Date of Award

2-22-2018

Document Type

Dissertation - SCU Access Only

Publisher

Santa Clara : Santa Clara University, 2018.

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering

First Advisor

Sarah Kate Wilson

Second Advisor

Ahmed Amer

Abstract

Sensor networks deployed in high-latency environments such as underwater acoustic and satellite channels find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves. In order to extend the lifetime of these sensors, energy must be conserved in all layers of the protocol stack. In addition to long propagation delays, these channels are characterized by limited bandwidth and a lack of well-established closed-form analytical models. This fact makes finding cross-layer energy-optimal solutions a difficult problem to solve. The objective of this research is to compute near-optimal routes, schedules and transmit power levels for delay-constrained applications of high-latency sensor networks. The proposed approach uses a mixed-integer programming relaxation of the energy optimization problem. The relaxed problem is then decomposed into subproblems that can be solved iteratively in a decentralized manner. Comparative simulation analysis shows that the proposed approach is more energy-efficient and throughput-efficient than the heuristic, time-sensitive greedy forwarding and least-cost routing algorithms.

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