Internet Of Things Edge Research Articles

Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.

Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning

A significant subset of ubiquitous computing known as vehicular ad hoc networks (VANETs) serves as a foundational piece of technology for VANET applications. Blockchain technology can be used in microgrids to conduct safe point-to-point transactions between anonymous parties while also addressing needs for security. This research is o proposes novel method in IoT(internet of things) based edge cloud computing architecture with microgrid energy management of VANET. here the VANET communication is carried out based on IoT edge cloud computing module and the smart microgrid architecture is used for energy management in VANET. then each vehicle energy has been analysed using structural reinforcement variational encoder neural networks. experimental analysis is carried out in terms of energy efficiency, network lifetime, training accuracy, QoS, communication overhead. the proposed technique attained energy efficiency of 96%, network lifetime of 85%, training accuracy of 98%, QoS of 95%, communication overhead of 55%.

Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment

With the development of the Internet of Things (IoT), more and more devices are connected in edge-cloud environment, and spectrum scarcity has become a major bottleneck for the employment of IoT devices in the end side of cloud computing. In addition, due to the broadcast nature of RF link, the potential eavesdropper may be presented in the wireless environment. To improve spectrum efficiency (SE) and ensure safe transmission of the users, in this paper, the two-way relay cooperative Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) with massive multiple-in multiple-out (MIMO) is proposed. Our objective is to maximize the security energy efficiency (SEE) of the cognitive system subject to the quality of service (QoS) of users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e., the optimization of transmit power, power allocation, and antenna selection, respectively. The Lagrange dual algorithm based on the first-order Taylor series expansion function is proposed to solve the non-convex problem. Moreover, a novel orthogonal antenna selection algorithm is proposed to decrease the cost of radio frequency chains and limit the interference to the primary user. The simulation results show that the proposed scheme has significant performance gain on SEE. It can effectively increase the number of access for cloud computing edge IoT users. In addition, due to the impact of correlation between the antennas, as the number of relay antennas increases, the SEE converges to a specific value, which exists the optimal number of antenna.

Enhancing human activity recognition using features reduction in IoT edge and Azure cloud

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These technologies have triggered huge traffic and delay by continuously transmitting telemetry data to the cloud. IoT edge choice made decision-making closer to the environment, which decreases traffic and speeds up response time. Human activity recognition (HAR) systems, among other IoT applications, require systems with quick response time; reduce costs using constrained traffic to the cloud while maintaining accurate prediction results. This study proposes an application of HAR for predicting activities using up to three smartphone accelerometers. Three models are developed, trained, and deployed to achieve the necessary accuracy at the IoT edge and in the cloud, with an acceptable response time. Since each coordinate value from the three accelerometers has different importance in activity category prediction, focusing on the data from the most related values can help minimize the amount of information transferred from the edge to the cloud. Six models were trained in the cloud; three were deployed and tested at the edge with different features by selecting the most important ones using Principal Component Analysis (PCA). Different experiments showed that traffic and processing time decreased significantly based on the time required to predict HAR categories with acceptable accuracy. Since there is significant latency between the edge and the cloud and within the cloud, sending samples for verification save bandwidth, and processing requests locally at the edge speed up predictions. Results illustrate that the time required to serve one request from the environment where smartphones generate traffic through the internet connection to the cloud took about 5.8 s on average, including transmission delays and the prediction process. During this time, the model at the edge can serve 150 requests with the same accuracy using nine features. In addition, the edge can serve 286 requests in 5.8 s with 94.8 % accuracy when choosing the top four features at the edge.

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

The 6th generation (6G) mobile communications technology will realize the interconnection of humans, machines, things as well as virtual space. The development of digital twins (DTs) and 6G has accelerated the Internet of Things (IoT) in an unprecedented way. The combination of DTs and edge intelligence (EI) enables powerful digital space synchronized with the real world constructed in the intelligent edge, bringing real-time, and adaptive services delivery of IoT. However, the dynamic features and heterogeneous resources in 6G-enabled IoT make the resource allocation for computation-intensive and delay-sensitive DTs services more challenging. In this paper, we first define the DTs implementation process as a DT service function chain (DTSFC) and address the resource allocation problem of DTs-empowered networks in form of dynamic DTSFCs orchestration. We further propose a novel collective reinforcement learning (CRL) method which is inspired by human collaboration, to realize the effective resource allocation of DTSFCs. Numerical results verify that the proposed CRL algorithm improves the learning efficiency and generalization ability compared with the benchmarks.

