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A wireless multimedia sensor network (WMSN) is a network of wirelessly interconnected sensors that can gather multimedia information, such as sound and vision. One of the most important design issues of a WMSN is to maximize the coverage, while preserving the network connectivity. Although there are many studies about coverage for WMSNs, most of them are based on two-dimensional terrain assumptions. However, particularly for outdoor applications, three-dimensional (3-D) terrain structure affects the performance of the WMSN remarkably. In this paper, a novel 3-D WMSN simulation environment for connected coverage issues is presented. There are four main modules of our simulation environment. The terrain generator (TerGen) generates a synthetic 3-D landscape with different weather conditions (snow, rain, and fog), object occlusions (artificial or natural objects), and toughness levels of terrain (smooth or rough). The scenario editor (SenEd) is used to define various sensor types that have various behavioral and locational attributes. The outputs of TerGen and SenEd are the inputs of the simulator engine (SimEn), which simulates the WMSN and gives the performance results. The Optimization Module (OptMod), which is optional, can be used to determine the location of the sensors optimally, while satisfying a set of predefined constraints. Different scenarios are simulated to show the capabilities of the simulation environment. The performance results show that the 3-D terrain structure affects the coverage performance of the WMSN directly. The object occlusions and weather conditions are also very important for WMSN coverage.

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In this paper, a novel hybrid method for path planning problem of multiple mobile sensors on a 3-D terrain is proposed. Our method proceeds in two phases: the global path-planning phase, and the local path planning phase. The first phase constructs a connectivity graph generated by a probabilistic roadmap (PRM) method and selects the control points of sensors' paths from the set of nodes generated by the PRM method. In the local path-planning phase, a hybrid evolutionary algorithm is proposed to determine the intermediate points, which are between control points of sensors' paths in order to complete the paths. The local-path planner considers the accessibility of control points, smoothness of each path, visibility of terrain covered by mobile sensors and the total cost of all paths (i.e. the total length of all paths). The experimental study points out the effectiveness of our framework under various terrain and sensor characteristics.