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Unmanned aerial vehicles are a synergistic technology that complements other new technologies and is in constant development. The paper focuses on using artificial intelligence (AI) in security surveillance. This article aims to develop an unmanned aerial system to monitor border areas and detect human silhouettes in challenging environmental conditions. For this purpose, thermal imaging technology was used for remote sensing in combination with artificial intelligence, particularly Yolo algorithms. After testing various Yolo versions, the target algorithm was implemented on an NVIDIA Jetson Xavier NX edge device. Prototyping of the AI-based thermal detection system was carried out on the DJI S900 multi-rotor aircraft. The final solution was implemented on a vertical take-off and landing aircraft. A summary containing observations and conclusions, as well as perspectives for the development of future work, are included at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC), as a computing model that provides services on the user side, can effectively solve the problems of high delay and resource shortage in traditional cloud computing when processing massive data. However, existing edge computing resources are still limited, and difficult to provide services to users in inaccessible remote areas. Considering that unmanned aerial vehicle (UAV) has the advantages of easy deployment, high flexibility and low cost, a UAV-assisted MEC hierarchical computation offloading framework is proposed. Firstly, contract theory is used to solve the information asymmetry problem between the platform and the UAV, and the UAV is encouraged to provide computing services. By analyzing the attributes and conditions of feasible contracts, the optimal contract is designed using the Lagrange multiplier method. Secondly, by constructing the preference set between UAV and mobile user (MU), a mobile user and unmanned aerial vehicle bilateral matching (MUBM) algorithm is proposed to establish the connection between user tasks and UAV computing resources. Finally, the feasibility and effectiveness of the contract were verified through experiments. The experimental results also prove the stability of the MUBM. [ABSTRACT FROM AUTHOR]

The development of communication network connects all over the world in device driven technology through 6G. With the development of 6G defense, the problems in the Network of resources in edge computing cause latency dependencies. The main problem is switch-to-switch way has connection disappointment, and numerous packets can get lost. Along these lines, some kinds of connection quality checking and way recuperation plans are expected to conquer the connection disappointment. To propose a Proactive Delay Aware Self-Scheduling Protocol (PDAS2P) for Edge Enabled network slicing in 6G Networks. The proposed method initially analysis the Congestion Aware Neighbor Discovery (CAND) based on optimized energy levels in the node for efficient data transfer. Then the Hyper Intensive Link Stability Rate (HILSR) estimates table-driven routing information to stabilize the transmission route. Based on that estimation, the Pipelined Time Variant Transmission Rate (PTVTR) is calculated by transmission in each duty cycle to improve the transmission delays by assigning priorities on operative routing in multi-point consideration. Then Proactive Delay Aware Self-Scheduling Protocol (PDAS2P) is attained to the frequency limits of transmission rate to allocate the route and improve Quality of Service (QoS). The proposed 6G-QoS framework has been implemented in the 6G network communication-based scheduling. The simulation result throughput performance is 99% and routing performance is 99% for 100 nodes. It provides high accuracy of QoS prediction and has been validated by comparing the predicted QoS values with other methods like Energy Efficient Resource Allocation Model (EERAM), Main Task Off-loading Scheduling Algorithm (MTOSA), and Improved Multi Objective Cuckoo Search (IMOCS). [ABSTRACT FROM AUTHOR]

Nowadays, the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms is increasingly affecting the performance of innovative systems. At the same time, the advent of the Internet of Things (IoT) and the Edge Computing (EC) as means to place computational resources close to users create the need for new models in the training process of ML schemes due to the limited computational capabilities of the devices/nodes placed there. In any case, we should not forget that IoT devices or EC nodes exhibit less capabilities than the Cloud back end that could be adopted for a more complex training upon vast volumes of data. The ideal case is to have, at least, basic training capabilities at the IoT-EC ecosystem in order to reduce the latency and face the needs of near real time applications. In this paper, we are motivated by this need and propose a model that tries to save time in the training process by focusing on the training dataset and its statistical description. We do not dive into the architecture of any ML model as we target to provide a more generic scheme that can be applied upon any ML module. We monitor the statistics of the training dataset and the loss during the process and identify if there is a potential to stop it when not significant contribution is foreseen for the data not yet adopted in the model. We argue that our approach can be applied only when a negligibly decreased accuracy is acceptable by the application gaining time and resources from the training process. We provide two algorithms for applying this approach and an extensive experimental evaluation upon multiple supervised ML models to reveal the benefits of the proposed scheme and its constraints. [ABSTRACT FROM AUTHOR]

In recent years, containerization is becoming more and more popular for deploying applications and services and it has significantly contributed to the expansion of edge computing. The demand for effective and scalable container image management, however, increases as the number of containers deployed grows. One solution is to use a localized Docker registry at the edge, where the images are stored closer to the deployment site. This approach can considerably reduce the latency and bandwidth required to download images from a central registry. In addition, it acts as a proactive caching mechanism by optimizing the download delays and the network traffic. In this paper, we introduce an edge-enabled storage framework that incorporates a localized Docker registry. This framework aims to streamline the storage and distribution of container images, providing improved control, scalability, and optimized capabilities for edge deployment. Four demanding XR applications are employed as use cases to experiment with the proposed solution. [ABSTRACT FROM AUTHOR]

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]

Summary: Edge computing has the capability to process data closer to its point of origin, leading to the development of critical autonomous infrastructures with frequently communicating peers. The proposed work aims to evaluate the effectiveness of security and privacy mechanisms tailored for distributed systems, particularly focusing on scenarios where the nodes are closed‐circuit television (CCTV) systems. Ensuring public safety, object tracking in surveillance systems is a vital responsibility. The workflow has been specifically crafted and simulated for the purpose of weapon detection within public CCTV systems, utilizing sample edge devices. The system's primary objective is to detect any unauthorized use of weapons in public spaces while concurrently ensuring the integrity of video footage for use in criminal investigations. The outcomes of prior research on distributed machine learning (DML) techniques are compared with modified federated machine learning (FML) techniques, specifically designed for being Gossip verifiable and Quantum Safe. The conventional federated averaging algorithm is modified by incorporating the secret sharing principle, coupled with code‐based McEliece cryptosystem. This adaptation is designed to fortify the system against quantum threats. The Gossip data dissemination protocol, executed via custom blockchain atop the distributed network, serves to authenticate and validate the learning model propagated among the peers in the network. It provides additional layer of integrity to the system. Potential threats to the proposed model are analyzed and the efficiency of the work is assessed using formal proofs. The outcomes of the proposed work demonstrate that the trustworthiness and consistency are meticulously preserved for both the model and data within the DML framework on the Edge computing platform. [ABSTRACT FROM AUTHOR]

This research introduces a groundbreaking collaborative defense mechanism that utilizes end-to-end edge computing to bolster the security of decentralized hospital cloud systems. By integrating intrusion detection systems, firewalls, anomaly detection, and threat intelligence in a unified manner through the efficiency of edge computing, this approach marks a significant advancement in healthcare cybersecurity. Through rigorous testing with a substantial dataset, the system demonstrated exceptional performance metrics, including a remarkable 95% accuracy in threat detection, a low false positive rate, and a swift response time of merely 0.25 seconds. Notably, the system effectively mitigates computational overhead, thereby optimizing resource utilization. Comparative analysis with existing methodologies underscores the superiority of this novel framework, particularly in terms of geolocation accuracy, the minimization of false positives, and expedited reaction capabilities. This study's collaborative defense strategy, underpinned by end-to-end edge computing, presents a holistic and innovative solution to the escalating cyber threats facing healthcare infrastructures. By redefining the parameters of security in medical settings, it paves the way for a safer and more resilient healthcare information technology ecosystem. [ABSTRACT FROM AUTHOR]

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model's performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE. [ABSTRACT FROM AUTHOR]

The current distribution network is developing toward the direction of information and intelligent distribution Internet of Things (IoT). In order to explore the online monitoring system for transient electromechanical simulation of distribution networks, this paper is based on the equivalent model of generator sets. Firstly, it describes the relevant theoretical knowledge of edge computing, designs the distribution IoT based on edge computing, and briefly introduces its network architecture. Secondly, based on the discrete-time domain equivalent model of generators, the electromechanical transient simulation distribution network is constructed by introducing the machine network division of the power network. Finally, the Western System Coordinating Council (WSCC)-3 units and 9 nodes are taken as an example, and simulation experiments are conducted under two fault simulation conditions to verify the effectiveness of the simulation model. The results show that under the two fault simulation conditions, the results of equivalent model simulation of generator terminal voltage and relative power angle change are like those of the Power System Analysis Software Package (PSASP). The changing trend of the two is similar and stable. After the PSASP results are stabilized at a value, the simulation results fluctuate extremely up and down the value. For example, under fault condition 1, the changing trend of the relative power angle of the No. 2 generator is first fluctuating and then becomes stable. After violent fluctuation, the relative power angle tends to be stable from about 20s to -12.35°. The result of PSASP software is stable at -12.35°. The transient electromechanical simulation of the generator equivalent model can provide some ideas for the online monitoring system of the distribution network. [ABSTRACT FROM AUTHOR]

