Mobile Computing Research Articles

Decentralized computation offloading via multi-agent deep reinforcement learning for NOMA-assisted mobile edge computing with energy harvesting devices

Supporting latency-sensitive and computation-intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server’s limited computing resources, available communication channel quality, and time-limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non-orthogonal multiple access (NOMA)-assisted MEC with multiple EH-enabled MDs. To learn decentralized offloading policies, we propose a multi-agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi-agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional-based prioritized experience replay (pPER) and propose an algorithm called multi-agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

Tennis is highly explosive, continuous, intense sport that involves numerous continuous short-term explosive actions. This type of training is short-term, high-intensity, and focuses on competitive skills. Athletes must be physically fit and have long-term endurance to excel in technical and tactical skills during competition. In this manuscript, Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation (MMARSCNN-PAM-TT-DM-IR) is proposed. The input data are collected from training levels of technical – sportive actions of 12-14 years old tennis players’ dataset. Initially the input data is preprocessed using Generalized Multi-kernel Maximum Correntropy Kalman Filter for exploring the data. The pre-processed data is given into Gradient Descent Namib Beetle Optimization (GDNBO) for choosing features from the dataset. Then, selected features are given to Dual Tree Complex Discrete Wavelet Transform to extract gray-scale statistical features such as Homogeneity, Entropy, Energy and Smoothness. Finally, the extracted feature attributes are given to MMARSCNN is used to Tennis Teaching Based on Data Mining. In general, MMARSCNN does not express some adaption of optimization strategies for determining optimal parameters to assure accurate Teaching of Tennis. Therefore, Zebra Optimization Algorithm (ZOA) is proposed to enhance weight parameter of MMARSCNN, which precisely Teaching of tennis depend on Data Mining. The proposed model is implemented, its efficacy is assessed utilizing some performance metrics such as accuracy, precision, F1-score, MSE, AUC. The proposed MMARSCNN-PAM-TT-DM-IR method provides 22.55%, 24.72% and 29.63% higher accuracy; 32.66%, 34.97% and 29.57% higher precision; 25.18%, 21.52% and 28.68% higher F1-Score is compared with existing method such as analysis characteristics of Tennis singles matches depend on 5G with data mining technology (AOC-TSM-DM), Tennis online teaching information platform depend on android mobile intelligent terminal (AMIT-TTP-DM), optimization analysis of Tennis players’ physical fitness index depend on data mining with mobile computing (OA-TP-DM-MC) respectively.

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

Deep reinforcement learning based computing offloading in unmanned aerial vehicles for disaster management

Abstract The emergence of Internet of Things enabled with mobile computing has the applications in the field of unmanned aerial vehicle (UAV) development. The development of mobile edge computational offloading in UAV is dependent on low latency applications such as disaster management, Forest fire control and remote operations. The task completion efficiency is improved by means of using edge intelligence algorithm and the optimal offloading policy is constructed on the application of deep reinforcement learning (DRL) in order to fulfill the target demand and to ease the transmission delay. The joint optimization curtails the weighted sum of average energy consumption and execution delay. This edge intelligence algorithm combined with DRL network exploits computing operation to increase the probability that at least one of the tracking and data transmission is usable. The proposed joint optimization significantly performs well in terms of execution delay, offloading cost and effective convergence over the prevailing methodologies proposed for UAV development. The proposed DRL enables the UAV to real-time decisions based on the disaster scenario and computing resources availability.

Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client Enhancement

With the advancement of multimedia technology and wireless networks, there is a growing demand for high-quality video streaming. Delivering stable video streaming in extremely dynamic wireless networks, nevertheless, is still an open problem. Recent developments in client computing and mobile edge computing (MEC) technologies have both shown promise in enhancing the adaptive bitrate (ABR) streaming services. In this paper, we consider a video streaming system in multi-tier computing networks, enabled by joint edge-side video transcoding and client-side video enhancement. By “enhancement,” we mean that the client improves the video chunk quality via client-side image processing modules. In particular, we aim to design a joint bitrate adaptation, edge transcoding, and client image-processing algorithm, maximizing the quality of experience (QoE) of streaming services. The majority of the prior art has concentrated on super-resolution-enabled video streaming. Contrarily, we show that the video enhancement method outperforms the super-resolution approach in terms of signal-to-noise ratio and frames per second, implying a superior alternative for client-side processing in ABR streaming. We formulate the problem as an event-triggered Markov decision process (E-MDP), and propose a deep reinforcement learning (DRL)-based framework, named EDTEA. To deal with the delayed feedback induced by multi-tier computing, the entropy and the expected re-buffering terms are introduced to the objective and the reward, respectively. Extensive simulations based on real-world videos and bandwidth traces manifest that compared with state-of-the-art approaches, EDTEA provides <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.4\%\sim 78.4\%$</tex-math></inline-formula> extra QoE while reducing re-buffering time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$85.5\%\sim 91.7\%$</tex-math></inline-formula> .

Elite‐guided equilibrium optimiser based on information enhancement: Algorithm and mobile edge computing applications

AbstractThe Equilibrium Optimiser (EO) has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems. Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research. To alleviate these limitations, an EO variant named adaptive elite‐guided Equilibrium Optimiser (AEEO) is introduced. Specifically, the adaptive elite‐guided search mechanism enhances the balance between exploration and exploitation. The modified mutualism phase reinforces the information interaction among particles and local optima avoidance. The cooperation of these two mechanisms boosts the overall performance of the basic EO. The AEEO is subjected to competitive experiments with state‐of‐the‐art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite. Experimental results demonstrate that AEEO outperforms several well‐performing EO variants, DE variants, PSO variants, SSA variants, and GWO variants in terms of convergence speed and accuracy. In addition, the AEEO algorithm is used for the edge server (ES) placement problem in mobile edge computing (MEC) environments. The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting LSTM Autoencoder

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services' large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services' current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services' runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services' historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services' historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services' runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

Intelligent Reflecting Surface Assisted Secure Computation of Wireless Powered MEC System

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users' offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Pengembangan Sistem Pemantauan Potensi Banjir Online di Desa Mangun Jaya Kabupaten Bekasi

Mangun Jaya Village is an area affected by flooding every year. There are at least two characteristics that cause flooding, namely local rain with high rainfall and the overflow of the Bekasi River. Kali Bekasi is a river located in the village of Mangun Jaya, South Tambun sub-district, Bekasi Regency, which has a large current, especially when it rains. In this community service activity, the team from Bhayangkara University, Jakarta Raya, has succeeded in developing a potential flood monitoring system based on IoT technology as a disaster mitigation measure. This system consists of a sensor device that can measure the height of the river surface and rainfall where these physical data are sent to the Internet. Access to this potential flood data can be done in real time and remotely using mobile devices and internet computers. This system has also been tested in the field by installing sensors on the edge of the Bekasi River where an average of 1429 data is collected daily. The percentage of data loss is 0.75%. This proves that the system can be used as a preventive effort in dealing with potential flooding in South Tambun Regency.

Web Framework Content Based Filtering for Picture Sharing

Information technology has greatly impacted the multimedia industry, particularly in the areas of video and audio content. There are various applications available for managing individual videos and converting them into formats suitable for online sharing. With the widespread use of mobile phones and computers, capturing and storing pictures and videos has become a common practice. These files can be easily shared on different networks, including popular platforms like Facebook, YouTube, and WhatsApp. The rise of web 2.0 has led to an increase in file sharing, including explicit content, as uploads are often not properly filtered. Social media platforms play a significant role in facilitating real-time video sharing, but they also host unwanted content such as violent and pornographic videos. This research aims to develop a web framework application that filters images and videos before they are uploaded to interconnected networks, ensuring that unwanted content is detected and flagged. Users will not be blocked from sharing content, but the system will help identify and address inappropriate material. Keywords: Picture Sharing, Web Framework, Content-Based Filtering, Online Security . CISDI Journal Reference Format Ehigiator Egho-Promise &amp; Mensah Sitti (2024 Web Framework Content Based Filtering for Picture Sharing. Computing, Information Systems, Development Informatics &amp; Allied Research Journal. Vol 15 No 1, Pp 29--46. dx.doi.org/10.22624/AIMS/CISDI/V15N1P3. Available online at www.isteams.net/cisdijournal

