The Influence of Communication Range on Connectivity for Resilient Wireless Sensor Networks Using a Probabilistic Approach

Abstract

Wireless sensor networks (WSNs) consist of thousands of nodes that need to communicate with each other. However, it is possible that some nodes are isolated from other nodes due to limited communication range. This paper focuses on the influence of communication range on the probability that all nodes are connected under two conditions, respectively: (1) all nodes have the same communication range, and (2) communication range of each node is a random variable. In the former case, this work proves that, for 0 < < −1 , if the probability of the network being connected is 0.36 , by means of increasing communication range by constant ( ), the probability of network being connected is at least 1 − . Explicit function ( ) is given. It turns out that, once the network is connected, it also makes the WSNs resilient against nodes failure. In the latter case, this paper proposes that the network connection probability is modeled as Cox process. The change of network connection probability with respect to distribution parameters and resilience performance is presented. Finally, a method to decide the distribution parameters of node communication range in order to satisfy a given network connection probability is developed.

Self Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

“…If the target comes in the SR of any MSN, it detects the target. Communication Range (CR) / radio range is a radius of MSN in which MSNs can communicate with each other for sharing their location coordinates and sharing a message mentioning the target is in its SR. [27], [28], [29]. SR and CR are shown in Fig.…”

Probability of Interference-Optimal and Energy-Efficient Analysis for Topology Control in Wireless Sensor Networks

“…As shown in [24,28,29], the energy required to transmit a data packet from node u to node v relates to the distance between these two nodes, which can be expressed as:…”

Probability Model Based Energy Efficient and Reliable Topology Control Algorithm

“…In general, there are three models can express the connection mode between nodes, which are shown in Figure 1 [20]. Figure 1a illustrates the k nearest neighbor model; each node in this model has constant node degree and maintains the node degree by changing communication range dynamically.…”

“…Figure 1c illustrates the Erdos-Renyi random graph that connects any two nodes by the same probability which is not appropriate in WSNs. Disc model is more plausible in WSN since obtaining k neighbors is not always feasible due to the communication range limitation [20]. Therefore, in this paper, we only interested in the Disc model.…”