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  • Meng, Luoming  (2)
  • Yang, Yang  (2)
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  • 1
    Online Resource
    Online Resource
    Task coalition formation and self‐adjustment in the wireless sensor networks
    Wiley ; 2014
    In:  International Journal of Communication Systems Vol. 27, No. 10 ( 2014-10), p. 2241-2254
    Details
    In: International Journal of Communication Systems, Wiley, Vol. 27, No. 10 ( 2014-10), p. 2241-2254
    Abstract: Coalition is an essential mechanism in the multi‐agent systems in the research of task‐oriented area. Self‐interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering‐based coalition formation and self‐adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX–MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self‐repairing autonomously. The stimulus‐response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 1074-5351 , 1099-1131
    URL: Issue
    URL: Article
    DOI: 10.1002/dac.v27.10
    DOI: 10.1002/dac.2470
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 2024893-3
    SSG: 24,1
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  • 2
    Online Resource
    Online Resource
    Data Fault Detection in Medical Sensor Networks
    MDPI AG ; 2015
    In:  Sensors Vol. 15, No. 3 ( 2015-03-12), p. 6066-6090
    Details
    In: Sensors, MDPI AG, Vol. 15, No. 3 ( 2015-03-12), p. 6066-6090
    Abstract: Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s150306066
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2052857-7
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