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Titlebook: Wireless Algorithms, Systems, and Applications; 13th International C Sriram Chellappan,Wei Cheng,Wei Li Conference proceedings 2018 Springe

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發(fā)表于 2025-3-23 12:47:25 | 只看該作者
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發(fā)表于 2025-3-23 14:54:33 | 只看該作者
Approximate Minimum-Transmission Broadcasting in Duty-Cycled WSNs,ing the transmitting time slots into the network. Then, a .-approximation algorithm is proposed for MTBDCA. Finally, the theoretical analysis and experimental results verify the efficiency of the proposed algorithm.
13#
發(fā)表于 2025-3-23 18:38:23 | 只看該作者
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發(fā)表于 2025-3-24 00:25:43 | 只看該作者
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發(fā)表于 2025-3-24 04:58:47 | 只看該作者
Interest-Aware Next POI Recommendation for Mobile Social Networks, to describe users location interest, based on which we form comprehensive feature representations regarding user interest and contextual information. We propose a supervised learning prediction model for next POI recommendation. Experiments based on the Gowalla dataset verify the accuracy and efficiency of the proposed approach.
16#
發(fā)表于 2025-3-24 07:50:33 | 只看該作者
Hop-Constrained Relay Node Placement in Wireless Sensor Networks,hop constraint given by the end user. We present a heuristic-based algorithm to solve the above optimization problem and evaluate its performance by extensive simulation. The experimental results demonstrate that the efficiency of our designs in comparison with two baselines.
17#
發(fā)表于 2025-3-24 10:49:47 | 只看該作者
18#
發(fā)表于 2025-3-24 16:32:31 | 只看該作者
Hypergraph Based Radio Resource Management in 5G Fog Cell,ditional cloud computing paradigm is unable to effectively solve the problem of 5G resource management, such as limited system capacity and low utilization rate of resource management. As a new paradigm, fog computing has the characteristics of low delay and geo-distribution. It can enable the resou
19#
發(fā)表于 2025-3-24 22:55:18 | 只看該作者
20#
發(fā)表于 2025-3-25 03:02:26 | 只看該作者
Interest-Aware Next POI Recommendation for Mobile Social Networks,d services. In this paper, we propose an interest-aware next POI recommendation approach, which consider the location interest among similar users and the contextual information (such as time, current location, and friends preference) for POI recommendation. We develop a spatial-temporal topic model
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