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We address the problem of resource allocation with multi-attribute demand considering heterogeneous servers in mobile edge computing (MEC). A mobile device (MD) may be within the coverage area of multiple access points and can get resources from any server that can establish direct communication. However, the configurations of heterogeneous servers are different, so the demand for each MEC server is different. We tackle this problem and consider multi-attribute demand, where each MD is assigned exactly one of multiple demands. We formulate the allocation problem in an auction-based setting to provide server scalability. We propose a family of greedy mechanisms to solve the resource allocation problem. However, each MD is self-interested and may try to manipulate the system to maximize its utility. We show our mechanisms are truthful, and they drive the system into an equilibrium where no MD can declare an untrue value to obtain higher utility. We also analyze the approximation ratios of our greedy mechanisms. Experimental results demonstrate that the greedy mechanisms obtain the near-optimal allocation in a short period while benefiting MDs and edge cloud providers. [ABSTRACT FROM AUTHOR]

The proliferation of mobile devices and the increasing use of networked applications have generated enormous data that require real-time processing and low-latency responses. For real-time applications, Edge Computing (EC) gained a lot of attention which helps to overcome the challenges with traditional computing systems. There is a massive growth of data generated at the web edge, but its limited computing resources and boundary dynamics pose significant challenges. For proficient resource utilization and network management, Software-Defined Networks (SDN) integration with EC can provide control and programmability in edge networking management. However, the optimization of resources is a fundamental need and challenge of EC based on SDN. Besides, the utilization of SDN in EC deals with a centralized and distributed infrastructure further enables the processing of data in closer proximity to its origin and helps in the growing demand for efficient and high-performance computing systems. This study presents a systematic review of the current state-of-the-art pertaining to resource optimization in EC and its integration with SDN. The review of challenges, benefits, and various proposed methodologies for managing resources in Edge and SDN-based Edge environments are investigated. This paper offers valuable insights into the current state and future directions of Edge, SDN-based EC systems, which can assist organizations in designing and deploying more efficient resource consumption solutions [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

As wireless communication technology continues to advance, the number of intelligent devices, such as computers, mobile phones, and iPads, is increasing rapidly. To keep up with the growing demand for data and improved network efficiency, additional base stations (BSs) are deployed, and bandwidth allocation is being increased in the sixth-generation (6G) wireless network. While these improvements have enhanced the physical layer of wireless communication, deploying excessive BSs can be costly and may lead to issues such as backhaul congestion and decreased performance. Additionally, frequent and asynchronous access to popular information generates a significant amount of duplicate data, thereby wasting energy and processing resources. Therefore, we use content caching, which involves storing content at the network's edge, such as at BSs or terminal devices. This allows the direct delivery of content to requesters, eliminating the need for backhaul or core network transmission. In this survey paper, we examine state-of-the-art content caching techniques that are specifically designed for mobile edge computing environments. The article classifies the existing content caching scheme into various categories based on a set of parameters, including caching techniques, criteria, location, objective functions, and supporting algorithms. Furthermore, a comprehensive analysis is conducted to investigate the critical aspects of existing schemes. Finally, the study identifies and discusses open research issues and challenges that necessitate further research to enhance content caching schemes. [ABSTRACT FROM AUTHOR]

To solve the problem of multi-SDN controller load balancing, a low-latency controller load balancing switch migration algorithm is proposed, and a load balancing framework consisting of three modules of load monitoring, decision-making, and switch migration is designed. Migrate the switch with the highest request rate to the controller with stronger processing capacity and closer distance, and achieve load balancing through multiple iterations, effectively solving the problem that the static controller deployment scheme cannot cope with the dynamic network environment. Since the current research on computing offloading does not consider the stability of the MEC system, an offloading decision-generation algorithm based on Lyapunov optimization is proposed. This algorithm designs a task queue scheduling model to transform the system stability problem into a queue backlog problem, considering the profit of the edge server and the delay of task processing, and establishing a resource optimization model to maximize the profit of the MEC system under the premise of meeting the stability and delay requirements of the MEC system. The experimental results show that the proposed controller load-balancing algorithm can speed up the load-balancing process and reduce the average response delay of the system by about 22.1% while maintaining high throughput. The proposed computing offload algorithm can reduce the average delay of the system by 52%, better allocate computing tasks, and make the edge server obtain higher profits. [ABSTRACT FROM AUTHOR]

KubeEdge is an open-source system extending native containerized application orchestration and device management to hosts at the edge. One of the main disadvantages of edge computing is the lack of an effective resource allocation and privacy-preserving framework. The objective of this study dynamic task offloading is an important concept in resource allocation and privacy-preserving framework in Kubeedge-based edge computing using machine learning. This research focuses on enhancing the efficiency and security of edge computing within the framework of IoT devices within a KubeEdge cluster. It begins by gathering crucial data on computational power, memory, and network bandwidth from IoT devices, which is essential for informed decision-making. The study evaluates KubeEdge in terms of computational resource distribution and delay, introducing a privacy-preserving multi-layer Blockchain-enabled data aggregation mechanism. This approach ensures efficient data storage with adaptable and reliable data access control. The research addresses privacy concerns in IoT applications, balancing information loss and disclosure risk, and highlights the impact of forwarding traffic on cluster throughput and service delays in edge computing environments. Dynamic resource allocation algorithms are employed, considering profiling data and machine learning models for real-time task offloading decisions, guaranteeing sufficient allocated resources. A Multi-Agent Collaborative-Reinforcement Learning with Salp Swarm Algorithm is proposed for resource allocation in the edge computing environment, enhancing resource efficiency and secrecy performance. Blockchain ensures secure and transparent transactions, reinforcing trust in the dynamic edge environment. Reinforcement learning optimizes task offloading decisions, adapting to changing conditions. Auction games introduce a competitive mechanism, enhancing efficiency in resource allocation. When combined, they create a robust framework where blockchain guarantees data integrity, reinforcement learning optimizes resource usage, and auction games introduce a fair and efficient task allocation mechanism, ensuring both performance and privacy in computing environments. Furthermore, a Partitioning-Dynamic Collaborative energy-aware task offloading scheme is developed to securely offload tasks while preserving data privacy, enhancing trust computing and task offloading capabilities. The study also proposes a Hybrid Greedy Randomized Adaptive Stackelberg-Auction Game Approach to optimize offloading performance, reduce information loss, and decrease time consumption. This comprehensive framework includes task scheduling to reduce energy consumption, enhance privacy, and security, and reduce latency. The proposed work is analysed based on the Matlab software, and it shows a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network. Continuous performance evaluation of resource utilization, response time, and privacy protection further improves offloading decision accuracy over time. [ABSTRACT FROM AUTHOR]

