Chieh-Yih Wan
Abstract
Event-driven sensor networks operate under an idle or light load and then suddenly become active in response to a detected or monitored event. The transport of event impulses is likely to lead to varying degrees of congestion in the network depending on the distribution and rate of packet sources in the network. It is during these periods of event impulses that the likelihood of congestion is greatest and the information in transit of most importance to users. To address this challenge we propose an energy-efficient congestion control scheme for sensor networks called CODA (COngestion Detection and Avoidance) that comprises three mechanisms: (i) receiver-based congestion detection; (ii) open-loop hop-by-hop backpressure; and (iii) closed-loop multisource regulation. We present the detailed design, implementation, and evaluation of CODA using simulation and experimentation. We define three important performance metrics (i.e., energy tax, fidelity penalty, and power) to evaluate the impact of CODA on the performance of sensing applications. We discuss the performance benefits and practical engineering challenges of implementing CODA in an experimental sensor network testbed based on Berkeley motes using CSMA. Simulation results indicate that CODA significantly improves the performance of data dissemination applications such as directed diffusion by mitigating hotspots, and reducing the energy tax and fidelity penalty on sensing applications. We also demonstrate that CODA is capable of responding to a number of congestion scenarios that we believe will be prevalent as the deployment of these networks accelerates.
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Index Terms
- Energy-efficient congestion detection and avoidance in sensor networks
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Reviews
Debraj De
In wireless sensor networks, reliable, fair, and energy-efficient data collection at the base station has been a widely explored problem. Still, unsolved issues of organized congestion detection and avoidance exist in designing such data collection protocols. Problems occur, especially during bursts of events when a large set of event-triggered sensor nodes tries to simultaneously send data streams to the base station. The data traffic and the feedback acknowledgement traffic vie for access to the medium, radically reducing data throughput. In an effort to solve the problem, this paper proposes the congestion detection and avoidance (CODA) scheme. It includes three main mechanisms: receiver-based congestion detection, open-loop hop-by-hop backpressure, and closed-loop multisource regulation. The receiver-based congestion detection mechanism utilizes monitoring of buffer queue length and sampling-activated channel load monitoring. With open-loop hop-by-hop backpressure, the receiver node, on detecting congestion, sends a backpressure signal upstream toward the data sources. The closed-loop multisource regulation approach dynamically regulates all of the data sources with aggregated acknowledgment (ACK) control. The authors propose a hybrid window-based and rate-based scheme for the closed-loop control. Simulation experiments in ns-2 indicate that CODA improves the performance of data collection applications by mitigating congestion nodes and reducing the energy expense and fidelity penalty. The main contribution of this paper is the presentation of open- and closed-loop control after detecting congestion. Individually, the control methods are valid and effective; however, together, they are not particularly structured for a distributed system design. The congestion control and avoidance mechanism can execute simultaneously, but the interplay among them and its final effect on congestion is not clear. The proposed model and solution, however, will effectively help readers with a view of all of the local and distributed effects of congestion in data collection sensor networks. This work (an extension of the authors' 2003 work [1]) and its references are a bit dated. Still, it is worth reading for assessing the unsolved congestion problems in event-driven data collection sensor networks. One point to clarify for readers: the paper frequently uses the term "data dissemination" as the application scenario, even though the solution is for data collection at a base station. Online Computing Reviews Service
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