Search

With the rapid proliferation of edge computing-based solutions, many edge computing applications use the cloud for data processing and analysis. However, latency-sensitive applications have low latency requirements and can be very bandwidth hungry, so processing their collected data through cloud servers is not efficient and cost-effective. For example, traffic management and health condition monitoring applications, require real-time data processing at the network edge to respond immediately to unexpected events. These applications are typically executed as workflows with dependent tasks that require careful scheduling to allocate the appropriate resources to the tasks so that the execution is done in a way that satisfies the users' target functions. In this work, we evaluate some traditional scheduling heuristics for the execution of workflow tasks in an edge-computing scenario. Our goal is to compare their performance in terms of execution time and cost and show that these heuristics, previously used in scheduling in the context of cloud environments, can also be used in edge computing scenarios. The results show that the MinMin and PSO scheduling algorithms offer the best results with regard to execution time and cost. [ABSTRACT FROM AUTHOR]

New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks' computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost. [ABSTRACT FROM AUTHOR]

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions. [ABSTRACT FROM AUTHOR]

Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques. [ABSTRACT FROM AUTHOR]

To the superiority of low cost and convenience, large-scale Internet of things has been deployed for various monitoring pervasively. Typically cloud platform is utilized as a remote data and control center. However, it is a challenge to the bandwidth and real-time feedback to the server because of mass data with uploading and processing. In this paper, to cope with the above problems, we design a gateway based on edge computing. The gateway not only retains the function of connecting multiple sensors and the interaction between devices and cloud, but also can provide real-time local services for terminal devices. It also can drop abnormal data through sensors' correlation analysis and reduce the amount of data upload by diverting the computing tasks of a server. In this paper, an anomaly detection method based on the bag of words model is introduced. At first, the gateway makes a bag of words representation by the data collected from each sensor, including normal and abnormal data. Then it aggregates these class predictions using the bag of words to calculate the final prediction. Through the simulation analysis based on the data such as humidity, temperature, and lighting monitored by sensors in agricultural greenhouses, the results show that the bag of words model can detect abnormal sensor data. [ABSTRACT FROM AUTHOR]

With the explosive growth of global data volume, the centralized cloud computing computing model cannot provide low-latency, high-efficiency video surveillance services. Therefore, a distributed computing model is proposed, which directly processes peripheral video data to reduce transmission pressure, the burden of the central cloud server and the processing delay of the video surveillance system. Combined with the federated learning algorithm, a light neural network model is proposed. The light neural network is used to generate calculation models in different scenarios, and the generated models are reasonably arranged in edge devices. With the continuous development of technology, human motion analysis has become a hot research field in recent years, and human motion recognition is developed on the basis of human motion analysis. With the development of society, the level of Chinese Taekwondo has improved compared to before. Therefore, Chinese Taekwondo classes have gradually developed, but behind this phenomenon there is a problem of uneven teaching quality. In order to help solve the teaching problems in the learning process of Taekwondo and further improve the level of Chinese Taekwondo, the author carried out specific research in conjunction with D.A. Cooper's empirical learning theory. Future work will continue to study federated learning algorithms, optimize general neural network weight update methods, further improve detection results, and introduce neural network compression acceleration algorithms to make the system meet real-time requirements. This research uses scientific and technological means as a guide to study technical actions and strategies, and apply these strategies to specific teaching experiments for testing. [ABSTRACT FROM AUTHOR]

As a branch of cloud computing, edge computing has received extensive attention. Edge computing offloads computing-intensive tasks to the computing-capable devices (i.e., edge servers) at the network edge of the cloud, and the edge servers assist in mobile devices to perform data analysis, which can significantly reduce the execution time of computing-intensive tasks on mobile devices. However, due to the poor quality of wireless channel communication, it may degrade the overall quality of the edge offloading when the mobile devices are linked to edge servers, that is, the communication link between the mobile device and the edge server may become a bottleneck. In this study, we propose a new edge computing scheme to support remote object detection by splitting the deep neural network (DNN), and we design a framework to reduce the amount of data transmitted over wireless links. Specially, the DNN is divided into a head modular and a tail modular, which are deployed on mobile devices and edge servers, respectively. Furthermore, the wireless link transmits the output of the last layer in the head modular to the edge server, instead of sending all the initial tensors of images to the edge server. And we employ three object detection models with complex structures, that is, Faster RCNN (regions with CNN features), Mask RCNN and Keypoint RCNN as the basic models, and modify their network architectures from the following aspects: 1) we introduce the information bottleneck principle and split the network architecture into a head modular and a tail modular, and we perform knowledge distillation to compress the head modular; 2) we embed a neural filter (NF) into the head modular to pre-filter images with no detection objects by using a simple convolutional neural (CNN) network, and the NF performs a binary classification task in the head modular. Lastly, we conduct experiments on the real COCO 2017 dataset and the PASCAL VOC 2012 dataset, and the results show that the proposed method outperforms other baselines. Especially, in person keypoint detection, the mAP (mean average precision) of the proposed method is at least 1.64% higher than other baselines on the COCO 2017 dataset. In addition, the prediction latency of the proposed splitting scheme at the data transmission rate of 1Mbps is at least 49.17% lower than that of local computing scheme. [ABSTRACT FROM AUTHOR]

The growing technology of the fifth generation (5G) of mobile telecommunications has led to the special attention of cloud service providers (CSPs) to mobile cloud computing (MCC). Due to the limitations in processing power, storage space and energy capacity of mobile devices, cloud resources can be moved to the edge of the network to improve the quality of service (QoS). Server placement is a crucial emerging problem in both typical and edge types of MCC, different proposed methods of which are reviewed and evaluated in this paper. Proper placement of servers leads to more efficient utilization of these servers, reduces their response time and optimizes their energy consumption. A variety of techniques and approaches, including machine learning-based techniques, evolutionary models, optimization algorithms, heuristics and meta-heuristics have been employed by different server placement methods of the literature to find the optimal deployment map of servers. This paper provides a comprehensive analysis of these server placement methods in edge computing, fog computing and cloudlet, investigates their various aspects, dimensions and objectives, and evaluates their strengths and weaknesses. Furthermore, open challenges for server placement in MCC are provided, and future research directions are also explained and discussed. [ABSTRACT FROM AUTHOR]

The Internet of Things technology is developing rapidly, and the data generated has also exploded. Traditional cloud computing technology can no longer meet the demand for efficient processing of massive data. Edge computing technology can move the amount of calculation down to the edge of the network, which can greatly improve computing efficiency. Applying edge computing to the field of equipment health prediction, the combination of strong responsiveness and computing capabilities of edge computing and high-precision prediction technology makes production operation and maintenance more reliable and efficient. At the same time, a neural network prediction model combining Variational Auto-Encoder (VAE) and Time Convolutional Network (TCN) is proposed to improve the accuracy of equipment health prediction. This model uses VAE for dimensionality reduction, extracts the hidden information in the original data, reconstructs high-quality sample data, and then uses TCN to mine the internal connection between the features and the target in the long sequence information. Compared with five benchmark prediction models on the C-MAPSS dataset, experiments show that the proposed model has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k , n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners. [ABSTRACT FROM AUTHOR]

Rapid advancements in vehicular technology and increased vehicle modernization have led to the emergence of intelligent and interconnected entities. As a result, the Vehicular Edge Computing (VEC) paradigm has gained prominence. This paradigm enables the provision of cloud computing services close to vehicular users by utilizing the idle computational resources of vehicles to execute tasks that require computing power beyond what is available locally. Aggregating these computational resources in the vehicular context is known as Vehicular Cloud (VCloud) formation. However, leveraging and aggregating these resources poses several challenges due to the dynamic nature of the vehicular environment. One of the main challenges is the efficient selection of vehicles to assume management roles in the distribution of computational power within the group, often referred to as leading vehicles. This research presents a mobility-aware mechanism called PREDATOR to enhance the VCloud formation process. In this mechanism, the Roadside Unit (RSU) provides vehicular mobility predictions, enabling the selection of the most stable vehicles within the RSU coverage area to assume leadership roles in the VCloud. In this context, vehicle stability is associated with a vehicle's time within the RSU coverage area, known as dwell time. PREDATOR employs a microscopic perspective to select vehicles with the longest dwell time in the VCloud, allowing for efficient management of computational resource utilization. Simulation results have demonstrated that PREDATOR not only increases the VCloud lifetime but also minimizes leader changes, reduces network message exchange, mitigates packet collisions, and facilitates the effective utilization of aggregated vehicular resources compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The study systematically reviews the integration of Fog and Edge Computing within Learning Analytics to enhance data privacy and security in educational settings that use cloud computing. Employing the PRISMA methodology, we analyze current literature from Web of Science and Scopus databases to examine how these decentralized computing technologies can mitigate the risks associated with centralized cloud storage by processing data closer to its source. Our findings highlight the significant potential of Fog and Edge Computing to transform Learning Analytics by enabling real-time, context-aware data analysis that supports personalized learning while ensuring stringent data privacy. This approach challenges conventional data management practices, advocating for privacy by design in developing new strategies and frameworks. The research underscores the need for collaborative efforts in establishing standards and guidelines for secure and effective technology use in education, pointing towards the necessity of addressing technical, operational, and ethical challenges to maximize the benefits of fog and edge computing in Learning Analytics. • Fog and Edge computing improve data privacy for Learning Analytics and minimizes dependence on centralized cloud storage. • Nearer analytical technologies to users promotes a personalized and secure learning environment. • Future research should focus on creating secure architectures and policies to leverage Fog and Edge Computing in education. [ABSTRACT FROM AUTHOR]

In the recent past, many studies have been conducted to elucidate the determinants affecting cloud computing adoption at different institutional levels. The study aims to identify how technological, organizational, environmental, and socio-culture factors influence the adoption of cloud edge technologies. The proposed theoretical framework is based on the main determinants of the TOE theory and extends it by incorporating a socio-cultural predictor. Eighteen core manifests were extracted from previous literature and the standard theories. The researcher used a proper validation process to create a valid questionnaire. Two-stage analytical methods, i.e., SEM and ANN, test the proposed model. SEM is applied to test all core hypotheses, and identified significant predictors are considered input items to the neural network model to ascertain the relative magnitude of each manifest in a subsequent stage. The outcomes of the quantitative investigation outline the relationship between chosen constructs and cloud-edge computing adoption (CECA). The study supports all core hypotheses using SEM. The study is one of the novel attempts to empirically confirm and estimate the synergy of multimodal structure to elucidate the relative importance of manifests to be considered by top management of HEIs. [Display omitted] • This research discussed parameters and techniques used in an integrated model of cloud-edge adoption. • Eighteen core manifests were extracted from previous literature and the standard theories. • SEM identified significant predictors, which are considered inputs to the Artificial Neural Network (ANN). • ANN outlines that in technological factors, security, and privacy of the cloud is the highest explanatory factor. • Organizational constructs denote innovative technology, reduced cost, and robust output of the cloud edge. • Environmental factors consist of geographical reach and vendor support 24x7 vital to adopt cloud edge. • Social values, customs & beliefs, awareness of cloud, collaboration & trust were significant in adoption of cloud edge. • Theoretical implications of the research offer practical insight to stakeholders on cloud-edge computing adoption. [ABSTRACT FROM AUTHOR]

With the popularity of IoT (Internet of Things) applications, edge computing has received lots of attention. To meet data privacy protection requirements of edge nodes and cope with their unbalanced data distribution, federated learning (FL), a distributed learning framework, is widely used in intelligent edge computing applications. However, recent studies have shown that FL still suffers from privacy leakage problems, including membership inference, data reconstruction, etc. However, these studies mainly focus on the feature information of private data. In this paper, we concern the user-level label privacy in FL. We propose LDIA, a label distribution inference attack against FL in edge computing, exploring the possibility that an honest but curious cloud server can infer the proportions of samples per label in the edge user's private data. LDIA is inspired by the observation that parameter changes in the output layer of a model can reflect the label distribution of training data. We use a neural network to learn individual features of the output layer updates over different label distributions, and then perform inference from local models uploaded by users. Our comprehensive evaluation shows that LDIA is effective on various datasets in different settings, demonstrating the severe privacy leakage in FL-based edge computing. [ABSTRACT FROM AUTHOR]

Since ancient times, humanity has envisioned a world where people and things connect and interact. The advent of generation mobile systems, the so-called 5 G in the early century, has realized this dream. 5 G can provide network services with extremely-high throughput and extremely-low delay and allows a huge device number to connect based on Internet infrastructure, forming the Internet of Things (IoT). In recent years, IoT has been applied in various fields serving humanity, such as smart cities, smart agriculture, healthcare, education, military, and IoT ecosystems. One of the main challenges of IoT applications is computing solutions to reduce service response times. This study proposes an efficient edge computing management mechanism for IoT applications in smart cities. Our mechanism proposes a small database (called an information map) that allows edge computing servers of smart cities to store edge service information. When the mobile end-users move to the new edge server' managed coverage, properties related to the EC service are exchanged between the edge servers. The experiment results have shown that our proposed mechanism improves service response time and energy consumption many times compared to the traditional mechanism. We hope that this mechanism will be strongly applied to sustainable smart cities in the future. • Our mechanism establishes a small database, the so-called information map. • Generate a map stores information about the EC services that are performed by edge servers. • Thanks to this information map, as mobile users move through the city, related properties to EC services can be exchanged among edge servers. [ABSTRACT FROM AUTHOR]

In recent years, the Industrial Internet of Things (IIoT) has played an indispensable role in the age of intelligence. The data of IIoT is extremely private and valuable. Therefore, privacy protection and secure communication in the IIoT have been the focus of attention. Since terminal devices in the IIoT collect massive amounts of sensitive data in real time, traditional cloud computing architectures cannot satisfy the requirements of the IIoT. Edge computing is an emerging computing paradigm to solve the problems of delay and bandwidth in traditional cloud computing architectures. In edge computing architecture, one-to-many communication is a secure and highly efficient communication method. Due to the complex heterogeneity of the IIoT, it faces various potential threats. In order to solve the dilemma mentioned above, we propose an efficient heterogeneous signcryption scheme based on edge computing for IIoT to achieve multi-message and multi-receiver communication between edge servers and data users, where the edge server belongs to identity-based cryptography while data users belong to certificateless cryptography. In the random oracle model, our proposed scheme is proved to satisfy confidentiality and unforgeability based on the discrete logarithm assumption and the computational Diffie–Hellman assumption. Experimental results indicate that our proposal achieves better performance than existing schemes. Hence, our scheme has more advantages for IIoT. [ABSTRACT FROM AUTHOR]

• A method of cloud edge computing is proposed to solve the economic dispatch. • Multi-agent deep reinforcement learning is adopted to realize cloud edge computing. • The cloud server and each edge server respond to each agent. • The proposed method can protect data privacy. In view of the risks and challenges of privacy data leakage and the communication burden in the traditional economic dispatch for active distribution network with multi-microgrids, this paper proposes a cloud edge computing method for economic dispatch of active distribution network with multi-microgrids. In this method, the cloud server is responsible for the calculation of active distribution network, and each edge server is in charge of the calculation of its own microgrid. The multi-agent deep reinforcement learning is employed to realize cloud edge collaborative computing, where each edge server and cloud server corresponds to an agent. Through case analysis, the reliability of the cloud edge computing method is confirmed. The simulation results show that the proposed method can provide a high-quality solution for economic dispatch of active distribution network with multi-microgrids on the premise of protecting data privacy and reducing the communication burden. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may correspond to a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented as the category of sensory data in this paper. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted value in the cloud. Experimental results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, nearly 40 commonly used deep neural network(DNN) models are selected, and their cross-platform and cross-inference frameworks are deeply analysed. The main metrics of accuracy, the total number of model parameters, the computational complexity, the accuracy density, the inference time, the memory consumption and other related parameters are used to measure their performance. The heterogeneous computing experiment is implemented on both the Google Colab cloud computing platform and the Jetson Nano embedded edge computing platform. The obtained performance is compared with that of two previous computing platforms: a workstation equipped with an NVIDIA Titan X Pascal and an embedded system based on an NVIDIA Jetson TX1 board. In addition, on the Jetson Nano embedded edge computing platform, different inference frameworks are investigated to evaluate the inference efficiency of the DNN models. Regression models are established to characterize the variation in the computing performance of different DNN classification algorithms so that the inference results of unknown models can be estimated. ANOVA methods are proposed to quantify the differences between models. The experimental results have important guiding significance for the better selection, deployment and application of DNN models in practice. Codes are available at this https URL https://github.com/Foreverzfy/Model-Test. [ABSTRACT FROM AUTHOR]

With the increase of smart devices, edge computing is becoming a new computing paradigm that coexist with centralized cloud computing to process data distributed at the edge of the network. Based on this new paradigm, app vendors can store data replicas on geographically distributed edge servers to serve surrounding users to reduce access delay. However, since edge servers have limited storage space and computing ability, these edge data are vulnerable to various corruption. Therefore, how to efficiently audit the integrity of edge data has become an urgent problem to be solved. To tackle the Edge Data Integrity (EDI) problem, we propose a lightweight auditing scheme, namely EDI-SA. Firstly, inspired by the shuffle algorithm and the bucket sorting algorithm, we propose an improved sampling algorithm, which is a new lightweight challenge block sampling method. Secondly, based on algebraic signature, EDI-SA can achieve efficient aggregation verification and the signature computation cost is independent of the number of data blocks, which greatly reduces the computation overhead of app vendors and edge servers. Thirdly, EDI-SA supports batch auditing and provable dynamic update, app vendors can also uniquely locate the corrupted edge data. Finally, security and performance analyses demonstrate the security and effectiveness of EDI-SA. • A lightweight integrity auditing scheme for distributed edge data is proposed. • Achieve efficient aggregation verification via algebraic signature technology. • Support batch audit, provable dynamic update and the localization of corrupted data. • Computation costs of edge servers and app vendors during auditing phase are constant. • Security and efficiency are proved by numerical analyses and simulation experiments. [ABSTRACT FROM AUTHOR]

• The multi-sensor fusion method has high accuracy for tillage depth and area detection. • The designed edge computing device could process subsoiling information in real-time. • The cloud-end data interactive mechanism ensuring safety and stability of the system. • The system has reliably ability for monitoring and management subsoiling on a large scale. An edge computing based monitoring system using Internet of Things architecture (IoT) was proposed, aiming at automatically monitoring subsoiling parameters, counting subsoiling quantity, evaluating subsoiling quality, analyzing subsoiling efficiency and managing subsoiling operation. In this study, tillage depth computing algorithm based on motion attitudes of tractor and subsoiler was studied, and an edge computing device was developed to collect and process subsoiling data in real-time. A cloud-end data interaction mechanism was designed to provide a safe, stable and reliable way for data transmission between the device and cloud. High-performance cloud computing was used to count indicators of subsoiling quantity such as total area, effective area and operation mileage, evaluate subsoiling quality indicators such as average tillage depth and area compliance rate, and analyze subsoiling efficiency indicators such as time utilization rate and effective mileage rate. Field tests showed that the error of tillage depth detection was less than 1.2 cm, and the error of subsoiling area detection was less than 1%. The subsoiling data in 2021 from a selected units in the system showed that the system could count the subsoiling quantity, analyze the subsoiling quality and efficiency, and meet the requirements of subsoiling monitoring and management by the friendly human-computer interaction interface. The subsoiling data collected for 7 years by the system was analyzed, and results showed that the total registered edge computing device number was 51,252, the peak value of annual cumulative on-line number was 20,560, the peak value of single-day on-line was 6,694, the peak data transmission rate was 0.034 GB/s, the peak value of single-day concurrent calculation subsoiling area was 43,900 ha, and the cumulative monitoring subsoiling area was 8.98 million hectares, which showed that the system has good concurrent calculation ability and stable and reliable operation. This paper provides an efficient monitoring, statistics, analysis and management tool for large-scale subsoiling, and has high practical value. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging computing paradigm, which extends the functions of cloud center to the edge of the networks, and brings great convenience to solving problems of delay and bandwidth in the traditional cloud computing paradigm. In edge computing architecture, one-to-many communication is a important communication mode that supports the edge nodes to send one message to multiple terminal devices in one broadcast report. To build a secure one-to-many communication, several multi-receiver signcryption (MRSC) schemes have been brought forward to ensure the security of broadcast messages. However, the existing MRSC schemes need the heavy computation and communication cost, and is not suitable for IoT terminal devices with limited resources. Besides, most of the existing MRSC schemes are vulnerable to the security and privacy leakage, and fail in achieving the decryption fairness. To solve these problems, based on the implicit certificate (IC) cryptosystem and polynomial interpolation evaluation, this paper proposes an provably secure multi-receiver IC-based signcryption (MRICSC) scheme for one-to-many communication in edge computing. The security analysis evidences the proposed MRICSC scheme can ensure the security. With the experimental results indicating that the proposed MRICSC scheme achieves the better performance than existing schemes. [Display omitted] [ABSTRACT FROM AUTHOR]

During the last decade, research and development in the field of multi access edge computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC's evolution is the ability to deploy edge servers capable of providing both communication and computational services in close proximity to the mobile user terminal. MEC has been regarded as a potentially transformative technique for fifth-generation (5G) and beyond 5G (B5G) wireless communication systems, as well as a possible complement to traditional cloud computing. Additionally, unmanned aerial vehicles (UAVs) integrated with MEC will play a critical role by introducing an additional mobility based computational layer to provide more secure, efficient and faster services. UAV enabled MEC offers seamless connectivity, fulfilling the promise of 5G's ubiquitous connectivity. Due to the enormous interest in UAV enabled MEC, there has been a tremendous increase in the number of published research articles in this domain; however, the research area still lacks a systematic study and categorization. We present a systematic literature review (SLR) on UAV enabled MEC, examining and analyzing data on the current state of the art using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To streamline our assessment, this study analyzes several research papers carefully selected through a multi-stage process satisfying the eligibility criteria defined in the paper. One of the SLR's primary contributions is to broadly classify the research in the UAV enabled MEC domain into different categories including energy efficiency, resource allocation, security, architecture, and latency. We have identified key findings, technology, and pros and cons for the selected articles under each category. Additionally, we discuss the key open issues related to scalability and fairness, resource allocation and offloading optimization, service delivery with a focus on quality of experience (QoE) and quality of service (QoS), and standardization. Finally, we discuss several future research directions that would address the aforementioned issues and emerging use cases for UAV enabled MEC. [ABSTRACT FROM AUTHOR]

The advent of technologies like IoT and 5G brought a new computing paradigm called cloud computing into the world. Cloud computing has become the main platform for data warehousing and processing. However, storing data into the cloud has its own set of challenges and security concerns. Moreover, with increasing data being generated from each device; the traditional cloud computing paradigm has become ineffective in addressing problems like high latency, bandwidth limitation, and resource limitation. So, new computational paradigms like edge and fog computing are proposed to solve the former's issues at the device itself or near the device. Both these paradigms provide computing and memory storage options close to the device itself. Despite their advantages, no system is perfect. In this paper, I list down the different privacy and security problems in all three computing paradigms and their proposed solutions. [ABSTRACT FROM AUTHOR]

In traditional cloud computing, tasks will be offloaded to the could, which often leads to high latency and low quality of service. To avoid this disadvantage, edge computing was introduced. The fundamental issue in edge computing is selecting some tasks to be computed in the edge, which is called the offloading problem. In most previous studies, the communication cost between any two tasks is often assumed to be symmetric in different sides and is often ignored within the same side. In this paper, we consider a heterogeneous offloading model, where the communication cost exists everywhere and is asymmetric. With the help of semidefinite relaxation, we give an algorithm for the offloading problem. If the Laplacian matrix with respect to the offloading problem is positive semidefinite, the theoretical guarantee can be proved to be 2 π. The performance of the proposed algorithm is also evaluated via numerical analysis. The experimental results show that the algorithm is very effective. [Display omitted] • Edge computing can avoid long delay between the cloud and users. • A deep theoretical analysis for the offloading problem. • Design a semidefinite relaxation based approximation algorithm. [ABSTRACT FROM AUTHOR]

The advancements in positioning technologies have led to the emergence of various location-based services, resulting in a drastic increase in location-based data generation, producing big-data. Location data are often linked with user privacy, as they can reveal sensitive information such as the places visited by a person. Moreover, most location-based services involve resource-constrained devices, needing lightweight data processing approaches. Due to these reasons, privacy and efficiency have been two of the primary components of location-based data processing. The existing approaches do not study both issues in the same setting. Consequently, current methods fail to provide efficient privacy preservation solutions towards location-based data stream processing. To address these issues, we investigate the effective integration of edge computing, cloud computing and differential privacy for location-based data clustering, which is an essential area in service recommendations (e.g. recommending the closest hotels to a particular location). In the proposed setup, we use local differential privacy to ensure user privacy. Next, we apply edge-based clustering on the differentially private input data using mobile edge devices. Next, the centroids of the clusters are collected at a cloud server to generate final clustering in a privacy-preserving manner. Our experiments show that the proposed approach provides maximum accuracy of 90% on lower privacy budgets (e.g. ɛ = 0.45-0.5). • Location data stream clustering requires advanced computing and storage capabilities. • Edge–cloud interplay can provide a potential solution to this problem. • The privacy of location data is another major issue that limits the effective use of cloud resources. • It is essential to look at both efficiency and privacy of location data stream clustering. • An efficient distributed edge–cloud scenario for location data clustering with differential privacy is proposed. [ABSTRACT FROM AUTHOR]

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods. [ABSTRACT FROM AUTHOR]

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) nowadays represents a fundamental technology for the Industry 4.0 initiatives for connecting devices, operators and processes so as to eventually fuel digital transformation. However, if based exclusively on Cloud Computing, the IIoT faces inevitable limitations and barriers. Therefore, in this paper we deal with the Edge Intelligence (EI), a novel paradigm at the confluence of Edge Computing and artificial Intelligence, and, particularly, we analyze its impact on the processing of the IIoT data in the proximity of the events of interest rather than on Cloud servers. Therefore, a general discussion on the most relevant benefits, limitations and open challenges of the IIoT-EI duo is proposed hereinafter and exemplified through an emblematic IIoT use case: this is related to safety-critical tasks based on video analytics and provides a full-fledged comparison between Cloud-and EI-based IIoT deployments (in terms of reliability, responsiveness, bandwidth usage, energy footprint and usability), finally outlining a general strategy for large-scale EI-aided IIoT systems engineering. [ABSTRACT FROM AUTHOR]