An optimized container consolidation is a key factor in Mobile Edge Computing. It is a challenging problem as its complexity is NP-hard. In addition to structural properties of a network which reveal nodes features and their connectivity, we believe that the spectral properties of correlation between rows of similarity matrix of the network could be more beneficial in placement algorithm. Most existing literature focus on reducing energy consumption in cloud or edge environments based on structural properties of the given network. However, they undermine the concept of locality which could affect excessive network traffic. Our proposed method utilizes structural and spectral properties of the network to solve the problem. We expected to improve locality factor and link reduction as well as reducing the problem space by utilizing a centroid-based approach and a queue. Our proposed algorithm preserves topology architecture without consuming more energy in comparison to the existing popular algorithms. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18–31% and can be applied to scenarios with tight task deadlines. [ABSTRACT FROM AUTHOR]

With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and dynamic directions. The existing scheduling strategies based on machine learning or deep learning are only applicable to specific scenarios, which is difficult to meet the requirements of dynamic real-time scheduling in multiple scenarios. The proposed digital twin technology provides a new solution for real-time scheduling of multiple scenarios. In this paper, a digital twin-oriented multi-scene real-time scheduler (GNN-RL) is proposed. This scheduler converts task sequences into node trees and sets up two learning layers. The first layer is an online learning representation layer, which uses GNN to learn node features of embedded structures in real time to boost large instances without additional training. The second layer is the online learning policy layer, which introduces imitation learning mappings into optimal scheduling behavior policies adapted to multiple scenarios. Finally, our approach is validated in several scenarios in 3D digital twin factories, such as computationally intensive, delay-sensitive, and task-urgent scenarios. Since the scheduler proposed in this paper learns general features of the embedding graph rather than instance-specific features, it has good generality and scalability, with good generalization and scalability, outperforming other scheduling rules and schedulers on various benchmarks. [ABSTRACT FROM AUTHOR]

The task offloading of mobile edge computing (MEC) is to find proper edge or cloud resources for the execution tasks to efficiently utilize resources and meet different user's requirements. However, it is difficult for task offloading when the number of tasks and resources providers increases and to optimize multiple objectives while satisfying users' requirements. In this paper, a new multi-objective strategy based on the biogeography-based optimization (BBO) algorithm is proposed for MEC offloading to satisfied users' multiple requirements (the execution time, energy consumption and cost). In this strategy, a time-energy consumption model and a cost model are constructed for task offloading firstly. Based on these models, the BBO algorithm is introduced into task offloading for MEC to solve the problem of multi-objective optimization. Compared with the traditional strategies, the offloading strategy based on BBO decreases the average task completion time by an average of 25.03%, and compared with the technique for order preference by similarity to an ideal solution (TOPSIS) strategy, the BBO offloading strategy proposed in this paper reduces energy consumption 75% and cost by 36.9%. The proposed strategy can well solve the problem of multi-objective optimization in the task offloading for MEC. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices' energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network's objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object's movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile devices for computation-intensive mobile applications via supporting computation offloading efficiently. However, the limitation of wireless resources and computational resources of edge servers often becomes the bottlenecks to realizing the developments of MEC. In order to address the computation offloading problem in the time-varying wireless networks, the offloading decisions and the allocation of radio and computation resources need to be jointly managed. Traditional optimization methods are challenging to deal with the combinatorial optimization problem in complex real-time dynamic network environments. Therefore, we propose a deep reinforcement learning (DRL)-based optimization approach, named DRJOA, which jointly optimizes offloading decisions, computation, and wireless resources allocation. The optimization algorithm based on DRL has the advantages of fast solving speed and strong generalization ability, which makes it possible to solve combinatorial optimization problems online. Simulation results show that our proposed DRJOA in this study dramatically outperforms the benchmark methods for offloading decisions and system utility. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale of the industrial Internet, edge computing has become an indispensable part of the industrial Internet. In order to quickly handle the massive computation tasks of production devices and monitoring devices in industrial production and ensure the safety and efficiency of industrial production. This article considers the Joint Task Offloading and Resource Allocation (JTORA) optimization problem, which is measured by the weighted sum of task completion time and energy consumption, and includes the joint optimization of task offloading decision, uplink power allocation, and computing resource allocation. Also further decompose the JTORA problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem for hierarchical optimization. This paper adopts Deep Reinforcement Learning (DRL) algorithm to solve the RA problem and a heuristic algorithm for the TO problem. Simulation experimental results show that the proposed JTORA optimization scheme can significantly reduce the production time and device energy consumption in industrial production over traditional approaches. [ABSTRACT FROM AUTHOR]

