Optimal Task Processing and Energy Consumption Using Intelligent Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks.

Section: Offloading Modelmentioning

confidence: 99%

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

Rijal Abdullah,

Noorulsadiqin Azbiya Yaacob,

Anas A. Salameh

et al. 2024

“…It receives tasks from multiple users or consumers and then publishes them to the users in its capacity as a centralised controller. The cloud policy facilitates communication between the other two tiers and maintains user personal facts, such as histories, whereabouts, and reputations [17,18]. The cloud platform in our system will set up a manipulator popularity component to choose the appropriate users based on their previous ability to carry out the activities effectively and efficiently.…”

Section: Fig 3 Mcs System With Edge Supportmentioning

confidence: 99%

Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities

Muhammad Alkirom Wildan,

Mahmudah Enny Widyaningrum,

T. Padmapriya

et al. 2023

As more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities.

“…Maftah S. et al [23], presented a strategy for optimizing task processing and energy usage in mobile edge computing environments. The suggested system employs an intelligent offloading strategy to dynamically distribute workloads between mobile devices and adjacent edge servers based on a number of variables, including available resources, network circumstances, and energy usage.…”

Section: Contributionsmentioning

confidence: 99%

Joint Microservices Caching and Task Offloading Framework in VEC Based on Deep Reinforcement Learning

Ghorab

Rasheed

Hatem

2023

Vehicle capacities and intelligence are rapidly increasing, which will likely support a wide range of novel and interesting applications. However, these resources are not effectively utilized. To take advantage of these invaluable capabilities in smart vehicles, they can be used in the cloud environment and can be operated through distributed computing platforms in order to benefit from their combined processing power, storage capacity, and memory resources. Vehicular edge computing (VEC) is a promising field that allows computing tasks to be transferred from cloud servers to vehicular edge servers for processing, allowing data and apps to be placed closer to vehicles (users). This paper proposes a framework that combines two modules, the first one for managing microservice caching in vehicle-mounted edge networks, such that we use cluster-based caching technique to deal with the case where similar microservices are frequently requested in VEC. The second one integrates the computational capabilities of the edge servers with the capabilities of vehicles to perform task offloading in a collaborative manner. Our solution addresses the limitations of existing edge computing platforms during peak times by combining microservices caching with computational task offloading to improve overall system performance.