LPTA: Location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks

This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.The work is supported by the Science and Technology Pillar Program of Changzhou (Social Development), no. CE20135052. Joel J. P. C. Rodrigues's work has been supported by the Fundamental Research Funds for the Central Universities (Program no. HEUCF140803), by Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Zhu, C.; Wang, Y.; Han, G.; Rodrigues, JJPC.; Lloret, J. (2014). LPTA: Location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks. Scientific World Journal. https://doi.org/10.1155/2014/476253SHan, G., Xu, H., Jiang, J., Shu, L., Hara, T., & Nishio, S. 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Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs