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Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been growing quickly since the 5 G era arrived, which has boosted the quantity of data stream and computational demands. Ultra-dense Edge Computing (UDEC) is a result of the growing popularity of Mobile Edge Computing (MEC) and Ultra-dense Network (UDN), two vital technologies with bright futures. Task offloading is viewed in the UDEC network as a successful strategy to offer mobile users low-latency and flexible computing capabilities. However, the edge cloud's constrained computing capacity and mobile users' dynamically shifting computing requirements present difficulties for the reasonable scheduling of computing requests to the edge cloud. The Power Allocation (PA) problem for mobile users to address this issue. This problem attempts to use quasiconvex technique to minimize energy consumption. Next, the Joint Request Offloading and Resource Scheduling (JRORS) problem is treated as a mixed-integer nonlinear programming problem to minimize request delay and improve welfare. The request offloading problem and the computational resource scheduling challenge are two subproblems of the JRORS problem that can be further broken down, the JRORS issue is therefore viewed as a dual decision-making problem. A Ratio of Power Consumption to Welfare (RCW) problem through mathematical modeling. An improved snake optimizer (SO) is therefore proposed, which was added with oscillation factor to optimize the location of the weight parameter in the location update and the variation of food production. It can enhance the global search capability and local search ability. By conducting simulation experiments, divide the mobile users into nine groups for testing, select energy consumption, welfare, and RCW as the objective functions, respectively, and compare them with other swarm optimization algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant-Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Gray Wolf Optimizer (GWO). The improved algorithm achieved minimal energy consumption and optimal welfare in nearly every test case, contributing to the enhanced effectiveness and cost-efficiency of edge systems, thereby achieving the optimization of task offloading and scheduling. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an essential technique in next-generation networks to serve ultra-low latency and computation-intensity applications. At the same time, nonorthogonal multiple access (NOMA) is a technique to help multi-user service, saving energy and increasing spectrum efficiency. In this study, we investigate the NOMA MEC-based wireless Tactile Internet of Things (IoT) network and propose the optimization algorithms for system and users performance: We propose a network model consisting of a MEC server at the access point that supports computation for two sensor clusters in the Tactile IoT environment. We analyze the performance of the system and cluster heads (CHs) using the successful computation probability (SCP). Asymptotic SCP at high SNRs was analyzed and compared by us to give a better view of the system's behavior. Then, we maximize the SCP of the proposed system and simultaneously maximize the SCP of the CHs to clarify the performance trade-off problem in the NOMA MEC network by proposing low-complexity meta-heuristic algorithms. Monte-Carlo simulation results show that our proposed approach can significantly improve system performance by up to 30% compared to OMA traditional methods. [ABSTRACT FROM AUTHOR]

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users' QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation, optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks. The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, the new paradigm is faced with security and privacy challenges due to the diversity and the limited capability of edge components. In particular, data privacy is one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in edge computing, which not only gives the strong privacy guarantee, but also provides a certain amount of data utility. Firstly, we design a preservation framework to implement private random decision trees satisfying local differential privacy. Secondly, we present the concrete implementations of algorithms and the corresponding task that each participant needs to undertake. Thirdly, we analyze the key factors to influence privacy and utility, including the allocation of data and privacy budget. Fourthly, we give the improved algorithms to further increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework. [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]

The implementation of the "Internet +" policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site. [ABSTRACT FROM AUTHOR]

As the IoT devices are highly ubiquitous and connected, their computational tasks can be processed on the Multi-access edge computing (MEC) servers because of the IoT devices' limited battery, computing power and storage capacities. Due to the heterogeneity of IoT devices and MEC devices' limited capabilities, tasks should be offloaded efficiently to MEC servers mainly and then to the Clouds (fog nodes) if necessary for fulfilling tasks' computational requirements. The proposed solution presents a task offloading decision using base stations of the network and queued task scheduling. The base station, which is the edge orchestrator, will decide on where to offload (edge servers or cloud) or compute locally. For handling heterogeneous tasks, a maximum tolerable latency is included. It is shown that a 3% to 7% improvement in the number of failed tasks, more than 7.4% in the number of successfully completed tasks, an improvement by more than 0.85 s in the average execution time and more than 7% in network communication, which directly impacts the energy of edge devices. The simulation results show that this work performed better in decreasing the number of failed tasks due to latency, decreasing energy consumption and minimizing the average execution time of tasks. [ABSTRACT FROM AUTHOR]

Driven by the Quality of Experience (QoE) demands for video analysis applications within contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles (IoV), networks are confronted with numerous challenges, including, but not limited to, the requirements for ultra-high bandwidth, ultra-low latency, reliability, and stability. Mobile Edge Computing (MEC) as a technology aims to decentralize computational and storage resources closer to data sources and end-users, meeting the demands of computation-intensive and latency-sensitive video analysis applications and thereby effectively addressing QoE requirements. This has led to a new intersection, edge video offloading, which has attracted widespread attention. However, to date, there are but a few extant papers on this topic with limited relevance. To bridge this gap, we offer a comprehensive review of the state-of-the-art approaches in computational offloading techniques applied to video data, innovatively revisiting existing task offloading technologies from the perspective of mobility of edge server nodes, including solutions based on both fixed and dynamic MEC servers. Additionally, we explore the differences in video offloading techniques between scenarios involving a single MEC server and those involving multiple MEC servers. Meanwhile, open challenges are presented to direct future research in this field. [ABSTRACT FROM AUTHOR]

