Wireless access to Internet services will become typical, rather than the exception as it is today. Such a vision presents great demands on mobile networks. Mobile IP represents a simple and scalable global mobility solution but lacks the support for fast handoff control and paging found in cellular telephony networks. In contrast, second-and third-generation cellular systems offer seamless mobility support but are built on complex and costly connection-oriented networking infrastructure that lacks the inherent flexibility, robustness, and scalability found in IP networks. In this article we present Cellular IP, a micro-mobility protocol that provides seamless mobility support in limited geographical areas. Cellular IP, which incorporates a number of important cellular system design principles such as paging in support of passive connectivity, is built on a foundation of IP forwarding, minimal signaling, and soft-state location management. We discuss the design, implementation, and evaluation of a Cellular IP testbed developed at Columbia University over the past several years. Built on a simple, low-cost, plug-and-play systems paradigm, Cellular IP software enables the construction of arbitrary-sized access networks scaling from picocellular to metropolitan area networks. The source code for Cellular IP is freely available from the Web (comet.columbia.edu/cellularip).
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.
Abstract-As wireless devices and sensors are increasingly deployed on people, researchers have begun to focus on wireless body-area networks. Applications of wireless body sensor networks include healthcare, entertainment, and personal assistance, in which sensors collect physiological and activity data from people and their environments. In these body sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This paper proposes BodyQoS, the first running QoS system demonstrated on an emulated body sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource rich aggregator, minimizing the load on resource limited sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.
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