Conserving energy in WSN through clustering and power control

Wireless Sensor Networks raise a growing interest among industries and civil organizations where monitoring and recognition of physical phenomena are a priority. Their possible applications are extremely versatile and are expected to be intensely applied in different domains such health, agriculture, habitat monitoring, routing traffic, security and military. WSN represent a significant technology that attracts more and more considerable research attention in recent years. It has emerged as a result of recent advances in low-power digital and analog circuitry, low-power RF design and sensor technology. Wireless Sensor Networks consist of a large number of miniature devices called sensor nodes scattered over a geographical area called sensor field. These nodes are attempted to collect information or data which is forwarded through gateways called base stations. The communication scenario through sensor nodes leads to some amount of energy wasting. Therefore we need to design suitable techniques and protocols in order to optimize the energy consumption and increase the network lifetime. This paper proposes a novel energy-aware framework for a long-lived sensor network. Our framework is based on clustering architecture and achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes. In fact, it's an energy optimization approach based on cross-layer for wireless sensor networks, joining optimal design of the physical, medium access control, and routing layer. The framework enables the network to be dynamically reconfigured and efficiently using every node's energy and distributes management tasks to support the scalability of the management system in densely deployed sensor networks. This approach focuses on the computation of optimal transmission power, routing, and duty-cycle schedule that optimize the WSNs energy-efficiency and by the way, reduces node energy consumption and contributes to extending the lifetime of the entire network. The approach is evaluated using the NS2 simulator. Simulation results show that it is an energy-efficient approach and able to achieve significant performance improvement as well.