Energy Aware Wireless Sensor Networks

Self-configuring wireless sensor networks can be invaluable in many civil and military applications for collecting, processing, and disseminating wide ranges of complex environmental data. They have therefore, attracted considerable research attention in the last few years. The WINS [1] and SmartDust [2] projects for instance, aim to integrate sensing, computing, and wireless communication capabilities into a small form factor to enable low-cost production of these tiny nodes in large numbers. Several other groups are investigating efficient hardware/software system architectures, signal processing algorithms, and network protocols for wireless sensor networks [3], [4], [5]. Sensor nodes are battery-driven, and hence operate on an extremely frugal energy budget. Further, they must have a lifetime on the order of months to years, since battery replacement is not an option for networks with thousands of physically embedded nodes. In some cases, these networks may be required to operate solely on energy scavenged from the environment through seismic, photovoltaic, or thermal conversion. This transforms energy consumption into the most important factor that determines sensor node lifetime. Conventional low-power design techniques [6] and hardware architectures only provide point solutions which are insufficient for these highly energy constrained systems. Energy optimization, in the case of sensor networks, is much more complex, since it involves not only reducing the energy consumption of a single sensor node, but also maximizing the lifetime of an entire network. The network lifetime can be maximized only by incorporating energy-awareness into every stage of wireless sensor network design and operation, thus empowering the system with the ability to make dynamic tradeoffs between energy consumption, system performance, and operational fidelity. This new networking paradigm, with its extreme focus on energy efficiency, poses several system and network design challenges that need to be overcome to fully realize the potential of the wireless sensor systems. A quite representative application in wireless sensor networks is event tracking, which has widespread use in applications such as security surveillance and wildlife habitat monitoring. Tracking involves a significant amount of collaboration between individual sensors to perform complex signal processing algorithms such as Kalman Filtering, Bayesian Data Fusion, and Coherent Beamforming. This collaborative signal processing nature of sensor networks offers significant opportunities for energy management. For example, just the decision of whether to do the collaborative signal processing at the user end-point or somewhere inside the network has significant implication on energy and lifetime. We will use tracking as the driver to …