One of the most important and yet most challenging issues in body area networks (BANs) is to provide diverse quality of service (QoS). Most physiological data monitoring applications require low rate periodic reporting while real-time entertainment applications require high rate continuous streaming. Emergency alarm, the most time-critical but unpredictable data, must be delivered instantaneously. A BAN should satisfy these diverse requirements since applications each with distinctive QoS requirement may run simultaneously. We propose PNP-MAC protocol that can flexibly handle variety of applications with diverse requirements through fast, preemptive slot allocation, non-preemptive transmission in the allocated slots, and flexible superframe adjustments. Performance evaluation using OPNET network simulator shows that PNP-MAC can satisfy diverse delay and throughput requirements of various applications such as continuous streaming, routine periodic monitoring, and time-critical emergency alarm.

This paper introduces a new IEEE standard, IEEE 802.15.5,which provides mesh capability for wireless personal area network (WPAN) devices. The standard provides an architectural framework enabling WPAN devices to promote interoperable, stable, and scalable wireless mesh topologies. It is composed of two parts: low-rate WPAN mesh and high-rate WPAN mesh. In this paper, we present only low-rate WPAN mesh because it is designed to support wireless sensor networks. IEEE 802.15.5 low-rate part is a light-weight scalable mesh routing protocol that caters well to the requirements of resource-constrained wireless sensor networks. By binding logical addresses to the network topology, IEEE 802.15.5 obviates the need for route discovery. This eliminates the initial route discovery latency, saves storage space and reduces the communication overhead and energy consumption. A distributed link state scheme is further built atop the block addressing scheme to improve the quality of routes, robustness, and load balancing. The routing scheme scales well with regard to various performance metrics. The standard also provides enhanced functions such as multicast, reliable broadcast, power saving, time synchronization, route tracing and portability. We also present the performance evaluation of major functions performed with a 50-nodes tested deployed over a whole floor (100 × 140 ft2) at CUNY Engineering building. The results testify that the IEEE 802.15.5 will serve well for wireless personal area networks and wireless sensor networks.

We present BikeNet, a mobile sensing system for mapping the cyclist experience. Built leveraging the MetroSense architecture to provide insight into the real-world challenges of people-centric sensing, BikeNet uses a number of sensors embedded into a cyclist's bicycle to gather quantitative data about the cyclist's rides. BikeNet uses a dual-mode operation for data collection, using opportunistically encountered wireless access points in a delay-tolerant fashion by default, and leveraging the cellular data channel of the cyclist's mobile phone for real-time communication as required. BikeNet also provides a Web-based portal for each cyclist to access various representations of her data, and to allow for the sharing of cycling-related data (for example, favorite cycling routes) within cycling interest groups, and data of more general interest (for example, pollution data) with the broader community. We present: a description and prototype implementation of the system architecture based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones; an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces.

We describe our experiences deploying BikeNet, an extensible mobile sensing system for cyclist experience mapping leveraging opportunistic sensor networking principles and techniques. BikeNet represents a multifaceted sensing system and explores personal, bicycle, and environmental sensing using dynamically role-assigned bike area networking based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones. We investigate real-time and delay-tolerant uploading of data via a number of sensor access points (SAPs) to a networked repository. Among bicycles that rendezvous en route we explore inter-bicycle networking via data muling. The repository provides a cyclist with data archival, retrieval, and visualization services. BikeNet promotes the social networking of the cycling community through the provision of a web portal that facilitates back end sharing of real-time and archived cycling-related data from the repository. We present: a description and prototype implementation of the system architecture, an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces. Visit [4] to see how the BikeNet system visualizes a user's rides.

Sensor networks exhibit a unique funneling effect which is a product of the distinctive many-to-one, hop-by-hop traffic pattern found in sensor networks, and results in a significant increase in transit traffic intensity, collision, congestion, packet loss, and energy drain as events move closer toward the sink. While network (e.g., congestion control) and application techniques (e.g., aggregation) can help counter this problem they cannot fully alleviate it. We take a different but complementary approach to solving this problem than found in the literature and present the design, implementation, and evaluation of a localized, sink-oriented, funneling-MAC capable of mitigating the funneling effect and boosting application fidelity in sensor networks. The funneling-MAC is based on a CSMA/CA being implemented network-wide, with a localized TDMA algorithm overlaid in the funneling region (i.e., within a small number of hops from the sink). In this sense, the funneling-MAC represents a hybrid MAC approach but does not have the scalability problems associated with the network-wide deployment of TDMA. The funneling-MAC is 'sink-oriented' because the burden of managing the TDMA scheduling of sensor events in the funneling region falls on the sink node, and not on resource limited sensor nodes; and it is 'localized' because TDMA only operates locally in the funneling region close to the sink and not across the complete sensor field. We show through experimental results from a 45 mica-2 testbed that the funneling-MAC mitigates the funneling effect, improves throughput, loss, and energy efficiency, and importantly, significantly outperforms other representative protocols such as B-MAC, and more recent hybrid TDMA/CSMA MAC protocols such as Z-MAC.

No abstract available.

We propose SWAN, a stateless network model which uses distributed control algorithms to deliver service differentiation in mobile wireless ad hoc networks in a simple, scalable and robust manner. We use rate control for UDP and TCP best-effort traffic, and sender-based admission control for UDP real-time traffic. SWAN uses explicit congestion notification (ECN) to dynamically regulate admitted real-time traffic in the face of network dynamics brought on by mobility or traffic overload conditions. We use the term "soft" real-time services to indicate that real-time sessions could be regulated or dropped due to mobility or excessive traffic overloading at mobile wireless routers. SWAN is designed to limit such conditions, however. A novel aspect of SWAN is that it does not require the support of a QOS-capable MAC. Rather, soft real-time services are built using existing best effort wireless MAC technology. Simulation, analysis, and results from an experimental wireless testbed show that real-time applications experience low and stable delays under various multi-hop, traffic and mobility conditions. The wireless testbed and ns-2 simulator source code are available from the Web (comet.columbia.edu/swan).

We propose SWAN, a stateless network model which uses distributed control algorithms to deliver service differentiation in mobile wireless ad hoc networks in a simple, scalable and robust manner. The proposed architecture is designed to handle both real-time UDP traffic, and best effort UDP and TCP traffic without the need for the introduction and management of per-flow state information in the network. SWAN supports per-hop and end-to-end control algorithms that primarily rely on the efficient operation of TC/IP protocols. In particular, SWAN uses local rate control for best-effort traffic, and sender-based admission control for real-time UDP traffic. Explicit congestion notification (ECN) is used to dynamically regulate admitted real-time sessions in the face of network dynamics brought on by mobility or traffic overload conditions. SWAN does not require the support of a QoS-capable MAC to deliver service differentiation. Rather, real-time services are built using existing best effort wireless MAC technology. Simulation, analysis, and results from an experimental wireless testbed show that real-time applications experience low and stable delays under various multihop, traffic, and mobility conditions.

We present the design, implementation, and evaluation of INSIGNIA, an IP-based quality of service framework that supports adaptive services in mobile ad hoc networks. The framework is based on an in-band signaling and soft-state resource management approach that is well suited to supporting mobility and end-to-end quality of service in highly dynamic environments where the network topology, node connectivity, and end-to-end quality of service are time varying. Architecturally INSIGNIA is designed to support fast reservation, restoration, and end-to-end adaptation based on the inherent flexibility and robustness and scalability found in IP networks. We evaluate the framework, paying particular attention to the performance of the in-band signaling system, which helps counter time-varying network dynamics in support of the delivery of adaptive services. Our results show the benefit of our framework under diverse mobility, traffic, and channel conditions.