Spatial modelling and analysis of wireless ad-hoc and sensor networks: an energy perspective
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This dissertation focuses on modelling and analyzing the spatial characteristics of traffic in these networks so as to extend network lifetime for various application/traffic scenarios. In the first part of the dissertation we consider large-scale sensor networks that systematically sample a spatio-temporal field. Firstly we formulate a distributed compression problem subject to aggregation costs to a single sink. We show that the optimal solution is based on ordering sensors according to aggregation costs. Next we consider a hierarchical model for a sensor network including sinks, compressors and sensors. We show that the optimal organization is associated with the Johnson-Mehl tessellation induced by nodes’ locations. Our analysis and simulations show the proposed scheme can yield 8-28% energy savings depending on the compression ratio. In the second part of the dissertation we investigate the use of proactive multipath routing in ad hoc wireless networks. The focus is on optimizing tradeoffs between the increased energy cost associated with spreading traffic and the improved spatial balance of energy burdens. We show how its optimization depends on the relative values of the energy reserves/storage, replenishing rates, and network load characteristics. In particular, we show that the degree of spreading should roughly scale as the square root of the bits-meters load offered by a session. Simulation v results confirm that proactive multipath routing decreases the probability of energy depletion by orders of magnitude versus that of a shortest path routing scheme when the initial energy reserve is high. In the third part of the dissertation we consider a large sensor network with mobile sinks. The network makes use of aggregation nodes (AGNs), for compression and/or data fusion of locally sensed data. Since the aggregated data may cause a concentration of energy burdens when routed to sinks, we use proactive multipath routing between AGNs to mobile sinks. We show that the scale of aggregation and degree of spreading can be optimized. Particularly if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.