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Summary: Intrusion Detection (ID) is a critical component in cybersecurity, tasked with identifying and thwarting unauthorized access or malicious activities within networked systems. The advent of Edge Computing (EC) has introduced a paradigm shift, empowering Wireless Sensor Networks (WSNs) with decentralized processing capabilities. However, this transition presents new challenges for ID due to the dynamic and resource‐constrained nature of Edge environments. In response to these challenges, this study presents a pioneering approach: an Improved Federated Transfer Learning Model. This model integrates a pre‐trained ResNet‐18 for transfer learning with a meticulously designed Convolutional Neural Network (CNN), tailored to the intricacies of the NSL‐KDD dataset. The collaborative synergy of these models culminates in an Intrusion Detection System (IDS) with an impressive accuracy of 96.54%. Implemented in Python, the proposed model not only demonstrates its technical prowess but also underscores its practical applicability in fortifying EC‐empowered WSNs against evolving security threats. This research contributes to the ongoing discourse on enhancing cybersecurity measures within emerging computing paradigms. [ABSTRACT FROM AUTHOR]

The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions. [ABSTRACT FROM AUTHOR]

As a promising platform on the Internet of Things (IoT), the smart Internet of Vehicle (IoV) has emerged with the advent of the key connectivity to Industry 4.0, i.e. Fifth-Generation Mobile Communication (5G). However, problems with adequate battery life, powerful computing, and energy economy have hampered the development of this technology in light of the enormous increase in data traffic in 5G and 6G mobile communication networks. To address these limitations, this study proposes an Internet of Vehicles (IoV) system empowered by Edge Computing (EC), wherein intelligent vehicle nodes interact with an anchor node integrated with an EC server for data upload and download. Rather than solely focusing on enhancing the central cloud infrastructure, the integration of EC and IoT enables real-time and efficient services, thereby bolstering the storage and processing capabilities of underlying networks. By employing an offloading strategy within the Edge Computing-based Internet of Vehicles (EC-IoV) framework, users can allocate their workloads to suitable EC servers, leading to improved resource management and computational capabilities. However, challenges persist in evaluating the impact of uncertain user-EC server connectivity on offloading decision-making and mitigating potential declines in offloading efficiency.

Distributed denial of service (DDoS) is an awful cyber threat, becoming more prevalent with mature heterogeneous IoT (HetIoT) applications like intelligent agriculture, wearables, and self-driving cars. Developing intelligent intrusion detection systems (IDS) using deep learning (DL) techniques to protect HetIoT has sparked a lot of attention. Federated learning (FL) helps to train the IDS locally with local data while respecting data privacy. Edge computing (EC) enhances security by processing data closer to the edge network. Therefore, the research contributed by integrating EC, FL, and DL and the proposed Edge-FL-based IDS. The proposed Edge-FL-based IDS aims to enhance HetIoT security by safeguarding data privacy against DDoS attacks. The research developed a DL-based convolutional neural network (CNN) classifier and used the CICDDoS2019 dataset to evaluate the success rate of the proposed Edge-FL-based IDS against DDoS attacks. The research employed IID and non-IID data distributions with participant clients K =3, K =5, and K =7. The findings indicate that the proposed Edge-FL-based IDS outperforms centralized and other state-of-the-art FL models. The proposed Edge-FL-based IDS correctly detects and classifies DDoS attacks with the following accuracy: (a) 8-class IID: K =3 is 99.98%, K =5 is 99.97%, K =7 is 99.96%. (b) 8-class non-IID: K =3 is 99.97%, K =5 is 99.94%, K =7 is 99.90%. (c) 12-class IID: K =3 is 99.96%, K =5 is 99.96%, K =7 is 99.95%. (d)12-class non-IID: K =3 is 99.07%, K =5 is 96.44%, K =7 is 52.39%.

Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.

