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The deployment of large-scale, low-cost, low-power, multifunctional sensory networks brings forward numerous and diverse research challenges. Critical to the design of systems that must operate under extreme resource constraints, the understanding of the fundamental performance limits of sensory networks is a research topic of particular importance. This thesis examines, in this respect, an essential function of sensory networks, viz., data collection, that is, the aggregation at the user location of information gathered by sensor nodes. In the first part of this dissertation we study, via simple discrete mathematical models, the time performance of the data collection and data distribution tasks in sensory networks. Specifically, we derive the minimum delay in collecting sensor data for networks of various topologies such as line, multi-line, tree and give corresponding optimal scheduling strategies assuming that the amount of data observed at each node is finite and known at the beginning of the data collection phase. Furthermore, we bound the data collection time on general graph networks. In the second part of this dissertation we take the view that the amount of data collected at a node is random and study the statistics of the data collection time. Specifically, we analyze the average minimum delay in collecting randomly located/distributed sensor data for networks of various topologies when the number of nodes becomes large. Furthermore, we analyze the impact of various parameters such as lack of synchronization, size of packet, transmission range, and channel packet erasure probability on the optimal time performance. Our analysis applies to directional antenna systems as well as omnidirectional ones. We conclude our study with a simple comparative analysis showing the respective advantages of the two systems. |