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are critical for modern intelligent processing but cause significant latency and energy consumption issues on mobile devices due to their high computational demands. Moreover, different tasks have different accuracy demands for DNN inference. To balance latency and accuracy across various tasks, we introduce PreDNNOff, a method that offloads DNNs at a layer granularity within the Mobile Edge Computing (MEC) environment. PreDNNOff utilizes a binary stochastic programming model and Genetic Algorithms (GAs) to optimize the expected latency for multiple exit points based on the distribution of task inference accuracy and layer latency regression models. Compared to the existing method Edgent, PreDNNOff has achieved a reduction of about 10% in the expected total latency, and due to the consideration of different tasks' varying requirements for accuracy, it has a broader applicability. [ABSTRACT FROM AUTHOR]

The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. [ABSTRACT FROM AUTHOR]

The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application. [ABSTRACT FROM AUTHOR]

In mobile edge computing, unmanned aerial vehicles (UAVs) equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility, flexibility, rapid deployment, and terrain agnosticism. These attributes enable UAVs to reach designated areas, thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable. However, the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks. Meanwhile, there are often obstacles that affect flight safety in real UAV operation areas, and collisions between UAVs may also occur. To solve these problems, we propose an innovative A*SAC deep reinforcement learning algorithm, which seamlessly integrates the benefits of Soft Actor-Critic (SAC) and A* (A-Star) algorithms. This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function. Furthermore, our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point. This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption, thereby optimizing system-level energy efficiency. Extensive simulation results demonstrate that, compared to other algorithms, our approach achieves superior system benefits. Specifically, it exhibits an average improvement of 13.18% in terms of different computing task sizes, 25.61% higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs, and 35.78% higher on average in terms of the maximum computing frequency of different auxiliary UAVs. As for path planning, the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV, enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments. [ABSTRACT FROM AUTHOR]

In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60–200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), data aggregation techniques are used to reduce energy consumption of the sensors and extend the network lifetime. Recent work in this area considers the utilization of external mobile agents (elements) in data aggregation to further improve the WSN lifespan. The sensitivity of data collected by WSNs varies based on the application environment, necessitating data aggregation that adheres to specific conditions and constraints. One of the challenges in using external mobile elements is establishing trust between these elements and WSNs to ensure that data aggregation conditions set by the WSNs are met. In this paper, we propose a fog-cloud framework for a distributed trust service that enables WSNs to recruit trusted mobile elements for data aggregation. Our proposed trust framework provides WSNs with the flexibility to align their trust policy with the conditions of their data aggregation processes, facilitating efficient access to trust services. Simulation results demonstrate that our framework reduces energy loss while ensuring consistent and reliable data aggregation in WSNs. [ABSTRACT FROM AUTHOR]

Edge computing has become quintessential in commercial, healthcare, and industrial applications. It enables real-time data processing at the edge device, thus reducing the data traffic to the cloud and increasing the processing time efficiency. As an edge device, modern System-on-Chips (SoCs) provide scalability, security, and development in an integrated platform. Intellectual Property (IP) core reuse is a boon in SoCs that bridges the gap between integrated circuit design and fabrication. Such edge devices modeled by vendors are bound to ensure high security to avoid piracy. The proposed architecture provides a two-step authentication utilizing a Finite State Machine (FSM) with a secured key obtained from the newly structured Physical Unclonable Function (PUF) within the same edge device, with the primary goal of verifying several heterogeneous IPs to achieve the least power and energy. Two PUF designs, Anderson Arbiter PUF (AA-PUF) and Balanced AA-PUF, have been proposed for two different placements taking advantage of SoC-based architecture. The PUF characteristics have been experimentally validated with and without majority voting and demonstrate their proximity close to the desired value in ZedBoard. The proposed design is a strong PUF with less than 15% area overhead and power dissipation of 1.982 W for a 64-bit response. The experimental validation has evaluated that the power and energy consumptions are 2.56 W and 2.17 J for 52 heterogeneous IPs. [ABSTRACT FROM AUTHOR]

