Boundary node selection algorithms in WSNs

Abstract

Physical damage and/or node power exhaustion may lead to coverage holes in WSNs. Coverage holes can be directly detected by certain proximate nodes known as boundary nodes (B-nodes). Due to the sensor nodes' redundant deployment and autonomous fault detection, holes are surrounded by a margin of B-nodes (MB-nodes). If all B-nodes in the margin take part in the hole recovery processes, either by increasing their transmission power or by relocating towards region of interest (ROI), the probability of collision, interference, disconnection, and isolation may increase affecting the rest of the network's performance and QoS. Thus, distributed boundary node selection algorithms (BNS-Algorithms) are proposed to address these issues. BNSalgorithms allow B-nodes to self-select based on available 1-hop information extracted from nodes' simple geometrical and statistical features. Our results show that the performance of the proposed distributed BNS-algorithms approaches that of their centralized counterparts.

A Case Study for Choosing Proper Relocation Algorithms to Recover Large Scale Coverage Hole(s) in Wireless Sensor Networks

“…Locations of sensors may be known by GPS or any other localisation methods [38]. Similar to [29], [35], [36], sensor nodes are classified into into damaged nodes if they reside inside the damaged area (coverage hole); otherwise, they are considered as undamaged nodes. Those undamaged nodes proximate to the coverage hole which directly detect the damage event within their ranges are further defined as boundary nodes (Fig.…”

“…(i.e. signal loss or disconnection as the result of the failure of their neighbours) Similar to [29], [35], [36], coverage hole is modelled as a circle of radius r Hole with the centre at x Hole , y Hole (Fig. 1).…”

“…In another scenario which DSSA only is applied to limited or select a set of nodes close (proximate)to the coverage hole (e.g. CH boundary nodes) [29], [35], [36] to limit node movements and power consumptions, DSSA ability of recovery worsen.…”

A Tuned Fuzzy Logic Relocation Model in WSNs Using Particle Swarm Optimization

“…Among these algorithms in WSNs, the amount of unnecessary movements, oscillations and power exhaustion of nodes with local interactions in the distributed relocation algorithms especially with a harsh and hostile environments with lack of central supervision and operation should be reduced as possible. In order to save nodes' power and to localize movement to a specific area in the network, relocation algorithm can be applied to a selected set of nodes [17], [18], fully or partially to avoid unnecessary node oscillations or energy consumption caused by careless movement strategies. Reduction in overhead and delay of centralized relocation paradigm comes at the price of increased uncertainty among autonomous nodes who have local interactions within their ranges.…”

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

“…The monitoring area is a sensitive area (named SA), which is usually an irregular pattern, and the position of sensitive areas may shift or change throughout the WSNs within a certain time. Figure 1 shows that the marked range belonging to SA is called region of interest (named RoI), and sensor data are transmitted to the upper node through the backbone node in the WSNs [28]. The scenario of locating the SA involves acquiring the coordinates or grids of the sensitive areas in the WSNs through the preset threshold and the value of each sensor in the network.…”