Computation offloading, as an efficient and promising computing paradigm for mobile edge computing (MEC), provides optimal computation allocation between MEC servers and Internet of Things (IoT) devices. However, in modern wireless communications, most of the computational tasks are latency-sensitive, presenting an efficiency challenge for offloading services. Moreover, to make the best use of computation resources, it remains an issue to ensure stable optimal offloading for the allocation of computation resources. In this paper, a novel adaptive deep reinforcement learning-based offloading (ADRLO) framework is developed to achieve a stable optimal computation revenue with low execution latency for MEC offloading. Structurally, we implement a residual connected deep neural network (DNN) to generate a series of offloading strategies and use them to obtain the global system computation rate. Especially, the strategy that achieves the maximum computation rate is then selected as experience to support the DNN training, forming a closed-loop deep reinforcement learning (DRL) mechanism. To further enhance the efficiency and stability of the MEC offloading, an adaptive regulator is presented for the framework parameters including the size of action space and the DNN learning rate. Numerical results show that, compared to existing state-of-the-art approaches, our proposal achieves optimal computation revenue, exhibits better convergence performance, and significantly reduces the execution latency by almost 50%. [ABSTRACT FROM AUTHOR]

Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. [ABSTRACT FROM AUTHOR]

Landslides are phenomena widely present around the world and responsible each year of numerous life loss and extensive property damage. Researchers have developed various methodologies to identify area of high susceptibility of landslides. However, these methodologies cannot predict 'when' landslides are going to take place. Indeed, Wireless Sensors Network (WSN), Internet of Things (IoT) and Artificial Intelligence (AI) offer the possibility to monitor in real-time parameters causing the triggering factors of rapid landslides. In this paper, we suggest a real-time monitoring of landslides in order to precociously alert population in dangerous situation by means of a warning system. The novelty of this paper is the coupling of wireless sensors network and a multi-agent system deployed on an edge AI-IoT architecture by means of Kubernetes and Docker. [ABSTRACT FROM AUTHOR]

Today, an emerging paradigm of edge computing, called Multi-Access Edge Computing (MEC), is widely used in a broad range of applications. MEC has been recently used as a driving technology for 5G networks. However, MEC encompasses some security problems, such as communicating with untrusted third-party service providers commonly known as location-based service (LBS) providers. In this paper, we propose a novel fuzzy-based misbehavior detection system for Internet of Things (IoT) in a 5G-based MEC environment to solve the previous problems. Our proposed system is comprised of a multi-tier architecture that is linked by IoT gateways and mobile core network. In our architecture, we employ a novel mechanism that embeds a security entity called Regional Certificate Authority in a 5G macro base station. This security entity acts as a passport for the IoT gateway to communicate with other security entities present in our system that are located in the core infrastructure. Besides that, our system utilizes a distributed certificate revocation mechanism for malicious LBS providers via simultaneously updating the internal blocklists employed by more than one system entity. In addition, we adopt in our architecture a novel penalty system for malicious LBS providers, which is used for penalizing providers in case proved to be malicious as well as actively isolating them from the network. On the other hand, our system provides conditional anonymity for the incident's reporter. Also, we present various security features maintained by the proposed system to thwart well-known attacks as well as we demonstrate efficiency and robustness of our system. Finally, we provide the overhead of our system and compare it with other state-of-the-art approaches where results show that the proposed system relatively incurs the least overhead. [ABSTRACT FROM AUTHOR]

The huge amount of exchanged data in the internet between entities and the quick development of edge computing has increased users frustration about the future generations. We expect a future where transactions will be based on clouds and virtual machines, gathered from sensors and IoT devices and processed by different artificial intelligence algorithms or agents. The speed and backlash change in the world has driven researchers to work on privacy and security for entities' information and transactions. Different concepts arise and still in their early stage as digital identity, self sovereign identity, global unique identifier and identity of things (IDoT). Therefore, motivated by the recent explosion of interest around Blockchains, we examine whether they make a good fit for the Identity Internet of Things (IDoT) sector. Blockchain a major distributed peer-to-peer network where non-trusting members can interact together without a need for a trusted third party, it actually make available many advantages to providers and consumers and solve data protection features lifelong a transaction existing. As being immutable, transparent and trustful platform the Blockchain allows managing identities and privacy of its nodes information. Our contribution is a new architecture eventually using a public Blockchain and creating a smart identity wallet. It contains standard node data and will also integrate proactive data behavior and the reactive one. The main target is to protect users from sybil attack at early stage. We will define a new digital wallet based on entities behavior in order to prevent 51% attack and Sybil attack [ABSTRACT FROM AUTHOR]