The emergence of Mobile Edge Computing (MEC) not only provides low-latency computing services for the User Equipment (UE), but also extends the battery life of the UE. However, the computational resources of MEC servers are usually limited, and how to efficiently offload UE's task and allocate the resources of MEC servers has become a research hotspot in MEC. In this paper, we develop an improved arithmetic optimization algorithm (IAOA) to optimize the convergence speed and convergence accuracy of the arithmetic optimization algorithm. Then a task offloading algorithm based on IAOA is designed to reduce the cost of offloading tasks in the framework including a single MEC server and multi-UE. The proposed algorithm jointly optimizes the task offloading strategy of the UEs and the resource allocation of the MEC server, meanwhile, models the weighted sum of delay and energy consumption as the system cost, with the goal of minimizing the system cost while satisfying the delay and energy consumption constraints of the tasks. Simulation results show that the proposed algorithm can effectively reduce the system cost and achieve a performance improvement of up to 20% compared with the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system's performance. The model is parameterizable, allowing adjustments to the components' resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed. [ABSTRACT FROM AUTHOR]

The deep integration and rapid development of new technologies such as edge computing and the Internet of Things (IoT) have brought new security challenges. Relying on mainstream security evaluation standards and security architecture, a security evaluation model for multi-source heterogeneous systems based on IoT and edge computing is proposed. An attribute-oriented security strategy decomposition system is established, and an indicator tree suitable for multi-source heterogeneous systems is constructed through mapping relationships. A comprehensive weighting method based on the optimal function theory is proposed. The weights are determined by the method based on indicator correlation and the method based on the amount of indicator information. According to the optimal function theory, the indicator weights obtained by the two methods are combined and optimized to obtain the optimal combination weight of the indicators. A comprehensive indicator measurement method based on the second-order grey cluster model is proposed, to deal with this situation of lack of persuasiveness in security ranking caused by the similar degree of membership in grey cluster evaluation. With this method, grey clustering of the initial score set can be used to obtain security evaluation results, and to rank the security evaluation results. According to the effectiveness evaluation indicator based on the discrimination coefficient and end-to-end consistency, the validity of the evaluation results is analyzed. Based on experiments, the model's good performance in security evaluation and security ranking is verified. [ABSTRACT FROM AUTHOR]

AI-driven edge computing is a development trend of the Industrial Internet of Things (IIoT). However, most existing solutions ignore the limitations of flexibility, security, and real-time performance caused by the rigid architecture of industrial control systems and the "end-to-end" computing paradigm of IIoT. This paper proposes an edge computing scheme for AI-driven IIoT. Specifically, we design a novel software-defined industry control architecture to enhance the flexibility and security of IIoT edge systems. The architecture decouples the software and hardware of Industrial devices by virtualization and industrial modeling technologies, which improves the flexibility and programmability of IIoT edge systems and alleviates the privacy issue of industrial data. Moreover, we adopt a new edge computing method, dispersed computing, to AI-driven IIoT to achieves better real-time performance and resource utilization. The proposed computing method optimizes the computing and networking of AI-driven industrial applications jointly by a multiobjective optimization scheduling algorithm. We also evaluated the performance of our scheme through experiments. [ABSTRACT FROM AUTHOR]

In the harsh mobile edge computing (HMEC) environment, there are many dynamic changes such as interference from noise, the impact of extreme environmental conditions, and the mobility of devices. It is a great challenge to the online realtime task offloading scheduling for delay-sensitive applications. However, the dynamic changes in HMEC environment have been ignored in almost all previous studies. Therefore, we propose the federated deep reinforcement learning-based online task offloading and resource allocation (FD-OTR) algorithm to address the task offloading in HMEC. Additionally, the FD-OTR algorithm performs resource allocation for offloaded tasks. The task offloading part of FD-OTR algorithm can be divided into two layers: the deep reinforcement learning (DRL) layer and the federated learning (FL) layer. The online algorithm in the DRL layer can adapt to the dynamic HMEC environment and make real-time task offloading decisions. In the FL layer, federated learning with low communication overhead is used for model aggregation to form a better global model. Resource allocation is done by using a new meta-heuristic algorithm: the Sparrow Search Algorithm (SSA). Finally, the simulation results demonstrate that the FD-OTR algorithm performs well in HMEC. The convergence speed of FD-OTR is three times faster than the centralized method. Compared to the baseline algorithms, FD-OTR reduces costs by 14.3%, 11.2% and 9.28%, respectively. [ABSTRACT FROM AUTHOR]

In order to relieve the pressure of the local devices, some tasks need to be offloaded to the edge computing system. In order to guarantee the service level of the edge computing system, virtual machines (VMs) deployed in the edge server should be as active as possible. For light load application scenarios such as smart home, to meet the quality of service of tasks while coping with occasional VM failures, we propose a task offloading strategy with real-time VM repair in an edge computing system. Accordingly, we establish a repairable queueing model with multiple servers and VM-dependent failure rates. By using quasi-birth-death process and matrix-geometric solution method, we give the average latency of tasks in steady state. Considering that the number of tasks and the VM state are fully unobservable, we construct profit functions to study the Nash equilibrium arrival rate of tasks and the socially optimal arrival rate of tasks. In order to maximize the social profit, we present a pricing policy for tasks in the edge computing system with the proposed strategy. [ABSTRACT FROM AUTHOR]

