Article
Chemistry, Analytical
Ajoze Abdulraheem Zubair, Shukor Abd Razak, Md. Asri Ngadi, Arafat Al-Dhaqm, Wael M. S. Yafooz, Abdel-Hamid M. Emara, Aldosary Saad, Hussain Al-Aqrabi
Summary: This paper proposes a modified symbiotic organisms search-based scheduling algorithm for efficient mapping of heterogeneous tasks to different capacity cloud resources. The algorithm improves the mutualism process and achieves better performance and convergence speed. Experimental results show significant improvement of the proposed algorithm over traditional methods and other algorithms.
Article
Computer Science, Information Systems
Ali Belgacem, Kadda Beghdad-Bey, Hassina Nacer
Summary: Cloud computing is a popular paradigm for leasing IT services over the Internet, which requires dynamic allocation and release of resources to ensure service quality. A new dynamic resource allocation model is proposed along with the MOSOS algorithm to minimize completion time and cost for improved cloud performance.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Mathematics
Narayanan Ganesh, Rajendran Shankar, Kanak Kalita, Pradeep Jangir, Diego Oliva, Marco Perez-Cisneros
Summary: The effectiveness of a novel optimizer called MOSOS/D for multi-objective problems was investigated in this research. It was based on the symbiotic organisms' search and incorporated a decomposition framework for better performance. Both qualitative and quantitative analyses were conducted, showing the superiority of MOSOS/D in solving large complex multi-objective problems.
Article
Computer Science, Artificial Intelligence
Anata-Flavia Ionescu, Raluca Vernic
Summary: The paper introduces a new approach called MOSOSS to address partner selection problem in strategic alliances, which considers cost, duration, and quality objectives simultaneously and utilizes evolutionary operators to handle incomplete scheduling solutions.
Article
Multidisciplinary Sciences
Jun Li, Xinxin Guo, Yongchao Yang, Qiwen Zhang
Summary: To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm integrates the symbiotic organisms search (SOS) strategy and introduces a parasitic natural enemy insect mechanism to overcome premature convergence and destruction of optimal solution. Experimental results demonstrate the algorithm's effectiveness in terms of convergence and distribution compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Mohammed Abdullahi, Md Asri Ngadi, Salihu Idi Dishing, Shafi'i Muhammad Abdulhamid
Summary: This paper proposes an adaptive benefit factors based symbiotic organisms search (ABFSOS) algorithm to strike a balance between local and global search procedures for faster convergence speed. The algorithm also integrates an adaptive constrained handling strategy to avoid infeasible solutions and premature convergence. Experimental results demonstrate that the proposed algorithm outperforms compared algorithms in terms of convergence, diversity, and hypervolume improvement.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Review
Computer Science, Theory & Methods
Ajoze Abdulraheem Zubair, Shukor Bin Abd Razak, Md Asri Bin Ngadi, Aliyu Ahmed
Summary: The SOS algorithm, inspired by symbiotic relationships in nature, has been modified and applied in various fields such as engineering, medicine, and finance. However, further improvements are needed for its application in cloud task scheduling.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Sachi Gupta, Sailesh Iyer, Gaurav Agarwal, Poongodi Manoharan, Abeer D. Algarni, Ghadah Aldehim, Kaamran Raahemifar
Summary: Cloud computing is widely used for virtual machine-based activities, but task scheduling remains a fundamental issue. This paper proposes enhanced versions of the HEFT algorithm to improve scheduling outcomes. By modifying the rank generation and processor selection methods, the proposed algorithms outperform the basic HEFT method in terms of schedule length for various workflow problems.
Article
Computer Science, Information Systems
Arslan Nedhir Malti, Mourad Hakem, Badr Benmammar
Summary: Nowadays, cloud computing is widely used and continuously developing in various fields. Task scheduling is an important issue that affects system performance in cloud computing. A novel hybrid optimization algorithm based on flower pollination behavior and grey wolf optimizer strategy is proposed to deal with multi-objective task scheduling in heterogeneous IaaS cloud environments. The algorithm strikes a balance between exploring new solutions and exploiting discovered ones using evolutionary algorithms crossover operators. Experimental results demonstrate the merits of the proposed algorithm in terms of various optimization criteria.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Samar Hussni Anbarkhan, Mohamed Ali Rakrouki
Summary: This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm to address the issue of high time and cost in scheduling workflow tasks in a cloud computing environment. The algorithm combines intensive tasks to reduce particle dimensions and ensure initial particle quality. It optimizes particle initialization and integrates a self-adaptive function to determine the best direction of the particles. Experimental results show that the proposed enhanced PSO algorithm achieves faster convergence speed and better performance in task execution.
Article
Mathematics
Ibrahim Attiya, Laith Abualigah, Samah Alshathri, Doaa Elsadek, Mohamed Abd Elaziz
Summary: This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD, which effectively improves the search performance and addresses the issue of local optima. Experimental results show that the proposed method achieves promising results in various benchmark functions and real-world applications.
Article
Computer Science, Information Systems
Behzad Saemi, Ali Asghar Rahmani Hosseinabadi, Azadeh Khodadadi, Seyedsaeid Mirkamali, Ajith Abraham
Summary: The task scheduling problem in Mobile Cloud Computing (MCC) is a difficult problem to solve, and this study proposes a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique to address this issue. By comparing with other algorithms, it is found that the proposed method performs better in terms of job completion time and energy savings.
Article
Chemistry, Multidisciplinary
Vamsheedhar Reddy Pillareddy, Ganesh Reddy Karri
Summary: Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. This study proposes a method focusing on makespan, average utilization, and cost. The authors propose a task's dynamic priority for workflow scheduling using MONWS, to minimize finish time and maximize resource utilization. Experimental results show significant improvements compared to existing algorithms.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Honglin Zhang, Yaohua Wu, Zaixing Sun
Summary: In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. Through ordering rules based on priority constraints and utilizing the HEFT algorithm, the algorithm proves to be effective and superior in simulation experiments.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Tao Hai, Jincheng Zhou, Dayang Jawawi, Dan Wang, Uzoma Oduah, Cresantus Biamba, Sanjiv Kumar Jain
Summary: Cloud computing is a crucial infrastructure for performing tasks, but faces challenges in efficient workflow submission. The HEFT algorithm has proved to be an efficient method for scheduling tasks in a heterogeneous environment. This study proposes altered versions of the HEFT algorithm that improve schedule length in virtual machines' workflow submissions.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Mohammed Abdullahi, Md Asri Ngadi, Salihu Idi Dishing, Shafi'i Muhammad Abdulhamid, Mohammed Joda Usman
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Nureni Ayofe Azeez, Sanjay Misra, Ihotu Agbo Margaret, Luis Fernandez-Sanz, Shafi'i Muhammad Abdulhamid
Summary: Phishing is a significant issue in cyberspace, with challenges such as low detection rates and slow access times for existing anti-phishing solutions. However, a new automated white-list approach has been proposed, showing high accuracy in detecting phishing attacks, especially with lower-level datasets. This approach outperforms similar benchmarks in accuracy and efficiency, demonstrating robust detection performance in comparison to other techniques.
COMPUTERS & SECURITY
(2021)
Article
Engineering, Multidisciplinary
Nadim Rana, Muhammad Shafie Abd Latiff, Shafi'i Muhammad Abdulhamid, Sanjay Misra
Summary: The study proposed a hybrid algorithm (M-WODE) based on evolutionary algorithm and whale optimization algorithm for solving virtual machine scheduling problems. Experimental results show that the algorithm outperformed previous algorithms in most cases in terms of makespan and cost trade-offs.
ENGINEERING OPTIMIZATION
(2022)
Article
Computer Science, Artificial Intelligence
Suleiman Sa'ad, Abdullah Muhammed, Mohammed Abdullahi, Azizol Abdullah, Fahrul Hakim Ayob
Summary: This study proposes an eDSOS algorithm for optimizing task scheduling in the cloud computing environment. Based on the standard SOS, the algorithm diversifies the local search space of DSOS to avoid local optima, leading to significant performance improvements compared to DSOS in solving large-scale task scheduling problems, as demonstrated through experiments and statistical analysis.