The widespread adoption of utility-based real-time applications has placed the necessity of widescale deployment of edge computing infrastructure. Crowdsourced edge computing is deemed a suitable way out. On the other hand, a collection of today's powerful smart mobile devices (SMDs) can cumulatively offer high-performance computing. The public-owned SMDs are utilized opportunistically to form a dynamic ad-hoc computing grid known as mobile crowd computing (MCC). This paper aspires to establish a proof-of-concept for the feasibility and use of MCC as a sustainable edge computing solution (MCC-edge). A typical smart HVAC system of an office building has been considered for the experiment case. We aim to process the HVAC data in real-time using the MCC-edge setup within the building for auto adjustment of the AC controller and error notifications. To maintain the ideal comfort level of the occupants, we present an extensive calculation using the dew point and heat index of the room. A high-level layered architecture of the MCC-edge for HVAC is presented along with a general framework of the MCC-edge. We report the module-wise design and implementation procedures with exhaustive details. The performance of MCC-edge is statistically compared with the commercial edge and cloud computing solutions in terms of cost, energy consumption, latency, and environmental impact, showing a significant advantage over the two. Every procedural detail of each module's design, development, and implementation is meticulously presented, which would aid interested readers and researchers in rebuilding such an application. [ABSTRACT FROM AUTHOR]

In the contemporary landscape of edge computing, the deployment of services with stringent real-time requirements on edge devices is increasingly prevalent. However, the challenge of designing an effective service deployment strategy that optimally leverages both cloud and edge resources to deliver high-quality services in production environments persists, primarily due to resource constraints in edge devices. To tackle this issue, we introduce an adaptive service deployment algorithm that utilizes speedup weights for cloud-edge collaborative environments (SWD-AD). First, by comparing the execution and communication times of tasks in the cloud and at the edge, the speedup weights are calculated, and a service deployment algorithm is designed that takes into account both the speedup weights and resource consumption weights. Then, during the cluster operation, information on the task processing for each service is collected and their cumulative speedup weights are calculated. Utilizing a dynamic service adjustment strategy based on these cumulative speedup ratio weights, services are migrated between the cloud and the edge. Our performance evaluation experiments reveal that this strategy notably reduces the average response time of tasks by 29.38 and 25.86% compared to Swarm and Kubernetes (K8s) algorithms, respectively. [ABSTRACT FROM AUTHOR]

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

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

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Edge computing has emerged as a prominent trend in the field of information technology, offering flexible and robust resources for the industrial Internet. How to migrate container accurately is crucial for edge computing in the industrial Internet, as it plays a vital role in enhancing service response speed and safeguarding uninterrupted continuity of production operations. In this paper, we explore the problem of container migration in edge computing within the industrial Internet, aiming to reduce latency and enhance reliability. We establish a two-objective optimization model to comprehensively capture the container migration problem and formulate it as a constrained optimization model. The formulated model provides a systematic framework that effectively balances the trade-off between reducing latency and enhancing reliability. To tackle the migration strategy derived from the optimization model, we propose a migration algorithm based on the improved binary whale optimization algorithm. Our migration algorithm incorporates the adaptive probability and adaptive position weight within the hunting and searching operations, effectively enhancing the search efficiency during the solving process. The experimental results demonstrate the effectiveness of the established model in reducing the objective value, while the proposed migration algorithm surpasses existing algorithms by achieving an average reduction of at least 15.59% in the objective value. [ABSTRACT FROM AUTHOR]

With the development of computing technology, the storage space and computing power of IoT devices have developed rapidly. Due to the social nature of people, users who have social relationships with each other IoT devices are relatively close in some time periods, but are far away from traditional edge nodes. It is far away, so service providers can temporarily rent suitable IoT devices to act as edge storage nodes based on the storage computing power and social status of user equipment. In the process of selecting edge nodes by service providers, this paper firstly describes the social relationships of users to which the edge nodes belong and some requirements for storage. Then, based on the above considerations, a method based on social and The edge node selection algorithm of equipment storage, and the subsequent modeling of the problem of service providers' selection of edge nodes as the problem of maximizing social benefits, through the use of matching algorithms, proves that the algorithm proposed in this paper can effectively select edge nodes, and subsequent simulation experiments The experimental results also show that the proposed algorithm can maximize the social benefits of service providers and users. [ABSTRACT FROM AUTHOR]

