Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

Abstract

Abstract. Wireless sensor networks will be used in a wide range of challenging applications where numerous sensor nodes are linked to monitor and report distributed event occurrences. In contrast to traditional communication networks, the single major resource constraint in sensor networks is power, due to the limited battery life of sensor devices. It has been shown that data-centric methodologies can be used to solve this problem efficiently. In data-centric storage, a recently proposed data dissemination framework, all event data is stored by type at designated nodes in the network and can later be retrieved by distributed mobile access points in the network. In this paper we propose Resilient Data-Centric Storage (R-DCS) as a method to achieve scalability and resilience by replicating data at strategic locations in the sensor network. Through analytical results and simulations, we show that this scheme leads to significant energy savings in reasonably large-sized networks and scales well with increasing node-density and query rate. We also show that R-DCS realizes graceful performance degradation in the presence of clustered as well as isolated node failures, hence making the sensornet data robust.

Dynamic Data-Centric Storage for long-term storage in Wireless Sensor and Actor Networks

“…Some works [1,4,6,7,15] demonstrate that using several home nodes (replicas) for a particular application reduces the overall network traffic, which leads to a lower energy consumption in the network. Some of those studies [7,15] propose allocating replica nodes following a gridstructure, while the authors in [1] propose to use a uniform replica deployment.…”

Programming a Sensor Network in a layered middleware architecture

“…DCS-GHT must route queries to all mirror nodes, to find all data stored into the 4 d mirrors of r. Structured replication is efficient in limiting the quantity of data stored around a single home node, but this is not sufficient by itself to ensure load balancing, in fact the storage load can become unbalanced even if there is not an unbalance in the meta-data. Resilient Data Centric Storage (R-DCS) (Ghose et Al., 2003) is an extension of DCS-GHT that addresses the issue of having all data of the same type stored on the same set of nodes. It divides the sensing area into zones, and each sensor can either be a monitor node, a replica node, or a normal node, with respect to a given event type.…”

Prediction of near likely nodes in data-centric mobile wireless networks

“…[2] developed schemes for resilient datacentric storage from the viewpoint of energy savings and scalability in sensor networks. Whereas the security and privacy concerns in data-centric storage are addressed in [3].…”

“…• Data-centric storage We assume that the storage is data-centric, Le., the particular node that stores a given data object is determined by the object's type such as event type [1], [2]. Hence, all data with the same type will be stored at the same node (not necessarily the collector node), so that the subsequent data retrieval requests could be efficiently directed.…”