Review
Engineering, Multidisciplinary
Haruna Chiroma, Shafi'i M. Abdulhamid, Ibrahim A. T. Hashem, Kayode S. Adewole, Absalom E. Ezugwu, Saidu Abubakar, Liyana Shuib
Summary: The Internet of Vehicles (IoV) is a developing technology attracting attention from both industry and academia, with the potential for hundreds of millions of connected vehicles by 2035. However, surveys on leveraging deep learning in IoV within the context of big data analytics are currently scarce. This study presents a survey exploring the theoretical perspective of the role of deep learning in IoV within the context of big data analytics, highlighting research opportunities that cut across deep learning, IoV, and big data analytics.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mohammed Abdullahi, Md Asri Ngadi, Salihu Idi Dishing, Shafi'i Muhammad Abdulhamid
Summary: This paper proposes an adaptive benefit factors based symbiotic organisms search (ABFSOS) algorithm to strike a balance between local and global search procedures for faster convergence speed. The algorithm also integrates an adaptive constrained handling strategy to avoid infeasible solutions and premature convergence. Experimental results demonstrate that the proposed algorithm outperforms compared algorithms in terms of convergence, diversity, and hypervolume improvement.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Article
Computer Science, Software Engineering
Jeremiah Isuwa, Mohammed Abdullahi, Yusuf Sahabi Ali, Abdulrazaq Abdulrahim
Summary: Scientific and technological advancements generate a large amount of data, which is computationally analyzed to reveal patterns and trends. However, the presence of noisy and irrelevant features in these datasets negatively affects the performance of classification techniques. Feature selection has emerged as an important pre-processing method to improve classification accuracy by selecting the most informative features. Recent research has shown that particle swarm optimization (PSO) is a promising algorithm for feature selection, but it may get stuck in local optima. To address this issue, a new update mechanism and hybridization with a local search method have been proposed for PSO.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Review
Chemistry, Multidisciplinary
Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Muhammad Abdulkarim, Yusuf Alhaji Surakat, Abdullateef Oluwagbemiga Balogun, Ganesh Kumar, Aliyu Nuhu Shuaibu, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Tanko Yahaya Mohammed, Bashir Abubakar Abdulkadir, Abdallah Alkali Abba, Nana Aliyu Iliyasu Kakumi, Ahmad Sobri Hashim
Summary: Artificial intelligence and wearable sensors are transforming healthcare service delivery by providing automated solutions for assessing patients' general health. This review explores the benefits and challenges of embracing AI techniques and wearable sensors in healthcare data analysis tasks.
APPLIED SCIENCES-BASEL
(2022)
Review
Chemistry, Multidisciplinary
Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Aliyu Nuhu Shuaibu, Shuib Basri, Ganesh Kumar, Yusuf Alhaji Surakat, Abdullateef Oluwagbemiga Balogun, Muhammad Abdulkarim, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Yahaya Tanko Mohammed, Bashir Abubakar Abdulkadir, Abdullah Alkali Abba, Nana Aliyu Iliyasu Kakumi, Ammar Kareem Alazzawi
Summary: Large amounts of patient vital/physiological signs data are often acquired manually in hospitals, making it challenging for doctors to integrate and analyze the data. The interconnection of medical devices through IoT or IoHT and AI/ML can help overcome these limitations by integrating data from different sources to enhance the diagnosis and prognosis of patients' health state.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Mohammed Abdullahi, Ibrahim Hayatu Hassan, Muhammad Dalhat Abdullahi, Ibrahim Aliyu, Jinsul Kim
Summary: This paper provides an in-depth examination of recent research on the MRFO algorithm, including its natural inspiration context, conceptual optimization framework, modifications, hybridizations, and applications in different domains. It also presents a meta-analysis of the developments of the MRFO and suggests possible future research directions. This study is useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.