A significant challenge in high-performance computing is to ensure the even distribution of applications across computational resources, preventing issues such as resource fragmentation and network congestion. While cloud computing offers advantages, it introduces scheduling delays caused by data transmission. To address this issue, edge computing has emerged as an alternative to traditional cloud systems, aiming to minimize latency. While various methods have been proposed to address this challenge, they often prioritize one aspect at the expense of overall system performance. In this paper, we present a novel algorithm utilizing ant colony optimization to compute a fitness function and prioritize multiple objectives in scheduling. The algorithm effectively determines how to distribute applications between edge and cloud servers to enhance computational efficiency. This entails a delicate balance between scheduling delays and energy consumption in two distinct phases. Initially, the algorithm identifies applications sensitive to delays and ensures their execution on local edge servers. Subsequently, it identifies applications that require intensive computation and migrates them to the cloud layer, where cloud servers can process them. The results demonstrate that this approach reduces delay costs by 21.19% and decreases energy consumption by 13.76%. [ABSTRACT FROM AUTHOR]

[Display omitted] • We propose decentralized adaptive resource-aware computing, caching, and communication framework to orchestrate the proximity resources as a result, IoE devices can exploit and explore the dynamic network environment & leverage users' ultra-reliable low latency connectivity. • The problem formulation is expressed using non-cooperative game theory which is prominent for resource allocation to smart mobile technologies. Subsequently, a decentralized deep reinforcement learning approach is exploited to achieve the optimal solution. Hence, we analyze the complex network environment and show that the game admits a Nash Equilibrium. • The DARMEC algorithm is introduced and designed using DRL. It can exploit and explore the complex dynamic network environment to promote cognitive and efficient resource utilization at the proximity of RAN for cellular subscribers. • Theoretical and simulation analyses are performed to reveal the outcomes of our proposed MEC systems and DARMEC algorithm. As a result, we observed that the explored DARMEC algorithm outperform than the baseline schemes. Decentralized computation offloading and caching in Multi-Access Edge Computing (MEC) is a promising approach to evolve the forthcoming network generation. MEC is the emerging technology that provides adaptive micro cloud services to the edge of proximity resource-constrained smart communication and Internet of Everything (IoE) devices for cellular subscribers. Nowadays, Massive IoE devices are exponentially connected to the global ecosystem. As a result, the backhaul network traffic grows enormously and users' ultra-reliable low latency communications are challenging as well. In this paper, we explored decentralized adaptive resource-aware communication, computing, & caching framework which can orchestrate the dynamic network environments based on Deep Reinforcement Learning (DRL). Subsequently, the framework can perform augmented decision-making capabilities to enhance users' connectivity and resource utilization requirements. Basically, every IoE device user are attempting to capitalize their own utilities. Hence, the problem is formulated using Non-cooperative game theory which is non-deterministic polynomial to solve the structural property of the MEC networks. We analyze and show that the game admits a Nash Equilibrium. Moreover, we have introduced a decentralized cognitive scheduling algorithm by exploiting DRL technology to leverage the utility of IoE & smart communication devices. Therefore, numerical results and theoretical analysis revealed that the proposed algorithm outperform, ultra-reliable low latency, and scalable than the baseline schemes. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Multi-Access Edge Computing (MEC) can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching scheme in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load. [ABSTRACT FROM AUTHOR]

Edge computing relies on devices at the edge of the network to best meet the application's needs, such as low communication latency and data caching. However, unbalanced networks and resource heterogeneity are challenging factors when optimizing data transfers and applications' performances in the edge. In this paper, we explore these two concerns through a comprehensive set of benchmarks and illustrate their importance with the help of two elements that should be part of any edge computing toolkit: data locality and context awareness. Thanks to the benchmarks, we highlight some pitfalls and opportunities that may be met when trying to deploy applications on the edge. • Benchmarks for edge-cloud and edge-edge data transmission and storage. • Use of real devices presenting a variety of performance profiles and capabilities. • Data-locality strategies for DHT storage and their impact on low-end edge devices. • Assessment of a context-aware scheduler on a network with heterogeneous devices. [ABSTRACT FROM AUTHOR]

With the development of Internet of things (IoT), billions of sensors, actuators and other intelligent terminal devices (ITDs) are connected to the Internet. In this situation, traditional cloud computing models are not fully suitable because these ITDs generate too much data which may cause severe network congestion. As a result, edge computing is proposed to solve this problem, which can provide computation and storage services for ITDs with a distributed model on the edge networks. While, this novel edge paradigm brings emerged problems of restricted computation, limited storage and unstable network, as the storage is distributed typically as a key role. So, in this paper, it proposes a distributed multi-level storage (DMLS) model with a multiple-factors least frequently used (mLFU) algorithm to solve the problem. In this model, storage levels are composed of ITDs on the edge, so when the storage space of a node is not enough, the mLFU is used to remove a part of data from the current nodes and upload the date to the upper storage levels. To reduce the impact of data loss caused by unstable edge networks, a factor of importance is introduced in the mLFU, which means that data with high importance is uploaded to the upper levels first. Experiments show that the hit rate of the mLFU is stabilized at 74% that is almost equal to the typical LFU, while the important data loss rate is about 35% lower than the LFU and the random replacement algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a hierarchical and multilayered computing framework, specifically tailored to analyze the benefits of Named Data Networking (NDN) enabled microservice-centric in-network computations in autonomous vehicular networks. The proposed framework comprises three layers: an autonomous vehicular network layer, an Edge computing layer, and a centralized Cloud.Modifications were made to the vanilla NDN codebase to facilitate communication between the vehicular network layer, Edge computing and Cloud layer, utilizing the C++ Boost Asio library and IP tunneling mechanism. A microservices-based driver assistance application, consisting of various heterogeneous microservices, was emulated in.NET Core and deployed on different Edge computing terminals and the Cloud. Additionally, RESTful APIs have been developed to enable the vehicular network layer and the physical Edge layer to offload microservice-centric computation requests and retrieve the corresponding computational outcomes. The Entity framework is employed to ensure proper tracking and management of these requests. An HTML-based user interface has also been developed for a visual representation of the request pattern. Extensive testbed experiments reveal that the proposed system significantly optimizes bandwidth consumption, reduces latency, and increases the computed results delivery ratio when compared to conventional monolithic systems. [ABSTRACT FROM AUTHOR]

