Collaborative compression and transmission of distributed sensor imagery

Abstract

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the network lifetime. In this dissertation, we consider a network of imaging sensors. We address the problem of energy-efficient communication of the resulting measurements. First, we develop a heuristic-based method that exploits the redundancy in the measurements of imaging sensors. The algorithm attempts to maximize the lifetime of the network without utilizing inter-sensor communication. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized Root-Mean-Squared- Error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the lossless case. Second, we develop a novel theory for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto Optimal solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not Pareto Optimal a second iteration is performed which maximizes the second minimum lifetime subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n−1)th minimum lifetime being maximum, subject to the (n−2)th minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. Third, we present a novel algorithm for the purpose of exploiting the inherent inter- and intra-sensor correlation in a network of imaging sensors while utilizing inter-sensor communication. This algorithm combines a collaborative compression method in conjunction with our cooperative multi-hop routing strategy in order to maximize the lifetime of the network. This CMT algorithm is demonstrated to achieve average gain in lifetime as high as 3.2 over previous methods. Finally, we discuss practical implementation considerations of our CMT algorithm. We first present some experimental results that illustrate the practicality of our method. Next, we develop a realistic optical model that permits us to consider a more heterogeneous network of cameras by allowing for varying resolution, intrinsic and extrinsic parameters, point-spread function and detector size. We show that our previous CMT algorithm can be extended to successfully operate in such a diverse imaging model. We propose new object-domain quality metrics and show that our proposed method is able to balance lifetime and fidelity according to expectations.