Next-generation wireless networks (NGWNs) are moving towards a more advanced and exemplary environment encompassing data-intensive, delay-sensitive and energy-efficient services. To accommodate the stringent requirements of these radical and multifarious services, Next-generation wireless heterogeneous networks (NGWHNs) have been envisioned as an exemplary connectivity solution which enables flow based association among user devices (UDs) and radio access technologies (RATs). However, designing an intelligent RAT association scheme for NGWHNs is a significant challenge as the network heterogeneity tends to intensify in terms of access technologies and niche Quality of Service (QoS) requirements. Recently, substantial research endeavours have been carried out in this line of work but they are insufficient in sustaining adequate service levels and RAT capacity constraints concurrently. Inspired by the pitfalls in the pertinent work, an intelligent bilateral RAT selection framework has been developed. The proposed solution facilitates QoS provisioning while adhering to the RAT capacity limitations through well-defined preference functions. Within this paradigm, the proposal explores the diversity of matching game theory and double deep reinforcement learning (DDRL) that facilitates faster convergence to stable and balanced RAT selection policy. Finally, the proposed solution validated with the help of extensive simulations exhibited a significant gain of 42% and 46.35% respectively in terms of system utility and robustness in comparison to other schemes. Eventually, the performance assessment underlines the supremacy of the proposed solution by 22% in terms of system satisfaction with the varying network size. [ABSTRACT FROM AUTHOR]

The automation of insect pest control activities implies the use of classifiers to monitor the temporal and spatial evolution of the population using computer vision algorithms. In this regard, the popularisation of supervised learning methods represents a breakthrough in this field. However, their claimed effectiveness is reduced regarding working in real-life conditions. In addition, the efficiency of the proposed models is usually measured in terms of their accuracy, without considering the actual context of the sensing platforms deployed at the edge, where image processing must occur. Hence, energy consumption is a key factor in embedded devices powered by renewable energy sources such as solar panels, particularly in energy harvesting platforms, which are increasingly popular in smart farming applications. In this work, we perform a two-fold performance analysis (accuracy and energy efficiency) of three commonly used methods in computer vision (e.g., HOG+SVM, LeNet-5 CNN, and PCA+Random Forest) for object classification, targeting the detection of the olive fly in chromatic traps. The training and testing of the models were carried out using pictures captured in various realistic conditions to obtain more reliable results. We conducted an exhaustive exploration of the solution space for each evaluated method, assessing the impact of the input dataset and configuration parameters on the learning process outcomes. To determine their suitability for deployment on edge embedded systems, we implemented a prototype on a Raspberry Pi 4 and measured the processing time, memory usage, and power consumption. The results show that the PCA-Random Forest method achieves the highest accuracy of 99%, with significantly lower processing time (approximately 6 and 48 times faster) and power consumption (approximately 10 and 44 times lower) compared with its competitors (LeNet-5-based CNN and HOG+SVM). [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) poses a critical health concern, affecting millions of individuals globally, particularly in light of the increasing prevalence of diabetes. Early diagnosis and treatment can prevent vision loss in up to 95% of DR patients. However, the shortage of ophthalmologists, coupled with the limitations of traditional DR diagnostic methods, necessitates innovative solutions to provide timely and accessible DR screening and diagnosis, especially in underserved regions. In this study, we present a deep learning (DL) based framework for DR screening, with a specific focus on its suitability for resource-constrained edge devices. Our methodology is built upon addressing five key research questions to enhance the model's accuracy, ensure scalability, establish robustness, optimize computational complexity, and provide a thorough performance comparison with existing models. The developed framework attains a very high accuracy of 98.4%, accompanied by a sensitivity of 95.6% and a specificity of 98.6%. It not only exhibits efficient resource utilization, with a 40-fold improvement in the accuracy-to-size ratio, but also excels with minimal CPU and GPU inference times, and low memory requirements. Our proposed framework combines enhanced accuracy and faster inference times with a lean DL model. Its adaptability facilitates inferences on edge devices in remote areas, even when cloud connectivity is intermittent. Looking forward, our focus will be on enhancing model generalization through cross-validation and hybrid databases, integrating security through federated learning, and implementing adaptive learning to ensure the model's continued relevance in evolving healthcare scenarios. This work represents a significant contribution to the field of DR screening, providing an accessible and efficient solution for mass screening, particularly in regions with limited access to healthcare resources. [ABSTRACT FROM AUTHOR]

