Mobile sensor networks (MSNs) are often used for monitoring large areas of interest (AoI) in remote and hostile environments, which can be highly dynamic in nature. Due to the infrastructure cost, MSNs usually consist of a limited number of sensor nodes. In order to cover large AoI, the mobile nodes have to move in an environment while monitoring the area dynamically. MSNs that are controlled by most of the previously proposed dynamic coverage algorithms either lack adaptability to dynamic environments or display poor coverage performances due to considerable overlapping of sensing coverage. As a new class of emergent motion control algorithms for MSNs, antiflocking control algorithms enable MSNs to self-organize in an environment and provide impressive dynamic coverage performances. The antiflocking algorithms are inspired by the solitary behavior of some animals who try to separate from their species in most of daily activities in order to maximize their own gains. In this paper, we propose two distributed antiflocking algorithms for dynamic coverage of MSNs, one for obstacle-free environments and the other for obstacle-dense environments. Both are based on the sensing history and local interactions among sensor nodes.
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