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In this paper we propose a new scheduling algorithm called Real Time Scheduling (RTS). This algorithm deals with all the contributing components of the ...
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Being independent of any fixed equipment, Ad Hoc wireless sensor networks, a kind of acentric and self-organized wireless network, possesses some features such as easiness of deployment, strong invulnerability and flexibility of networking, which leads to a promising application prospect in terms of UAV military and civilian use. This paper proposes a new slot adaptive 4D network clustering algorithm based on UAV autonomous formation and reconfiguration to solve the problem of UAV Ad Hoc network such as networking confusion, poor network reconstruction performance, huge energy consumption and other issues. The algorithm can optimize the topology of UAVs network. We build the network topology and generate clustering network by the slot adaptive 4D network clustering algorithm in Matlab. According to the real combat of UAV, four states are simulated and analyzed. The simulation results validate the feasibility of the slot adaptive 4D network clustering algorithm. The clustering structure generated by the slot adaptive 4D network clustering algorithm is robust and the algorithm is suitable for the UAV group operation.
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Online Learning is a model of incremental learning where the learning algorithm makes predictions based on data as it arrives sequentially.
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This paper proposes a loss-free time-slot allocation (LTA) algorithm working on Optical Burst Switching (OBS) for ring and mesh hybrid networks.
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Slot shifting is an algorithm that aims to provide schedulability guarantees for event-based tasks alongside periodic tasks.
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In computer science, a sorting algorithm is an algorithm that puts elements of a list into an order. The most frequently used orders are numerical order and lexicographical order, and either ascending or descending.
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The document proposes a dynamic K-means clustering algorithm to improve routing in mobile ad hoc networks (MANETs). It aims to address limitations of the basic K-means algorithm like fixed cluster heads and members.
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