Main Reference PaperA game theoretic optimization of RPL for mobile Internet of Things applications., 2018. [Contiki-Cooja Simulator]
  • Kharrufa, Harith, Hayder Al-Kashoash, and Andrew H. Kemp.

Author Name(s):
  • Kharrufa, Harith, Hayder Al-Kashoash, and Andrew H. Kemp.

  • IEEE Sensors Journal, 2018

Journal Name and year:
  • IEEE Sensors Journal, 2018

  • 10.1109/JSEN.2018.2794762

DOI:
  • 10.1109/JSEN.2018.2794762

  • Internet of Things (IoT)

Research Area:
  • Internet of Things (IoT)

  • In this paper, a game scenario is formulated, where nodes compete for network resources in a selfish manner, to send their data packets to the sink node

  • Each node counts as a player in the non-cooperative game.

  • The optimal solution for the game is found using the unique Nash equilibrium (NE), where a node cannot improve its pay-off function while other players use their current strategy.

  • The proposed solution aims to present a strategy to control different parameters of mobile nodes (or static nodes in a mobile environment) including transmission rate, timers, and operation mode, in order to optimize the performance of routing protocol for low-power and lossy networks (RPL) under mobility in terms of packet delivery ratio, throughput, energy consumption, and end-to-end-delay.

  • The proposed solution monitors the mobility of nodes based on received signal strength indication readings, and it also takes into account the priorities of different nodes and the current level of noise in order to select the preferred transmission rate.

  • An optimized protocol called game-theory based mobile RPL (GTM-RPL) is implemented and tested in multiple scenarios with different network requirements for Internet of Things applications.

  • Simulation results show that in the presence of mobility, GTM-RPL provides a flexible and an adaptable solution that improves throughput whilst maintaining lower energy consumption showing more than 10% improvement compared to related work.

  • For applications with high throughput requirements, GTM-RPL shows a significant advantage with more than 16% improvement in throughput and 20% improvement in energy consumption.

Abstract:
  • In this paper, a game scenario is formulated, where nodes compete for network resources in a selfish manner, to send their data packets to the sink node

  • Each node counts as a player in the non-cooperative game.

  • The optimal solution for the game is found using the unique Nash equilibrium (NE), where a node cannot improve its pay-off function while other players use their current strategy.

  • The proposed solution aims to present a strategy to control different parameters of mobile nodes (or static nodes in a mobile environment) including transmission rate, timers, and operation mode, in order to optimize the performance of routing protocol for low-power and lossy networks (RPL) under mobility in terms of packet delivery ratio, throughput, energy consumption, and end-to-end-delay.

  • The proposed solution monitors the mobility of nodes based on received signal strength indication readings, and it also takes into account the priorities of different nodes and the current level of noise in order to select the preferred transmission rate.

  • An optimized protocol called game-theory based mobile RPL (GTM-RPL) is implemented and tested in multiple scenarios with different network requirements for Internet of Things applications.

  • Simulation results show that in the presence of mobility, GTM-RPL provides a flexible and an adaptable solution that improves throughput whilst maintaining lower energy consumption showing more than 10% improvement compared to related work.

  • For applications with high throughput requirements, GTM-RPL shows a significant advantage with more than 16% improvement in throughput and 20% improvement in energy consumption.

  • Operating System : Ubuntu 12.04 LTS 64bit

  • Simulator: Cooja, Instant Contiki-3.0 and Vmware Player 12.5.6

  • Language: C

Software Tools & Technologies
  • Operating System : Ubuntu 12.04 LTS 64bit

  • Simulator: Cooja, Instant Contiki-3.0 and Vmware Player 12.5.6

  • Language: C

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