Mobile edge computing requires more and more high-performance servers, resulting in increased energy consumption. As an effective means to reduce energy consumption, virtual machine placement (VMP) has been widely studied. In the edge computing environment, as the number of terminal device requests continues to increase, the scale of VMP becomes larger and larger, and existing research algorithms may take a long time to converge. The reason is that as the number of VMs increases, the search space of the policy becomes larger and the agent needs to interact with the environment for a longer time to make the best decision. In addition, existing research methods only consider reducing energy consumption, rarely consider the response latency of virtual machines, and almost ignore the dynamic changes of the edge environment. To overcome these drawbacks, we propose a virtual machine placement algorithm based on meta-reinforcement learning, which consists of an inner and outer loop. The inner loop designs a deep reinforcement learning algorithm combined with the order exchange and migration mechanism to generate the best decision, and the outer loop provides meta-strategy parameters for the inner loop based on meta-learning to accelerate the convergence capability of the inner loop, thereby obtaining efficient virtual machine placement decisions quickly from a new environment. Through simulation experiments, we demonstrate that our approach effectively reduces the energy consumption of the edge server and the response latency of VMs at different problem sizes compared to the three baseline algorithms. At the same time, it quickly adapts to the new environment with only a small number of gradient updates. [ABSTRACT FROM AUTHOR]

Maximizing the utility of large-scale Internet of Things (IoT) is an important issue in practice. In this paper, we attempt to improve the performance of IoT edge computing system (IoT ECS) from a perspective of task value, which decays with execution time. We consider such an IoT ECS which is composed of multiple mobile equipments (MEs) and edge nodes (ENs). Each ME holds a task with a certain task value decay curve (TVDC) that decides whether to execute locally or at the edge nodes. Further more, we use a system utility function to describe the overall performance of the network by trading-off task value, calculation cost, and network risk factor. We convert the IoT ECS utility maximization problem into a multi-knapsack and multi-dimensional knapsack problem and prove it's NP-hard. Then, we adopt the piecewise linearization method to conquer the non-linear, even non-convex challenge of the objective function, and develop a distributed task offloading scheme based on Lagrange relaxation framework (TOS-LRPLM). Finally, numerical experiments prove the effectiveness of our proposed strategies and its superiority to others. [ABSTRACT FROM AUTHOR]

Nowadays, Smart Healthcare Systems (SHS) are frequently used by people for personal healthcare observations using various smart devices. The SHS uses IoT technology and cloud infrastructure for data capturing, transmitting it through smart devices, data storage, processing, and healthcare advice. Processing such a huge amount of data from numerous IoT devices in a short time is quite challenging. Thus, technological frameworks such as edge computing or fog computing can be used as a middle layer between cloud and user in SHS. It reduces the response time for data processing at the lower level (edge level). But, Edge of Things (EoT) also suffers from security and privacy issues. A robust healthcare monitoring framework with secure data storage and access is needed. It will provide a quick response in case of the production of abnormal data and store/access the sensitive data securely. This paper proposed a Secure Framework based on the Edge of Things (SEoT) for Smart healthcare systems. This framework is mainly designed for real-time health monitoring, maintaining the security and confidentiality of the healthcare data in a controlled manner. This paper included clustering approaches for analyzing bio-signal data for abnormality detection and Attribute-Based Encryption (ABE) for bio-signal data security and secure access. The experimental results of the proposed framework show improved performance with maintaining the accuracy of up to 98.5% and data security. [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing is the specific application of Mobile Edge Computing in vehicular scenarios. In this work, to efficiently provide on-demand computational resource access for Mission Vehicles, Road Side Units (RSUs) and Cooperative Vehicles are both mounted with edge servers. To further improve the performance of this system, we firstly study the dynamic and cooperative task caching between RSUs. Under the control of the central controller, our caching scheme fully utilizes edge servers' storage resources. Then, we resort to Deep Deterministic Policy Gradient (DDPG) algorithm to adapt to the time-varying wireless environment. Moreover, we propose the DDPG-OCWB algorithm to improve the convergence performance of DDPG. Finally, numerical results verify the improved performance of our proposed algorithm. Based on a detailed comparison with existing research schemes, it is evident that our scheme dramatically reduces system delay while utilizing edge servers' energy efficiently. [ABSTRACT FROM AUTHOR]

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. [ABSTRACT FROM AUTHOR]

With the increasing advancements in the Internet of Things (IoT) and the growing production of tasks by IoT devices, the demand for cloud computing centers has become more critical than ever. The energy consumption in cloud computing servers has a significant impact on the overall costs and environmental pollution. This article addresses the task allocation problem to cloud computing servers with the aim of reducing energy consumption in those servers while maintaining Quality of Service (QoS). Evolutionary algorithms have been employed to solve this NP-hard problem. In this paper, a novel version of Equilibrium Optimization algorithm is defined and used for finding good solutions for this problem. In the proposed algorithm, a tournament operator is introduced to control selection pressure and enhance the algorithm's exploration capability during local optima convergence, added to the EO algorithm. The utilization of this operator in the proposed algorithm eliminates the need for sorting all search agents at each iteration, resulting in reduced execution time. The simulation results indicate that the proposed algorithm has demonstrated a 24% improvement in performance compared to existing algorithms in solving the task allocation problem to servers in cloud computing environments. [ABSTRACT FROM AUTHOR]