The ongoing realization of a transition towards increased reliance on edge computing is evident. This shift not only underscores the importance of interdependence in knowledge sharing but also contributes to a reduction in adaptation and transmission time through cloud computing directly, along with decreased computational demands compared to edge-to-edge computing. A persistent challenge in knowledge transfer stems from the shift in dependence from high-performance computing systems to compact models, making the adjustment cumbersome. Even with cross-edge knowledge transfer, pre-processing remains essential to facilitate seamless adaptation—a necessity that endures despite the escalating volume of data. Consequently, the system tends to become overburdened with instance filtering instead. To address this issue, we present an optimization for small-to-small inter-edge transfer learning of a comparable size. This is achieved through a self-supervised domain-adaptive protocol, utilizing self-target curriculum-based pseudo-labeling creation, new statistical alignment, and lightweight attention. In the multi-view verification using public visual DA datasets, we demonstrate a remarkable improvement of 73.6 (+4.2 %) in Top-1 performance. Our results consistently outperform other methods, particularly when using the latent feature input technique. This advantage is most noticeable at the 10-th layer for latent feature input. Crucially, our approach incurs the modest increase in computational demands, adding just 100 K-units of parameters and MFLOPS of computational power. Furthermore, through model enhancement via acceleration and compression, we achieve significant compression—up to 6.28 times faster at FP32 and 7.18 times faster at FP16 runtimes—resulting in an increased FPS to 124.9 and 142.9, respectively. This demonstration effectively illustrates the feasibility of advancing inter-edge transfer learning with our approach. [ABSTRACT FROM AUTHOR]

Accurate and fast short-term traffic flow prediction plays a vital role in the construction of future smart cities. To address large bandwidth consumption and high delay in traditional cloud solutions, and improve the accuracy and timeliness of traffic prediction, this paper introduces a novel three-layer Cloud-Edge-IoT traffic flow edge computing architecture, and proposes a short-term Traffic Flow Prediction Method based on Spatial–Temporal Correlation (TFPM-STC), in which Principal Component Analysis (PCA) is adopted for analyzing the intersection correlation, and the Convolution-Gated Recurrent Unit (Conv-GRU) and Bidirectional GRU (Bi-GRU) are used for extracting the spatial–temporal and periodic features of the traffic flow. Experiment results show that compared with existing methods, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of TFPM-STC are reduced by about 16.5 points on average, and there is a significant reduction in training time and prediction time. • A novel traffic flow edge computing architecture is proposed. • A traffic flow prediction method based on spatial–temporal correlation is proposed. • Using principal component analysis method saves training time and prediction time. • Extracting spatial–temporal and periodic feature improves the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) recognized as an emerging technology that provides cloud computing services in the proximity of Radio Access Network (RAN). Mobile subscribers could offload their computation-intensive or memory-intensive tasks to the nearest Base Stations (BS) and deliver the cloud services from MEC servers. This technology can meet the requirements of low latency and location awareness. Smart map applications are an indispensable part of our digital life in large cities. In this paper, we introduce how the map application could gain from MEC technology. We concentrate on performance modeling and analysis of direction-finding in the map application. We study the impact of variation of workload, the number of servers, queue length, and connection failure on the task rejection probability and mean sojourn time. We model each phase of direction-finding service by the Markov Reward Model (MRM). The numerical results presented by applying models in the SHARPE software package. Furthermore, inter-dependencies between sub-models resolved by the fixed-point iteration technique. Moreover, the analytical results verified by the Discrete-Event Simulation (DES), which conducted in MATLAB. The result could help the MEC providers to assess their platform performance. [ABSTRACT FROM AUTHOR]

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic and network congestion, IoT devices with limited computational power and energy resources face challenges in analyzing, processing, and storing data. To address this issue, computing technology has emerged as an effective means of conserving energy for IoT devices by providing high-performance computing capabilities and efficient storage to support data collection and processing. As such, energy-efficient computing, or "green computing," has become a focal point for researchers seeking to deploy large-scale IoT networks. This study provides a comprehensive Survey of recent research efforts aimed at achieving energy-efficient computing and green computing for IoT networks. To the best of our knowledge, none of the studies in the literature have discussed all types of green computing (edge, fog, cloud) and their role in enabling massive IoT networks in terms of energy efficiency. The article starts with an overview of computing technologies and then goes with a discussion of the empowering energy-saving techniques for computing (edge, fog, and cloud) environments including, energy-aware architecture, data aggregation and compression, low-power hardware, energy-aware scheduling, task offloading, switching on/off unused resources, virtualization, energy harvesting, and cooling optimization. This article is an outline of a roadmap toward realizing the vision of a sustainable computing environment for massive IoT networks; in addition, open the door for interested researchers to follow and continue the vision of Energy-Efficient Computing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates a significant volume of geo-tagged images via surveillance sensors in edge–cloud computing environments. Image search is essential to facilitate information sharing, data analysis, and strategic decision-making. However, outsourced images are typically encrypted for privacy protection, posing a challenge in simultaneously searching for visual and geographical relevance on encrypted images. To address this, this paper proposes an edge intelligence empowered privacy-preserving top- k geo-tagged image search scheme for IoT in edge–cloud computing. The scheme presents a novel single-to-multi searchable encryption method for geo-tagged images that enables multiple users to perform secure nearest neighbor queries on a data source. Additionally, an extended anchor-based position determination method and an inner product-based distance calculation method are designed to enable geo-tagged image similarity calculation on ciphertext. Finally, a secure pruning method is introduced to improve query performance. Experiments are conducted to verify the performance of the scheme in terms of high efficiency and accuracy of the search. • Single-to-multi searchable encryption model on geo-tagged images. • Secure nearest neighbor queries on geo-tagged images. • Secure pruning based on the extended locality-sensitive hashing. • Distance calculation from an encrypted data point to an encrypted space. • Extended anchor-based position determination. [ABSTRACT FROM AUTHOR]