With the advancement of the 5G networks, edge computing (EC) assisted Internet of Things (IoT) based applications demand real-time computation and high-volume data-intensive services. Due to the heterogeneity and limited resources of the edge nodes (ENs), and dynamic resource demand of the IoT applications, it is challenging to place the IoT services into the available ENs by ensuring performance measurements on quality of services (QoS). In this paper, a novel quantum-inspired particle swarm optimization-based service placement (QPSO-SP) algorithm is proposed for EC environment. The QPSO-SP is intended to achieve desired service placement while optimizing throughput, energy consumption, delay, and computation load of the system. Quantum particle (QP) is designed to represent a complete solution for IoT service placement in an EC environment. Decoding of the QP is done by using a novel double-hashing technique. The fitness function uses throughput, delay, energy consumption, and load balancing parameters. Extensive simulation is performed and comparison is done with the standard existing algorithms. The parametric study, Taguchi method is conducted. The statistical analysis, ANOVA, followed by Friedman test is also done. The simulation results indicate that the proposed QPSO-SP outperforms existing works in terms of energy consumption, delay, throughput, and load balancing. • A Quantum-Inspired PSO is proposed to address IoT service placement. • Quantum particle is designed to represent complete IoT service placement solution. • The decoding of quantum particle is done using a novel double-hashing technique. • Fitness function considers throughput, energy consumption, delay and load balancing. • Simulation shows efficacy of the proposed work with statistical analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G networks and the IoT(Internet of Things), intelligent networked vehicles have attracted more and more attention. Due to the mobility characteristics of the vehicle and the range of communication between the roadside unit and the vehicle, it is limited. Therefore, there will be a situation where the vehicle has left the communication range of the RSU before the computing task is completed. So a novel approach based on V2I direct transmission and V2V auxiliary transmission is proposed. Firstly, by estimating the vehicle movement, the amount of data returned, the maximum transmission delay and the effective life of the link, the transmission strategy is determined. Secondly, when designing the auxiliary transmission strategy, the factors such as the speed, direction and position of the vehicle are comprehensively considered, and these factors are measured by the stability effect value. When constructing the return link, a greedy selection method is adopted, and the neighbor node with the maximum stability and efficiency value is selected as the relay node. Experimental results show that the data return approach proposed in this paper is better than other algorithms in terms of transmission delay and packet delivery rate. [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]

Asynchronous Federated Learning (AFL) has been introduced to improve the efficiency of FL by reducing the latency of Machine Learning (ML) model aggregation, particularly in the Internet of Things (IoT) environment. Meanwhile, decentralized FL, such as leveraging blockchain and directed acyclic graph (DAG)-based ledgers, also has drawn much attention to the integration of FL owing to the security benefit against single-point-failure and Byzantine fault tolerant consensus. We observe that the inherent network asynchrony of DAG-based ledgers is beneficial for implementing asynchronous FL particularly in edge computing domains, even though there is no existing survey work that provides a fundamental overview of such integration. This paper surveys on the integration of asynchronous FL with DAG, which we call AFL-DAG, as a promising approach to realize the intersection of decentralized FL and asynchronous FL. Motivated by the lack of a concrete taxonomy of asynchronous FL and a global model, especially with decentralized FL, we introduce universally applicable terminologies and the extensive classification method of FL in terms of asynchrony. Based on the proposed taxonomy, we present a generic system model of AFL-DAG for edge computing applications. To provide a horizontal overview and cover fundamental concepts in AFL-DAG, we identify four critical design factors and the state-of-the-art solutions are discussed accordingly. Future directions to achieve a practical AFL-DAG are also highlighted. Finally, we explore the opportunities of AFL-DAG by investigating a popular edge computing application, the on-device crowdsourcing, and provide a high-level evaluation of AFL-DAG compared to blockchained-FL. [ABSTRACT FROM AUTHOR]

In the era of 5G and beyond, Mobile Edge Computing (MEC) has emerged as a technology that seamlessly integrates wireless networks and the Internet, enabling low-latency and high-reliability computing services for mobile users. A crucial prerequisite for deploying MEC is the strategic selection of edge server locations that can satisfy computing demands and improve resource utilization. In this paper, we study the problem of efficient and intelligent dynamic edge server placement considering time-varying network states and placement costs. We present a long-term dynamic decision-making process that models edge server placement as a Markovian decision process and dynamically adjusts server layout. To achieve intelligent decision-making, we propose two deep reinforcement learning-based algorithms. Namely, the DBPA algorithm based on D3QN and the PBPA algorithm based on PPO, which significantly improve the efficiency and performance of model training. We also propose a novel method for transforming network states into network inputs using heat map and grayscale map to enhance the agent's learning efficiency. Experimental results demonstrate that our proposed algorithms achieve intelligent and dynamic placement of edge servers, and outperform comparison algorithms by 13.20% to 61.84% and 23.09% to 66.32%, respectively. [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]

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments. [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]

Edge computing is important for the efficiency of an entire system. It makes the Internet of Things technologies more sustainable and improves users' experience and satisfaction. Generally, owing to the heterogeneity of edge servers, computing resources are unbalanced, resulting in energy wastage. Existing studies, however, have only focused on incentive mechanisms for edge collaboration; therefore, the willingness of edge servers to share resources needs further exploration. Furthermore, few studies have considered more than one edge service provider (ESP) in the incentive mechanism, which is unsustainable for selfish ESPs to optimize their computing resource allocation process. To deal with this issue, a gamified process is introduced into edge computing. Then, a system throughput maximization problem is proposed to utilize idle resources through gamification-based edge cooperation. This is proven to be a non-deterministic polynomial-time hard problem. To resolve this problem, a greedy algorithm is proposed. The experimental results demonstrate that the proposed algorithm improves the throughput by ranges of [36.9%, 87.2%] and [10.0%, 13.1%] when compared with that from no gamification and an existing greedy algorithm, respectively, when considering the influential factors. The proposed gamified mechanism helps to improve system efficiency by reducing idle computing resources and motivating ESPs to process share during production, which further enhances the smart utilization of limited global resources. • Promote external cooperation among edge service providers for process sharing. • A gamified mechanism is introduced to incentivize edge computing sharing. • A system throughput maximization problem is proposed to utilize idle resources. • A greedy algorithm for throughput maximization is proposed. [ABSTRACT FROM AUTHOR]

