Abstract-Duty-cycle MAC protocols have been proposed to meet the demanding energy requirements of wireless sensor networks. Although existing duty-cycle MAC protocols such as S-MAC are power efficient, they introduce significant end-to-end delivery latency and provide poor traffic contention handling. In this paper, we present a new duty-cycle MAC protocol, called RMAC (the Routing enhanced MAC protocol), that exploits crosslayer routing information in order to avoid these problems without sacrificing energy efficiency. In RMAC, a setup control frame can travel across multiple hops and schedule the upcoming data packet delivery along that route. Each intermediate relaying node for the data packet along these hops sleeps and intelligently wakes up at a scheduled time, so that its upstream node can send the data packet to it and it can immediately forward the data packet to its downstream node. When wireless medium contention occurs, RMAC moves contention traffic away from the busy area by delivering data packets over multiple hops in a single cycle, helping to reduce the contention in the area quickly. Our simulation results in ns-2 show that RMAC achieves significant improvement in end-to-end delivery latency over S-MAC and can handle traffic contention much more efficiently than S-MAC, without sacrificing energy efficiency or network throughput.

Recent advances in technology have made low-cost, low-power wireless sensors a reality. A network of such nodes can coordinate among themselves for distributed sensing and processing of certain phenomena. In this paper, we propose an architecture to provide a stateless solution in sensor networks for efficient addressing and routing. We name our architecture TreeCast. We propose a unique method of address allocation, building up multiple disjoint trees which are geographically intertwined and rooted at the data sink. Using these trees, routing messages to and from the sink node without maintaining any routing state in the sensor nodes is possible. Next, we use this address allocation method for scoped addressing, through which sensor nodes of a particular type or in a particular region can be targeted. Evaluation of our protocol using ns-2 simulations shows how well our addressing and routing schemes perform.

As wireless devices become more pervasive, mobile ad hoc networks are gaining importance, motivating the development of highly scalable ad hoc networking techniques. In this paper, we give an overview of the Safari architecture for highly scalable ad hoc network routing, and we present the design and evaluation of a specific realization of the Safari architecture, which we call Masai. We focus in this work on the scalability of learning and maintaining the routing state necessary for a large ad hoc network. The Safari architecture provides scalable ad hoc network routing, the seamless integration of infrastructure networks when and where they are available, and the support of self-organizing, decentralized network applications. Safari's architecture is based on (1) a self-organizing network hierarchy that recursively groups participating nodes into an adaptive, locality-based hierarchy of cells; (2) a routing protocol that uses a hybrid of proactive and reactive routing information in the cells and scales to much larger numbers of nodes than previous ad hoc network routing protocols; and (3) a distributed hash table grounded in the network hierarchy, which supports decentralized network services on top of Safari. We evaluate the Masai realization of the Safari architecture through analysis and simulations, under varying network sizes, fraction of mobile nodes, and offered traffic loads. Compared to both the DSR and the L+ routing protocols, our results show that the Masai realization of the Safari architecture is significantly more scalable, with much higher packet delivery ratio and lower overhead.

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Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as measurement compression, de-noising, and query routing. A number of factors complicate realizing such a transform in real-world deployments, including irregular spatial placement of nodes and a potentially prohibitive energy cost associated with calculating the transform in-network. In this paper, we address these concerns head-on; our contributions are fourfold. First, we propose a simple interpolatory wavelet transform for irregular sampling grids. Second, using ns-2 simulations of network traffic generated by the transform, we establish for a variety of network configurations break-even points in network size beyond which multiscale data processing provides energy savings. Distributed lossy compression of network measurements provides a representative application for this study. Third, we develop a new protocol for extracting approximations given only a vague notion of source statistics and analyze its energy savings over a more intuitive but naïve approach. Finally, we extend the 2-dimensional (2-D) spatial irregular grid transform to a 3-D spatio-temporal transform, demonstrating the substantial gain of distributed 3-D compression over repeated 2-D compression.