Edge analytics receives an ever-increasing interest since processing streaming data closer to where they are produced, rather than transferring them to the cloud, ensures lower latency while also addresses data privacy issues. In this work, we deal with the placement of analytic tasks to heterogeneous geo-distributed edge devices while targeting three objectives, namely latency, quality of results, and resource utilization. In addition, we investigate this multi-objective problem in a multi-query setting, where we jointly optimize multiple analytic jobs while dynamically adjusting task placement decisions. We explore multiple solutions that we thoroughly evaluate; interestingly, in a multi-query setting, all three objectives can be improved simultaneously by our proposals in many cases. Furthermore, we develop a proof-of-concept prototype using Apache Storm. Our solutions are thoroughly evaluated and shown to yield improvements by more than 50% compared to advanced baselines targeting only latency. Moreover, our software prototype managed to achieve speedups of up to 6 × over the Resource Aware Apache Storm scheduler, with an average speedup of 2.76 × , when deployed over a small-scale infrastructure. [ABSTRACT FROM AUTHOR]

Green edge computing aims to reasonably allocate computing resources on the premise of ensuring the reliability of information services. The computing power gap between terminal and edge server makes the traditional encryption algorithm waste too much energy when dealing with massive redundant data. How to improve encryption efficiency and reduce the computing consumption of massive data terminal equipment on the premise of ensuring data security is one of the goals of green edge computing. We proposed a data compression and encryption scheme based on compression sensing, which greatly reduces the computing consumption of computing limited data terminals; At the same time, the hyper chaotic system is used to further encrypt the data by Arnold transform, bitwise XOR and data random scrambling. In order to solve the problem that compressed sensing can not accurately recover data, we designed a nonlinear encryption scheme based on Chinese Remainder Theorem as a supplement. The simulation results show that the proposed data compression and encryption method is effective and reliable, which has high security performance, compression ability for text data and images, and high recovery ability when the compression ratio is more than 0.5. [ABSTRACT FROM AUTHOR]

With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these "mobile" edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) presents complex challenges in task offloading, especially within Mobile Edge Computing (MEC) environments and under conditions of data insecurity. Addressing these challenges, particularly the balance between energy consumption, cost, and latency, necessitates intelligent decision-making strategies. This study introduces a blockchain-based offloading framework that leverages a Double Deep Q-Network (DDQN), a cutting-edge algorithm of Deep Reinforcement Learning (DRL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision-Making (MCDM) technique. This approach strategically frames the MEC-based offloading problem using a Markov Decision Process (MDP), allowing the DDQN to learn the optimal policy. Subsequently, TOPSIS is applied to finalize offloading decisions based on predetermined criteria. The proposed framework significantly outperforms other strategies, demonstrating improvements in energy consumption, cost, and latency in the most complex scenarios by at least 35.83%, 4.65%, and 14.17%, respectively. These results underscore the efficiency and robustness of the presented multi-faceted approach in addressing the inherent complexities of task offloading within IoT environments. [ABSTRACT FROM AUTHOR]

D2D-assisted mobile edge computing has attracted much attention due to its great potential to reduce the pressure on edge servers and improve the utilization of mobile devices. However, users are usually reluctant to share resources with neighbor devices, considering the limitations on resources and energy, and the concerns about security and privacy. In this paper, we firstly propose a blockchain-based resource-sharing system to solve the trust issues between unfamiliar participants. Under this system, we jointly consider resource trading and computation offloading to reduce energy consumption on the premise of maximizing the utilities of both providers and requesters. Resource trading is modeled as Stackelberg Game, and computation offloading is formalized as a mixed optimization problem. Finally, we propose the corresponding algorithms to efficiently get the optimal trading and offloading strategy. The simulation results show the effectiveness of incentivizing more participants. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT) devices and the growth of users' demand for computation and real-time services, artificial intelligence has been applied to reduce the system cost for future network systems. To meet the demand of network services, the paradigm of edge networks is increasingly shifting towards the joint design of computation, communication and caching services. This paper investigates a multi-user cache-enabled mobile edge computing (MEC) network and proposes an intelligent particle swarm optimization (PSO) based offloading strategy with cache mechanism. In each time slot, the server selects one file among multiple ones to pre-store, according to the proposed cache replacement strategy. In the next time slot, the requested files by the users needn't to be computed and offloaded, if these files have been cached in the server. For the files that have not been cached in the server, PSO algorithm is adopted to find an appropriate offloading ratio to implement the partial offloading. Simulation results are finally presented to validate the proposed studies. In particular, we can find that incorporating the proposed cache replacement strategy into the computation offloading can effectively reduce the system latency and energy consumption for the future networks. [ABSTRACT FROM AUTHOR]

Technology advancement in semiconductors enables integration of large number of cores on a single chip that leads to the design and development of Multi-Processor System on Chip (MPSoC). In Network-on-Chip (NoC) based MPSoCs dozens and even 100s of cores exist on single chip. To improve the energy efficiency of such systems, workload consolidation has been extensively used. NoC based multicore systems face various challenges due to energy efficient workload consolidation. Workload consolidation leads to imbalanced task mapping that increases the power density of the chip. Consequently, NoC based MPSoCs suffer from overheating and thermal issues leading to faster aging of certain processing cores, which results in lower average lifetime reliability of the system. The uneven aging of some cores exacerbates time constraints that may further affect lifetime reliability of critical systems, such as embedded medical cyber physical systems. Existing reliability aware task mapping algorithms attempt to increase lifetime of MPSoCs by avoiding hotspots that increase communication overhead leading to application performance degradation. Therefore, in this article we propose algorithmic solutions that optimize aging of the system while reducing the communication overhead. Experiments show that proposed communication and aging aware algorithm reduces the communication overhead while marginally sacrificing lifetime reliability compared with some of the baseline and state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