In recent years, the evolving Internet of Things (IoT) technology has been widely used in various industrial scenarios, whereby massive sensor data involving both normal data and anomalous data are generated. However, the anomalies involved might have a great impact on the industrial sustainability. Actually, in most scenarios, it is of vital importance to detect the anomalies and processed accordingly in real time. In previous work, some methods, which analyze the collected industrial sensor data on cloud computing center to detect anomalies, casually increase both the transmission pressure of bandwidth and the computing pressure on cloud center. By contrast, some other methods based on edge computing detect anomalies on edge nodes, but they only take the anomalies of sensor data from single-source time series into account and ignore the correlation anomalies between the data from multi-source time series. In this paper, we first present an anomaly detection model in distributed edge computing. Then, the Edge Computing based Anomaly Detection Algorithm (ECADA), which can detect the anomalies from both single-source time series or multi-source time series is proposed. Finally, we conduct a series comparison experiments in order to demonstrate the effectiveness of the algorithm proposed in the paper. And the experimental results demonstrate that both the anomalies from single-source time series and the anomalies from multi-source time series can be accurately detected by the proposed algorithm. Specifically, it performs more efficient and effective when there exist the anomalies of new type. • The anomalies involved in sensor data from time series might have a great impact on the industrial sustainability. • This paper presents an anomaly detection model in distributed edge computing. • An Edge Computing based Anomaly Detection Algorithm (ECADA) is proposed. • When there exist the anomalies of new type, ECADA can detect them accurately. [ABSTRACT FROM AUTHOR]

As blockchain technology merges with Mobile Edge Computing (MEC) and the Internet of Things (IoT), we encounter increasing challenges such as high energy consumption and latency due to the network's scale and complexity. Therefore, optimizing these communications and effectively managing computational task offloading is crucial, with data security being an essential concern. This paper studies these issues by examining networks that already operate within blockchain environments, where the complex process of transaction validations intensifies the demands on the system. We propose a new solution through the A3C-AHP offloading framework, which integrates the Asynchronous Advantage Actor-Critic (A3C) algorithm with the Analytic Hierarchy Process (AHP). This combination optimizes task offloading for multiple IoT users in MEC settings. The A3C algorithm improves decision-making by allowing agents to learn and adjust policies simultaneously, while AHP helps systematically evaluate and prioritize different network elements. Our framework adeptly manages the trade-offs between energy use, latency, and security when implemented in a blockchain context. Simulation results show that the A3C-AHP model notably enhances performance, cutting energy consumption by approximately 15.4 %, computational overhead by 49.5 %, latency by about 9.1 %, and operational costs by 8 % for networks with 100 users, highlighting its effectiveness and practical relevance. [ABSTRACT FROM AUTHOR]

Climatic chamber are enclosures where the ambient conditions, i.e. the temperature and humidity, are finely controlled. This one can play multiple roles such as the cultivation of plants (phytotron), the breeding of insects or habitat for exotic animals. The availability on the market of a wide variety of equipment makes it difficult to share settings and operating recipes. In this paper, we propose a versatile and automated modular climatic enclosure system that can be adapted according to the use cases and available material. In this paper, we use IoT device virtual representation, data validation by means AI algorithm, the semantic description of material, and ontology locally deployed from the cloud allowing to automate the local installation with container technology. [ABSTRACT FROM AUTHOR]

Smart Poultry acquires data from aviaries by means of sensor network at reduced intervals of time (every minute) that generate hundred thousands of data. The conjunction of Internet of Things and Artificial Intelligence open the field of the real-time monitoring of poultry and, advance analytics and automation if data is from high quality. In this paper, we propose a scalable monitoring of a poultry achieved with open hardware wireless sensors network and software. We use a Gated Recurrent Unit, an artificial intelligence algorithm to validate and predicate environmental parameters. [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]

With the introduction of edge computing into the field of Internet of Things (IoT), the Cognitive Internet of Things (CIoT) has emerged as the next-generation solution for trust and intelligent reasoning in the IoT. That also puts blockchain, with its unique consensus mechanism, transparency and trustworthiness, on the stage of IoT applications. At present, not much research is focused on blockchain's application in CIoT, whose development is to a large extent restricted by the inefficiency of the consensus algorithm. Considering the characteristics of CIoT, a multi-stage consensus algorithm of EIoT-PBFT is proposed on the basis of PBFT algorithm, which includes the Grouping stage, Scoring stage and Consensus reaching stage. EIoT-PBFT meets the IoT edge computing setup by adopting a two-phase improved PBFT algorithm and a scoring mechanism based on both location and reputation, thus achieving a great increase in consensus efficiency. Evaluation results show that EIoT-PBFT takes 36.4% less time than PBFT for a single consensus, and the performance remains stable over the 2500 node configurations we set up. Moreover, at a scale of 1000 nodes, the number of edge nodes to be configured to reduce the number of system communications by 90% compared to the PBFT algorithm is only 5, making blockchain more customized for CIoT settings. [ABSTRACT FROM AUTHOR]

