Recent advances in technology have made low cost, low power wireless sensors a reality. Clock synchronization is an important service in any distributed system, including sensor network systems. Applications of clock synchronization in sensor networks include data integration in sensors, sensor reading fusion, TDMA medium access scheduling, and power mode energy saving. However, for a number of reasons, standard clock synchronization protocols are unsuitable for direct application in sensor networks.In this paper, we introduce the concept of adaptive clock synchronization based on the need of the application and the resource constraint in the sensor networks. We describe a probabilistic method for clock synchronization that uses the higher precision of receiver-to-receiver synchronization, as described in Reference Broadcast Synchronization (RBS) protocol. This deterministic protocol is extended to provide a probabilistic bound on the accuracy of the clock synchronization, allowing for a tradeoff between accuracy and resource requirement. Expressions to convert service specifications (maximum clock synchronization error and confidence probability) to actual protocol parameters (minimum number of messages and synchronization overhead) are derived. Further, we extend this protocol for maintaining clock synchronization in a multihop network.

Abstract

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.

Abstract

Abstract. In wireless sensor networks, energy and communication bandwidth are precious resources. Traditionally, layering has been used as a design principle for network stacks; hence routing protocols assume no knowledge of the application behavior in the sensor node. In resource-constrained sensor-nodes, there is simultaneously a need and an opportunity to optimize the protocol to match the application. In this paper, we design a network architecture that efficiently supports multi-scale communication and collaboration among sensors. The architecture complements the previously proposed Abstract Regions architecture for local communication and collaboration. We design a self-organizing hierarchical overlay that scales to a large number of sensors and enables multi-resolution collaboration. We design effective Network Programming Interfaces to simplify the development of applications on top of the architecture; these interfaces are efficiently implemented in the network layer. The overlay hierarchy can adapt to match the collaboration requirements of the application and data both temporally and spatially. We present an initial evaluation of our design under simulation to show that it leads to reduced communication overhead, thereby saving energy. We are currently building our architecture in the TinyOS environment to demonstrate its effectiveness.