Energy-Efficient Machine Learning for IoT Edge Devices: A Federated Learning Approach

In the realm of modern-day IoT (Internet of Things) deployments, the quest for energy-efficient solutions stands paramount for different use cases. As the IoT ecosystem continues to burgeon, there arises an exigency for resource-conscious algorithms that can effectively navigate the intricacies of data routing while minimizing energy consumption levels. This paper illuminates a pivotal advancement in this domain, introducing a novel paradigm for different scenarios. Existing methodologies have long grappled with the challenge of balancing the ever-increasing computational demands of IoT devices with their constrained power resources. Conventional routing strategies often lack the depth required to optimize energy consumption, resulting in inefficiencies that hamper overall system performance. Prior research has primarily focused on rudimentary routing techniques that fall short in addressing the multifaceted demands of contemporary IoT ecosystems. In response to these limitations, this paper propounds a breakthrough model that leverages the power of Deep Dyna Q integrated with a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). This fusion of cutting-edge technologies not only enhances energy efficiency but also ushers in a new era of intelligent data routing within IoT deployments. The chosen methodology is deliberate; Deep Dyna Q harnesses the power of reinforcement learning to dynamically adapt routing decisions, while the LSTM-based RNN augments the model's ability to capture contextual dependencies crucial in IoT scenarios. This synergy engenders superior energy optimization, transforming the way data is routed in IoT edge devices & samples. The results are resoundingly positive, with empirical evidence showcasing the model's prowess. Tested across multiple contextual datasets, our approach exhibits a remarkable 10.5% improvement in energy efficiency, a commendable 8.5% boost in speed, a 3.9% uptick in throughput, a 4.5% surge in packet delivery ratio, and a notable 4.9% enhancement in consistency when compared against existing methods. Such substantial improvements underscore the transformative impact of our work, setting new standards for energy-efficient data routing in IoT deployments.

Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinforcement learning to optimize federated learning in heterogeneous device IoT environments. Additionally, a metric model for IoT devices is proposed to evaluate the performance of different devices. The experimental results demonstrate that the proposed method can improve training accuracy by 30% in a heterogeneous device IoT environment.

Hybrid of Supervised Learning and Optimization Algorithm for Optimal Detection of IoT Distributed Denial of Service Attacks

The high-speed internet has led to the development of Internet of Things (IoT) with a fundamental Three-Layer IoT architecture. However, small amount of un-indicative data captured at the end level of IoT network makes the edge IoT devices susceptible to cyber-security attacks aimed at its transport layer. The Distributed Denial of Service (DDoS) poses significant cyber-security threat to the heterogenous IoT devices which are rendered vulnerable by ineffectiveness of conventional cybersecurity softwares. The literature reveals numerous studies that employed machine learning for the mitigation of IoT DDoS attacks but they lack in terms of an extensive investigation on optimization of machine learning classifiers. Therefore, this study first evaluates the prediction performance of machine learning classification algorithms trained on an authenticated/validated real-time IoT traffic dataset. The results reveal Logistic Regression (LR) as the most effective supervised machine learning classifier for detecting IoT DDoS attacks with a prediction accuracy of 97%. Following this, another investigation on the hybridization of LR with optimization algorithms yields Grasshopper Optimizer Algorithms (GOA) as the most effective optimizer in improving its prediction accuracy to 99%. Hence, the LR hybridized by GOA is developed as the optimal IoT DDoS Attack detection solution. Thus, the study serves to lay the foundation of a data-driven approach for the mitigation of the emerging variants of malicious IoT DDoS attacks such as zero-day attacks.

