Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks

Ali, Zulfiqar; Qureshi, Kashif Naseer; Mustafa, Kainat; Bukhsh, Rasool; Aslam, Sheraz; Mujlid, Hana; Ghafoor, Kayhan Zrar

doi:10.3390/e24111607

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Once the GMM with K-Means probabilistically design the optimum number of profiles, now the ED i s are assigned to these profiles according to the readings/data. Further, the ED i from HPP is allowed to transmit data for 15 minutes at maximum [27]. The reason behind this is that having 20 bytes of a complete packet with SF 12, BW 125 Khz and CR 4/5, it takes 1318.912 milliseconds to reach GW j .…”

Section: Proposed System Modelmentioning

confidence: 99%

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

et al. 2023

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Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.INDEX TERMS Low power wide area network, long range wide area network, forward error correction, energy efficiency, internet of things, adaptive scheduling algorithm, Gaussian mixture model, spreading factor, adaptive data rate, end device, quality of service, chirp spread spectrum, packet success ratio.

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Section: Proposed System Modelmentioning

confidence: 99%

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

et al. 2023

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“…During the forward pass of the LSTM, the gates are computed based on the current input, previous output, and previous Ct. The equations governing the computation of the gates are as follows [21][22][23]:…”

Section: Cellmentioning

confidence: 99%

“…LSTMs can be used to recommend films by modelling users' sequential patterns and preferences based on their previous encounters with films. Here's a high-level explanation of how LSTM can be used to recommend films [22][23]:…”

Section: A Lstm For Movie Recommendationmentioning

confidence: 99%

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Ali,

Muhammad,

Al-Shamayleh

et al. 2023

IEEE Access

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Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their own likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, have showed substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra Metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter metapath analysis. The intra metapath analysis investigates interactions within a single metapath, whereas the inter metapath analysis investigates links between numerous metapaths. Intra and inter metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user's interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.

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“…Both ZigBee and Wi-Fi are the key communication technologies for data acquisition and transmission in IoT [15] and beyond 5G networks [16,17]. In this paper, we focus on IEEE 802.15.4 network characteristics in the presence of heavy Wi-Fi traffic in the proximity Jamming probability of ZigBee networks when operating in the same environment as Wi-Fi is addressed in [18]. A channel configuration mechanism based on fast channel switching is proposed for terminal node interference detection.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

Hassan,

Rubab,

Saadat

et al. 2023

IEEE Access

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The use of unlicensed frequency bands has gathered much interest recently to meet the bandwidth requirements of beyond 5G networks. However, the frequency overlap between different radio technologies such as LTE-U, Wi-Fi, and ZigBee can cause severe interference and hamper communication reliability. This inter-technology interference is particularly unfavorable for IoT devices operating on the ZigBee network that shares the same unlicensed band with Wi-Fi which is capable of transmitting at 10-100 times higher power. Our real-time experiments study this transmission power asymmetry and examine the interference patterns between IEEE 802.11 Wi-Fi and IEEE 802.15.4 ZigBee networks at bitlevel granularity, the simulations were performed on the Zolertia Z1 platform. In this paper, we evaluate the system performance under different settings and the scalability of a real-world ZigBee network. We observed that under certain conditions, Wi-Fi activity can trigger a nearby ZigBee device to back off for a certain time and starve. We also demonstrate that most of the link disconnectivity and packet loss are mainly due to channel access failure rather than errors in data transmission. The observations illustrated in this work would help researchers to propose efficient solutions for heterogeneous IoT networks and design modern data communication protocols for ZigBee devices that are likely to operate in an environment that coexists with dense Wi-Fi deployment

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Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks

Cited by 7 publications

References 37 publications

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Real-Time Investigation of Cross-Technology Interference in Heterogeneous IoT Networks

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