Review
Health Care Sciences & Services
Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Abdullateef Oluwagbemiga Balogun, Liyanage Chandratilak De Silva, Yusuf Alhaji Surakat, Ganesh Kumar, Muhammad Abdulkarim, Aliyu Nuhu Shuaibu, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Tanko Yahaya Mohammed, Bashir Abubakar Abdulkadir, Abdallah Alkali Abba, Nana Aliyu Iliyasu Kakumi, Saipunidzam Mahamad
Summary: In recent times, the growth of IoT, AI, and Blockchain technologies in healthcare has been significant. This survey paper evaluates the application of these emerging technologies in healthcare management systems and discusses related issues and successful deployments.
Review
Multidisciplinary Sciences
Emmanuel Gbenga Dada, Stephen Bassi Joseph, David Opeoluwa Oyewola, Alaba Ayotunde Fadele, Haruna Chiroma, Shafi'i Muhammad Abdulhamid
Summary: This paper presents the recent progress, variants, and applications of the Grey Wolf Optimization (GWO) algorithm, highlighting the potential for development of more robust variants. The review aims to stimulate researchers in advancing the effectiveness of GWO in solving complex optimization problems.
GAZI UNIVERSITY JOURNAL OF SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Ibrahim Bello, Haruna Chiroma, Usman A. Abdullahi, Abdulsalam Ya'u Gital, Fatsuma Jauro, Abdullah Khan, Julius O. Okesola, Shafi'i M. Abdulhamid
Summary: Recently, there has been a growing interest in using intelligent algorithms, particularly deep learning algorithms, for ransomware attack detection. However, there is a lack of comprehensive literature review on the applications of intelligent algorithms in detecting ransomware attacks, indicating a potential direction for future research.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Olutosin Taiwo, Absalom E. Ezugwu, Nadim Rana, Shafi'i M. Abdulhamid
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT VI
(2020)
Article
Computer Science, Artificial Intelligence
Fatsuma Jauro, Haruna Chiroma, Abdulsalam Y. Gital, Mubarak Almutairi, Shafi'i M. Abdulhamid, Jemal H. Abawajy
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Hardware & Architecture
Zihang Zhen, Xiaoding Wang, Hui Lin, Sahil Garg, Prabhat Kumar, M. Shamim Hossain
Summary: In this paper, a blockchain architecture based on dynamic state sharding (DSSBD) is proposed to solve the problems caused by cross-shard transactions and reconfiguration. By utilizing deep reinforcement learning, the number of shards, block spacing, and block size can be dynamically adjusted to improve the performance of the blockchain. The experimental results show that the crowdsourcing system with DSSBD has better performance in terms of throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, while ensuring security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Gabriel F. C. de Queiroz, Jose F. de Rezende, Valmir C. Barbosa
Summary: Multi-access Edge Computing (MEC) is a technology that enables faster task processing at the network edge by deploying servers closer to end users. This paper proposes the FlexDO algorithm to solve the DAG application partitioning and offloading problem, and compares it with other solutions to demonstrate its superior performance in various test scenarios.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shahid Latif, Wadii Boulila, Anis Koubaa, Zhuo Zou, Jawad Ahmad
Summary: In the field of Industrial Internet of Things (IIoT), networks are increasingly vulnerable to cyberattacks. This research introduces an optimized Intrusion Detection System based on Deep Transfer Learning (DTL) for heterogeneous IIoT networks, combining Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and ensemble techniques. Through rigorous evaluation, the framework achieves exceptional performance and accurate detection of various cyberattacks.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Rongji Liao, Yuan Zhang, Jinyao Yan, Yang Cai, Narisu Tao
Summary: This paper proposes a joint control approach called STOP to guarantee user-perceived deadline using curriculum-guided deep reinforcement learning. Experimental results show that the STOP scheme achieves a significantly higher average arrival ratio in NS-3.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Miguel Rodriguez-Perez, Sergio Herreria-Alonso, J. Carlos Lopez-Ardao, Raul F. Rodriguez-Rubio
Summary: This paper presents an implementation of an active queue management (AQM) algorithm for the Named-Data Networking (NDN) architecture and its application in congestion control protocols. By utilizing the congestion mark field in NDN packets, information about each transmission queue is encoded to achieve a scalable AQM solution.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Angel Canete, Mercedes Amor, Lidia Fuentes