Framework Design of an Edge Gateway System Supporting Multi-Protocol Standardized Access Detection

Recently, intelligent city construction has been promoted with the development of the Internet of things (IoT). The edge IoT gateway plays a critical role as the data aggregation core and processing center. Most existing gateways mainly solve heavy data storage and processing loads in cloud computing centers. There is less attention paid to multi-protocol data transmission and fusion. However, multiple products with different protocols in an IoT system require a flexible gateway compatible with multiple protocols. This paper proposes a multi-protocol edge gateway. The frame design was based on the actual demand for edge data acquisition. The gateway hardware platform used an RK3399 chip transplanted from the embedded operating system. It could support simultaneous multi-protocol access to ZigBee, LoRa, Bluetooth, and Wi-Fi. We combined the plug-and-play (PnP) hardware device access detection scheme with the system onboard interface driver to realize dynamic access detection and unified device management. In addition, the gateway also integrated data storage and access functions and partial edge computing functions. Finally, the experiment results verified that the multi-protocol edge gateway could meet the demand for data access and device control.

MIM-Graph: A multi-sensor network approach for fault diagnosis of HSR Bogie bearings at the IoT edge via mutual information maximization

The Internet of Things (IoT) is crucial in developing next-generation high-speed railways (HSRs). HSR IoT enables intelligent diagnosis of trains using multi-sensor data, which is critical for maintaining high speeds and ensuring passenger safety. Graph neural network (GNN)-based methods have gained popularity in HSR IoT research due to the ability to represent the sensor network as intuitive graphs. However, labeling monitoring data in the HSR scenario takes time and effort. To address this challenge, we propose a semi-supervised graph-level representation learning approach called MIM-Graph, which uses mutual information maximization to learn from a large amount of unlabeled data. First, the multi-sensor data is converted into association graphs based on their spatial topology. The unsupervised encoder is trained using global–local mutual maximization. The teacher–student framework transfers knowledge from the unsupervised encoder learned to the supervised encoder, which is trained using a small amount of labeled data. As a result, the supervised encoder learns distinguishable representations for intelligent diagnosis of HSR. We evaluate the proposed method using CWRU dataset and data from HSR Bogie test platform, and the experimental results demonstrate the effectiveness and superiority of MIM-Graph.

IoT Networks-Aided Perception Vocal Music Singing Learning System and Piano Teaching with Edge Computing

The research on Internet of Things (IoT) network and edge computing has been a research hotspot in both industry and academia in recent years, especially for the ambient intelligence and massive communication. As a typical form of IoT network and edge computing, the intelligent perception vocal music singing learning system has attracted the attention of researchers in education and academia. Piano teaching is an important course for music majors in higher education. Strengthening piano teaching can cultivate outstanding piano talents for the country and promote the development of music art. This paper applies IoT perception technology to piano teaching, constructs an intelligent piano teaching system, and uses edge computing algorithms to accurately deploy sensors into the system by exploiting the ambient intelligence and massive communication. The system includes data acquisition, data perception, data monitoring, and other modules, making piano teaching more humanized and intelligent. Experiments show that the research in this paper provides important guidance for the application of IoT networks and edge computing, especially for the ambient intelligence and massive communication.

DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

Blockchain-Aided Network Resource Orchestration in Intelligent Internet of Things

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

Edge computing-based digital management system of game events in the era of Internet of Things

With the great development of Internet of Things (IoT) and edge computing, the development of sports activities depends on the development of information technology and it is inevitable to pay attention to the combination and optimization of resources. The combination of IoT and edge computing will be critical in sports activities. This paper elaborates on the application of network skill in sports event information management, that is, through the effective gathering of sports event data, to realize the use of sports event information, to achieve the purpose of information and digitization. Furthermore, the goal is to investigate the effect of sports event in the era of IoT. The impact of sports events on the economy and culture of the hosting city is investigated using IoT concept of edge computing. By analyzing the advantages and disadvantages of traditional centralized optimization method, we present a series of performance indicators and utility functions and show that the method is effective and achieves the optimal purpose. Through vital research, it is found that with the development of the edge computing and IoT industry, the scale of sports events is constantly expanding. By 2019, there has been a scale of 1,271 billion yuan. An increase of 981 billion yuan, compared with 290 billion yuan in 2013. Therefore, the use of the IoT technology in combination with edge computing to manage sports events will greatly encourage the expansion of sports activities. Furthermore, the holding of sporting events reflects a city’s overall strength and enhances the city’s exposure and fame. The investigation offers a certain reference point for cities looking to increase their influence through events.