The autonomous fault diagnosis of mechanical systems is crucial to addressing smart manufacturing product issues. In this article, we propose intelligent diagnosis and prediction technologies based on acoustic emission (AE) for mechanical motors. The integration of practical technologies, such as acoustic analysis, artificial intelligence (AI), edge computing (EC), electromagnetics, communication, and other theory-based subjects, is convenient for achieving flexible changes made in response to the edge operation trend. The proposed model, developed using acoustic information links with machine learning (ML) platforms to collect acoustic information via feature extraction (FE), is novel in that it can detect system health and prevent system failures. It can inspire innovative design concepts once the above model is combined with the EC migration module. In addition, in this paper, we discuss the embedded system in smart manufacturing applications, including AE, to establish an ML framework that is trained using audio emission data. The valuable results from the proposed algorithm experiments show that the audio judgment accuracy rate can be above 90%. At the current stage, the metric accuracy and precision of mechanical motor discrimination can reach 93.5% and 0.97, respectively. In this paper, we present an analytical method for performing motor axis misalignment judgment based on tiny machine learning (TinyML) techniques, which will enable the IoT field to move toward smart energy savings. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios.

Dynamic service orchestration is becoming more and more necessary as IoT and edge computing technologies continue to advance due to the flexibility and diversity of services. With the surge in the number of edge devices and the increase in data volume of IoT scenarios, there are higher requirements for the transmission security of privacy information from each edge device and the processing efficiency of SFC orchestration. This paper proposes a kind of dynamic SFC orchestration security algorithm applicable to EC-IoT scenarios based on the federated learning framework, combined with a block coordinated descent approach and the quadratic penalty algorithm to achieve communication efficiency and data privacy protection. A deep reinforcement learning algorithm is used to simultaneously adapt the SFC orchestration method in order to dynamically observe environmental changes and decrease end-to-end delay. The experimental results show that compared with the existing dynamic SFC orchestration algorithms, the proposed algorithm can achieve better convergence and latency performance under the condition of privacy protection; the overall latency is reduced by about 33%, and the overall convergence speed is improved by about 9%, which not only achieves the security of data privacy protection of edge computing nodes, but also meets the requirements of dynamic SFC orchestration. [ABSTRACT FROM AUTHOR]

With the rapid development of Industry 4.0, the industrial cyber-physical systems (ICPS) are expected to realize the digital sensing, automatic control, and refined management in smart factories. However, limited bandwidth resources and severe industrial interference make it difficult to meet the real-time and ultrahigh reliability in edge computing (EC)-based next-generation industrial automation networks. To tackle these challenges, in this article, we propose a real-time transmission optimization scheme to accelerate EC. First, we establish a hierarchical system model for smart manufacturing and automation scenarios. Then we present a power control optimization method based on noncooperative game to alleviate interference and reduce energy consumption. Finally, we propose a path optimization scheme based on Q-learning for low-latency and ultrahigh reliability transmission requirements. Extensive simulation results reveal that our proposals perform better in terms of transmission delay and packet-loss rate compared with traditional methods, and therefore, contributes to EC deployment in ICPS. [ABSTRACT FROM AUTHOR]

The development of the Internet of vehicles (IoV) has spawned a series of driving assistance services (e.g., collision warning), which improves the safety and intelligence of transportation. In IoV, the driving assistance services need to be met in time due to the rapid speed of vehicles. By introducing edge computing into the IoV, the insufficiency of local computation resources in vehicles is improved, providing high quality services for users. Nevertheless, the resources provided by edge servers are often limited, which fail to meet all the needs of users in IoV simultaneously. Thereby, how to minimize the tasks processing latency of users in the case of limited edge server resources is still a challenge. To handle the above problem, a task offloading scheme fuzzy-task-offloading-and-resource-allocation (F-TORA) based on Takagi–Sugeno fuzzy neural network (T–S FNN) and game theory is designed. Primarily, the cloud server predicts the future traffic flow of each section through T–S FNN and transmits the prediction results to the roadside units (RSUs). Then, the RSU adjusts the current load based on the captured future traffic flow data. After the load balancing of each RSU, the optimal task offloading strategy is determined for the users by game theory. Following, the edge server acts as an agent to allocate computing resources for the offloaded tasks by $Q$ -learning algorithm. Finally, the robust performance of the proposed method is validated by comparative experiments. [ABSTRACT FROM AUTHOR]

