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The method examines the challenging issues in tracking multiple autonomous ground vehicles (AGVs) using a motion capture system (MoCap) accessible by a wireless network such as standard IEEE 802.11a WIFI protocol. A state-of-art technology such as global positioning system may have limitations in their accuracy and restricted lines of sight. Tracking in these environments entail various complexities such as target (AGV) occlusion, clutter, and the electromagnetic interference that can interrupt communication between AGV and ground-based control station. We present a novel idea that exploits IR signals emitted by the MoCap to fetch position data reflected from AGVs. MoCap uses the Motive software to manipulate the position data necessary for the tracking filter. This method adopted the fixed-interval smoothing based on the joint integrated track splitting filter (FIsJITS) for detecting and tracking the vehicles. FIsJITS obtains track state estimation in both forward and backward directions within fixed time measurement intervals. The multi-track backward predictions are fused in the forward-path track to obtain a-priori smoothing predictions, followed by a smoothing state estimation. This approach also calculates the smoothing target existence probability (TEP) to reinforce target detection, allowing a tracking system to simultaneously track multiple vehicles efficiently within cluttered environments. For a seamless data processing, we utilize the WIFI through a wireless access point (WAP) to transfer the data from MoCap to the computer system where Motive and tracking system softwares are running simultaneously. We conducted the real-time experiments to demonstrate the tracking performance of the proposed AGV system outperforming existing algorithms.

Recent embedded middleware platforms enable the structuring of an application as a set of collaborating agents deployed on various nodes of the underlying wireless sensor network (WSN). Of particular importance is the network cost incurred due to agent communication, which in turn depends on how the agents are placed within the WSN system. In this paper, we present two agent migration algorithms with the aim of minimizing the total network overhead. The first one takes independent single agent migration decisions, while the second one considers groups of agents for migration. Both algorithms work in a fully distributed fashion based on the knowledge available locally at each node, and can be used both for one-shot initial application deployment as well as for the continuous updating of agent placement. We also propose two methodologies to tackle the problem when WSN nodes have limited capacity. We show through theoretical analysis that one of our algorithms (called GRAL\ast) always results in an optimal placement, while for the rest of the algorithms, we derive approximation ratios pertaining to their performance. We evaluate the performance of our algorithms through a series of simulation experiments. Results show that group migration algorithms are superior compared to single agent migration algorithms with the performance difference reaching 34% for some settings. [ABSTRACT FROM AUTHOR]

The availability of large volumes of Semantic Web data has created the potential of discovering vast amounts of knowledge. Semantic relation discovery is a fundamental technology in analytical domains, such as business intelligence and homeland security. Because of the decentralized and distributed nature of Semantic Web development, semantic data tend to be created and stored independently in different organizations. Under such circumstances, discovering semantic relations faces numerous challenges, such as isolation, scalability, and heterogeneity. This paper proposes an effective strategy to discover semantic relationships over large-scale distributed networks based on a novel hierarchical knowledge abstraction and an efficient discovery protocol. The approach will effectively facilitate the realization of the full potential of harnessing the collective power and utilization of the knowledge scattered over the Internet. [ABSTRACT FROM PUBLISHER]

Abstract-With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a paramount research issue. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to simultaneously minimize the energy consumption and the makespan subject to the constraints of deadlines and tasks' architectural requirements. We propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution (NBS). In this cooperative game, machines collectively arrive at a decision that describes the task allocation that is collectively best for the system, ensuring that the allocations are both energy and makespan optimized. Through rigorous mathematical proofs we show that the proposed cooperative game in mere O(nmlog(m)) time (where n is the number of tasks, and m is the number of machines in the system) produces a NBS that guarantees Pareto optimally. The simulation results show that the proposed technique achieves superior performance compared to the Greedy and Linear Relaxation (LR) heuristics and with competitive performance relative to the optimal solution implemented in LINDO for small-scale problems. [ABSTRACT FROM AUTHOR]