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show the results of tuning such parameters to obtain a low-latency and low-energy embedded LSNN (eLSNN) implementation. We demonstrate that it is possible to adapt the firing rate so that the samples belonging to the most frequent class are processed with less spikes. We implemented the eLSNN on a microcontroller-based sensor node and we evaluated its performance and energy consumption using a structural health monitoring application processing a stream of vibrations for damage detection (i.e. binary classification). We obtained a cycle count reduction of 25% and an energy reduction of 22% with respect to a baseline implementation. We also demonstrate that our methodology is applicable to a multi-class scenario, showing that we can reduce spiking activity between 68 and 85% at iso-accuracy. [ABSTRACT FROM AUTHOR]

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing. [ABSTRACT FROM AUTHOR]

Edge computing has much lower elasticity than cloud computing because cloudlets have much smaller physical and electrical footprints than a data center. This hurts the scalability of applications that involve low-latency edge offload. We show how this problem can be addressed by leveraging the growing sophistication and compute capability of recent wearable devices. We investigate four Wearable Cognitive Assistance applications on three wearable devices, and show that the technique of offload shaping can significantly reduce network utilization and cloudlet load without compromising accuracy or performance. Our investigation considers the offload shaping strategies of mapping processes to different computing tiers, gating, and decluttering. We find that all three strategies offer a significant bandwidth savings compared to transmitting full camera images to a cloudlet. Two out of the three devices we test are capable of running all offload shaping strategies within a reasonable latency bound. [ABSTRACT FROM AUTHOR]

In the context of edge environments with constrained resources, realizing real-time and robust crosswalk and guide arrow detection poses a significant challenge for autonomous driving systems. This paper proposes a crosswalk and guide arrow detection network (CGADNet), a lightweight visual neural network derived from YOLOv8. Specifically designed for the swift and accurate detection of crosswalks and guide arrows within the field of view of the vehicle, the CGADNet can seamlessly be implemented on the Jetson Orin Nano device to achieve real-time processing. In this study, we incorporated a novel C2f_Van module based on VanillaBlock, employed depth-separable convolution to reduce the parameters efficiently, utilized partial convolution (PConv) for lightweight FasterDetect, and utilized a bounding box regression loss with a dynamic focusing mechanism—WIoUv3—to enhance the detection performance. In complex scenarios, the proposed method in the stability of the mAP@0.5 was maintained, resulting in a 4.1% improvement in the mAP@0.5:0.95. The network parameters, floating point operations (FLOPs), and weights were reduced by 63.81%, 70.07%, and 63.11%, respectively. Ultimately, a detection speed of 50.35 FPS was achieved on the Jetson Orin Nano. This research provides practical methodologies for deploying crosswalk and guide arrow detection networks on edge computing devices. [ABSTRACT FROM AUTHOR]

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge "intelligence" to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet. [ABSTRACT FROM AUTHOR]

Deep networks play a crucial role in the recognition of agricultural diseases. However, these networks often come with numerous parameters and large sizes, posing a challenge for direct deployment on resource-limited edge computing devices for plant protection robots. To tackle this challenge for recognizing cotton diseases on the edge device, we adopt knowledge distillation to compress the big networks, aiming to reduce the number of parameters and the computational complexity of the networks. In order to get excellent performance, we conduct combined comparison experiments from three aspects: teacher network, student network and distillation algorithm. The teacher networks contain three classical convolutional neural networks, while the student networks include six lightweight networks in two categories of homogeneous and heterogeneous structures. In addition, we investigate nine distillation algorithms using spot-adaptive strategy. The results demonstrate that the combination of DenseNet40 as the teacher and ShuffleNetV2 as the student show best performance when using NST algorithm, yielding a recognition accuracy of 90.59% and reducing FLOPs from 0.29 G to 0.045 G. The proposed method can facilitate the lightweighting of the model for recognizing cotton diseases while maintaining high recognition accuracy and offer a practical solution for deploying deep models on edge computing devices. [ABSTRACT FROM AUTHOR]

Bioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection of diseases, offering insights into the physiological conditions of various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy in addressing diverse medical conditions through the precise delivery of stimuli directly to specific targets. This comprehensive review explores developments and applications of bioelectronic devices within the biomedical field. Special emphasis is placed on the evolution of closed‐loop systems, which stand out for their dynamic treatment adjustments based on real‐time physiological feedback. The integration of Artificial Intelligence (AI) and edge computing technologies is discussed, which significantly bolster the diagnostic and therapeutic functions of these devices. By addressing elemental analyses, current challenges, and future directions in implantable devices, the review aims to guide the pathway for advances in bioelectronic devices. [ABSTRACT FROM AUTHOR]

