CareNet is an integrated wireless sensor environment for remote healthcare that uses a two-tier wireless network and an extensible software platform. CareNet provides both highly reliable and privacy-aware patient data collection, transmission and access. This paper describes our system architecture, software development, and the results of our field studies.

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Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms -mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.

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This paper considers two important issues for video sensor networks: (1) timely delivery of captured video stream and (2) energy-efficient network design. Based on network calculus, it presents a unified analytical framework that is able to quantitatively weigh the tradeoff between these two factors. In particular, it derives the service curve, buffer and delay bound under single-hop and multi-hop scenarios for video sensor networks under power management. To the best of our knowledge, this is the first work that extends the network calculus theory to the domain of wireless network with the consideration of power management and wireless interference. Our analysis has been validated through experiments conducted on a video sensor network testbed.