Mobile sensor networks (MSNs) can provide sensing coverage to large areas of interest (AoIs). Area coverage and target tracking capabilities of MSNs are heavily depending on their motion control and coordination mechanisms. Many existing MSN motion control algorithms ignore or poorly utilize available information from their operating environment, thus lead to unsatisfactory monitoring performances. This paper proposes a fully distributed semi-flocking algorithm which enables mobile nodes to self-organize themselves based on mobility and sensing information via information exchanges among nearby nodes. A distributed mechanism is designed to maximize area coverage and target tracking performances of MSNs. Mobile nodes perform evaluations based on received information and switch between searching and tracking modes. Behaviors of MSNs controlled by the proposed algorithm are studied under different levels of information exchanges. Our study shows that the proposed semi-flocking algorithm is capable of delivering desirable area coverage and target tracking performances in MSNs.

Efficient path planning has an utmost importance in the domain of autonomous navigation. Even though shortest path planning has been well discussed in the past, the same techniques might not always be used to find the fastest path in outdoor environments due to the inability of mobile agents to travel at their peak speed everywhere in irregular terrains. Mobility maps are an effective way for dealing with such irregularities. In this paper, we first introduce a grid-based mobility maps for representing speed limitations in outdoor terrains. Then, we propose a heuristic for finding the fastest path on such maps. The proposed heuristic is proven to be both admissible and consistent. Therefore, it can be used with A*-like heuristic search algorithms for obtaining fastest paths efficiently. Simulation results provided in this paper verify the optimality of paths that are found with the help of the proposed heuristic.

Anti-flocking controlled mobile sensor networks (MSNs) have demonstrated impressive dynamic area coverage performances. Even though MSNs are often utilized in outdoor environments that consist of uneven terrains, existing antiflocking control protocols are designed for flat terrain navigation. Thus they tend to maneuver mobile sensory units along shortest paths between navigation goals in an area of interest. Even though navigating along shortest paths can be both time-and energyefficient on flat terrains, such motions can often result in excessive energy consumptions on uneven terrains. This paper proposes an energy-efficient anti-flocking control protocol for MSNs based on a terrain adaptation force and a navigation goal selection method. The proposed control protocol encourages mobile sensory units to follow terrain contours whenever feasible. Test results show that the proposed control protocol is a promising energy-efficient solution for MSNs operating on uneven terrains.

Recently proposed distributed anti-flocking algorithms have enabled mobile sensor networks (MSNs) to deliver impressive area coverage performances. However, due to lack of information about each other's traverse history, mobile sensor nodes tend to travel extra distances to achieve 100% cumulative area coverage. Inspired by the territorial marking behaviour of solitary animals, this paper proposes a new information map and map updating methods for anti-flocking controlled MSNs. The proposed territorial marking anti-flocking control enables MSNs to achieve improved area coverage performances by encouraging nodes to remain in a part of the terrain. According to the results provided in this paper, the proposed algorithm can be more energy efficient for MSNs in continues monitoring applications. Index Terms-Mobile sensor networks, territorial marking, anti-flocking, area coverage, distributed control This is the Pre-Published Version.

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