Integrating Software-Defined Networking (SDN) and Edge Computing (EC) technologies becomes a promising approach to harness enormous data traffic in the Internet. Recent studies examined the Quality of Service (QoS) based routing paradigms for different data applications such as IoT, heterogeneous networks, and 5G network slicing in such integrated networks. In this paper, our study formulates the QoS-specific routing optimization as a minimum-cost resource allocation problem, while multi-dimensional factors of bandwidth cost, latency, and packet loss are jointly considered. Since we use the fittingness factor to help the problem solving, we propose a path selection scheme based on the measure of fittingness factor, briefly named as FitPath. The FitPath scheme can appropriately adjust network resources to maintain delivery time against various service requirements of user demands. Simulation results show that the FitPath scheme can perform better than the Open Shortest Path First (OSPF) and Greedy heuristic schemes in terms of the total of link costs, delivery latency, and packet loss rate under both of normal and heavy data traffic experiments. The effects by the FitPath scheme can be further enhanced with the top- $B$ % path selection policy in integrated network environments.

The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios. [ABSTRACT FROM AUTHOR]

This survey explores the convergence of Internet of Things (IoT) technologies with Water Distribution Systems (WDSs), focusing on large-scale deployments and the role of edge computing (EC). Effective water management increasingly relies on IoT monitoring, resulting in massive deployments and the generation of Big Data. While previous research has examined these topics individually, this work integrates them into a comprehensive analysis. We systematically reviewed 255 studies on IoT in WDS, identifying key challenges such as interoperability, scalability, energy efficiency, network coverage, and reliability. We also examined technologies like LPWAN and the growing use of EC for real-time data processing. In large-scale WDS scenarios, where vast amounts of data are generated, we highlighted the importance of technologies like NB-IoT, SigFox, and LoRaWAN due to their low power consumption and wide coverage. Based on our findings, we provide guidelines for sustainable, large-scale IoT deployment in WDS, emphasizing the need for edge data processing to reduce cloud dependency, improve scalability, and enable smarter cities and digital twins. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), various latency-critical and data-intensive applications have recently emerged to support smart traffic solutions. The sixth generation mobile networks (6G) greatly reduce the communication delay for the latency-critical tasks. However, the computing resources and execution efficiency for data-intensive tasks are still inadequate. The stationary edge computing (EC) servers, on the other hand, lack the flexibility to give service to moving vehicles. Therefore, Cybertwin is introduced in the IoV paradigm to provide a unified access point for EC. In addition, the 6G-enabled network in box (NIB) is deployed in vehicles to provide flexible computing power. However, in this article, the optimization of NIB task migration is still a challenge; thus, NIB task migration method (NTM) for IoV is proposed. The Pareto envelope-based selection algorithm is employed to determine the strategy. Finally, NTM is evaluated by a real-world dataset of the service requests. [ABSTRACT FROM AUTHOR]

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98 $\%$. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed. [ABSTRACT FROM AUTHOR]

In view of the inherent defects of traditional network protocols, unreasonable video content distribution process, content scheduling and routing mechanism need to be optimized in the current edge computing (EC) network. A centralized control EC network architecture of named data networking (NDN) is proposed in this paper. EC nodes are deployed on the edge of the network to provide caching and computing capabilities, and distribute video streams. So that transmission delay is reduced, bandwidth cost is saved, and the quality of experience (QoE) is improved. Firstly, the communication mechanism of NDN is introduced to distribute content and improve the efficiency of content distribution. In the scheme, the content distribution mechanism of video stream is improved in a distributed way. Different bit rate versions of video clips are distributed and buffered on the distributed EC nodes, so as to avoid redundant cache and save storage resources. In addition, the user interest content model is constructed. That is, video is divided into different categories according to content and user content preference model. Finally, with the limited cache and computing resources of EC nodes, the software defined network (SDN) mechanism is introduced for centralized management and control. And it provides a solution for content scheduling problem under resource constraints. In the scheme, the content distribution mode is decided by the effective arrangement of network resources, and the user's request is processed and responded cooperatively to avoid redundant cache and transmission. Based on the selection of forwarding response mode, the distributed characteristics of EC nodes and the routing and forwarding mechanism of NDN are fully utilized to provide users with response. It is shown by the experimental results that the proposed scheme optimizes the load on the network side and the experience of the client to a certain extent.

