Rendezvous Regions: A Scalable Architecture for Self-Configuration and Data-Centric Storage in Sensor Networks

Sensor networks are large-scale, self-organizing, distributed networks of small sensing devices embedded in the physical world. Due to the limited resources of the devices and the uncertainty of the environment, efficient and robust data-access mechanisms are both essential and challenging. Rendezvous-based data-access mechanisms provide an efficient and valuable solution for provisioning a wide range of services. In this paper, we describe Rendezvous Regions (RRs) a novel scalable rendezvous-based architecture for sensor networks. RR is a general architecture proposed for data-centric storage, in addition to bootstrapping, resource discovery, configuration, and task assignment of sensor nodes. In RR the network topology is divided into rendezvous regions. Rendezvous regions are geographical regions dividing the network topology space, where each region is responsible for a range of keys representing the data or information of interest. A hash-table-like mapping scheme maps a key to its region. Inside a region, a simple local election mechanism is used to dynamically promote servers that are responsible for maintaining the mapped information. Replication between servers in the region is provided to deal with mobility and failures. We run extensive detailed and high-level simulations to investigate the design space, and study the architecture in various environments including node mobility and failures. We evaluate it against other approaches (e.g., GHT) to identify its merits and limitations. The results show high success rate with low overhead in networks with low to moderate mobility. The overhead increases moderately with the dynamics to preserve a high success rate. RR scales to large number of nodes and is highly robust and efficient with failures and localized movements.