Smart and self‐organised routing algorithm for efficient IoT communications in smart cities

Hamrioui, Sofiane; Hamrioui, Camil Adam Mohamed; LIoret, Jaime; Lorenz, Pascal

doi:10.1049/iet-wss.2018.5022

Cited by 13 publications

(9 citation statements)

References 28 publications

(41 reference statements)

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“…Recently, significant research efforts have been made to apply RL to wireless sensor networks (WSN) and IoT. Several approaches were proposed, including the cooperative approach, which is one of the most used in many works, such as SSAR in [8], FROMS in [25], OPT-Q-Routing in [26], EQR-RL in [27], and the work by [28]. is approach is deemed to be suitable for multiagent RL models where agents are required to cooperate and work together for the same purpose.…”

Section: Related Workmentioning

confidence: 99%

“…In [8], the authors proposed a smart and self-organizing routing algorithm (SSAR), which selects the best route based on some communication parameters, namely, the distance between nodes, the stability of links, and residual energy of IoTdevices. ese parameters have an impact on the stability and energy-efficient routing.…”

Section: Related Workmentioning

confidence: 99%

“…IoT consists of the interconnection of heterogeneous wireless devices including smartphones, wireless sensors, actuators, identification by radio frequency (RFID) tags, and real-world things with sensing capabilities [8,9]. Generally, a sensor device comprises four units, namely, power unit, sensing unit, processing unit, and communication unit [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

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Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

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“…In the literature, several protocol proposals for IoT networks can be found, [18][19][20][21] developed based on different methodologies. Routing protocols can be divided into two types: proactive and reactive.…”

Section: Introductionmentioning

confidence: 99%

A mobility solution for low power and lossy networks using the LOADng protocol

Gonçalves

Rabêlo

Rodrigues

et al. 2020

Trans Emerging Tel Tech

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Many devices on an Internet of Things (IoT) network have severe constraints on computing resources in terms of memory, processing, and power, which make these networks generally known as Low power and Lossy Networks (LLNs). Device mobility in these scenarios has several negative impacts on the network, particularly in terms of connectivity, as node movement affects the routes established by the routing protocol. The network performance is highly related to the adjacent routing protocol. The protocol considered as standard for IoT networks was designed for static networks, and it has a low reactivity in mobility scenarios, besides other limitations. Lightweight On-demand Ad hoc Distance-vector Routing-Next Generation (LOADng) is currently the primary reactive protocol for LLNs. Considering that mobility is a crucial feature for the future of IoT, this article proposes Extended Kalman Filter (EKF)-LOADng, a solution built from LOADng for LLNs in the mobility context. The solution aims to make the mobile nodes aware of its location, as well as to predict the nonlinear trajectory of these nodes through the EKF. The location obtained is used to improve the connectivity of the mobile nodes and to shorten paths while messages are exchanged on the network. EKF-LOADng was compared with the standard version and an enhanced version of the LOADng protocol in two different scenarios in the Contiki OS. Simulation results show the proposed solution is capable of locating mobile nodes with an average error of 1.46 m while maintaining connectivity to other nodes, as well as outperforms the compared proposals in some network metrics in terms of packet delivery ratio, latency, power consumption, and control bit overhead.

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“…Internet of things (IoTs) is the network of devices, and recent research proposed a performance degradation of IoTs communications in smart cities by using a new routing algorithm to improve them. This study has figured out an algorithm defined as smart and self-organised routing algorithm (SSRA), which can be selected as the best route for the packets and for which SSRA can be extended to the devices lifetime by parameter of fair and efficient energy consumption (Hamrioui et al, 2018). Due to this reason, the IoTs growing popularity is gaining more attention as the security and sensitivity problem in IoTs device applications and sensor-based threats to IoT devices is developed.…”

Section: Introductionmentioning

confidence: 99%

Impact of electromagnetic interface of soft magnetic sensor on IoTs current detector: Design and development

Hsu¹,

Chang²,

Fung³

2019

J. Electrical Electron. Eng. Res.

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This paper presents the electromagnetic interface (EMI) impact on the current transformer with amorphous HB1-M core, printed circuit board (PCB) power transformer and the filter of inductor for Internet of Things (IoTs) device. According to the antenna principle, the radiation emission power responses of PCB transformer and current transformer are estimated and found to be negligible due to the EMI experimental report. Based on this reason, consideration of the amorphous core in design manufacturing and EMI filter development is indispensable. Thus, the current transformer of amorphous HB1-M core is annealed at 350°C and soaked for 1.5 h. The simulation results of electromagnetic field show an EMI impact on filter performance. EMI frequency between 50 to 400 MHz for filter is found to be suitable for PCB. Experimental results have confirmed that the application of PCB transformer with HB1-M would be performed as well in high permeability and sensitivity.

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Smart and self‐organised routing algorithm for efficient IoT communications in smart cities

Cited by 13 publications

References 28 publications

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

A mobility solution for low power and lossy networks using the LOADng protocol

Impact of electromagnetic interface of soft magnetic sensor on IoTs current detector: Design and development

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