With the recent progress of science and the development of society, the development of artificial intelligence technology and robot theory have become increasingly mature. Compared to the development of Internet, robot industry is a branch of traditional industry, and the popularization of social robots has always been the bottleneck. Robot intelligence is an important part of artificial intelligence, which brings new challenges to human production and life for it usually needs cloud computing system for the systematic implementations. Compared to cloud computing, edge computing brings powerful computing resources and efficient services to the network edge, which has lower latency, lower bandwidth consumption, higher energy efficiency and better privacy protection. Data envelopment analysis (DEA) enriches theory of production function and its application technology in microeconomics. Meanwhile, it has advantages in avoiding subjective factors, simplifying algorithm and reducing errors. Based on these novel technologies, this paper analyzes development trend of intelligent social robot with specialzed analysis of the electric engineering and data analytic perspectives. The sample expeirment has shown rapid development. Compared with the other state-of-the-art methodologies, the proposed framework is efficient. [ABSTRACT FROM AUTHOR]

Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modeling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 available cloud/edge locations with different system setups. Thus, they can be used to model distribution of functions through multi-location aware scheduling mechanisms. The experiments include different traffic arrival rates, different setups for the FaaS system, as well as different configurations for the hardware and platform used. We analyze the acquired data for the three FaaS system setups and discuss their differences presenting interesting conclusions with relation to transient effects of the system, such as the effect on wait and execution time. We also demonstrate interesting trade-offs with relation to system setup and indicate a number of factors that can affect system performance and should be taken under consideration in modeling attempts of such systems. • Experimental data analysis of 3 different setups of a FaaS system. • Insights into the transient effects of the system. • Considerations in terms of modeling approaches. • Explore interesting conclusions regarding the system setup and its trade-offs • Examine hidden factors influencing performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-to-Device (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state-of-the-art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility. • We present a method for task offloading across the D2D, Edge, and Cloud spectrum, crucial for elevating operational efficiency in the IoT systems. • We incorporate the Rainbow DQN, an advanced DRL algorithm. • We establish compelling evidence of noteworthy improvements in key performance indicators. [ABSTRACT FROM AUTHOR]

• A taxonomy discussing application execution strategies in fog computing. • A comprehensive discussion about components of application execution in fog computing. • Critical analysis providing in detail information about key aspects of application execution strategies. • Identifying research gaps and highlight new research directions which needs to be addressed in the future. With tremendous advancements in smart phone industry and IoT devices, edge computing has emerged to provide computational services at edge of the network. As a result, applications can be executed in a distributive manner to meet the latency requirements of applications. Despite these advancements, these devices are still considered to have limited energy, memory and computational resources compared to stationary standalone devices. Mobile edge computing extends mobile cloud computing close to end-users, decreasing latency and bandwidth utilisation by offloading tasks to edge servers. Fog computing places nodes between cloud and edge servers. A challenging approach in these paradigms is establishing offloading decision to edge/fog server, and distributed application execution at run time for heterogeneous devices. This paper reviews execution of resource-intensive tasks on mobile devices in distributed frameworks. The objective is to identify research gaps and highlight new research directions that can be pursued in future. Application execution involves application partitioning, task allocation, task execution and task resumption. In this paper, we propose a separate taxonomy for each step in application execution, reviews state-of-the-art techniques, and analyses critical aspects of application execution in a distributed manner. It further investigates similarities and differences among these techniques based on each step of application execution. [ABSTRACT FROM AUTHOR]

The cloud and the Internet of Things (IoT) provide optimal service responses at the user's request through edge devices. Resource sharing and computations are distributed between the users and edge devices, reducing the complex processing instances. In such computations, the pipelined process due to one-to-many resource sharing increases the service lags. For addressing this issue, this article presents a Service-Intensive Computation Offloading Framework (SICOF). The proposed framework guarantees service quality preventing lag in resource-oriented computations. This process is forwarded based on support vector classification for pipelined and distributed computations. The modifications in classifier intervals ensure distributed computations with offloading. In the offloading process, the service-free edge and cloud platform devices are identified, and the pipeline is split for validation. The classification outputs generate allocated request processing or service distribution, achieving a high response ratio. The service quality-based results are verified using the metrics service delay, computation time, process distribution ratio, and service lag. Simulation result shows that the proposed framework SICOF achieves reduce service delay, computation time, and service lag. [ABSTRACT FROM AUTHOR]