As the application complexity increases, it is desired to offload the computationally intensive tasks from the end devices to the cloud. In the cloud, virtual machines (VM) conduct the computations on behalf of the end devices. To reduce the processing delay of the offloaded tasks at the cloud, VMs may be created in advance by anticipating the arrival of offload requests, which is termed 'VM pre-provisioning'. For certain types of applications, however, long delay or high cost for the communication between the end devices and the cloud is not acceptable, and edge computing is used as an alternative. Since the edge servers are typically equipped with far smaller resource capacity than the cloud servers, the conventional VM pre-provisioning becomes inefficient. In this paper, we focus on VM pre-provisioning for resource-constrained edge servers. The VMs to be pre-provisioned are carefully chosen to effectively utilize the limited resources of the edge servers. When there exist pre-provisioned VMs that match the offload requests, those VMs are cloned to serve the offload requests, which enables quick start of the VMs. Extensive evaluation shows that the proposed scheme outperforms the existing schemes in the resource-constrained edge computing environment, particularly when the application diversity is high. For realistic evaluation, we collect a large-scale measurement trace of global users and use it in conjunction with the existing Google cluster trace. [ABSTRACT FROM AUTHOR]

Software defined networks and the Internet of Things (IoT) are two major and emerging developments in networking that have much in common and their survival depends on each other. Software Defined Networking (SDN) is one of the 5G enabling innovations that can help design complex, manageable, cost-effective and adaptable networks. On the other hand, Edge Computing (EC) will do automatic analytical computing on data from sensors, network switches, or other devices instead of waiting for data to be returned to a centralized data store. The IoT also requires a decentralized Internet, as the demand for real-time data analysis is growing and centralized processing systems are being overrun. So, it is necessary to provide a solution to encompass the advantages of EC, IoT and SDN simultaneously, as an integrated platform. In this research, after presenting a brief explanation about the key points of the SDN, IoT, EC and related concepts, and recent articles in this regard are investigated. This systematic literature review (SLR) study focuses on different frameworks and platforms that meet the mentioned requirements by considering the advantages of integrating EC, SDN, and IoT technologies. This platform provides centralized management of heterogeneous devices and architectures and supports the problem of resource limitations in IoT. The short output of this literature contains the following: (1) presenting a summary of review studies and research articles in this area which have been published from 2013 till 2021; (2) providing some key technical questions; (3) presenting some different technical classifications to categorizing the characteristics and features of EC and SDN in IoT; (4) discussing the key challenges; (5) Presenting future directions for research and open issues. [ABSTRACT FROM AUTHOR]

Compared with the traditional network tasks, the emerging Internet of Vehicles (IoV) technology has higher requirements for network bandwidth and delay. However, due to the limitation of computing resources and battery capacity of existing mobile devices, it is hard to meet the above requirements. How to complete task offloading and calculation with lower task delay and lower energy consumption is the most important issue. Aiming at the task offloading system of the IoV, this paper considers the situation of multiple MEC servers when modeling, and proposes a dynamic task offloading scheme based on deep reinforcement learning. It improves the traditional Q-Learning algorithm and combines deep learning with reinforcement learning to avoid dimensional disaster in the Q-Learning algorithm. Simulation results show that the proposed algorithm has better performance on delay, energy consumption, and total system overhead under the different number of tasks and wireless channel bandwidth. [ABSTRACT FROM AUTHOR]

The synergy of edge computing and Machine Learning (ML) holds immense potential for revolutionizing Internet of Things (IoT) applications, particularly in scenarios characterized by high-speed, continuous data generation. Offline ML algorithms struggle with streaming data as they rely on static datasets for model construction. In contrast, Online Machine Learning (OML) adapts to changing environments by training the model with each new observation in real-time. However, developing OML algorithms introduces complexities such as bias and variance considerations, making the selection of suitable estimators challenging. In this challenging landscape, ensemble learning emerges as a promising approach, offering a strategic framework to navigate the bias-variance tradeoff and enhance prediction accuracy by amalgamating outputs from diverse ML models. This paper introduces a novel ensemble method tailored for edge computing environments, designed to efficiently operate on resource-constrained devices while accommodating various online learning scenarios. The primary objective is to enhance predictive accuracy at the edge, thereby empowering IoT applications with robust decision-making capabilities. Our study addresses the critical challenges of ML in resource-constrained edge computing environments, offering practical insights for enhancing predictive accuracy and scalability in IoT applications. To validate our ensemble's efficacy, we conducted comprehensive experimental evaluations leveraging both synthetic and real-world datasets. The results indicate that our ensemble surpassed state-of-the-art data stream algorithms and ensemble regressors across a range of regression metrics, underlining its superior predictive prowess. Furthermore, we scrutinized the ensemble's performance within the realm of auto-scaling for Virtual Network Function (VNF)-based applications situated at the network's edge, thereby elucidating its applicability and scalability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Efficient privacy-preserving algorithms are used in internet of things (IoT) applications, virtualization and edge computing environments to decrease the high data disclosure risk. However, due to the strict security principles of current privacy-preserving algorithms, they ignore the time consumption. On the other hand, most existing privacy-preserving algorithms suffer from the overgeneralization problem, creating unnecessary information loss. Thus, two privacy-preserving algorithms are proposed in this paper for IoT applications in the virtualization and edge computing environment to address this problem and balance the information loss and disclosure risk. We first propose an overall metric to measure the performance of privacy-preserving algorithms and balance the information loss and disclosure risk. Second, the proposed algorithms can significantly decrease the time consumption by utilizing cached anonymized datasets. Moreover, the proposed algorithms also optimize the distribution of anonymized datasets and decrease the information loss by deleting the small equivalent classes. Experimental results show that our proposed algorithms are relatively fast, only consuming 34.3% operation time of current algorithms. Next, we tested the usability of anonymized datasets by putting them into an IoT application. The two datasets generate similar results, indicating that the proposed algorithms can satisfy the information loss requirements of IoT applications. [ABSTRACT FROM AUTHOR]

