Density and transmission power in intelligent wireless sensor networks

Michele Chincoli, Stavros Stavrou, Antonio Liotta

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case.

TaalEngels
Titel2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's1518-1523
Aantal pagina's6
ISBN van geprinte versie9781538620700
DOI's
StatusGepubliceerd - 28 aug 2018
Evenement14th International Wireless Communications and Mobile Computing Conference, (IWCMC2018) - Limassol, Cyprus
Duur: 25 jun 201829 jun 2018
http://iwcmc.org/2018/

Congres

Congres14th International Wireless Communications and Mobile Computing Conference, (IWCMC2018)
Verkorte titelIWCMC2018
LandCyprus
StadLimassol
Periode25/06/1829/06/18
Internet adres

Vingerafdruk

Power transmission
Wireless Sensor Networks
Wireless sensor networks
Power control
Power Control
Interference
Vertex of a graph
Quality of service
Quality of Service
Network protocols
Communication
Game theory
Reinforcement learning
IEEE 802.15.4
Internet of Things
Sensor
Sensor nodes
Cage
Telecommunication links
Energy efficiency

Trefwoorden

    Citeer dit

    Chincoli, M., Stavrou, S., & Liotta, A. (2018). Density and transmission power in intelligent wireless sensor networks. In 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018 (blz. 1518-1523). Institute of Electrical and Electronics Engineers. DOI: 10.1109/IWCMC.2018.8450432
    Chincoli, Michele ; Stavrou, Stavros ; Liotta, Antonio. / Density and transmission power in intelligent wireless sensor networks. 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018. Institute of Electrical and Electronics Engineers, 2018. blz. 1518-1523
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    abstract = "This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case.",
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    Chincoli, M, Stavrou, S & Liotta, A 2018, Density and transmission power in intelligent wireless sensor networks. in 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018. Institute of Electrical and Electronics Engineers, blz. 1518-1523, Limassol, Cyprus, 25/06/18. DOI: 10.1109/IWCMC.2018.8450432

    Density and transmission power in intelligent wireless sensor networks. / Chincoli, Michele; Stavrou, Stavros; Liotta, Antonio.

    2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018. Institute of Electrical and Electronics Engineers, 2018. blz. 1518-1523.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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    AU - Stavrou,Stavros

    AU - Liotta,Antonio

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    Y1 - 2018/8/28

    N2 - This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case.

    AB - This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case.

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    Chincoli M, Stavrou S, Liotta A. Density and transmission power in intelligent wireless sensor networks. In 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018. Institute of Electrical and Electronics Engineers. 2018. blz. 1518-1523. Beschikbaar vanaf, DOI: 10.1109/IWCMC.2018.8450432