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Pervasive and Mobile Computing, Volume 51

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Volume 51, December 2018

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  - journals/percom/Garcia-CejaRNJO18
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Enrique Garcia-Ceja, Michael Riegler, Tine Nordgreen, Petter Jakobsen, Ketil J. Oedegaard, Jim Tørresen:
Mental health monitoring with multimodal sensing and machine learning: A survey. 1-26

Research papers

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  - journals/percom/ValerioCP18
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Lorenzo Valerio, Marco Conti, Andrea Passarella:
Energy efficient distributed analytics at the edge of the network for IoT environments. 27-42
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  - journals/percom/RahimMZTAX18
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Azizur Rahim, Kai Ma, Wenhong Zhao, Amr Tolba, Zafer Al-Makhadmeh, Feng Xia:
Cooperative data forwarding based on crowdsourcing in vehicular social networks. 43-55
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Ming Zeng, Kai Zhang, Jie Chen, Haifeng Qian:
P3GQ: A practical privacy-preserving generic location-based services query scheme. 56-72
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Xing Jin, Mingchu Li, Xiaomei Sun, Cheng Guo, Jia Liu:
Reputation-based multi-auditing algorithmic mechanism for reliable mobile crowdsensing. 73-87
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  - journals/percom/ShiQWM18
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Fengrui Shi, Zhijin Qin, Di Wu, Julie A. McCann:
Effective truth discovery and fair reward distribution for mobile crowdsensing. 88-103
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You-Chiun Wang, Jiun-Wen Huang:
Efficient dispatch of mobile sensors in a WSN with wireless chargers. 104-120
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Vinícius M. A. de Souza, Rafael Giusti, Antonio J. L. Batista:
Asfault: A low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning. 121-137
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Yanli Xu, Feng Liu, Ping Wu:
Interference management for D2D communications in heterogeneous cellular networks. 138-149
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Zachary Wemlinger, Lawrence B. Holder:
Cross-environment activity recognition using a shared semantic vocabulary. 150-159
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Yen-Wen Chen, Po-Yin Liao, Yu-Ching Chen:
Study of relay-based ad hoc rendezvous and data transmission in cognitive radio networks. 160-173
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Rafal Kapelko:
On the maximum movement to the power of random sensors for coverage and interference. 174-192
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Mattia Tomasoni, Andrea Capponi, Claudio Fiandrino, Dzmitry Kliazovich, Fabrizio Granelli, Pascal Bouvry:
Why energy matters? Profiling energy consumption of mobile crowdsensing data collection frameworks. 193-208

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