Edge–IoT Computing and Networking Resource Allocation for Decomposable Deep Learning Inference

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.

Dynamic Reliability Management of Multigateway IoT Edge Computing Systems

The emerging paradigm of edge computing envisions to overcome the shortcomings of cloud-centric Internet of Things (IoT) by providing data processing and storage capabilities closer to the source of data. Accordingly, IoT edge devices, with the increasing demand of computation workloads on them, are prone to failures more than ever. Hard failures in hardware due to aging and reliability degradation are particularly important since they are irrecoverable, requiring maintenance for the replacement of defective parts, at high costs. In this article, we propose a novel dynamic reliability management (DRM) technique for multigateway IoT edge computing systems to mitigate degradation and defer early hard failures. Taking advantage of the edge computing architecture, we utilize gateways for computation offloading with the primary goal of maximizing the battery lifetime of edge devices, while satisfying the Quality of Service (QoS) and reliability requirements. We present a two-level management scheme, which work together to 1) choose the offloading rates of edge devices; 2) assign edge devices to gateways; and 3) decide multihop data flow routes and rates in the network. The offloading rates are selected by a hierarchical multitimescale distributed controller. We assign edge devices by solving a bottleneck generalized assignment problem (BGAP) and compute optimal flows in a fully distributed fashion, leveraging the subgradient method. Our results, based on real measurements and trace-driven simulation, demonstrate that the proposed scheme can achieve a similar battery lifetime and better QoS compared to the state-of-the-art approaches while satisfying reliability requirements, where other approaches fail by a large margin.

AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

The paradigm of edge computing has formed an innovative scope within the domain of the Internet of Things (IoT) through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous, distributed and resource-constrained essence of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in Artificial Intelligence (AI) (especially Machine Learning), and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge computing framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology to offer automated and secure AI-based applications at the network edge.

Service Home Identification of Multiple-Source IoT Applications in Edge Computing

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveillance

Advances in communication technologies and computational capabilities of Internet of Things (IoT) devices enable a range of complex applications that require ever increasing processing of sensors' data. An illustrative example is real-time video surveillance that captures videos of target scenes and process them to detect anomalies using deep learning (DL). Running deep learning models requires huge processing and incurs high computation delay and energy consumption on resource-constraint IoT devices. In this article, we introduce methods for distributed inference over IoT devices and edge server. Two distinct algorithms are proposed to split the deep neural network layers computation between IoT device and an edge server; the early split strategy (ESS) for battery powered IoT devices and the late split strategy (LSS) for IoT devices connected to regular power source. The evaluation shows that both the ESS and LSS schemes achieve the target inference delay deadline when tested over VGG16 and MobileNet_V2 CNN models. In terms of computational load, the ESS scheme achieves nearly 15–20% reduction whereas LSS scheme achieves up to 60% reduction. The gains in energy saving of IoT devices for both the ESS and LSS schemes are nearly 18% and 52%, respectively.

A Deep Subdomain Adaptation Network With Attention Mechanism for Malware Variant Traffic Identification at an IoT Edge Gateway

The prevailing of malware variants in ubiquitous Internet of Things (IoT) devices causes enormous losses. Accurate and timely identification of malware variant traffic at an IoT edge gateway can effectively reduce the loss. TransNet, the state-of-the-art technology for malware variant traffic detection, considers only global domain adaptation and ignores the alignment of distributions between different subdomains, which fails to capture the fine-grained information of classification targets. Besides, TransNet converges very slowly, which may use up precious resources in IoT devices. This article proposes a deep subdomain adaptation network with attention mechanism (DSAN-AT) to accurately and efficiently identify malware variant traffic at an IoT edge gateway. DSAN-AT utilizes local maximum mean discrepancy (LMMD) to align the traffic feature distributions of subdomains in the source and target domains. It also exploits channel and spatial attention mechanisms to accelerate learning traffic features between different subdomains to save precious computing resources at the IoT edge gateway. Our experimental study demonstrates that DSAN-AT achieves an average accuracy of 97.15% (96.37% for TransNet) and converges fast without using a large target domain training data set. DSAN-AT has strong practicality for identifying malware variant traffic at an edge IoT gateway.