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field. [ABSTRACT FROM AUTHOR]

As the adoption of electric vehicles increases, the challenge of managing bidirectional energy flow while ensuring grid stability and respecting user preferences becomes increasingly critical. This paper aims to develop an intelligent framework for vehicle-to-grid (V2G) energy management that balances grid demands with user autonomy. The research presents VESTA (vehicle energy sharing through artificial intelligence), featuring the semantic-aware vehicle access control (SEVAC) model for efficient and intelligent energy sharing. The methodology involves developing a comparative analysis framework, designing the SEVAC model, and implementing a proof-of-concept simulation. VESTA integrates advanced technologies, including artificial intelligence, blockchain, and edge computing, to provide a comprehensive solution for V2G management. SEVAC employs semantic awareness to prioritise critical vehicles, such as those used by emergency services, without compromising user autonomy. The proof-of-concept simulation demonstrates VESTA's capability to handle complex V2G scenarios, showing a 15% improvement in energy distribution efficiency and a 20% reduction in response time compared to traditional systems under high grid demand conditions. The results highlight VESTA's ability to balance grid demands with vehicle availability and user preferences, maintaining transparency and security through blockchain technology. Future work will focus on large-scale pilot studies, improving AI reliability, and developing robust privacy-preserving techniques. [ABSTRACT FROM AUTHOR]

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85 % compared to 80.88 % for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model. [ABSTRACT FROM AUTHOR]

Advancements in technology have driven the miniaturization of embedded systems, making them more cost-effective and energy-efficient for wireless applications. As a result, the number of connectable devices in Internet of Things (IoT) networks has increased significantly, creating the challenge of linking them effectively and economically. The space industry has long recognized this challenge and invested in satellite infrastructure for IoT networks, exploiting the potential of edge computing technologies. In this context, it is of critical importance to enhance the onboard computing capabilities of satellites and develop enabling technologies for their advancement. This is necessary to ensure that satellites are able to connect devices while reducing latency, bandwidth utilization, and development costs, and improving privacy and security measures. This paper presents the GPU@SAT DevKit: an ecosystem for testing a high-performance, general-purpose accelerator designed for FPGAs and suitable for edge computing tasks on satellites. This ecosystem provides a streamlined way to exploit GPGPU processing in space, enabling faster development times and more efficient resource use. Designed for FPGAs and tailored to edge computing tasks, the GPU@SAT accelerator mimics the parallel architecture of a GPU, allowing developers to leverage its capabilities while maintaining flexibility. Its compatibility with OpenCL simplifies the development process, enabling faster deployment of satellite-based applications. The DevKit was implemented and tested on a Zynq UltraScale+ MPSoC evaluation board from Xilinx, integrating the GPU@SAT IP core with the system's embedded processor. A client/server approach is used to run applications, allowing users to easily configure and execute kernels through a simple XML document. This intuitive interface provides end-users with the ability to run and evaluate kernel performance and functionality without dealing with the underlying complexities of the accelerator itself. By making the GPU@SAT IP core more accessible, the DevKit significantly reduces development time and lowers the barrier to entry for satellite-based edge computing solutions. The DevKit was also compared with other onboard processing solutions, demonstrating similar performance. [ABSTRACT FROM AUTHOR]

The advent of 6G technology will transforms aviation, particularly in the realm of aircraft health monitoring systems (AHMSs). This paper explores the transformative potential of 6G in enhancing real-time data exchange, predictive maintenance, and overall communication efficiency within the aviation sector. By using ultra-fast data transmission, low latency, and advanced AI integration, 6G enables the development of a unified AHMS architecture that significantly improves aircraft safety, operational efficiency, and reliability. The proposed eight-layer AHMS model, incorporating digital twins, federated learning, and edge computing, showcases how 6G can revolutionize aircraft maintenance by providing continuous, real-time monitoring and decision-making capabilities. [ABSTRACT FROM AUTHOR]

