On perimeter coverage issues in wireless sensor networks

Perimeter coverage made practical in wireless sensor networks

“…Since no known polynomial time solution exists to solve the problem of finding M C (M CC) in a practical scenario, this justifies us to look for an approximation solution to solve the problem. We know that the distributed algorithm proposed in [18] ( [19]) can find M C (M CC) in the network in a non-practical scenario with O(|M C|) (O(N )) number of messages. Here, we modify the algorithms proposed in [18] ( [19]) as followings:…”

“…[8], [9] were the first to propose distributed algorithms to solve these problems. Previously, we have extended the works of [8] and [9] by further proposing two distributed algorithms to find the minimum cover (MC) in [18] and the minimum cost cover (MCC) in [19] with reduced message overheads. On the other hand, Chow et.…”

“…Generally speaking, the algorithms proposed in [8], [9], [18], [19], regardless of using the fixed or the adjustable sensing range technique, work as long as the sensors can cover a single continuous portion of the perimeter of the target object and the perimeter of the target object forms a loop. Unfortunately, in a practical scenario, some special situations may exist.…”

“…For instance, the irregularity of the sensing area of a sensor or the existence of some long convex and concave parts on the perimeter of the target object similar to that shown in Figure 1. Under these special situations, a sensor may cover multiple continuous portions of the perimeter on the target object instead of a single continuous portion which is assumed in [8], [9], [18], [19]. In this paper, we are specifically interested in these special situations and we collectively call these special situations as a practical scenario.…”

“…In this paper, we are specifically interested in these special situations and we collectively call these special situations as a practical scenario. First, we modify the algorithms proposed in [8], [9], [18], [19] to achieve the approximation solutions with the use of fixed sensing range technique. Last but not least, we further show that an approximation solution is modified from the algorithm proposed in [19] with the use of the adjustable sensing range technique.…”