Biblio

In this work we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20°. For realtime tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor.