Edge Servers Research Articles

Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

Pimo: memory-efficient privacy protection in video streaming and analytics

Video streaming from cameras to backend cloud or edge servers for neural-based analytics has gained significant popularity. However, the transmission of data from cameras to a backend raises substantial privacy concerns, particularly regarding sensitive information like facial data. To offer privacy protection, visual processing techniques, such as Generative Adversarial Networks (GANs), have been employed on cameras to blur and safeguard such data intelligently. However, these techniques frequently face memory challenges, particularly when dealing with high-resolution videos. In this paper, we propose PIMO, a memory-efficient visual privacy protection scheme designed to effectively blur video content leveraging adaptive slicing of frames and resolution degradation. Our extensive experimental evaluations validate that PIMO’s adaptive mechanism proficiently navigates fluctuating memory constraints. Furthermore, utilizing a content-based blur scheme, our approach can maintain an impressive mean precision of 95.2%, as compared to the original, non-blurred images.

Minimizing Task Age upon Decision for Low-Latency MEC Networks Task Offloading with Action-Masked Deep Reinforcement Learning.

In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wirelessly reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce the task of Age upon Decision (AuD) aimed at the timeliness of tasks used for decision-making, which highlights the timeliness of the information at decision-making moments. For the case in which dynamic task generation and random fading channels are considered, we provide a task AuD minimization design by jointly selecting UE and allocating blocklength. In particular, to solve the task AuD minimization problem, we transform the optimization problem to a Markov Decision Process problem and propose an Error Probability-Controlled Action-Masked Proximal Policy Optimization (EMPPO) algorithm. Via simulation, we show that the proposed design achieves a lower AuD than baseline methods across various network conditions, especially in scenarios with significant channel Signal-to-Noise Ratio (SNR) differences and low average SNR, which shows the robustness of EMPPO and its potential for real-time applications.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

Task offloading scheme of vehicular cloud edge computing based on Digital Twin and improved A3C

In traditional vehicle edge computing research, the limited computing resources of edge servers are not fully considered. At the same time, when using deep reinforcement learning (DRL) to solve vehicle edge computing problems, it is not fully considered that DRL requires a large amount of real-time data and is prone to gradient explosion or disappearance, over-fitting and local optimal solution problems. Therefore, this article proposes a digital twin (DT)-assisted vehicular cloud edge computing offloading solution. To solve the problem of limited edge server computing resources, this article proposes a vehicle, cloud, and edge server collaboration solution. In response to the problem that DRL requires a large amount of real-time data, this paper proposes a real-time data acquisition method based on DT technology. Aiming at the gradient explosion or disappearance, overfitting, and local optimal solution problems of the asynchronous dominant actor–critic (A3C) algorithm, this paper proposes an improved algorithm of A3C, which introduces the ɛ-greedy strategy and dynamic baseline to increase the possibility of exploration and increase the generalization ability of the model through Dropout technology. During the training process, this article also uses the stochastic gradient descent (SGD) algorithm to accelerate the training process and reduce the computational complexity. The simulation results show that compared with other solutions, the improved algorithm proposed in this paper can effectively reduce the delay and energy consumption of offloading tasks at the vehicular edge cloud.

IoV-6G+: A secure blockchain-based data collection and sharing framework for Internet of vehicles in 6G-assisted environment

The growing need for wide and ubiquitous accessibility to advanced Intelligent Transportation Systems (ITS) has led to the evolution of conventional Vehicle to Everything (V2X) paradigms into the Internet of Vehicles (IoVs). Next-generation IoVs establish seamless connections among humans, vehicles, Internet of Things (IoT) devices, and service platforms to enhance transit efficiency, road safety, and environmental conservation, with notable advancements in IoV technologies, specifically in secure data exchange and user privacy protection. Additionally, Unmanned Aerial Vehicles (UAVs) are envisioned as scalable and adaptable solutions for comprehensive IoV service coverage. The current approaches in IoV mainly concentrate on local infrastructure setups, overlooking the potential of infrastructure-less IoVs that lack extensive edge facilities. Also, the transmission of data over public channels by vehicles equipped with servers is susceptible to interception and tampering by malicious attackers. Moreover, the substantial amount of real-time data generated by vehicles, IoT devices, travelers, and social interactions pose a significant strain on servers as well as latency issues. To address these security concerns, this research suggests a secure-lightweight data collection and sharing framework for 6G-assisted smart transportation using blockchain and UAV, called IoV-6G+. The process entails drones collecting information and transmitting it to dedicated edge servers that securely aggregate the information and generate transactions on a cloud server. A cloud server securely collects data from edge servers, creates transactions, combines them into blocks, and then confirms and adds these blocks to the blockchain through a voting-based consensus method in a peer-to-peer network of cloud servers. Additionally, the security analysis of our proposed IoV-6G+ is assessed using the “Informal and formal (i.e., Scyther Tool, and Real-or-Random (RoR) Model) methods”, showcasing its efficacy in delivering a secure and efficient authentication mechanism for IoV. Moreover, the proposed IoV-6G+ exhibits lower computational and communication costs as well as energy consumption, along with enhanced security features, when compared to existing authentication frameworks.

Asynchronous Federated Learning with Grey Wolf Optimization for the Heterogeneity IoT Devices

The Internet of Things (IoT) creates new options for real-time data collection and Machine Learning (ML) model development. Nonetheless, it's feasible that a particular IoT device does not have sufficient computational power to train and implement a complete learning model. However, there are significant communication costs and data security and privacy concerns associated with sending actual data to a centralised server with a lot of computational power. Federated Learning (FL) is a potential way to train ML models using low-powered devices and Edge Servers (ES) since it is a distributed ML architecture. However, the vast majority of the works in existence make the unsustainable assumption of a synchronized parameters update manner with similar IoT nodes and reliable communications networks. To increase training efficiency and accelerate the speed for heterogeneous IoT devices in an unreliable network environment, we designed an Asynchronous Federated Learning strategy with Grey Wolf Optimization (AFL-GWO) in this research. In particular, we develop a Lightweight Node Selection (LNS) technique and propose an AFL-GWO model to efficiently complete learning tasks. To ensure that diverse IoT nodes with varying computational capabilities and network connectivity are represented in the global learning aggregate, the proposed technique makes node selections on an iterative basis. We show through extensive experiments that our suggested AFL-GWO system outperforms the state-of-the-art techniques on identically and independently distributed (IID) and non-IID data distribution in a variety of contexts.

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

An efficient mechanism for function scheduling and placement in function as a service edge environment

With the advancement of Internet of Things (IoT) applications and the substantial surge in associated traffic, coupled with the rise in demand for low-latency mobile applications, the utilization of remote cloud computing infrastructure has encountered notable challenges. As a solution, edge computing has emerged, decentralizing computational resources closer to data sources. This results in quicker processing and diminished reliance on distant servers. To address resource limitations and prevent congestion in edge servers, applications are now being developed as smaller, modular components, referred to as the Function as a Service (FaaS) pattern. FaaS brings advantages such as distributing the workload across multiple edge servers and offering improved flexibility in resource management. However, the FaaS model introduces new challenges when compared to the traditional edge computing environment. These include issues like function placement and minimizing cold start delays. Assignment mechanisms play a crucial role in characterizing the interactions between edge servers and user devices. They excel at devising optimal allocation strategies and ensuring mutual satisfaction between edge servers and user devices, especially in scenarios with limited resources. This article introduces the Edge FaaS-Top Trading Cycles (EF-TTC), an efficient mechanism for implementing FaaS-based applications within the edge platform. The proposed approach models the problem of function placement and scheduling in a manner akin to the well-known School Choice problem. It subsequently employs the Top Trading Cycle mechanism to address this challenge. Experimental results demonstrate that the proposed approach significantly improves the average completion time for users’ requests, outperforming other baseline algorithm in this domain.

On incentivizing resource allocation and task offloading for cooperative edge computing

Cooperative edge computing serves as an effective solution to provide reliable and elastic edge computing services through pooling geographically proximate edge resources and efficiently allocating them to users. The incentive mechanism is critical for realizing cooperative edge computing. In this paper, we propose a virtual machine (VM) or container based resource allocation scheme and two market-based mechanisms to stimulate collaboration among edge servers (ESs). Our first mechanism is a novel two-stage double auction based approach where edge resources are allocated to services/VMs at the first stage and then distributed among ESs at the second stage via a two-sided multi-unit auction. To make it more practical, we use markups to set the ask and bid prices for the participants in the market. Moreover, with the concept of virtual sellers and virtual buyers, we cast the multi-unit auction as single-unit one so that the auction becomes tractable. We further propose a price-based decentralized mechanism in case there is no central authority. With an appropriately constructed objective function, we formulate the resource allocation task as a network utility maximization (NUM) problem and leverage the dual-based decomposition theory for a distributed solution. We theoretically prove that both mechanisms guarantee properties such as budget balance and individual rationality for all ESs. Numerical studies with real-world dataset are presented to demonstrate the superior performance of our mechanisms over baselines.

An efficient computation offloading in edge environment using genetic algorithm with directed search techniques for IoT applications

The contemporary computations in the IoT environment are often outsourced to remote infrastructures due to inherent computation-intensive nature of tasks or to circumvent the high expenditures associated with establishing local infrastructures. Traditional approaches, be it cloud-based or localized, are deemed impractical for computation offloading in this scenario due to the trade-off between benefits and increased latency which is unacceptable for many real-time IoT applications. Computation offloading at the edge environment is a promising solution in leveraging the untapped resources at Edge. Offloading application execution to edge servers offers the potential to reduce completion time and fulfill application requirements. However, the simultaneous offloading of multiple entire applications to edge servers may strain their hardware and communication channels and also poses significant challenge for ensuring Quality of Service. To overcome this, a multi-site workflow offloading problem is conceptualized wherein tasks are strategically distributed across various edge sites for efficient execution. The problem is combinatorial and an optimal solution is never guaranteed through deterministic procedures. Thus this work proposes a novel metaheuristic approach based on modified Genetic Algorithm with Directed Search (GADS) operators that aim at optimizing the overall Execution Time (ET) and Energy Consumed (EC). The efficiency of GADS is demonstrated against alternatives, and a relative comparison is carried out in terms of the quality of the solution and the pace of convergence to the solution and approximately a minimum of 1% and maximum 15% gain is achieved.

Computation Offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.

Reinforcement learning-based dynamic load balancing in edge computing networks

Edge computing (EC) has emerged as a paradigm aimed at reducing data transmission latency by bringing computing resources closer to users. However, the limited scale and constrained processing power of EC pose challenges in matching the resource availability of larger cloud networks. Load balancing (LB) algorithms play a crucial role in distributing workload among edge servers and minimizing user latency. This paper presents a novel set of distributed LB algorithms that leverage machine learning techniques to overcome the three limitations of our previous LB algorithm, EVBLB: (i) its reliance on static time intervals for execution, (ii) the need for comprehensive information about all server resources and queued requests for neighbor selection, and (iii) the use of a central coordinator to dispatch incoming user requests over edge servers. To offer increased control, custom configuration, and scalability for LB on edge servers, we propose three efficient algorithms: Q-learning (QL), multi-armed bandit (MAB), and gradient bandit (GB) algorithms. The QL algorithm predicts the subsequent execution time of the EVBLB algorithm by incorporating rewards obtained from previous executions, thereby improving performance across various metrics. The MAB and GB algorithms prioritize near-optimal neighbor node servers while considering dynamic changes in request rate, request size, and edge server resources. Through simulations, we evaluate and compare the algorithms in terms of network throughput, average user response time, and a novel LB metric for workload distribution across edge servers.

Privacy-security oriented chaotic compressed sensing data collection in edge-assisted mobile crowd sensing

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing data through intelligent terminal devices carried by workers. However, due to resource limitations, the confidentiality, integrity and communication cost issues of sensing data have not been well coordinated and resolved in the actual MCS data collection process. In this regard, this paper proposes an edge computing-assisted MCS Chaotic Compressed Sensing Secure Data Collection scheme (CCS-SDC), which supports the secure collection of sensing data and saves communication cost. In CCS-SDC, workers first use the encryption algorithm based on chaos theory to encrypt the collected sensing data, and then adopt the hash location algorithm based on chaos theory to calculate the corresponding hash verification code of the sensing data. After receiving the encrypted sensing data transmitted by the worker, the edge server recomputes the hash verification code of the encrypted sensing data and verifies the integrity of the data, which can locate the changed sensing task data to a certain extent. Then the sensing data is compressed and sampled based on the generated chaos measurement matrix to reduce the amount of data transmission and further enhance the confidentiality of the sensing data. In addition, the same hash positioning algorithm is used between the edge server and the sensing platform to protect data integrity. For the changed data located by integrity verification, in addition to choosing to let workers re-sense and submit, the sensing platform can also choose to discard the changed sensing data under appropriate circumstances, and still reconstruct and decrypt the remaining data through the proposed algorithm to obtain effective original sensing data. The experimental evaluation results on real data sets show that CCS-SDC achieves the best effects, not only achieving lower sensing data communication cost than other related schemes, but also better protecting the confidentiality and integrity of sensing data, which is very useful for resource-constrained MCS data collection scenarios.

Enabling privacy-preserving non-interactive computation for Hamming distance

Hamming distance is a measure of the similarity between two strings of the same length. Privacy-preserving Hamming distance computation allows data users to obtain the Hamming distance between their data without disclosing their respective private data. The existing privacy-preserving protocols for Hamming distance computation require multiple rounds of online interactive computation between two data users. To address this issue, we propose a new Privacy-preserving non-interactive Hamming Distance Computation (PHDC) protocol. Different from previous works, we adopt the strategy of secure outsourcing to avoid the online interactive computation between data users. We move the Hamming distance computation from the user side to the cloud side. The cloud server is responsible for the Hamming distance computation under privacy protection. To preserve data privacy, we propose a novel blinding technique for user data. Data users initially blind their data using homomorphic encryption and randomization techniques. The edge server is responsible for data aggregation and further data blindness. In this way, data users only need to outsource their encrypted data to the edge server, and there is no online interactive computation between data users. With the assistance of the edge server and the cloud server, the privacy-preserving Hamming distance computation is achieved. The security analysis demonstrates that the protocol guarantees the data privacy under the semi-honest adversarial model. The theoretical analysis and experimental results illustrate the efficiency of the proposed protocol.

Edge Computing Based Cardiac Monitoring and Detection System in the Internet of Medical Things

Currently, cardiac disease becomes one of the major health issues in the world. The death due to cardiac arrests has been increasing day by day. The survival rate for the sudden cardiac arrest is low. It is possible to save 60% human life by continuously monitoring the fitness of the person. In recent times, the Internet of Things is emerging promising technologies. If the doctor cannot monitor the patient face to face, there Internet of Things plays important role in remotely monitoring the patient’s condition. With the rise in the use of smart wearable gadgets, the contribution of IoT will be more. In this paper, about five predictor model are analyzed for a particular dataset and chosen the best as Random forest algorithm. The proposed system integrates oxygen saturation level and heart rate sensor data from MAX30100 sensor for monitoring the patient’s vital signs. The remaining parametric data of the patient (AGE, GENDER, BLOOD SUGAR LEVEL, BLOOD PRESSURE LEVEL, GENETIC ISSUE and CHLOESTEROL LEVEL) are fed through the matrix keypad. The designed system monitors the online patient’s vitals continuously. The patient’s vitals will be stored in the edge server and also analyzed. The chosen predictor model checks the vital data for any abnormality and based on abnormality data detection in the heart rate, the warning message is conveyed to the nearby health care center via Thingspeak cloud. By tracking the patient’s GPS location, the server cloud sends alert notification along with the GPS location to the nearby health center and to the registered mobile numbers.

Energy-Delay Joint Optimization for Task Offloading in Digital Twin-Assisted Internet of Vehicles

Vehicle edge computing (VEC) provides efficient services for vehicles by offloading tasks to edge servers. Notably, extant research mainly employs methods such as deep learning and reinforcement learning to make resource allocation decisions, without adequately accounting for the ramifications of high-speed mobility of vehicles and the dynamic nature of the Internet of Vehicles (IoV) on the decision-making process. This paper endeavours to tackle the aforementioned issue through the introduction of a novel concept, namely, a digital twin-assisted IoV. Among them, the digital twin of IoV offers training data for computational offloading and content caching decisions, which allows edge servers to directly interact with the dynamic environment while capturing its dynamic changes in real-time. Through this collaborative endeavour, edge intelligent servers can promptly respond to vehicular requests and return results. We transform the dynamic edge computing problem into a Markov decision process (MDP), and then solve it with the twin delayed deep deterministic policy gradient (TD3) algorithm. Simulation experiments demonstrate the adaptability of our proposed approach in the dynamic environment while successfully enhancing the Quality of Service, that is, decreasing total delay and energy consumption.

Robust Offloading for Edge Computing-Assisted Sensing and Communication Systems: A Deep Reinforcement Learning Approach.

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.

An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services Systems

The visual information processing technology based on deep learning (DL) can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning (TL) to unload deep neural networks (DNN) to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity is still challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of UAV authentication protocol and collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.