Recent advances in wireless networking and data acquisition have enabled us with a unique capability to remotely sense our environment. Data acquisition networks can be used to sense natural as well as human-created phenomena. As these applications may require deployment in remote and hard-to-reach areas, it is critical to ensure that such wireless sensor networks are capable of operating unattended for long durations. The lack of easy access to a continuous power source in most scenarios and the limited lifetime of batteries have hindered the deployment of such networks. Consequently, the central objective in wireless sensor network design is to utilize the available energy as efficiently as possible. In this thesis, we study the design of optimal or near-optimal energy management schemes for various wireless sensor networks composed of nodes with different capabilities.
Firstly, we derive theoretical upper bounds on the performance of a transmission scheduler for sensor networks. We do this by calculating the information theoretic channel capacity of finite-state Markov channels with imperfect feedback containing different grades of channel state information including that, obtained through Automatic Repeat Request (ARQ) feedback. Secondly, we consider the problem of energy optimal transmission scheduling over a finite state Markov channel with imperfect feedback. We propose a transmission controller that utilizes different "grades" of channel state information to schedule packet transmissions in an energy-optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the information-theoretic limit. Finally, we consider the problem of energy management in nodes with energy replenishment capabilities. Here, we derive the performance limits of sensor nodes with limited energy, being replenished at a variable and random rate. We provide a simple localized energy management scheme for nodes with limited energy storage space, and show that our scheme achieves a performance asymptotically close to that available with an unlimited energy source. Based on the insights developed, we address the problem of energy management for energy-replenishing nodes with finite data buffer capacities as well as limited energy storage space. To this end, we give an energy management scheme that is provably asymptotically optimal.