Role of Artificial Intelligence in the Prevention of Online Child Sexual Abuse: A Systematic Review of Literature

Online Child sexual abuse (OCSA) is a major menace in the digitalized world. Every year, more than a billion children between the ages of 2–17 years are sexually abused. Despite the harsh reality of most incidents, they remain unreported. Not only that but activities on dark web platforms go unmoderated and it becomes very difficult and complex to trace the origin of abuse and exploitation of children. In cases of online abuse, the sources through which detection can be done are evaded. Although several government and non-government initiatives have been implemented worldwide to curb this social menace, their effectiveness and accuracy remain questionable. AI-based services if regulated and executed cautiously can be effective in diagnosing and preventing sexual abuse in children on virtual platforms. Existing literature implies that AI can be a potent armor to detect and predict child sexual abuse online. The ability of artificial intelligence to predict and stop sexual abuse in children is very promising. AI-based technologies that can aid in the identification and prevention of violence against children include mobile computing, the Internet of Things, chatbots, machine learning, pattern recognition, and cloud computing. Therefore, it is essential to examine the existing literature pertaining to the research area, to highlight the emergent need for more applied and evidence-based research in the area. Thus, this study aimed to identify interventions driven by artificial intelligence in preventing online child sexual abuse, its limitations, and future implications. We conducted an extensive systematic literature review to understand the trends and efficacy of AI-based services in preventing and tracing child sexual abuse. The selection of research studies was performed in accordance with the PRISMA standards. Relevant studies were extracted from databases such as ScienceDirect, Springer, IEEE, and MDPI. Articles were selected and screened based on inclusion and exclusion criteria. This study identified 35 papers that were strictly limited to the prospect of AI interventions for online child sexual abuse. The review helped in deducing 3 major themes, namely, current trends in the field of AI for preventing online child sexual abuse, algorithm evaluation (advantages and disadvantages of AI tools) in preventing OCSA, and recommendation for technique advancements of AI tools. However, there is scarce evidence that proves AI interventions are effective in solving online child sexual abuse issues, as shown in the paper, thereby encouraging more extant research to be conducted in the area.

Research on the Design of Power Supply Gateway and Wireless Power Transmission Based on Edge Computing

There is a rising need for sensors under an IoT network to identify and monitor the environment as more IoT devices and services are made accessible for use. This movement presents challenges such as the proliferation of data and the scarcity of energy. This research presents a strategy for enhancing the service provision capabilities of WSN-aided IoT applications by combining mobile edge computation with wireless signal and control transmission. In order to reduce overall system energy consumption while maintaining data transmission rate and power needs, a new optimization problem integrating power allocation, CPU frequency, offloading weight factor, and energy harvesting is devised. The non-convex nature of the problem necessitates the development of a novel ideal solution group iterative process optimization model that divides the original problem into multiple subproblems, with each subproblem being optimized in turn. According to the results of simulations with a numerical model, our proposed method consumes considerably less energy than just the two benchmark methodologies.

Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning.

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices.

A secure cross-domain authentication scheme based on threshold signature for MEC

The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.

Computation offloading in NOMA-MEC-enabled aerial-vehicular networks exploiting mmWave capabilities

