A Deep-Neural-Network-Based Relay Selection Scheme in Wireless-Powered Cognitive IoT Networks

Nguyen, Toan-Van; Tran, Thong-Nhat; Shim, Kyusung; Huynh‐The, Thien; An, Beongku

doi:10.1109/jiot.2020.3038907

Cited by 49 publications

(29 citation statements)

References 34 publications

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“…Different supervised learning algorithms, including support vector machines (SVMs), feed forward neural networks (FNNs), deep neural networks (DNNs) and decision trees (DTs), have been used in existing studies considering relaying in IoT networks; see, e.g., [153]- [167]. A key bottleneck in conventional relaying is the acquisition of accurate CSI.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…However, with large-scale device deployment in future networks and real-time IoT applications, CSI acquisition can add to communication overhead and delays. Supervised learning combined with deep neural networks can offer real-time relay selection algorithms in IoT networks [153]. Considering the nonlinearity in EH relays, [153] developed a DNN-based model for relay selection by using throughput-dependent parameters such as SNR, number of users and relay position for offline training.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…Supervised learning combined with deep neural networks can offer real-time relay selection algorithms in IoT networks [153]. Considering the nonlinearity in EH relays, [153] developed a DNN-based model for relay selection by using throughput-dependent parameters such as SNR, number of users and relay position for offline training. One-time offline model training, as in [153], results in lower complexity selection algorithms and can offer a real-time selection advantage; however, offline training still needs the resources of central entities such as access points or base stations.…”

Section: A Supervised Learningmentioning

confidence: 99%

“…Considering the nonlinearity in EH relays, [153] developed a DNN-based model for relay selection by using throughput-dependent parameters such as SNR, number of users and relay position for offline training. One-time offline model training, as in [153], results in lower complexity selection algorithms and can offer a real-time selection advantage; however, offline training still needs the resources of central entities such as access points or base stations. Neural network relay selection was shown to offer throughput performance improvement over an SVM approach in [168].…”

Section: A Supervised Learningmentioning

confidence: 99%

See 3 more Smart Citations

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

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The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

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Section: A Supervised Learningmentioning

confidence: 99%

Section: A Supervised Learningmentioning

confidence: 99%

Section: A Supervised Learningmentioning

confidence: 99%

Section: A Supervised Learningmentioning

confidence: 99%

See 2 more Smart Citations

Relaying in the Internet of Things (IoT): A Survey

Uyoata¹,

Mwangama

Adeogun

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In another front, deep neural network (DNN) has recently gained recognition as a viable solution to deal with various practical problems, such as queue management, resource allocation, and security problem in IoT systems and contemporary wireless networks [9]. Due to the accuracy in approximating high non-linear functions at considerably low-complexity, it has activated various interesting applications including prediction of secrecy outage probability, relay selection, and routing optimizations [9], [10]. DNN-based relay selection helps to expedite real-time settings in IoT networks since DNN models can precisely estimate desired performance metrics from high dimensional raw data even with dynamic environments and complex radio conditions.…”

Section: Introductionmentioning

confidence: 99%

Short-Packet Communications in Multi-Hop WPINs: Performance Analysis and Deep Learning Design

Nguyen

Costa

et al. 2021

2021 IEEE Global Communications Conference (GLOBECOM)

Self Cite

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In this paper, we study short-packet communications (SPCs) in multi-hop wireless-powered Internet-of-Things networks (WPINs), where IoT devices transmit short packets to multiple destination nodes by harvesting energy from multiple power beacons. To improve system block error rate (BLER) and throughput, we propose a best relay-best user (bR-bU) selection scheme with an accumulated energy harvesting mechanism. Closed-form expressions for the BLER and throughput of the proposed scheme over Rayleigh fading channels are derived and the respective asymptotic analysis is also carried out. To support real-time settings, we design a deep neural network (DNN) framework to predict the system throughput under different channel settings. Numerical results demonstrate that the proposed bR-bU selection scheme outperforms several baseline ones in terms of the BLER and throughput, showing to be an efficient strategy for multi-hop SPCs. The resulting DNN can estimate accurately the throughput with low execution time. The effects of message size on reliability and latency are also evaluated and discussed.

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Applications of artificial neural networks for calculation of the Erlang B formula and its inverses

Marinković

Stosic

2023

Engineering Reports

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In this article, a novel approach to calculate the Erlang B formula and its inverses is proposed. It is based on application of artificial neural networks (ANNs). Namely, ANNs are trained to calculate the Erlang B call blocking probability as well as to solve the inverse problem, i.e. to calculate the required number of channels or the maximum amount of traffic for the given call blocking probability. Comparing to direct calculation of Erlang B formula, computational efficiency is significantly increased, while the accuracy is maintained. As far as the calculation of Erlang B inverse values is considered, as there is a lack of exact mathematical formulas, the proposed approach provides the closed‐form mathematical expressions.

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A Deep-Neural-Network-Based Relay Selection Scheme in Wireless-Powered Cognitive IoT Networks

Cited by 49 publications

References 34 publications

Relaying in the Internet of Things (IoT): A Survey

Relaying in the Internet of Things (IoT): A Survey

Short-Packet Communications in Multi-Hop WPINs: Performance Analysis and Deep Learning Design

Applications of artificial neural networks for calculation of the Erlang B formula and its inverses

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