GECSA: A game theory based energy efficient cluster-head selection approach in Wireless Sensor Networks

Mishra, Manas Ranjan; Panigrahi, Chhabi Rani; Sarkar, Joy Lal; Pati, Bibudhendu

doi:10.1109/mami.2015.7456580

Cited by 12 publications

(12 citation statements)

References 11 publications

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“…A link with 20 dB or above is considered as very good, a link with SNR 12 dB is considered as good and a link with SNR between 5 and 10 dB can hardly considered as bad, average or good. 68 LQI. The LQI designates the quality of received signal calculated either energy detection (ED), SNR or combination of both.…”

Section: Lq or Link Efficiency Computationmentioning

confidence: 99%

“…end for LQ, variance mean of the hardware-based LQEs (RSSI, SNR and LQI) is taken, which is named as 'hardwarebased triangle metric' and then this combined mean value of LQE is taken as input parameter in C:F. The idea of the mean of three metrics of LQEs has been taken from the literature. 68 The receiver estimates the variance of LQ (lq D ) to the origin using these equations…”

Section: Take Variance Of Rssi Snr and Lqimentioning

confidence: 99%

“…Selection of CLG and CLH is based on the value of C:F but selection of CN is totally based on noncooperative and recurring game theory. 67,68 CN and CLH further enhance network lifespan and energy efficacy. Only CMs of CLH start campaign for becoming CN.…”

Section: Selection Of Cnmentioning

confidence: 99%

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Trust-based energy-efficient routing protocol for Internet of things–based sensor networks

Ilyas

Ullah

Khan

et al. 2020

International Journal of Distributed Sensor Networks

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Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency.

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Section: Lq or Link Efficiency Computationmentioning

confidence: 99%

Section: Take Variance Of Rssi Snr and Lqimentioning

confidence: 99%

See 1 more Smart Citation

Trust-based energy-efficient routing protocol for Internet of things–based sensor networks

Ilyas

Ullah

Khan

et al. 2020

International Journal of Distributed Sensor Networks

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“…e algorithm examines the cooperation of cluster members and cluster heads (CHs) for reducing energy consumption in the network. e authors highlight that an algorithm based on game theory can be applied in the CH selection for the purpose of energy efficiency [24]. e algorithm is based on the subgame perfect Nash equilibrium (SPNE) approach which is used to find the Nash equilibrium (NE) in every subgame of the real game.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Coalition Games with Incentives in Machine-to-Machine Communications

Juma

Kurien

Olwal

2019

Journal of Computer Networks and Communications

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The need to achieve energy efficiency in machine-to-machine (M2M) communications has been a driver of the use of coalition game-based cooperative communication schemes. The proposed schemes have shown good energy-efficient performance results in the recent past. However, sustaining cooperation amongst coalition games of M2M devices from different network-operating authorities requires appropriate incentives. A review of the literature demonstrates that a limited number of contributions have considered the use of coalition games with incentives in M2M communications. In this paper, an energy-efficient coalition game with incentives in M2M communications is proposed. This work considers a Sierpinski triangle technique to partition M2M devices into multiple networks of hierarchical zones. Based on the constructed zones, a contract-modelled incentive is invoked to stimulate multihop transmissions between devices up to the BS/sink. The results obtained demonstrate that the proposed approach is on average 10% more energy efficient than the closely related existing algorithm, the coalition game theoretical clustering (CGTC).

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“…Because the IN node must be within the communication range of CH to monitor its behaviors. The selection of the CH and IN is crucial to improve the network life-cycle and energy efficiency [ 48 , 49 ]. In the GIN protocol, the IN election is based on a non-cooperative and repeated game theory.…”

Section: The Smart Collaborative Routing Protocol For Iotmentioning

confidence: 99%

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

Zhou

Song

2018

Sensors

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It is knotty for current routing protocols to meet the needs of reliable data diffusion during the Internet of Things (IoT) deployments. Due to the random placement, limited resources and unattended features of existing sensor nodes, the wireless transmissions are easily exposed to unauthorized users, which becomes a vulnerable area for various malicious attacks, such as wormhole and Sybil attacks. However, the scheme based on geographic location is a suitable candidate to defend against them. This paper is inspired to propose a smart collaborative routing protocol, Geographic energy aware routing and Inspecting Node (GIN), for guaranteeing the reliability of data exchanging. The proposed protocol integrates the directed diffusion routing, Greedy Perimeter Stateless Routing (GPSR), and the inspecting node mechanism. We first discuss current wireless routing protocols from three diverse perspectives (improving transmission rate, shortening transmission range and reducing transmission consumption). Then, the details of GIN, including the model establishment and implementation processes, are presented by means of the theoretical analysis. Through leveraging the game theory, the inspecting node is elected to monitor the network behaviors. Thirdly, we evaluate the network performances, in terms of transmission delay, packet loss ratio, and throughput, between GIN and three traditional schemes (i.e., Flooding, GPSR, and GEAR). The simulation results illustrate that the proposed protocol is able to outperform the others.

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GECSA: A game theory based energy efficient cluster-head selection approach in Wireless Sensor Networks

Cited by 12 publications

References 11 publications

Trust-based energy-efficient routing protocol for Internet of things–based sensor networks

Trust-based energy-efficient routing protocol for Internet of things–based sensor networks

Energy-Efficient Coalition Games with Incentives in Machine-to-Machine Communications

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

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