Recent advances in smart connected vehicles and intelligent transportation systems are based on the collection and processing of massive amounts of sensor data. There are various internal sensors integrated into modern vehicles that are useful to monitor multiple mechanical and electrical systems, and the shift to semi-autonomous vehicles adds outward-facing sensors such as cameras, lidar, and radar. Low-cost, dependable sensing, connectivity, computational capacity, and powerful analytics are ushering in a new era of vehicle context sensing and vehicular network (VANET). However, due to latency, bandwidth, cost, security, and privacy issues, as well as the growing capabilities of edge computing devices, it is necessary to examine both edge and cloud computing in order to make informed judgments based on their contexts and performances. So, in this paper, we have proposed an architecture that focuses on an intelligent transportation system based on both Cloud and Edge computing in order to monitor and analyze vehicle sensor data and its environment; known as monitoring and analyzing as a service. Our proposed architecture is able to react in real-time to any vehicle context changes in a dynamic, adaptive, and autonomous way. It generates many plans adapted to the vehicle context that will be useful for future research to achieve a self-learning and self-adaptive system. This suggested architecture helped us improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. Also, we have reduced latency and increased bandwidth. Finally, we have approved the performance of our architecture based on different evaluation metrics. [ABSTRACT FROM AUTHOR]

In mobile edge computing environment, due to resources constraints of edge devices, when user locations continue changing, the network will be delayed or interrupted, which affects the quality of user's service access. Previous studies have shown that deploying multiple microservice instances with the same function on multiple edge servers through container technology can solve this problem. However, how to choose the optimal microservice instance from multiple servers in a cloud-edge hybrid environment needs to be further investigated. This paper studies the selection of microservices problem based on the dynamic and heterogeneous characters of the cloud-edge collaborative environment, which is defined as a microservice selection and scheduling optimization problem (MSSP) to minimize users' service access delay. To cope with the complexity of cloud-edge collaborative environment and improve learning efficiency, MSSP is regarded as a Markov decision-making process, a Deep Deterministic Policy Gradient algorithm for microservice selection called MS_DDPG is then proposed to solve this problem, and the microservice selection strategy experience pool is established in MS_DDPG. Performance evaluations of MS_DDPG based on a real dataset and some synthetic dataset have been conducted, and the results show that MS_DDPG outperforms the other three baseline algorithms. In terms of average access delay, MS_DDPG is reduced by 23.82%. We also validate the performance of MS_DDPG by increasing the number of user requests, and the results also show that MS_DDPG obtains better performance in scalability. [ABSTRACT FROM AUTHOR]

This study introduces delay-aware cloudlet placement with a modified k-means (DACP-mk) algorithm to address the complexities of mobility and optimize cloudlet placement in a mobility-enhanced environment. DACP-mk enhances the traditional k-means algorithm by incorporating modified regional and global delay matrices to enable the simulation of a delay-aware cloudlet movement and placement strategy using a cluster-based approach within a smart healthcare platform. An optimized cloudlet position is generated to achieve a uniform distribution of mobile devices among placed cloudlets, maximize device coverage, and minimize network access delay within the cloudlet coverage distance. The proposed algorithm determines the optimal positions for cloudlets at coordinates (161.90, 103.24), (080.22, 139.52), and (080.29, 051.21) with an increase of 02.42%, 00.54%, 38.71%, and 49.00%, respectively, in terms of global mobile device coverage compared to modified k-means Manhattan (mkMAN), k-means, energy-efficient cloudlet placement method (ECPM) and enhanced adaptive cloudlets placement with covering algorithm (EACP-CA), and a decrease of 00.71% and 58.27% in global network access delay compared to mkMAN and k-means algorithms. The proposed algorithm offers enhanced healthcare support and cost-effective services, improving the Quality of Service (QoS) and ultimately contributing to saving lives. [ABSTRACT FROM AUTHOR]

Edge computing enables portable devices to provide smart applications, and the indoor positioning technique offers accurate location-based indoor navigation and personalized smart services. To achieve the high positioning accuracy, an indoor positioning algorithm based on particle filter requires a large number of sample particles to approximate the probability density function, which leads to the additional computational cost and high fusion delay. Focusing on real-time and accurate positioning, an edge computing-enabled green multi-source fusion indoor positioning algorithm called APFP is proposed based on adaptive particle filter in this paper. APFP considers both pedestrian dead reckoning (PDR) signals in mobile terminals and the received signal strength indication (RSSI) of Bluetooth, and effectively merges the error-free accumulation of trilateral positioning and the accurate short-range positioning of PDR, which enables mobile terminals adaptively perform particle filter to reduce the computing time and power consumption while ensuring positioning accuracy simultaneously. Detailed experimental results show that, compared with the traditional particle filter algorithm and the map-constrained algorithm, the proposed APFP reduces fusion computing cost by 59.89% and 54.37%, respectively. [ABSTRACT FROM AUTHOR]