F ederated L earning (FL) enabled edge computing has been widely used in training complex deep learning models by coordinating various heterogeneous resources in I ndustrial I nternet of T hings (IIoT). However, the existing FL solutions may cause long training time or huge energy consumption for resource-constrained terminal or edge nodes. To address this issue, we propose an adaptive edge intelligence offloading framework based on F ederated S plit L earning (FSL) and R einforcement L earning (RL), named FSLEdge, for IIoT systems. To begin with, a S plit L earning (SL) approach was added to FL to reduce the training time and energy consumption by allowing local devices to offload some training layers into the edge servers while ensuring the protection of private data. Additionally, FSLEdge adopts RL to find the optimal split point to adaptively identify which is the best layers of the deep learning models should be offloaded into the edge servers with the goal of reducing the training time and energy consumption. Experimental studies are conducted under two different datasets: one public, CIFAR10, and one private, GridDataset, a transmission line fault detection dataset in smart grid domain. And the results show that the training time and energy consumption of the system by our proposed FSLEdge can be reduced by 10% and 21%, respectively, compared to the classical FL, without sacrificing accuracy. [ABSTRACT FROM AUTHOR]

With the advent of 5G and the promise of 6G, edge computing is becoming increasingly important. Edge computing involves the proximity of computational resources to the devices and sensors responsible for data generation, reducing delay and improving real time data processing capabilities. EdgeX is an openly available framework designed for edge computing. that provides a platform for building IoT services and applications. In this paper, we evaluate the EdgeX open edge server for use in IoT services. We first deploy a web service application into the edge using a specific port, and then test the EdgeX server to see whether it can be used as a reliable and effective open-source edge server. Our results show that the EdgeX server is capable of supporting IoT services and can be used as an open-source edge server for a variety of applications. We conclude that EdgeX is a valuable resource for developing edge computing solutions, and that the open edge server can significantly contribute to the advancement of forthcoming IoT service innovations. [ABSTRACT FROM AUTHOR]

The edge computing-based Internet of Thing (IoT) has been growing drastically by taking advantage of edge computing which provides great assistance for IoT and mobile devices to complete sophisticated tasks. However, the rapid development leads to the neglect of security threats in edge computing platforms and their enabled applications, which has been one of the main limitations in the smart cities. In this article, we propose a fault and attack detection model for edge computing-based IoT systems to ensure the security of edge computing. Since the risk and fault cases are very imbalanced compared to normal cases, this paper proposes a novel fault detection algorithm by using the imbalance classification technique. Utilizing deep learning techniques, the proposed algorithm overcomes data overlapping problems occurring in many traditional oversampling methods and achieves outstanding performance. With this novel imbalance classification algorithm, the proposed security prediction model achieves pretty good performance on real-world edge computing applications. [ABSTRACT FROM AUTHOR]

• Wireless powered multi-access edge computing network is a future trend of Internet of Things in agriculture context. • The convolutional operation-based approach is able to handle mutual inference between different wireless devices with one AP. • The method based on convolutional operations can make offloading decisions quickly and effectively. Prompt, uninterrupted, accurate and reliable agricultural information plays an important role for agricultural decision making, which requires stable and efficient data transmission frame. Wireless powered multi-access edge computing (MEC) has recently emerged as a promising paradigm to improve the capability of data transmission with low-power network. Applying wireless powered MEC to agricultural information monitoring will benefit the development of smart agriculture. Management and scheduling of different applications (i.e., computing offloading) is one of the most important influencing factors for the performance of wireless powered MEC network. Of these, the mutual interferences of different WDs are an important factor which should be considered. To address this issue, an online computation offloading method based on convolutional operation is presented in this paper. And a fundamental wireless powered MEC network including one access point (AP) and multiple WDs is constructed to validate the efficacy of this approach. Impacts of three factors (including network size (i.e., the number of WDs), training intervals and memory size) and different application scenarios are studied and their influences on the performance of convolutional operation-based approach are analyzed. Additionally, convolutional operation-based method is compared with the other two offloading approaches (including DROO (a Deep Reinforcement learning-based Online Offloading) and CD (Coordinate Descent) algorithm). The results indicate that the convolutional operation-based approach is more suitable for large-scale wireless powered MEC networks (e.g., the number of WDs is more than 30) with moderate memory size (=512) and training interval (=50). [ABSTRACT FROM AUTHOR]

IoT malware dissemination seriously exacerbates the decline of IoT system availability, which deteriorates the users experience. To address the issue, we first predict the optimal IoT malware dissemination strategy based on a signaling game for edge computing-assisted IoT systems. We then develop an algorithm to obtain the solution of the signaling IoT availability assessment game, which is to factually reflect IoT malware dissemination in practice and reasonably express the probability of IoT system nodes being successfully infected by malware. Thus, a state transition diagram of IoT system nodes can be further designed, illustrating intercommunication among all six states during IoT malware dissemination. Upon this state transition diagram, we represent the state transition probability of IoT system nodes in each state utilizing a Markov matrix, and attain the steady-state availability of an IoT system node from reliability theory. Consequently, we deduce metrics to access the steady-state availability of the entire IoT system under typical star-, tree-, and mesh topologies, respectively. We also design the corresponding IoT system availability assessment algorithm from the view of practice. In this manner, an availability assessment mechanism for edge computing-based IoT systems with malware dissemination is constructed. Experiments demonstrate the influence of IoT system features on predicting IoT malware dissemination probability and assessing the steady-state availability of three typical IoT system topologies. Our results can be utilized to lay a theoretical foundation for guiding the implementation of higher availability for edge computing-assisted IoT systems with malware dissemination. [ABSTRACT FROM AUTHOR]