The demand for real-time communication and high performance of the Internet of Vehicles (IoV) system has caused researches to investigate new techniques in edge computing (EC). With the rapid development of the fifth- generation (5G) network, the features of low delay, high reliability and superior communication efficiency can bring historic opportunities for the development of the EC-IoV system. In the 5G-enabled EC-IoV system, extreme densification of 5G base stations (gNBs) provides rapid and reliable network access and information interaction. However, this densification also brings more complex connectivity to the network, which increases the difficulty of resource migration and scheduling for the edge devices. Thus, it is still a challenge to manage the resources of the edge devices under the premise of reducing the energy and time cost in the system while avoiding the situation of overload or underload to maintain the stability of the system. In this article, a 5G-enabled EC-IoV system framework is proposed to enhance the performance of *the existing EC-IoV system. Specific computation offloading in 5G-enabled EC-IoV system is presented under three different cases. Through the above cases, two communication modes are concluded and the corresponding resource allocation strategy is given in this article. The performance of the proposed system is evaluated and compared with the existing system. Finally, future research directions in this area are considered. [ABSTRACT FROM AUTHOR]

The development of the Internet of Vehicles (IoV) has made transportation systems into intelligent networks. However, with the increase in vehicles, an increasing number of data need to be analyzed and processed. Roadside units (RSUs) can offload the data collected from vehicles to remote cloud servers for processing, but they cause significant network latency and are unfriendly to applications that require real-time information. Edge computing (EC) brings low service latency to users. There are many studies on computing offloading strategies for vehicles or other mobile devices to edge servers (ESs), and the deployment of ESs cannot be ignored. In this paper, the placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity. In addition, the deployment problem of ESs is optimized by a many-objective evolutionary algorithm. By comparing with the state-of-the-art methods, the effectiveness of the algorithm and model is verified. [ABSTRACT FROM AUTHOR]

The edge computing paradigm introduces a hierarchy of multiple processing elements between the edge devices, the gateways, and the cloud endpoints, in order to address the Internet-of-Things (IoT) challenges in a scalable way. In order to support the computational demands of latency-sensitive video applications and efficiently utilize the available network resources, we present an edge-based resource management methodology for serving video processing applications in an IoT environment. In this letter, we propose a scalable solution for providing low-latency video analytics at the edge level, while maximizing the quality of service under device and network constraints. As use case, we evaluate the proposed methodology on a face detection video processing application. The experimental results show that the proposed methodology satisfies specific time thresholds, comparing to cloud-only, local-only, and other video-optimized offloading techniques, while taking into consideration the video resolution. [ABSTRACT FROM AUTHOR]

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power, and thus, is not well-suited for ML tasks. Consequently, this article aims to combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes to reduce data transfer. In order to process data close to the source, sensors are grouped according to locations, and feature learning is performed on the close by edge node. For comparison reasons, similarity-based processing is also considered. Feature learning is carried out with deep learning—the encoder part of the trained autoencoder is placed on the edge and the decoder part is placed on the cloud. The evaluation was performed on the task of human activity recognition from sensor data. The results show that when sliding windows are used in the preparation step, data can be reduced on the edge up to 80% without significant loss in accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, industrial Internet of Things (IIoTs) has gained attention as an emerging application area of the Internet of Things paradigm to improve efficiency and reliability in a wide class of manufacture processes. This article focuses on a combined edge-cloud computing architecture for IIoT systems, and addresses the minimization of both the mean system response time and the number of requests dropping due to a response time greater than their time deadline. The problem is formulated as a matching game with externalities between the edge computing servers, and the applications themselves. The proposed strategy adopts a discard policy with the aim at favoring the IIoT-devices requests with a not-expired deadline. Moreover, a suitable discussion of the matching stability is proposed. Finally, the strategy's validity has been confirmed by the system performance expressed in terms of mean and worst system response time and outage probability, in comparison to methods recently proposed in the literature. [ABSTRACT FROM AUTHOR]