Summary: This paper proposes an energy-aware placement of service function chains of Virtual Network Functions (VNFs) and a resource-allocation solution for heterogeneous edge infrastructures. The solution has been integrated with an open source management and orchestration project and has been successfully applied to augmented reality services, achieving significant reduction in power consumption and ensuring quality of service compliance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Sachin Kadam, Kaustubh S. Bhargao, Gaurav S. Kasbekar
Summary: This paper discusses the problem of estimating the node cardinality of each node type in a heterogeneous wireless network. Two schemes, HSRC-M1 and HSRC-M2, are proposed to rapidly estimate the number of nodes of each type. The accuracy and efficiency of these schemes are proven through mathematical analysis and simulation experiments.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Jean Nestor M. Dahj, Kingsley A. Ogudo, Leandro Boonzaaier
Summary: The launch of commercial 5G networks has opened up opportunities for heavy data users and highspeed applications, but traditional monitoring and evaluation techniques have limitations in the 5G networks. This paper presents a cost-effective hybrid analytical approach for detecting and evaluating user experience in real-time 5G networks, using statistical methods to calculate the user quality index.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O. Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Summary: The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. This study introduces a resource allocation framework that leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to improve energy efficiency. Through the use of a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG), the proposed approach optimizes user association, cache design, and transmission power control, resulting in enhanced energy efficiency and reduced time delays compared to existing methods.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Wu Chen, Jiayi Zhu, Jiajia Liu, Hongzhi Guo
Summary: With advancements in technology, large-scale drone swarms will be widely used in commercial and military fields. Current application methods are mainly divided into autonomous methods and controlled methods. This paper proposes a new framework for global coordination through local interaction.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Peiying Zhang, Zhihu Luo, Neeraj Kumar, Mohsen Guizani, Hongxia Zhang, Jian Wang
Summary: With the development of Industry 5.0, the demand for network access devices is increasing, especially in areas such as financial transactions, drone control, and telemedicine where low latency is crucial. However, traditional network architectures limit the construction of low-latency networks due to the tight coupling of control and data forwarding functions. To overcome this problem, researchers propose a constraint escalation virtual network embedding algorithm assisted by Graph Convolutional Networks (GCN), which automatically extracts network features and accelerates the learning process to improve network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Review
Computer Science, Hardware & Architecture
P. Anitha, H. S. Vimala, J. Shreyas
Summary: Congestion control is crucial for maintaining network stability, reliability, and performance in IoT. It ensures that critical applications can operate seamlessly and that IoT devices can communicate efficiently without overwhelming the network. Congestion control algorithms ensure that the network operates within its capacity, preventing network overload and maintaining network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shunmugapriya Ramanathan, Abhishek Bhattacharyya, Koteswararao Kondepu, Andrea Fumagalli
Summary: This article presents an experiment that achieves live migration of a containerized 5G Central Unit module using modified open-source migration software. By comparing different migration techniques, it is found that the hybrid migration technique can reduce end-user service recovery time by 36% compared to the traditional cold migration technique.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Fatma Foad Ashrif, Elankovan A. Sundararajan, Rami Ahmad, Mohammad Kamrul Hasan, Elaheh Yadegaridehkordi
Summary: This article introduces the development and current status of authentication protocols in 6LoWPAN, and proposes an innovative perspective to fill the research gap. The article comprehensively surveys and evaluates AKA protocols, analyzing their suitability in wireless sensor networks and the Internet of Things, and proposes future research directions and issues.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Pranjal Kumar Nandi, Md. Rejaul Islam Reaj, Sujan Sarker, Md. Abdur Razzaque, Md. Mamun-or-Rashid, Palash Roy
Summary: This paper proposes a task offloading policy for IoT devices to a mobile edge computing system, aiming to balance device utility and execution cost. A meta heuristic approach is developed to solve the offloading problem, and the results show its potential in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