The large increase in the number of Internet of Things (IoT) devices have revolutionised the way data is processed, which added to the current trend from cloud to edge computing has resulted in the need for efficient and reliable data processing near the data sources using energy-efficient devices. Two methods based on low-cost edge-IoT architectures are proposed to implement lightweight Machine Learning (ML) models that estimate indoor environmental quality (IEQ) parameters, such as Artificial Neural Networks of Multilayer Perceptron type. Their implementation is based on centralised and distributed parallel IoT architectures, connected via wireless, which share commercial off-the-self modules for data acquisition and sensing, such as sensors for temperature, humidity, illuminance, CO 2 , and other gases. The centralised method uses a Graphics Processing Unit and the Message Queuing Telemetry Transport protocol, but the distributed method utilises low-performance ARM-based devices and the Message Passing Interface protocol. Although multiple IEQ parameters are measured, the training and testing of ML models is accomplished with experiments focused on small temperature and illuminance datasets to reduce data processing load, obtained from sudden spikes, square profiles and sawteeth test cases. The results show a high estimation performance with F-score and Accuracy values close to 0.95, and an almost theorical Speedup with a reduction in power consumption close to 37% in the distributed parallel approach. In addition, similar or slightly better performance is achieved compared to equivalent IoT architectures from related research, but error reduction of 35–76% is accomplished with an adequate balance between performance and energy efficiency. • Computing methods based on Artificial Neural Networks of Multilayer Perceptron type (ANN-MLP). • Lightweight Machine Learning (ML) models that estimate IEQ parameters, such as temperature, humidity, illuminance, and CO 2. • Implementation of centralised and distributed parallel IoT architectures using COTS devices connected via a wireless LAN. • The centralised method uses a GPU and the MQTT protocol, and the parallel method uses ARM-based devices and the MPI protocol. • Experimental validation focused on temperature tests with high estimation performance (F-score, Accuracy) and energy efficiency. [ABSTRACT FROM AUTHOR]

• Swarm intelligence-based computation offloading algorithm. • Improved response time for IoTs. • Efficient resource management of Edge computing devices. The main aim of edge computing is to facilitate the low power computational IoT sensors in processing heavy tasks. Sensors should be enough efficient to offload the required task and get back the computation results in the stipulated time. However, acquiring the desired quality of services (QoS) is a challenging task. Therefore, efficient resource management is inevitable to reduce the response time and communication cost. In this paper, a computation offloading algorithm based on nature-inspired multi-objective bacterial foraging optimization (MO-BFO) is proposed for load management over edge servers. The performance of the proposed MO-BFO algorithm is quantitatively evaluated against standard ant colony optimization (ACO), particle swarm optimization (PSO), and round-robin (RR) scheduler. The detailed results show that the proposed algorithm reduces the response time, communication cost, and ensures effective load management compared to its counterparts. Smart Offloading for IoT Sensors in Edge Computing Using Bacterial Foraging Optimization [Display omitted] [ABSTRACT FROM AUTHOR]

• Access control is an essential mechanism in collaborative environments, which guarantees the security of shared resources. With the intertwinement of emerging technologies likes edge computing, cloud, Artificial Intelligence (AI), and Internet of Things (IoT), access control will encounter its innovation and development shortly. Whereas, traditional access control did not consider the adaptive and fine-grained requirements in edge computing. In addition, the integrity of the accessed data is not considered in the traditional access control scheme. 1) In this paper, inspired by the technologies in AI and cloud, an adaptive access control scheme based on trust degrees is proposed. The trust degrees of users and the quality of data are defined. On this basis, the trust degrees-based adaptive access control framework is proposed. 2) Moreover, in the proposed scheme, technologies in AI and cryptography are converged. In particular, the ciphertext policy attribute-based encryption (CP-ABE) is integrated with the classification and recommendation algorithms in AI, thereby providing adaptive and fine-grained properties access control in edge computing. 3) Moreover, the integrity of the accessed data is guaranteed by the auditing technique in cryptography. Finally, the proposed access control scheme is implemented by using the PBC library. 4) For supporting the efficient computation in edge computing, all types of elliptic curves provided by the PBC library are tested. The experimental result shows that Type D curve is the most efficient in implementing the proposed scheme. Access control is an essential mechanism in collaborative environments, which guarantees the security of shared resources. With the intertwinement of emerging technologies likes edge computing, cloud, Artificial Intelligence (AI), and Internet of Things (IoT), access control will encounter its innovation and development shortly. Whereas, traditional access control did not consider the adaptive and fine-grained requirements in edge computing. In addition, the integrity of the accessed data is not considered in the traditional access control scheme. In this paper, inspired by the technologies in AI and cloud, an adaptive access control scheme based on trust degrees is proposed. The trust degrees of users and the quality of data are defined. On this basis, the trust degrees-based adaptive access control framework is proposed. Moreover, in the proposed scheme, technologies in AI and cryptography are converged. In particular, the ciphertext policy attribute-based encryption (CP-ABE) is integrated with the classification and recommendation algorithms in AI, thereby providing adaptive and fine-grained properties for access control in edge computing. Moreover, the integrity of the accessed data is guaranteed by the auditing technique in cryptography. Finally, the proposed access control scheme is implemented by using the PBC library. For supporting the efficient computation in edge computing, all types of elliptic curves provided by the PBC library are tested. The experimental result shows that Type D curve is the most efficient in implementing the proposed scheme. [ABSTRACT FROM AUTHOR]