This review paper explores Urban Digital Twins (UDTs) and their crucial role in developing smarter cities, focusing on making urban areas more sustainable and well-planned. The methodology adopted an extensive literature review across multiple academic databases related to UDTs in smart cities, sustainability, and urban environments, conducted by a bibliometric analysis using VOSviewer to identify key research trends and qualitative analysis through thematic categorization. This paper shows how UDTs can significantly change how cities are managed and planned by examining examples from cities like Singapore and Dubai. This study points out the main hurdles like gathering data, connecting systems, handling vast amounts of information, and making different technologies work together. It also sheds light on what is missing in current research, such as the need for solid rules for using UDTs effectively, better cooperation between various city systems, and a deeper look into how UDTs affect society. To address research gaps, this study highlights the necessity of interdisciplinary collaboration. It also calls for establishing comprehensive models, universal standards, and comparative studies among traditional and UDT methods. Finally, it encourages industry, policymakers, and academics to join forces in realizing sustainable, smart cities. [ABSTRACT FROM AUTHOR]

Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value. [ABSTRACT FROM AUTHOR]

Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device. [ABSTRACT FROM AUTHOR]

With the development of deep learning foundation model technology, the types of computing tasks have become more complex, and the computing resources and memory required for these tasks have also become more substantial. Since it has long been revealed that task offloading in cloud servers has many drawbacks, such as high communication delay and low security, task offloading is mostly carried out in the edge servers of the Internet of Things (IoT) network. However, edge servers in IoT networks are characterized by tight resource constraints and often the dynamic nature of data sources. Therefore, the question of how to perform task offloading of deep learning foundation model services on edge servers has become a new research topic. However, the existing task offloading methods either can not meet the requirements of massive CNN architecture or require a lot of communication overhead, leading to significant delays and energy consumption. In this paper, we propose a parallel partitioning method based on matrix convolution to partition foundation model inference tasks, which partitions large CNN inference tasks into subtasks that can be executed in parallel to meet the constraints of edge devices with limited hardware resources. Then, we model and mathematically express the problem of task offloading. In a multi-edge-server, multi-user, and multi-task edge-end system, we propose a task-offloading method that balances the tradeoff between delay and energy consumption. It adopts a greedy algorithm to optimize task-offloading decisions and terminal device transmission power to maximize the benefits of task offloading. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Internet of Vehicles (IoV) technology, the demands for real-time navigation, advanced driver-assistance systems (ADAS), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and multimedia entertainment systems have made in-vehicle applications increasingly computing-intensive and delay-sensitive. These applications require significant computing resources, which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks, energy consumption, and cost constraints. To address this issue in IoV-based edge computing, particularly in scenarios where available computing resources in vehicles are scarce, a multi-master and multi-slave double-layer game model is proposed, which is based on task offloading and pricing strategies. The establishment of Nash equilibrium of the game is proven, and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium. Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing (MEC)-enabled IoV environments. Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility. Specifically, the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%. [ABSTRACT FROM AUTHOR]

Cloud and edge computing are necessary elements in the modern distributed network. To reflect the capacity variances of the system, stochastic capacities and costs of components (arcs and nodes) are considered such that stochastic capacity distributed networks (SCDNs) can be formed in the paper. To learn the capability of the SCDN for system monitoring and management, system reliability can be utilized which is defined as the probability of satisfying demands (data) under a budget for an SCDN. An exact-system reliability algorithm for system reliability is derived in advance for prudent management and decision-making. For immediate control and management, a deep neural network (DNN) architecture is proposed to create a prediction model for system reliability. Enough data points about SCDN information are generated and transformed into an appropriate format for training the prediction model. Note that the labels (system reliability) for all the data points would be calculated using the exact-system reliability algorithm. Necessary hyperparameters for determining the DNN of system reliability of the SCDN are also suggested. The model can be used for the future management of the distributed network. [ABSTRACT FROM AUTHOR]

Conventional infrastructures based on the cloud are not sufficient for the emerging Internet of Things (IoT) applications requirements. Many big problems are shortcomings, especially in terms of network bandwidth and latency. Throughout recent years, the idea to relieve fog computing and edge computing was suggested by bringing data processing capacities closer to these limits to the edge of the network. The authors assume that the full potential of IoT will, in many cases, only be activated by the combination of cloud, fog, and edge computing in a new computing paradigm, given IoT growth and development forecasts. This article discusses the possibility and need for such a paradigm by introducing steam computing as a new distributed type of computing using cloud, fog, and edge utilities to carry out data processing and storage. The authors treat steam computing through four planes: security and privacy plane, data analytics and fault tolerance plane and deployment and test beds plane. Finally, the authors focus on the open issues and future trends in steam computing. [ABSTRACT FROM AUTHOR]