# Probabilistic Coverage and Connectivity

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Wireless sensor networks have been proposed for many applications such as forest fire detection, area surveillance, and natural habitat monitoring. A common ground for all such applications is that every sensor can detect an event occurring within its sensing range, and sensors collaborate in some way to deliver events, or information related to these events, to processing centers for possible actions. In many of the previous works, the sensing range is assumed to be a uniform disk of radius $r_s$. The disk model assumes that if an event happens at a distance less than or equal to rs from the sensor location, the sensor will deterministically detect this event. On the other hand, an event occurring at a distance $r_s + \epsilon (epsilon > 0)$ can not be detected at all, even for very small $\epsilon$ values. The disk sensing model is appealing, because it makes coverage maintenance protocols less complicated to design and analyze. However, it is unlikely that physical signals drop abruptly from high, full-strength values to zero, as the disk model assumes. This implies that there might be a chance to detect an event occurring at distances greater than rs. By ignoring this extra sensing capacity, the disk model may not fully utilize the sensing capacity of sensors, which may lead to: (i) deploying more sensors than needed and thus incurring higher cost, (ii) activating redundant sensors which increases interference and wastes energy, and ultimately (iii) decreasing the lifetime of the sensor network.

Several studies have argued that probabilistic sensing models capture the behavior of sensors more realistically than the deterministic disk model. A probabilistic sensing model is more realistic because the phenomenon being sensed, sensor design, and environmental conditions are all stochastic in nature. For instance, noise and interference in the environment can be modeled by stochastic processes. Sensors manufactured by the same factory are not deterministically identical in their behavior, rather, sensor characteristics are usually modeled using statistical distributions.

In this project, we consider probabilistic sensing and radio communication models in designing protocols for large-scale sensor networks. Probabilistic models are more realistic in capturing the characteristics of actual sensors than the over-simplified regular disk model.

## People

• Hossein Ahmadi (M.Sc., Graduated Summer 2007)

## Software

• Implementation of the Probabilistic Coverage Protocol (PCP) in NS-2. [readme] [code]
• Implementation of the Probabilistic Coverage Protocol (PCP) in C++. [readme] [code]