The construction industry suffers from severe air pollution problems, and there lacks of a unified platform for integrated management of different information formats to achieve effective and rapid pollution detection. Hence, it is necessary to improve the quality of air monitoring utilizing more visible, integrated and intelligent methods. In this context, this study aims to establish a distributed sensor network based on a limited perception range and extended the monitoring information based on the IFC standard to realize the visualization. Then a monitoring system architecture based on edge computing was then established, and a sensor network interface module based on BIM was then designed to realize the fusion of multi-module sensor data. After that, the integrated dataset was developed in the prediction model of pollutants, and the ARIMA model was utilized in the air pollution emergency management platform to predict air pollutants concentration. The results showed that edge computing and BIM can be integratedly applied in a unified platform, which significantly improves the prediction, early warning and emergency management capabilities of air pollutant at construction sites. This study makes contributions to handling and predicting air pollutants issues and promoting the environmental management in the construction sites. • EC, BIM and predictive models were integratedly applied in a unified platform. • A distributed sensor network was established based on a Limited Sensing Range. • Monitoring information was extended based on IFC to achieve information sharing. • EC was developed to realize the multi-protocol conversion and data preprocessing. • LSTM model and ARIMA model were constructed to predict air pollutant concentration. [ABSTRACT FROM AUTHOR]

Smart Grid technology has been considered an attractive research issue due to its efficiency in solving energy demand, storage, and power transmission. The integration of IoT technology in the Smart Grids is a critical way to accelerate the digitization of the power grid and is useful for the efficient performance of the energy grid infrastructure. For efficient real-time data analysis and decision-making, the Internet of Things will incorporate various communication systems seamlessly. To achieve efficient communication between all Internet of Things, devices are expected to use multiple means, including smart sensors, cable and wireless communication. Improved Internet of Things sensor technologies and connectivity could theoretically prevent or minimize the potential to natural disaster transmission lines, improve transmission power capacity and reduce economic losses. A smart grid is a variety of sensors, devices, and data sets that continuously capture high-resolution data equal to individual IoT conditions. A vast amount of data is one of the biggest challenges on the Internet of Things. Edge Computing is trying to process data close to linked sensors to address this problem, where the data is gathered and processed. This paper aims to investigate the edge computing solutions for the smart grid. A comprehensive review of both emerging issues and edge computing in the Smart Grid environment is discussed and explained. There are two primary components to the energy sharing process among Prosumers: information/digital technologies and Artificial Intelligence Scheduling Techniques. Each of them is mentioned in detail to discuss the Prosumer smart Grid. Furthermore, Edge Computing and classifications (cloudlet, Fog computing and Multi-Access) are among the suitable network methods mentioned in this paper. Some techniques and methodologies have been extensively covered to improve reader awareness of the Prosumer smart grid system. [ABSTRACT FROM AUTHOR]

• Microservice-based edge computing apparatus in smart distribution transformer area. • Computing resource demand and supply model. • Sequential logic-based evolution algorithm. • Increase of vertical elastic rate improves computing resource utilization and delay. • Effectiveness in both stochastic and concurrent simulation scenarios. With the development of the Internet of Things technology in the smart grids, terminal devices such as advanced sensors and smart meters present a trend of massive access in distribution network, which poses great challenges to the information perception, analysis and processing ability of distribution automation system. The microservice-based edge computing apparatus is a novel technology in the smart distribution transformer area to improve the adaptability of the distribution automation system. Research on modeling methods and computing resource capacity planning methods for the microservice-based edge computing apparatus has become an urgent and significant research topic in the modern electrical power system. Therefore, this paper proposes a configuration method of computing resources for the microservice-based edge computing apparatus in the smart distribution transformer area. The suitability of the microservice technology and edge computing apparatus is analyzed, and a model of microservice-based edge computing apparatus is proposed. Inspired by power demand and supply in the power system, a computing resource demand and supply model is proposed to represent the computing resource demand of power Internet of Things services and the computing resource supply of the microservice-based edge computing apparatus. Furthermore, a sequential logic-based evolution algorithm is designed to determine the configured capacities of computing resources for the microservice-based edge computing apparatus considering multi-aggregated service flows. Finally, simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) is a relatively new subject in which intelligence comes from computer systems. Edge Computing (EC) processes data near the SG information source — potentially attenuating latency problems. While Internet of Things (IoT) collects, transmits and stores data, EC allows the employment of data analysis with Machine Learning (ML) at its edge. This work uses a systematic mapping methodology to encompass 15081 works related to four SGs technologies: data analysis, EC, IoT, and context awareness. With 37 works after the filtering process, this review revealed that most papers use one to three approaches, while only two use all four technologies. The results also indicate that EC has been extensively used in SG solutions, with 22 selected studies. Distinctively, only 9 works use context awareness, which may indicate a path for future developments in SG. The study also allowed the learning of 7 lessons that are presented in this paper. [Display omitted] • We review the state of the art related to smart grid computational techniques. • The paper covers internet of things, edge computing, and context-aware computing. • The work considers literature of the last decade through a well-defined methodology. • The study covers 15081 papers, answering one general and eight focused issues. • The paper proposes a taxonomy for smart grid Technologies. [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]

