On boundary recognition without location information in wireless sensor networks

Abstract

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d , a node lies inside of the construction for a d -quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

Hop-based approach for holes and boundary detection in wireless sensor networks

“…Topological approaches [4,5,7,14,15,18] use the information on the connectivity of sensor nodes to detect holes and WSN boundaries. In this context, Funke and Klein [4] have presented an algorithm based on the idea of iso-levels each of which represents a set of nodes having the same hop distance from a specific root node [19].…”

“…The proposed algorithm first attempts to detect holes inside the network and then moves towards the identification of the network boundary. In addition to the need for synchronising all the sensor nodes, the network node degree must be 10 or higher in order to reach high-performance levels [15].…”

“…The success of this algorithm heavily depends on the identification of at least one flower structure, which may not always be the case especially in a sparse network [7]. Moreover, a sensor node requires knowledge of its 8-hop neighbours to be able to start searching for a flower [15]. Saukh et al [15] have proposed an approach which approximates the boundary of the sensor network and considers all other nodes to be part of the outer boundary or the boundary of a hole.…”

“…Moreover, a sensor node requires knowledge of its 8-hop neighbours to be able to start searching for a flower [15]. Saukh et al [15] have proposed an approach which approximates the boundary of the sensor network and considers all other nodes to be part of the outer boundary or the boundary of a hole. The approach is based on geometric constructions, called patterns, wherein inner nodes lie.…”

“…Other works have been based on less information demanding approaches where the information on the connectivity of sensor nodes is used [4,5,7]. Such approaches generally require relatively high average of network connectivity degrees and result in communication overheads as they are based on finding specific network distribution patterns [14,15] or constructing special geometries rooted at arbitrary nodes [4,7]. In this paper, we propose a new topological approach that requires less network connectivity degree while assuring high performance.…”