Energy-aware path selection in mobile wireless sensor networks: A dynamic Bayesian game approach

Lin, Chih-Kuang; Kure, Øivind

doi:10.1109/pimrc.2009.5450183

Cited by 4 publications

(12 citation statements)

References 8 publications

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“…The proposals differ from each others in the game class used, or the solution proposed. For instance, GTEBR [55] takes the residual energy of nodes as a main metric, while in [69], three factors are taken into account: energy, location, and cooperation between sensors. DSR-G [62] chooses reliable routes to prolong the network lifetime.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…In VGTR, the selection of a next hop is rotated to achieve energy bal-1080 anced routing. It is worth mentioning that [69] considers WSNs' scenarios that allow mobility but it has a low remaining energy distribution.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Correlated equilibrium requires joint computation of strategies. In general, it is easier to computer those joint [21], [24], [25], [26], [28], [76],[100], [115], [139] Repeated game [21], [48], [50], [68], [96], [97], [98],[100],[105], [125], [157] Incomplete information [21], [23], [69], [105], [113], [114], [136], [153], [154], [156] Bayesian NE [23], [68], [113], [117] Dynamic game [66], [69], [76], [113] Static game [113] Mixed strategy NE [49], [55], [57], [69], [136], [138], [139], [157] Pure strategy NE [22], [76], [79], [125] Potential game [28],…”

Section: Cooperation Enforcement Gamesmentioning

confidence: 99%

“…In [69], the source/forwarder problem is formulated as a dynamic Bayesian game with incomplete information. This game is played by every node participating 1015 in the packet delivery, thereby helping the nodes to decide energy-aware paths toward a sink.…”

Section: Cooperation Enforcement Gamesmentioning

confidence: 99%

“…All the mathematical formulations of j's payoff in the the six different cases, illustrated in Fig. 3, are further described in [69]. Simulation results show that the proposal enhances the network lifetime, compared to the techniques such as Flood and AODV [70], by selecting the delivery path based on a sensor's energy.…”

Section: Cooperation Enforcement Gamesmentioning

confidence: 99%

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Section: Discussion and Future Directionsmentioning

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Section: Cooperation Enforcement Gamesmentioning

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Abstract-Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and contextawareness are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

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Energy-aware path selection in mobile wireless sensor networks: A dynamic Bayesian game approach

Cited by 4 publications

References 8 publications

Game theory for energy efficiency in Wireless Sensor Networks: Latest trends

Game theory for energy efficiency in Wireless Sensor Networks: Latest trends

On-demand updates after a node failure in a wireless network

Reconfigurable Wireless Networks

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