This work involves the design of an energy aware adaptive algorithm which tries to reduce current consumption in solar powered wireless sensor nodes. This proposed algorithm is optimized for use in solar powered MICAz nodes performing surface temperature monitoring. Laboratory results indicate significant current savings under low voltage conditions which can provide an extended lifetime of 23 hours compared to periodic non-adaptive algorithm. Current consumption model is also developed, this can estimate the lifetime of a sensor network without replacement of batteries. The proposed algorithm is implemented in an experimental wireless sensor network that is deployed in a power substation for equipment health monitoring. We present the results from the experimental deployment.

The harvesting of energy from ambient energy sources to power electronic devices has been recognized as a promising solution to the issue of powering the ever-growing number of mobile devices around us.Key technologies in the rapidly growing field of energy harvesting focus on developing solutions to capture ambient energy surrounding the mobile devices and convert it into usable electrical energy for the purpose of recharging said devices. Achieving a sustainable network lifetime via battery-aware designs brings forth a new frontier for energy optimization techniques. These techniques had, in their early stages, resulted in the development of low-power hardware designs. Today, they have evolved into power-aware designs and even battery-aware designs.This book covers recent results in the field of rechargeable sensor networks, including technologies and protocol designs to enable harvesting energy from alternative energy sources such as vibrations, temperature variations, wind, solar, and biochemical energy and passive human power.

Recent technological advances have enabled distributed micro-sensing for large scale information gathering through a network of tiny, low power devices or nodes equipped with programmable computing, multiple sensing and communication capabilities. This network of sensor nodes, known as a wireless sensor network, has revolutionized remote monitoring applications because of its ease of deployment, ad hoc connectivity and cost-effectiveness. In this dissertation, we design distributed routing protocols for minimizing energy consumption in a sensor network. There are two main contributions of this work. The first contribution is the design of an energy aware multiple path routing protocol to route heavy data traffic between a source and a destination node in a sensor network. The protocol spreads the routing load between the source and destination nodes over a large number of sensor nodes to minimize disparity in the energy levels of the sensor nodes. We also grade the multiple paths based on their route length to support time critical queries on the shortest available paths. The second contribution is the design of a communication architecture that supports distributed query processing to evaluate spatio-temporal queries within the network. We represent these queries by query trees and distribute query operators to appropriate sensor nodes. As operator execution demands high computation capability, we propose use of a heterogenous sensor network where query operators are assigned to sparsely deployed resource-rich nodes within a dense network of low power sensor nodes. We design an adaptive, decentralized, low communication overhead algorithm to determine an operator placement on the resource-rich nodes in the network to minimize cost of transmitting data in the routing tree constructed to continuously retrieve data from a set of spatially distributed geographical regions to the sink. To the best of our knowledge, this is the first attempt to build an energy aware routing infrastructure to enable in-network processing of spatio-temporal queries. In order to maximize energy savings the proposed multiple path routing protocol can be used to route data between the nodes that form the routing tree.

I implement this new synchronization technique using Matlab and show that it can provide significant power savings over both TPSN and RBS.