The progress in the development in the field of information technology has brought the Internet of Things (IoT) into existence to play a crucial role in our daily lives. There are interconnected sensors or devices that can both collect and also exchange various data among themselves by employing a modern network of communication as an infrastructure that has been connected by many millions of the IoT nodes. After this, there are various applications of the IoT that may be able to provide accurate and fine-grained services to the users. Using this as a strategy which can mitigate an escalation to the congestion of resources, Edge Computing is emerging as the new paradigm that solves the needs of localized computing and the IoT. The Mobile Edge Computing (MEC) has been emerging to handle the volume of data produced and this can reach a latency of demand of the IoT applications that are intensive in terms of computation. Even though the MEC has advanced in terms of latency of service and has been solidly investigated, the efficiency of data usage and security are not identified clearly. Replication of data is well suited for improving the time taken for a response, global traffic and data sharing as even at the time of server disconnection this can be done. In this work, efficient techniques of data replication for the Mobile Ad hoc Networks (MANET) like the simple and the random applications are evaluated for improving availability of data which considers all the issues that are related to the MANET like consumption of power, availability of resource, time taken for response and consistency management. The results of the experiment have shown that a random algorithm for replication can achieve a bandwidth that is better in terms of savings compared to a simple replication algorithm. [ABSTRACT FROM AUTHOR]

In the emerging paradigm of edge computing (EC) for Internet of Things (IoT), data processing is pushed to the edge of the IoT network (e.g., gateways and embedded IoT devices). IoT devices must support multiple operation modes in order to adapt to varying runtime situations, like preserving energy at low battery, while still maintaining some crucial functionality, etc. Adapting the optimal operation mode is a challenge for edge devices given the limited resources at the edge of the network (both bandwidth and processing power of the shared gateway), various constraints (e.g., battery lifetime), etc. This paper proposes a fast and low-overhead scheme to determine and adapt the operation mode of edge devices at runtime and orchestrate devices in a way that the efficiency of IoT devices is optimized with respect to the gateway’s resource constraints. The proposed scheme breaks the optimization problem into several smaller ones (i.e., subproblems) whose solutions are aggregated to find the final solution. We present a novel memoization technique that determines the solution to a range of subproblems based on subproblems that are already solved. In addition, we present a novel pruning technique that reduces the search space and consequently reduces both memory and execution time overhead. The experimental results show up to 50% reduction in memory overhead and $14 \times $ reduction in execution time overhead compared to the state-of-the-art solution which is a major step toward efficient EC for IoT. [ABSTRACT FROM AUTHOR]

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased. [ABSTRACT FROM AUTHOR]

Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account. [ABSTRACT FROM AUTHOR]

With the development of communication and control technology, intelligent transportation systems (ITS) have received increasing attention from both industry and academia. However, plenty of studies providing different formulations for ITS depend on Master Control Center and require a high level of hardware configuration. The systematized technologies for distributed architectures are still not explored in detail. In this paper, we proposed a novel distributed cyber–physical system for connected and automated vehicles, and related methodologies are illustrated. Every vehicle in this system is modeled as a double-integrator and supposed to travel along a desired trajectory for maintaining a rigid formation geometry. The desired trajectory is generated by reference leading vehicles using information from multiple sources, while ordinary following vehicles use velocity and position information from their nearest neighbors and sensor information from on-board sensors to correct their own performance. Information graphs are used to illustrate the interaction topology between connected and automated vehicles. Edge computing technology is used to analyze and process information, such that the risk of privacy leaks can be greatly reduced. The performance scaling laws for the network with a one-dimensional information graph are generalized to networks with D -dimensional information graphs, and the results of the experiments show that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study. [ABSTRACT FROM AUTHOR]