In recent years, serverless computing has received significant attention due to its innovative approach to cloud computing. In this novel approach, a new payment model is presented, and a microservice architecture is implemented to convert applications into functions. These characteristics make it an appropriate choice for topics related to the Internet of Things (IoT) devices at the network's edge because they constantly suffer from a lack of resources, and the topic of optimal use of resources is significant for them. Scheduling algorithms are used in serverless computing to allocate resources, which is a mechanism for optimizing resource utilization. This process can be challenging due to a number of factors, including dynamic behavior, heterogeneous resources, workloads that vary in volume, and variations in number of requests. Therefore, these factors have caused the presentation of algorithms with different scheduling approaches in the literature. Despite many related serverless computing studies in the literature, to the best of the author's knowledge, no systematic, comprehensive, and detailed survey has been published that focuses on scheduling algorithms in serverless computing. In this paper, we propose a survey on scheduling approaches in serverless computing across different computing environments, including cloud computing, edge computing, and fog computing, that are presented in a classical taxonomy. The proposed taxonomy is classified into six main approaches: Energy-aware, Data-aware, Deadline-aware, Package-aware, Resource-aware, and Hybrid. After that, open issues and inadequately investigated or new research challenges are discussed, and the survey is concluded. [ABSTRACT FROM AUTHOR]

Real-time accurate traffic congestion prediction can enable Intelligent traffic management systems (ITMSs) that replace traditional systems to improve the efficiency of traffic and reduce traffic congestion. The ITMS consists of three main layers, which are: Internet of Things (IoT), edge, and cloud layers. Edge can collect real-time data from different routes through IoT devices such as wireless sensors, and then it can compute and store this collected data before transmitting them to the cloud for further processing. Thus, an edge is an intermediate layer between IoT and cloud layers that can receive the transmitted data through IoT to overcome cloud challenges such as high latency. In this paper, a novel real-time traffic congestion prediction strategy (TCPS) is proposed based on the collected data in the edge's cache server at the edge layer. The proposed TCPS contains three stages, which are: (i) real-time congestion prediction (RCP) stage, (ii) congestion direction detection (CD2) stage, and (iii) width change decision (WCD) stage. The RCP aims to predict traffic congestion based on the causes of congestion in the hotspot using a fuzzy inference system. If there is congestion, the CD2 stage is used to detect the congestion direction based on the predictions from the RCP by using the Optimal Weighted Naïve Bayes (OWNB) method. The WCD stage aims to prevent the congestion occurrence in which it is used to change the width of changeable routes (CR) after detecting the direction of congestion in CD2. The experimental results have shown that the proposed TCPS outperforms other recent methodologies. TCPS provides the highest accuracy, precision, and recall. Besides, it provides the lowest error, with values equal to 95%, 74%, 75%, and 5% respectively. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users' quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users' preferences while estimating the popularity of content. Knowing users' preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users' preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In recent times, the Internet of Things and big data analytics have become a hot research topic in mobile edge computing (MEC) desires wide-ranging research works for intelligent decision making. In this view, this paper designs an intelligent outlier detection with machine learning empowered big data analytics (IODML-BDA) for MEC. The proposed model involves an adaptive synthetic sampling-based outlier detection technique to eradicate the existence of outliers. Besides, the oppositional swallow swarm optimization (OSSO) based feature selection technique is used to choose an effective set of features. Finally, long short-term memory based classification model is employed to identify different class labels. The design of OSSO algorithm for feature selection with ADASYN technique for big data analytics show the novelty of the work. A comprehensive experimental analysis is carried out on GPS trajectories, movement prediction, water treatment plant, hepatitis, and Twitter datasets to confirm the experimental results. The experimentation outcomes pointed out that the proposed IODML-BDA model achieves the higher accuracy of 0.9735, 0.9816, 0.9798, 0.9896, and 0.9912 on the applied datasets. [ABSTRACT FROM AUTHOR]

Mobile edge computing requires more and more high-performance servers, resulting in increasing energy consumption. As a well-established way to reduce energy consumption, virtual machine placement can be utilized to optimize the cost of wide-deployment of servers. However, traditional strategies tend to focus on single indicators, there are few existing research taking time delay limitation into account while solving energy consumption problems. In this paper, we propose a brand new method to settle the problems listed above, which is able to reduce the placement time of virtual machine and energy consumption. First, considering the excellent performance of bat swarm algorithm in NP-hard problem, we introduced the second order oscillation factor to avoid premature convergence, and combined the order exchange and migration local search technology. we proposed the OEMBA algorithm, which integrates underutilized servers to save energy. Subsequently, an improved Long Short-Term Memory model is utilized to fasten the placement of virtual machines and reduce latency based on historical data. Our results indicate that the improved learning model can save energy consumption and reduce placement latency. [ABSTRACT FROM AUTHOR]

Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%. [ABSTRACT FROM AUTHOR]

The promising technique of Wireless Power Transfer (WPT) to end devices and sensors has gained the attention of researchers recently. Mobile edge computing (MEC) is also succeeding from Cloud Computing due to its minimum latency constraints. In MEC, smart devices offload computation intensive tasks to the MEC server which achieves low latency. However, limitations exist for smart device battery lifetime and task execution delay because of an effective decision in the offloading scenario necessitating joint WPT and MEC offloading. The joint WPT and MEC offloading decisions are based on real time application requirements, placement of Base Station (BS) with power transfer capabilities for smart devices, and offloading opportunities in the MEC. To meet the energy consumption requirement, a BS integrated with MEC server and power transfer capability transfers wireless power to end devices as an incentive and offers opportunities for offloading. Transferring wireless power to end devices effectively meets the requirement of smart devices while extending battery lifetime. This article encapsulates the state of art work in methodologies of offloading in MEC and WPT to end nodes. We consider MEC offloading techniques with WPT and real time application requirements while summarizing related studies. We formulate a taxonomy of joint WPT and offloading in MEC. We compare the state-of-the-art studies based on parameters identified from taxonomy. Finally, we provide the challenges and debate future research directions relevant to the domain of joint MEC-WPT. [ABSTRACT FROM AUTHOR]