Cross-over-net: An energy-aware coordination algorithm for WANETs based on software-defined networking

Aparicio, Joaquín; Legarda, Jon; Larrañaga, Janire; Echevarria, Juan José

doi:10.1109/softcom.2015.7314080

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…However, these methods mainly are aimed at optimizing energy consumption [ 13 , 14 ] or focusing on three-layered routing to optimize the packet flow by reducing the routing overhead [ 15 – 18 ]. Recently, a Software Defined Network (SDN) has attracted significant attention in optimizing the routing in such networks [ 19 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Alkadhmi

Uçan

Ilyas

2020

Applied Bionics and Biomechanics

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With the reliance of humans on mobile smart devices that have wireless communication, modules have significantly increased in recent years. Using these devices to communicate with the survivors during a disaster or its aftermath can significantly increase the chances of locating and saving them. Accordingly, a method is proposed in this study to extend the lifetime of the nodes in a Mobile Ad Hoc Network (MANET) while maintaining communications with the nearest base station (BS). Such a methodology allows the rapid establishment of temporary communications with these survivors, as restoring the complex infrastructure is a time-consuming process. The proposed method achieves the longer lifetime of the network by balancing the load throughout the nodes and avoids exhausting those with limited remaining energy. The proposed method has shown significant improvement in the lifetime of the MANET while maintaining similar Packet Delivery Rate (PDR) and route generation time, compared to existing methods.

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Section: Introductionmentioning

confidence: 99%

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Alkadhmi

Uçan

Ilyas

2020

Applied Bionics and Biomechanics

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“…The central control abstraction manages all the processes without the reduction of the network services. One notion of the energy consumption is the energy factor, as it is discussed in [38]. Let denotes the remaining energy in node u, and the total energy of the node u at its maximum capacity.…”

Section: Energy Consumptionmentioning

confidence: 99%

A Software Defined Networking Approach to Improve the Energy Efficiency of Mobile Wireless Sensor Networks

Aparicio¹,

Echevarria²,

Legarda³

2017

KSII TIIS

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Mobile Wireless Sensor Networks (MWSN) are usually constrained in energy supply, which makes energy efficiency a key factor to extend the network lifetime. The management of the network topology has been widely used as a mechanism to enhance the lifetime of wireless sensor networks (WSN), and this work presents an alternative to this. Software Defined Networking (SDN) is a well-known technology in data center applications that separates the data and control planes during the network management. This paper proposes a solution based on SDN that optimizes the energy use in MWSN. The network intelligence is placed in a controller that can be accessed through different controller gateways within a MWSN. This network intelligence runs a Topology Control (TC) mechanism to build a backbone of coordinator nodes. Therefore, nodes only need to perform forwarding tasks, they reduce message retransmissions and CPU usage. This results in an improvement of the network lifetime. The performance of the proposed solution is evaluated and compared with a distributed approach using the OMNeT++ simulation framework. Results show that the network lifetime increases when 2 or more controller gateways are used.

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Cross-over-net: An energy-aware coordination algorithm for WANETs based on software-defined networking

Cited by 2 publications

References 13 publications

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

A Software Defined Networking Approach to Improve the Energy Efficiency of Mobile Wireless Sensor Networks

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