The collection and processing of real-time data from a disaster-affected area is challenging. Unmanned aerial vehicles (UAVs) can efficiently gather the data and then transfer it to the edge servers (ESs) to timely initiate the rescue process. Consideration of energy and delay in a UAV-assisted edge network is very important, as both the UAVs and the smart mobile devices (SMDs) in the network have energy constraints and low processing capacity. Offloading is a promising technique to preserve the precious energy of the SMDs. In this research, gravitational search algorithm (GSA)-based offloading is presented for UAV-assisted mobile edge computing (MEC)-enabled disaster-affected areas. The problem is first mathematically formulated and shown to be computationally hard. Efficient encoding of agents (solution vectors) is given for the offloading problem. Fitness function is designed by considering the energy, delay, and load balancing of the ESs. The proposed GSA is executed by considering multiple disaster scenarios, and its performance is compared with other evolutionary algorithms (EAs) like the genetic algorithm (GA), particle swarm optimization (PSO), and fireworks algorithm (FWA). It has been observed that the GSA outperforms the other EAs in almost all the considered experiment scenarios. GSA claims a 30%–40% improvement for delay, 3%–5% for energy consumption, and more than 40% for load balancing. Statistical and convergence analyses are also conducted. The convergence of the GSA is found to be faster than that of the other EAs. [ABSTRACT FROM AUTHOR]

Edge computing is a key paradigm for the various data-intensive Internet of Things (IoT) applications where caching plays a significant role at the edge of the network. This paradigm provides data-intensive services, computational activities, and application services to the proximity devices and end-users for fast content retrieval with a very low response time that fulfills the ultra-low latency goal of the 5G networks. Information-centric networking (ICN) is being acknowledged as an important technology for the fast content retrieval of multimedia content and content-based IoT applications. The main goal of ICN is to change the current location-dependent IP network architecture to location-independent and content-centric network architecture. ICN can fulfill the needs for caching to the vicinity of the edge devices without further storage deployment. In this paper, we propose an architecture for efficient caching at the edge devices for data-intensive IoT applications and a fast content access mechanism based on new clustering and caching procedures in ICN. The proposed cluster-based efficient caching mechanism provides the solution to the problem of the existing hash and on-path caching mechanisms, and the proposed content popularity mechanism increases the content availability at the proximity devices for reducing the content transfer time and packet loss ratio. We also provide the simulation results and mathematical analysis to prove that the proposed mechanism is better than other state-of-the-art caching mechanisms and the overall network efficiencies are increased.

• A four-layer edge optimized and user-personalized framework for life-logging human activities is proposed. • A lightweight edge intelligence module requiring low computation is designed, which reduces data transmission to the cloud. • A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is developed for the user-specific parameter selection. Additionally, it makes the framework resilient to certain sensor failures. • The MSP optimized Decision Tree classifier is developed for real-time activity recognition in the Spark environment. • Experimental evaluation demonstrates the outperformance of the proposed model with existing ones. The fostered use of smart wearables for lifelogging daily activities has fuelled massive data generation. Lack of personalization, massive network traffic, increased latency, and high vulnerability to missing and noisy data are the significant impediments that existing frameworks face. This paper proposes a user-personalized and edge-optimized four-layer framework for lifelogging activities to address these impediments. A lightweight Edge Intelligence (EI) module with low computation requirements is designed to reduce data transmission to the cloud, lowering energy consumption. A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is proposed to provide a user-specific and optimized set of features. MSP optimized Decision Tree (MSP-DT) classifier is developed for real-time activity recognition in the Spark environment. The classifier's performance is calibrated regularly, making the framework resilient to sensor failure. Experiments demonstrate that the proposed framework can recognize 12 physical activities of different subjects with a mean accuracy of 97.67% and 47.66% reduction in transmitted data. [Display omitted] [ABSTRACT FROM AUTHOR]

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators. [ABSTRACT FROM AUTHOR]