DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.
Abstract.Situation awareness is an important application category in cyber-physical systems, and distributed video-based surveillance is a good canonical example of this application class. Such applications are interactive, dynamic, stream-based, computationally demanding, and needing real-time or near real-time guarantees. A sense-process-actuate control loop characterizes the behavior of this application class. ASAP is a scalable distributed architecture for a multi-modal sensor network that caters to the needs of this application class. Features of this architecture include (a) generation of prioritization cues that allow the infrastructure to pay selective attention to data streams of interest; (b) virtual sensor abstraction that allows easy integration of multi-modal sensing capabilities; and (c) dynamic redirection of sensor sources to distributed resources to deal with sudden burstiness in the application. In both empirical and emulated experiments, ASAP shows that it scales up to a thousand of sensor nodes (comprised of high bandwidth cameras and low bandwidth RFID readers), significantly mitigates infrastructure and cognitive overload, and reduces false negatives and false positives due to its ability to integrate multi-modal sensing.
Abstract-We introduce a novel abstraction, the target container (TC), which serves as a parallel programming model and execution framework for developing complex applications for tracking multiple targets in a large-scale camera network. The key insight is to allow the domain expert (e.g., a vision researcher) to focus on the algorithmic details of target tracking and let the system deal with providing the computational resources (cameras, networking, and processing) to enable target tracking. Each TC has a one-to-one correspondence with a target, possibly tracked from multiple cameras. The domain expert provides the code modules for target tracking (such as detectors and trackers) as handlers to the TC system. The handlers are invoked dynamically by the TC system to discover new targets (detector) and to follow existing targets (tracker). The TC system also provides an interface for merging TCs whenever they are determined to be corresponding to the same target.This paper presents the design of the TC system, details of an experimental prototype, and an example application to demonstrate the simplicity of using the TC programming model.
Abstract-Wireless sensor networks (WSN) built using current Berkeley Mica motes exhibit low reliability for packet delivery. There is anecdotal evidence of poor packet delivery rates from several field trials of WSN deployment. All-to-one communication pattern is a dominant one in many such deployments. As we scale up the size of the network and the traffic density in this communication pattern, improving the reliability of packet delivery performance becomes very important.This study is aimed at two things. Firstly, it aims to understand the factors limiting reliable packet delivery for all-to-one communication pattern in dense wireless sensor networks. Secondly, it aims to suggest enhancements to well-known protocols that may help boost the performance to acceptable levels. We first postulate the potential reasons hampering packet delivery rates with current CSMA-based MAC layer used by the radios deployed in WSN. We then propose a set of enhancements that are aimed to mitigate the ill-effects of these factors. We pick three protocols, namely, Flooding, AODV, and Geographic routing as candidates for this study. Using TOSSIM, we perform a detailed study of these protocols and the proposed enhancements. This study serves several purposes. First, it helps us to quantify the detrimental effects of these factors. Second, it helps us to quantify the extent to which our proposed enhancements improves packet delivery performance. Concretely, we show that using Geographic routing in a WSN with 225 nodes spread over 150 feet x 150 feet, the proposed enhancements yield a 23-fold improvement in packet delivery performance over the baseline. Further, the enhancements result in fairness (measured by the number of messages received from each node at the destination). Lastly, we show that the overhead (in terms of retransmissions, acknowledgement messages, and control messages) is reasonable.
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