Emergency aware congestion control for smart grid neighborhood area networks

León, Juan Pablo Astudillo; Llopis, Luis J. de la Cruz

doi:10.1016/j.adhoc.2019.101898

Cited by 9 publications

(5 citation statements)

References 21 publications

(26 reference statements)

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“…On the other hand, unlike the strategy followed in [12] and [13] where relay nodes are in charge of discarding packets in network congestion situations, in this paper it is up to the source nodes to reduce their packet generation rate. Thus, depending on the sensed degree of network congestion, source nodes may increase or decrease the rate at which they generate their packets.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, different channels are used for data and control traffic. Later, in [13], a multi-channel allocation scheme together with a congestion control mechanism was presented and evaluated, considering the different service quality needs of the different applications. The congestion control mechanism takes into account if the network is in an emergency state caused by intrinsic or extrinsic reasons.…”

Section: Related Workmentioning

confidence: 99%

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A fair and distributed congestion control mechanism for smart grid neighborhood area networks

et al. 2020

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The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new fair and distributed congestion control mechanism for NANs is proposed, implemented and evaluated. The main goal of this mechanism is to provide fairness in the access to the network, thus avoiding that some network nodes monopolize the use of the channels due to their higher traffic generation rate, or to their geographical position. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The goal here is to provide the needed Quality of Service (QoS) to all traffic flows, especially when the traffic load is high. The proposal is evaluated in the context of a wireless ad hoc network composed by a set of smart grid meter devices. Applying our proposed congestion control mechanism leads to performance improvements in terms of packet delivery ratio, network throughput fairness between different traffic sources, packet network transit time and QoS provision.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A fair and distributed congestion control mechanism for smart grid neighborhood area networks

et al. 2020

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“…Instead of considering IEEE 802.11s mesh networks, the basic IEEE 802.11 adhoc networks and the recent IEEE 802.11ac physical layer standard have been chosen. On the other hand, unlike the strategy followed in [1] and [11] where relay nodes are in charge of discarding packets in network congestion situations, in this paper it is up to the source nodes to reduce their packet generation rate. Thus, depending on the sensed degree of network congestion, source nodes may increase or decrease the rate at which they generate their packets.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, different channels are used for data and control traffic. Later, in [11], a multi-channel allocation scheme together with a congestion control mechanism was presented and evaluated, considering the different service quality needs of the different applications. The congestion control mechanism takes into account if the network is in an emergency state caused by intrinsic or extrinsic reasons.…”

Section: Related Workmentioning

confidence: 99%

Towards a Distributed Congestion Control mechanism for Smart Grid Neighborhood Area Networks

León

Begin

Busson

et al. 2019

Proceedings of the 16th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &Amp; Ubiquitous Netw

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The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new distributed congestion control mechanism is proposed, implemented and evaluated for NANs. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The main goal is to provide with the needed Quality of Service (QoS) to all traffic flows, especially in high traffic load situations. The proposal is evaluated in the context of a wireless ad hoc network made up by a set of smart meter devices, using the Ad hoc On-Demand Distance Vector (AODV) routing protocol and the IEEE 802.11ac physical layer standard. The application of the proposed congestion control mechanism, together with the necessary modifications made to the AODV protocol, lead to performance improvements in terms of packet delivery ratio, network throughput and transit time, fairness between different traffic sources and QoS provision.

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“…We have previously proposed two alternatives for congestion control on SG NANs. One of them is implemented on the intermediate nodes and takes into account the possibility of being in emergency situations [28]. The second one delves into the need to offer an equitable distribution of network resources among all traffic generating nodes [29].…”

Section: Related Workmentioning

confidence: 99%

Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks

León¹,

Rico-Novella

Llopis

2020

IEEE Access

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Smart Grid (SG) networks include an associated data network for the transmission and reception of control data related to the electric power supply service. A subset of this data network is the SG Neighborhood Area Network (SG NAN), whose objective is to interconnect the subscribers' homes with the supplier control center. The data flows transmitted through these SG NANs belong to different applications, giving rise to the need for different quality of service requirements. Additionally, other subscriber appliances could use this network to communicate over the Internet. To avoid network congestion, as well as to differentiate the quality of service (QoS) received by the different data flows, a congestion control mechanism with traffic differentiation capabilities is required. The main contribution of this work is the proposal of a new congestion control mechanism based on machine learning techniques to try to guarantee the different QoS requirements to the different data flows. A main problem when applying machine learning techniques is the need for datasets to be used in the training steps. In this sense, a second contribution of this article is the proposal of a method to generate such datasets by means of simulation techniques. The proposed mechanism is then evaluated in the context of a wireless SG NAN. The nodes of this network are the subscriber's smart meters, which in turn perform the function of concentrating the data traffic sent and received by the rest of the home appliances. Besides, different machine learning classification methods are taken into account. The evaluation carried out shows significant improvements in terms of network throughput, transit time, and quality of service differentiation. Finally, the computational cost of the algorithms used in this proposal has also been evaluated, using real low-cost IoT hardware platforms.

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Emergency aware congestion control for smart grid neighborhood area networks

Cited by 9 publications

References 21 publications

A fair and distributed congestion control mechanism for smart grid neighborhood area networks

A fair and distributed congestion control mechanism for smart grid neighborhood area networks

Towards a Distributed Congestion Control mechanism for Smart Grid Neighborhood Area Networks

Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks

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