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Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

Computers, Materials & Continua 2024年第8期80卷 2065-2080页

作者： Dayong Wang Kamalrulnizam Bin Abu Bakar babangida isyaku Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Department of Computer Science Faculty of Information Communication TechnologySule Lamido UniversityJigawa741103Nigeria

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are *** addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time *** is resulting in TD missing potential offloading opportunities in the *** fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic *** Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time *** framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning *** results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

关键词： Decision making internet of things load prediction task offloading multi-access edge computing

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Managing Smart Technologies with Software-Defined Networks for Routing and Security Challenges: A Survey

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Computer Systems Science & Engineering 2023年第11期47卷 1839-1879页

作者： babangida isyaku Kamalrulnizam Bin Abu Bakar Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Department of Computer Science Faculty of Computing and Information TechnologySule Lamido UniversityP.M.B.048Jigawa StateNigeria

Smart environments offer various services,including smart cities,ehealthcare,transportation,and wearable devices,generating multiple traffic flows with different Quality of Service(QoS)*** the desired QoS with security in this heterogeneous environment can be challenging due to traffic flows and device management,unoptimized routing with resource awareness,and security *** Defined Networks(SDN)can help manage these devices through centralized SDN controllers and address these *** schemes have been proposed to integrate SDN with emerging technologies for better resource utilization and *** Defined Wireless Body Area Networks(SDWBAN)and Software Defined Internet of Things(SDIoT)are the recently introduced frameworks to overcome these *** study surveys the existing SDWBAN and SDIoT routing and security *** paper discusses each solution in detail and analyses its *** covers SDWBAN frameworks for efficient management of WBAN networks,management of IoT devices,and proposed security mechanisms for IoT and data security in *** survey provides insights into the state-of-the-art in SDWBAN and SDIoT routing with resource awareness and security ***,this study highlights potential areas for future research.

关键词： SDN WBAN IoT routing security

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Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

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Computers, Materials & Continua 2023年第4期75卷 785-798页

作者： Kamalrulnizam Bin Abu Bakar Fatima Tul Zuhra babangida isyaku Fuad A.Ghaleb Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Science and Technology Shaheed Benazir Bhutto University Sanghar CampusPakistan Faculty of Computing and Information Technology Sule Lamido UniversityKafin HausaP.M.B.048Nigeria

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

关键词： Artificial intelligence internet of things internet of medical things wireless body sensor network wireless endoscopic capsule gastric cancer

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Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

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Computer Systems Science & Engineering 2023年第9期46卷 3729-3748页

作者： babangida isyaku Kamalrulnizam Bin Abu Bakar Wamda Nagmeldin Abdelzahir Abdelmaboud Faisal Saeed Fuad A.Ghaleb Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Computing and Information Technology Sule Lamido UniversityKafin HausaP.M.B.048Nigeria College of Computer Engineering and Sciences PrinceSattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia Taibah University P.O.Box 344Medina41411Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUK

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data ***,communication reliability is the desired property in computer *** frequency of communication link failure degrades network performance,and service disruptions are likely to *** network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in ***,these schemes have various weaknesses,which may not always guarantee service *** paths differ in their roles;some paths are critical because of the higher frequency *** paths frequently share links between primary and *** the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system ***,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link *** paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery *** affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery *** the former and latter path with a minimal score is *** simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

关键词： SDN OpenFlow failure restoration critical path Bayesian probability

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