Abstract-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 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, anti-flocking control algorithms enable MSNs to self-organize in an environment and provide impressive dynamic coverage performances. The anti-flocking 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 anti-flocking algorithms for dynamic coverage of MSNs, one for obstacle free environments and the other one for obstacle dense environments. Both are based on the sensing history and local interactions among sensor nodes.

Motions of mobile robots need to be optimized to minimize their energy consumption to ensure long periods of continuous operations. Shortest paths do not always guarantee the minimum energy consumption of mobile robots. Moreover, they are not always feasible due to climbing constraints of mobile robots, especially on steep terrains. We utilize a heuristic search algorithm to find energy-optimal paths on hilly terrains using an established energy-cost model for mobile robots. The terrains are represented using grid-based elevation maps. Similar to A*-like heuristic search algorithms, the energy-cost of traversing through a given location of the map depends on a heuristic energy-cost estimation from that particular location to the goal. Using zigzag-like path patterns, the proposed heuristic function can estimate heuristic energy-costs on steep terrains that cannot be estimated using traditional methods. We proved that the proposed heuristic energy-cost function is both admissible and consistent. Therefore, the proposed path planner can always find feasible energy-optimal paths on any given terrain without node revisits, provided that such paths exist. Results of tests on real-world terrain models presented in this paper demonstrate the promising computational performance of the proposed path planner in finding energy-efficient paths.

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.

Abstract-Data clustering is a frequently used technique in finance, computer science, and engineering. In most of the applications, cluster sizes are either constrained to particular values or available as prior knowledge. Unfortunately, traditional clustering methods cannot impose constrains on cluster sizes. In this paper, we propose some vital modifications to the standard kmeans algorithm such that it can incorporate size constraints for each cluster separately. The modified k-means algorithm can be used to obtain clusters in preferred sizes. A potential application would be obtaining clusters with equal cluster size. Moreover, the modified algorithm makes use of prior knowledge of the given data set for selectively initializing the cluster centroids which helps escaping from local minima. Simulation results on multidimensional data demonstrate that the k-means algorithm with the proposed modifications can fulfill cluster size constraints and lead to more accurate and robust results.

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Abstract-Three dimensional (3D) printing can be used to manufacture many different objects range from toys to hitech robot parts. This paper investigates 3D printer trajectory planning to improve the speed of the printing process. The printing speed mainly depends on the motion speed and path of the printing nozzle. We use triangular and trapezoidal velocity profiles to minimize the transition time between print segments. In this work, several algorithms that were originally proposed as solutions for conventional traveling salesman problem are modified to adapt to the new problem. The proposed modifications are designed to obtain time-efficient trajectories for the printing nozzle.

Abstract-After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of partitioning process. The second step ensures the satisfaction of modelled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39-and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

Abstract-Wireless sensor networks are usually deployed in scenarios that are too hostile for human personnel to perform maintenance tasks. Wireless sensor nodes usually exchange information in a multi-hop manner. Connectivity is crucial to the performance of a wireless sensor network. In case a network is partitioned due to node failures, it is possible to re-connect the fragments by setting up bridges using mobile platforms. Given the landscape of a terrain, the mobile platforms should be able reach the target position using a desirable path. In this paper, an off-line robot path planner is proposed to find desirable paths between arbitrary points in a given terrain. The proposed path planner is based on ACO algorithms. Unlike ordinary ACO algorithms, the proposed path planner provides its artificial ants with extra flexibility in making routing decisions. Simulation results show that such enhancement can greatly improve the qualities of the paths obtained. Performances of the proposed path planner can be further optimized by finetuning its parameters.