An Underwater Sensor Allocation Scheme for Noncircular Sensing Coverage Regions

Intelligently allocating underwater sensors to a large area of interest whose acoustic characteristics vary throughout is a challenge, especially for an area clearance scenario. In these scenarios, there is no apparent target for an adversary to gravitate towards, such as a ship or a port. Thus, it is difficult to determine how the field designer should allocate sensors so that their deployment locations can be planned efficiently. The previously proposed Game Theory Field Design (GTFD) model can achieve an intelligent sensor allocation, using a game theoretic approach, for sensors with circular coverage regions. In practice, however, the sensing coverage of an underwater sensor will likely be noncircular due to the azimuthally dependent bathymetric phenomena and other underwater irregularities. As a result, an extension of the model is proposed for allocating sensors for the irregularly shaped sensing coverage regions. This work provides two validations of the extended GTFD model. The first is an analytical comparison with sensing coverage regions whose shape is well understood, and the second uses simulation to validate the model for the irregularly shaped regions. 1. Introduction When designing an underwater sensor field for an area clearance scenario, a field designer’s responsibility is to create a sensor field whose purpose may range from preventing the deployment of mines to averting illegal traversal and surveillance of a restricted water space. Sensor allocation to an area of interest (AOI) in an area clearance scenario is complex, as there are no obvious targets that an adversary would gravitate towards, such as a port [1]. Thus, the field designer is left to blindly guess as to how to allocate a fixed number of available sensors, unless an analysis of the acoustic characteristics of the AOI is done. The underwater environment introduces challenges that are not experienced terrestrially, such as signal transmission loss due to geometric spreading and absorption by the ocean [2–4]. Additionally, multipath, as well as man-made and ambient noise, can cause significant interference [2–4]. As a result, the sensing range of a sensor is limited, making underwater vehicle detection quite difficult [2–4]. Additionally, sound speed, and as a result, transmission loss, are dependent upon water temperature, depth, salinity, and time of year. Furthermore, transmission loss, and in turn, sensing range, is range dependent, meaning that it varies by a sensor’s physical location [4, 5]. Since the underwater environment exhibits range dependence, it is possible