4,705 research outputs found

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Using mobility and exception handling to achieve mobile agents that survive server crash failures

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    Mobile agent technology, when designed and used effectively, can minimize bandwidth consumption and autonomously provide a snapshot of the current context of a distributed system. Protecting mobile agents from server crashes is a challenging issue, since developers normally have no control over remote servers. Server crash failures can leave replicas, instable storage, unavailable for an unknown time period. Furthermore, few systems have considered the need for using a fault tolerant protocol among a group of collaborating mobile agents. This thesis uses exception handling to protect mobile agents from server crash failures. An exception model is proposed for mobile agents and two exception handler designs are investigated. The first exists at the server that created the mobile agent and uses a timeout mechanism. The second, the mobile shadow scheme, migrates with the mobile agent and operates at the previous server visited by the mobile agent. A case study application has been developed to compare the performance of the two exception handler designs. Performance results demonstrate that although the second design is slower it offers the smaller trip time when handling a server crash. Furthermore, no modification of the server environment is necessary. This thesis shows that the mobile shadow exception handling scheme reduces complexity for a group of mobile agents to survive server crashes. The scheme deploys a replica that monitors the server occupied by the master, at each stage of the itinerary. The replica exists at the previous server visited in the itinerary. Consequently, each group member is a single fault tolerant entity with respect to server crash failures. Other schemes introduce greater complexity and performance overheads since, for each stage of the itinerary, a group of replicas is sent to servers that offer an equivalent service. In addition, future research is established for fault tolerance in groups of collaborating mobile agents

    Wireless Networks Inductive Routing Based on Reinforcement Learning Paradigms

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    Evolutionary Computation of Multi-Robot/Agent Systems

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    More Improvement by Helping Ant to Fault-Tolerant Heuristic Routing Algorithm in Mesh Networks

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    Abstract: Routing with fault-tolerant mechanisms has a crucial effect on the fast exchange of information in variety of networks including mesh networks. This study attempts to choose an optimal path in terms of fault tolerance to transmit messages from source to destination while taking into account faulty nodes in such mesh networks. In this study, we take advantage of ant colony optimization algorithm to propose Adaptive Heuristic Routing algorithms to this problem. We use color pheromone ants to overcome problem of fail-recover behavior of network components. The proposed method is compared with fault-tolerant routing algorithm in mesh networks using the balanced ring. Simulation results depict that this method reacted quickly in terms of network faults, meanwhile in each time step the data can choose the optimal path to reach their destination. In this study, we improve performance of the proposed method using update ants to inform other nodes about the discovered shortest path. Simulation results show that the proposed method dramaticcaly increase efficiency of routing mechanism in mesh networks

    GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recognition

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    Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments
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