In recent years, there has been a significant interest in ubiquitous coverage, high data rate connectivity, and mobile edge computing (MEC) as crucial services within the future sixth-generation (6G) wireless networks. These services are regarded as essential components, exemplifying the advancements anticipated in 6G technology. Nevertheless, the successful implementation of these services in MEC-enabled vehicular networks significantly relies on the availability of robust network coverage and telecommunication framework. Unfortunately, in far-flung and isolated regions, such framework is often lacking, posing significant challenges in achieving uninterrupted connectivity, comprehensive coverage, and efficient computation offloading. Targeting the aforementioned horizon, in this paper, an uplink non-orthogonal multiple access (NOMA)-MEC-enabled aerial-vehicular network operating at millimeter wave (mmWave) is proposed in which the ground vehicles are provided edge computing services by an aerial autonomous vehicle, i.e., a high-altitude platform (HAP). We present a system model in which a HAP equipped with MEC servers offers computation offloading capabilities to a vehicular network. Our main objective is to minimize the transaction time of a NOMA cluster offloading its data to MEC servers located at the HAP. We devise a dual-layer optimization scheme for optimizing the transmission power and computational resource allocation by using the Lagrange multipliers method and then attain convergence by implementing a sub-gradient approach. We further extend our work by proposing a data-aware NOMA clustering scheme. The simulation results demonstrate the efficacy of our proposed approach, showing a notable reduction in the transaction time in comparison to the baseline scheme. The optimal power allocation enhances the data rates which subsequently reduces the transmission time, and the optimal cores assignment effectively minimizes the computation time. Additionally, the data-aware NOMA clustering scheme shows promising results by enhancing the system effective throughput and the spectral efficiency in comparison to the conventional NOMA clustering.

Enhancing information freshness in multi-class mobile edge computing systems using a hybrid discipline

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

Evaluation and prediction of fibrous filters' operating performance in residential fresh air system

Fibrous filters play a crucial role in determining indoor air quality and protect people from bacteria, particulates, and viruses such as COVID-19. However, there is a lack of comprehensive research on methods for effectively evaluating and predicting the operating performance of these filters. In this study, we conducted laboratory and field tests on coarse (G4) and medium (F9 or F7) filters from four different brands to assess both their initial and operating performance. We discovered that a fluffy fiber structure in coarse filters could achieve an impressive threefold increase in dust holding capacity (DHC) compared to normal filters. Utilizing initial pressure drop, initial particle capture efficiency, and DHC, we introduced a new quality factor (QF) that integrates these parameters to evaluate the filter's long-term performance. This approach addresses the limitations of traditional quality factor, which neglects DHC and tends to underestimate the performance of filters with high DHC. Additionally, we proposed a simple method to predict the filter's operating pressure drop, using particle concentration data from weather report. This allows for convenient assessment of the fresh air system's operating status, offering potential integration into a user-friendly mobile application or computer software. These findings contribute innovative ideas for enhancing the intelligent and energy-efficient operation of fresh air systems.

Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing

SummaryAs a burgeoning paradigm, collaborative mobile edge computing (C‐MEC) can cater to growing computation demand of mobile devices (MDs). However, there are great challenges for joint task offloading and resource allocation. In addition, failures on both MDs and edge servers greatly affect reliable task execution. This paper investigates the joint optimization problem of offloading decision, power allocation, and computation resource allocation in the multi‐user C‐MEC system, where nondivisible tasks may be executed locally, offloaded to nearby collaborative devices, or processed by the edge server. We aim at minimizing the total energy consumption of MDs while satisfying the reliability and delay constraints, and the replication technique is adopted to enhance task execution reliability. To tackle this problem, we first transform it into a bi‐level optimization problem, and then propose an iterative algorithm named JOC. Specifically, the offloading decision is constructed in the upper level on the basis of ant colony system (ACS), and the allocation of working power, transmission power, and computation resources is optimized in the lower level by using the monotonic optimization approach. Simulation experimental results reveal that the proposed algorithm saves more energy and achieves a higher task success rate in comparison with baseline schemes.

SoVEC: Social vehicular edge computing-based optimum route selection

This paper proposes a new architecture Social Vehicular Edge Computing (SoVEC) by integrating three domains: social network, vehicular ad-hoc network, and mobile edge computing. The users access various mobile applications and share various types of information on the social network during travel time. Using SoVEC three categories of social networks are generated based on the type of information shared among the users such as traffic information, professional information, and personal interests. To reach the destination in minimal time, this paper proposes an optimum route selection strategy based on TOPSIS method and genetic algorithm. The SoVEC is simulated using the network simulator Qualnet 7, and average delay, jitter, and throughput are determined. A case study of generating social network based on road traffic-related information is also demonstrated. Finally, the effectiveness of the proposed approach for selecting the optimum route is assessed, and the results present that the proposed method outperforms the existing algorithms.