Network-accessible task offloading is for low latency; offline task offloading is for low cost. Offline devices cannot directly access service nodes due to lack of resources. Accordingly, the latter involves more steps and optimization variables such as: where to offload tasks, how to allocate computation resources, how to adjust offloading ratio and transmit power, and such optimization variables and hybrid combination features are highly coupled with each other. In this paper, we first formulate a Mixed Integer Nonlinear Programming Problem (MINLP) for such task offloading under energy and delay constraints. Furthermore, we decompose it into two subproblems so as to efficiently solve the formulated MINLP, and design a low-cost and low-complexity Joint Optimization for Energy Consumption and Task Processing Delay (JOET) algorithm to optimize selection decisions, resource allocation, offloading ratio and transmit power adjustment. We carry out extensive simulation experiments to validate JOET. Simulation results demonstrate that JOET outperforms many representative existing schemes in quickly converge and effective reduction of energy consumption and delay. Specifically, the average energy consumption and the average delay have been reduced by 15.93% and 13.70%, respectively, and the load balancing efficiency has increased by 10.20%. [ABSTRACT FROM AUTHOR]

• Propose a privacy-preservation signaling game for edge-based IoT networks. • Obtain optimal privacy preservation strategies from theoretical and practical views. • Address the problem of obtaining the convergent game equilibrium in practice. • Have a lower computational complexity available to real-life implementation. Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends requests to gather private data from an edge-computing-based IoT cloud storage system across the edge nodes. To solve this problem, a privacy-preservation signaling game for edge-computing-based IoT networks is proposed to characterize the interactions between an IoT node and its corresponding edge node when managing an IoT resource-grant system. Optimal privacy preservation strategies for edge nodes are then theoretically derived. A signaling Q-learning algorithm is then designed to address the problem of achieving convergent equilibrium and game parameters from a practical perspective. The theoretical results are validated using simulations that focus on two statistical points (i.e., the optimal probability of an IoT node requesting maliciously and the posterior probability of an IoT node being malicious). By comparing the proposed signaling Q-learning algorithm with the greedy algorithm benchmark, the proposed algorithm is shown to more effectively decrease the optimal probability of an IoT node sending malicious requests. Thus, privacy preservation for edge-computing-based IoT cloud storage systems can be strengthened. [ABSTRACT FROM AUTHOR]

• The vibration edge computing architecture of a shield tunnel was proposed. • A prototype vibration test enhanced by the edge computing was implemented. • The vibration characteristics can be viewed on mobile devices simultaneously. • The dominant frequency of vibration in the low frequency band is determined. Edge computing can reduce the latency induced by the long communication path from end users to the cloud center in the Internet of Things (IoT). In this paper, we implement a vibration test of an underwater shield tunnel using a wired accelerometer network enhanced by edge computing to verify a vibration edge computing architecture that we propose. The cloudlet for edge computing can temporarily store raw data and analyze them simultaneously. It can also serve as the hub of the accelerometer. The acquired acceleration data and their results can be viewed on mobile devices in real time from the cloudlet. The results of measurements show that the amplitude of vibrations of components of the tunnel decreased distinctly from the pavement lane to the segment lining and the base. Moreover, the dominant frequency of a low-frequency band was determined by the tunnel–soil interaction, and represents the overall vibration characteristics of the tunnel–soil system along the Z-axis. The quickly analyzed online results agreed well with those of an offline simulation in terms of general trends. This study highlights the potential of the proposed vibration edge computing architecture for underwater shield tunnels. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of Internet of Things, the centralized cloud faces the performance problems caused by remote network access and real-time services. To ensure good performance, edge computing paradigm emerges, and requires a secure authentication between edge host and cloud server to safeguard resources against any illegitimate access. Securing edge host authentication is an effective method and cornerstone to protect the information security in edge computing. Existing methods most recognize an entity's identity by password only once at the time of initial stage, which may lead to security problem. Continuous authentication as a novel authentication modality has the ability to alleviate this issue. Unlike the authentication operating merely at point-of-entry, a continuous edge host authentication method is proposed on the basis of electromagnetic radiation which reflects the behavioral characteristics of edge host with heterogeneous hardware. We demonstrate the feasibility of the proposed method through analysis of success rate from ten thousand waterfall spectrograms, and experimental results show that the average success rate of 95% has been achieved by support vector machine combining multiple features. [ABSTRACT FROM AUTHOR]

Scientific research into methodologies and algorithms to improve support for medical diagnoses remains in high demand on the agenda. Computer-aided diagnostic systems that use the Internet of Things (IoT) enable greater accessibility and integration between patients and specialists. One of the steps covered by IoT systems is the segmentation of Regions of Interest (ROI) in digital images. The challenge to segment these ROI in IoT systems is to apply methods that have low computational and storage costs with reliable results. Thus this work proposes the use of edge computing with a new approach based on active contours to target the ROI in medical images called FLog Parzen Level Set (FPLS). The method can be divided into two stages. First is the initialization of the seed point using Parzen Window and clusterization using Floor of Log. Second the growth and refinement of the region with probabilistic estimates of the regional contour using the Parzen Window. The proposed method was evaluated using the metrics of Accuracy, Precision, Sensitivity, Specificity, Matthews Coefficient Correlation, Hausdorff distance, Dice, and Jaccard Similarity Coefficient. The stable and satisfactory results can be highlighted despite the low computational times and costs. The proposed method was submitted to stroke, lung, and skin disease datasets. The proposed method achieved the fastest mean segmentation time of 1.64s for the three datasets used in this work. The method obtained the highest values for Sensitivity (98.57%), Accuracy (98.77%) and MCC (94.73%) in the stroke dataset, the lowest value for Hausdorff distance (4.24) in the lung dataset, and the highest Dice Coefficient value (92.49%) in the skin dataset. In conclusion, the proposed method is a promising tool for an edge computing system that segments regions of interest with the high precision of a level set and a fast convergence rate. [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]