Abstract Recently, unmanned aerial vehicle (UAV) acts as the mobile edge computing (MEC) node to help battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, prolong lifetime operating. However, IoT can ONLY ask UAV for either or caching help, collaborative offloading services are rarely mentioned in literature. Moreover, device has multiple mutually independent tasks, which make policy design even more challenging. Therefore, we investigate a...

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service time-sensitive services. How jointly optimize task offloading resource allocation minimize subject latency requirement remains an open problem, which motivates this paper. When constraint taken into account, optimization variables, including ratio, transmission power, subcarrier computing allocation, are strongly coupled. To address issue, we...

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, limited battery capacity of UAV constrains its endurance time, makes energy efficiency one top priorities implementing UAV-enabled MEC architecture. In this backdrop, we aim minimize UAV’s consumption by jointly optimizing trajectory resource allocation, task decision bits scheduling users considering fairness. The...

With the growing concerns about energy saving and security problems in wireless sensor networks (WSNs), we investigated a joint scheduling secure routing algorithm for critical event reporting WSN. When has been detected, notification must be safely sent to sink node through uplink transmission, needs broadcast alarm messages whole network downlink transmission. In uplink, power allocation strategy (JPASR) proposed maximize connection probability (RSCP) under constraint of power. Meanwhile,...

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data is studied where mobile date can be computed at both local devices cloud with consideration of time-sensitive tasks users. Since NP-hardness considered optimization problem, propose an iterative algorithm decide proportion offload design in a...

The physical-layer security issue in the multiple non-regenerative wireless-powered relay (WPR) networks is investigated this letter, where idle treated as a potential eavesdropper. To guarantee secure communication, destination-based artificial noise sent to degrade receptions of eavesdroppers, and it also becomes new source energy powering relays forward information with power splitting (PS) technique. We propose an efficient algorithm ground on block-wise penalty function method jointly...

To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since considered problem is strongly non-convex with coupled variables, we decompose original into two subproblems: (1) decision; (2) communication resources allocation, further employ Block Coordinate Descent (BCD) method tackle them sequentially linear complexity....

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide services in the resource-shortage areas, e.g., rural and disaster rescue fields. However, satisfactory services, especially for computation-intensive time-sensitive tasks, UAVs are required operate at their full capacity due limited computation resources. This may lead excessive heat dissipation damage units. To tackle this dilemma, paper takes central processing unit (CPU) thermal constraints...

Since the flight of Unmanned Aerial Vehicle (UAV) greatly consumes limited on-board energy and affects ability to afford computation services, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">it is urgent challenging minimize UAV’s endurance time while satisfying varieties constraints from UAVs users, such as budgets, cache capacities, resources, latency requirements.</i> In this paper, are endowed with capability temporarily buffer some...

This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, base stations, and malicious eavesdropper. Specifically, the users adopt partial offloading strategy by partitioning computation task into several parts. One is executed locally others are securely offloaded to MEC servers integrated stations leveraging physical layer security combat eavesdropping. We jointly optimize power allocation, partition, subcarrier resource maximize secrecy rate...

Due to high flexibility and ease of deployment, Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged provide services for users meet the demands computing-intensive tasks at edge. However, MEC-server mounted UAV is inappropriate heavy-computation owing limitation energy supply hardware cost, which may make excessively CPU temperature. To tackle this issue, paper considers a UAV-and-basestation (BS) hybrid-assisted MEC network, where hover-fly-hover mode...

As an emerging computing paradigm, cloud-edge collaborative (CECC) combines resources at the back-end and edge of network to provide more flexible service delivery, thus striking a good balance between abundant high responsiveness. However, mobile devices (MDs) must make strategic offloading decisions in such environment. Although existing research has made remarkable progress computation strategies, most works ignore multi-priority settings complex application scenarios. In this article, we...

Although numerous state-of-the-art deep neural networks have recently been proposed for malware classification, effectively detecting on a large-scale sample set and identifying zero-day or new variants still pose significant challenges. To address this issue, hashing-based classification model is designed identification, including two parts: ResNet50-based hashing retrieval voting-based classification. Specifically, multiple models are developed by extracting the high-layer outputs (feature...

Mobile Edge Computing (MEC) enables rich services in close proximity to the end users provide high quality of experience (QoE) and contributes energy conservation compared with local computing, but results increased communication latency. In this paper, we investigate how jointly optimize task offloading resource allocation minimize consumption an orthogonal frequency division multiple access-based MEC networks, where time-sensitive tasks can be processed at both server via partial...

Recently, the metaverse has emerged as a focal point of widespread interest, capturing attention across various domains. However, construction pluralistic, realistic, and shared digital world is still in its infancy. Due to ultra-strict requirements security, intelligence, real-time, it urgent solve technical challenges existed building ecosystems, such ensuring provision seamless communication reliable computing services face dynamic time-varying complex network environment. In terms...

In this work, we maximize the secrecy rate of wireless-powered untrusted amplify-and-forward relay networks by jointly designing power splitting (PS) ratio and beamforming with proposed global optimal algorithm (GOA) local (LOA). To guarantee secure communication, destination-based artificial noise is sent to degrade reception relay, it also becomes a new source energy powering forward information technique. Simulation result shows that LOA can achieve satisfactory performance compared GOA,...

As the power density of modern CPUs is gradually increasing, thermal management has become one primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing system temperature. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CARTAD</i> , new reinforcement learning (RL)-based DVFS method temperature minimization latency...

Advances in edge computing significantly impact the development of mobile networks. As most important research goal related to networks, resource orchestration has been well studied recent years; however, existing approaches based on deep reinforcement learning share similar bottlenecks training inefficiency. In this paper, we treat drones, whose available time is limited by their batteries, as terminals a target network and aim maximize energy efficiency. The battery-constrained problem...

Mobile edge computing (MEC) provides users with a high quality of experience (QoE) by placing servers rich services close to the end users. Compared local computing, MEC can contribute energy saving, but results in increased communication latency. In this paper, we jointly optimize task offloading and resource allocation minimize consumption an orthogonal frequency division multiple access (OFDMA)-based networks, where time-sensitive tasks be processed at both server via partial offloading....

Higher communication rates are required given that cloud radio access network (C-RAN) becomes a significant component of 5G wireless communication, yet the problem using sparse beamforming to maximize achievable sum rate in long term subject transmit power constraints still remains open C-RAN. Inspired by success Deep Reinforcement Learning (DRL) solving dynamic programming problems, we propose DRL-based framework for online Particularly, DRL agent is charge remote head (RRH) activation...