameters has an impact in selecting the cluster head. They accurately observed hop size, energy of each sensor node, average energy of sensor neighbours, links to sensor nodes (HEEL) has better improvements in comparison of Node ranked Low Energy Adaptive Clustering Hierarchy (Nr-LEACH), Modified Low Energy Adaptive Clustering Hierarchy (ModLEACH), Low Energy Adaptive Clustering Hierarchy-B (LEACH-B), Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information System (PEGASIS), energy-aware clustering scheme with transmission power control for sensor networks (EACLE) and hybrid energy efficient distributed clustering (HEED) algorithms in possible case of network lifetime and throughput.

A Distributed TDMA Scheduling Algorithm with Distance-Measurement-Based Power Control for Sensor Networks

A Distributed TDMA Scheduling Algorithm with Distance-Measurement-Based Power Control for Sensor Networks

Cross-Layer Throughput Optimization With Power Control in Sensor Networks

In wireless sensor networks, transmission power has a significant impact on network throughput as wireless interference increases with transmission power, and interference negatively impacts the network throughput. In this paper, we try to improve the network throughput through cross-layer optimization. We first present two algorithms to compute the transmission power of each node with the objectives of minimizing the total transmission power and minimizing the total interference, respectively, from which we can obtain a network topology that ensures a connected path from each source to the sink; then, we compute the maximum achievable throughput from the obtained topology by using joint routing and link rate control. The power control algorithms can generate symmetric links or asymmetric links if so desired. Based on different link models, we use different algorithms to compute the maximum achievable throughput. Since computing the maximum throughput is an NP-hard problem, we use efficient heuristics that use a sufficient condition instead of the computationally expensive-to-get optimal condition to capture the mutual conflict relation in a collision domain. The formal proof for the sufficient condition is provided, and the proposed algorithms are compared with previous work. Simulation results show that the proposed algorithms improve the network throughput and reduce the energy consumption, with significant improvement over previous work on both aspects.

EACLE : Energy-Aware Clustering Scheme with Transmission Power Control for Sensor Networks

In this paper, we propose a new energy efficient clustering scheme with transmission power control named "EACLE" (Energy-Aware CLustering scheme with transmission power control for sEnsor networks) for wireless sensor networks, which are composed of the following three components; "EACLE clustering" is a distributed clustering method by means of transmission power control, "EACLE routing" builds a tree rooted at a sink node and sets the paths from sensor nodes taking energy saving into consideration, and "EACLE transmission timing control" changes the transmission timing with different levels of transmission power to avoid packet collisions and facilitates packet binding. With an indoor wireless channel model which we obtained from channel measurement campaigns in rooms and corridors and an energy consumption model which we obtained from a measurement of a chipset, we performed computer simulations to investigate the performance of EACLE in a realistic environment. Our simulation results indicate that EACLE outperforms a conventional scheme such as EAD (Energy-Aware Data-centric routing) in terms of communication success rate and energy consumption. Furthermore, we fully discuss the impact of transmission power and timing control on the performance of EACLE.