Wireless Link Quality Prediction in IoT Networks

Sindjoung, Miguel Landry Foko; Minet, Pascale

doi:10.23919/pemwn47208.2019.8986920

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…The prediction model utilizes lists of MEC servers and stored results to forecast the future flow of end devices. This prediction process utilizes link quality estimation techniques outlined in [26] and [?]. Based on these predictions, bandwidth allocation is carried out for each MEC server, considering the parameter γ to comply with the constraint specified in Equation 5.…”

Section: ) Computing and Storage Managementmentioning

confidence: 99%

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Kabdjou,

Shinomiya

2024

IEEE Access

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Creating a cyber deception framework for 5G networks, particularly in IoT and cellular applications, is complex due to critical constraints in managing resources, meeting low latency demands, and addressing security concerns. While cloud computing aids in alleviating some limitations, it often falls short in meeting low-latency requirements. Multi-Access Edge Computing (MEC) has emerged as a solution by bringing resources closer to User Equipment (UEs) to reduce latency. Various MEC architectures have leveraged Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC), Network Slicing (NS), decision-making systems, and deception components. However, none have integrated these technologies comprehensively to achieve superior Quality of Service (QoS) and strengthen security. In this paper, we unify SDN, NFV, SFC, NS, decision-making technologies, and deception to efficiently manage MEC server resources and lure attackers. We utilize cyber deception metrics, including request collection rates over time and variations in request numbers concerning different botnet sizes. Moreover, we meticulously address QoS parameters such as latency, computing, storage, and bandwidth resources. Our approach initiates with a mathematical model for MEC server resource allocation, introducing a novel architecture that reduces bandwidth, computing, and storage resource usage. We introduce a cyber deception strategy utilizing uniform distribution and random selection to divert potential attackers. Simulations validate efficient resource management, notably reducing end-to-end latency for requests processed on the edge and in the cloud. This enhancement improves QoS within the MEC system and provides valuable insights for advancing decision-making technologies.

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Section: ) Computing and Storage Managementmentioning

confidence: 99%

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Kabdjou,

Shinomiya

2024

IEEE Access

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“…The packet reception rate (PRR) therefore becomes an important indicator for evaluating link reliability. To calculate PRR, a large number of test fittings can be used to provide intuitive feedback, and it can adaptively and slowly track changes in transmission channel quality 25 . We regard the transmission process of the data packet between the destination node and the sending node as multiple Bernoulli experiments.…”

Section: Related Workmentioning

confidence: 99%

“…To calculate PRR, a large number of test fittings can be used to provide intuitive feedback, and it can adaptively and slowly track changes in transmission channel quality. 25 We regard the transmission process of the data packet between the destination node and the sending node as multiple Bernoulli experiments. The result of the data transmission to the destination node in each experiment can be a success (the sending node receives ACK) or a failure.…”

Section: System Modelmentioning

confidence: 99%

An energy‐efficient power allocation algorithm under reliability constraint for industrial Internet of Thing systems

Zheng

Yang

Yan

et al. 2023

Int J Communication

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SummaryWith the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms.

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“…Hardware-based metrics. They are defined to estimate the the Received Signal Strength (RSSI) and the Link Quality Indication (LQI) of the received packet [11]. They include:…”

Section: A Link Quality In Iotmentioning

confidence: 99%

QoS in IoT Networks based on Link Quality Prediction

Boucetta

Nour

Cusin

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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The success of the Internet of Things (IoT) depends on the ability to provide reliable communication to the billions of devices that are used in many applications. In essence, estimating the quality of wireless links ensures the optimization of several protocols, reduces the end-to-end latency, and increases the reliability and the network lifetime. In this paper, we study the link quality in the Time Slotted Channel Hopping (TSCH) network by analyzing the received signal strength (RSSI) and error rates. The objective is to understand the temporal properties of these parameters which is important to select the appropriate channels for the critical applications and to enhance the Quality of Service (QoS) of the network. We apply machine learning techniques to a real dataset collected from a testbed IoT network deployed at Grenoble, France. We define five classes and present a classification of the 16 channels by comparing the performances of KNN (k-Nearest Neighbor) and LSTM (Long Short-Term Memory) algorithms.

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Wireless Link Quality Prediction in IoT Networks

Cited by 12 publications

References 9 publications

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

An energy‐efficient power allocation algorithm under reliability constraint for industrial Internet of Thing systems

QoS in IoT Networks based on Link Quality Prediction

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