Smart Edge Research Articles

Smart Edge-Based Driver Drowsiness Detection in Mobile Crowdsourcing

Traffic accidents caused by drowsy drivers represent a crucial threat to public safety. Recent statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the widespread use of mobile devices and roadside units, these accidents can be significantly prevented using a drowsiness detection solution. While several solutions were proposed in the literature, they all fall short of presenting a distributed architecture that can answer the needs of these applications without breaching the driver’s privacy. This paper proposes a two-stage Driver Drowsiness Detection System using smart edge computing. Mobile devices in the car are used to capture and analyze the current condition of the drivers without sharing their data. The smart edge is deployed as a decision-maker where the drowsiness is confirmed when the information about the driver status received from the mobile client and the observed car path match. Our approach relies on a) a distributed edge architecture that has two levels of hierarchy, namely the Main Edge Node (MEN) and Local Edge Node (LEN), to better manage the area of interest and b) a data fusion offloading strategy that considers: 1) local detection of driver drowsiness through facial expressions using CNN model, 2) global detection of car path through acceleration readings using YoLov5 algorithm, and finally, 3) a two-layer LSTM algorithm for drowsiness detection based on the local and the global detection. The proposed framework achieves drowsiness detection with an average accuracy of 97.7%.

Real-time fruit detection using deep neural networks on CPU (RTFD): An edge AI application

Intelligent harvesting can greatly reduce the manual labor for fruit harvesting and is one of the main application areas of intelligent agricultural machinery. Smart edge sensors must detect the growth status of tomatoes and strawberries in real-time for intelligent harvesting to be possible. Due to edge devices' relatively low image processing power, it remains challenging to implement deep learning algorithms for real-time detection in field applications. Therefore, scholars are strongly interested in utilizing computationally small, accurate, and quick models for edge harvesting machinery. In this research, we propose RTFD, a lightweight algorithm for edge CPU devices that detects fruit. Based on the PicoDet-S model, RTFD optimizes the efficiency of real-time detection for edge CPU computing devices by enhancing the model's structure, loss function and activation function. The experimental results demonstrate that the mAP of RTFD for tomato and strawberry datasets at various growth phases is 0.78 and 0.96, respectively, with a computational effort of 1.44 GFLOPs. RTFD improves mAP by 1.9% and 2.3% on the tomato and strawberry datasets, respectively, with minimal loss in computation and model parameters, making it more suitable for edge device deployment. Meanwhile, we proposed an approach for 8-bit quantization-aware training of the Conv2D and Linear layers of the model for improved edge deployment. This approach quantizes 32-bit floating-point values in the Conv2D and Linear layers into 8-bit integers, thereby reducing the model's size and accelerating detection speed. The testing findings indicate that the quantized RTFD model has a size of 1.33 MB and a CPU detection speed of around 11 FPS. Using the quantified trained model, we developed the Android application RTFD-CPU to evaluate the model's performance in test scenarios. RTFD-CPU was assessed on the smartphone Redmi K30pro (Qualcomm Snapdragon 865 CPU and 8 GB of RAM) with a detection speed of 19 FPS utilizing only the CPU. In summary, the proposed RTFD has great potential for intelligent picking machines, and the concept of redesigning the model structure, loss function, and activation function, as well as training by quantization to speed up the detection of deep neural networks, is anticipated to be successfully implemented in edge computing.

Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing technology. Decentralized deep learning (DDL), such as federated learning and swarm learning, as a promising solution to privacy-preserving data processing for millions of smart edge devices leverages distributed computing of multilayer neural networks within the networking of local clients, without disclosing the original local training data. Notably, in industries such as finance and healthcare, where sensitive data of transactions and personal medical records are cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey article, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state of the art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.

Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach.

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.

Hardware Solutions for Low-Power Smart Edge Computing

The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.

Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.

Exploiting Hardware-Based Data-Parallel and Multithreading Models for Smart Edge Computing in Reconfigurable FPGAs

Current edge computing systems are deployed in highly complex application scenarios with dynamically changing requirements. In order to provide the expected performance and energy efficiency values in these situations, the use of heterogeneous hardware/software platforms at the edge has become widespread. However, these computing platforms still suffer from the lack of unified software-driven programming models to efficiently deploy multi-purpose hardware-accelerated solutions. In parallel, edge computing systems also face another huge challenge: operating under multiple conditions that were not taken into account during any of the design stages. Moreover, these conditions may change over time, forcing self-adaptation mechanisms to become a must. This paper presents an integrated architecture to exploit hardware-accelerated data-parallel models and transparent hardware/software multithreading. In particular, the proposed architecture leverages the ARTICo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> framework and ReconOS to allow developers to select the most suitable programming model to deploy their edge computing applications onto run-time reconfigurable hardware devices. An evolvable hardware system is used as an additional architectural component during validation, providing support for continuous lifelong learning in smart edge computing scenarios. In particular, the proposed setup exhibits online learning capabilities that include learning by imitation from software-based reference algorithms. Experimental results show the benefits of the proposed approach, exposing different run-time tradeoffs (e.g., computing performance versus functional correctness of the evolved solutions), and highlighting the benefits of using scalable data-parallel models to perform circuit evolution under dynamically changing application scenarios.

Flame and smoke recognition on smart edge using deep learning

Flame and smoke recognition on smart edge using deep learning

Industrial Edge Computing: Architecture, Challenges, Applications

Edge computing is the deployment of network operations and data-handling activities toward more solitary sources of data collecting or storage. Industrial edge computing, which includes industrial applications, describes the method of controlling data-handling operations using distinct data sources, such as smart edge devices. As a result, whether collecting, processing, or evaluating data in smart factories, IIoT devices and smart equipment do not need to contact centralised cloud platforms. Information can also be sent to the cloud for additional analysis or integration into a larger system, depending on how important the data analysis performed at the edge is. Industrial edge computing is a term used to describe a distributed platform that combines communication, processing, and storage capabilities to run real-time applications that may be accessed directly from the cloud. The Industrial Internet of Things (IIoT) revolution has raised demands for flexible connectivity, realtime control, data optimization, intelligent applications, security, and privacy protection. Edge computing nodes serve as intelligent gateways for assets, services, and systems, bridging the gap between the real and digital worlds.

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.

PCFed: Privacy-Enhanced and Communication-Efficient Federated Learning for Industrial IoTs

Federated learning (FL) is capable of analyzing tremendous data from smart edge devices in Industrial Internet of Things (IIoTs), empowering numerous industrial applications. However, the increasing privacy concerns and deployment costs of IIoT environment have been posing new challenges for FL. This article proposes PCFed, a novel privacy-enhanced and communication-efficient FL framework to provide higher model accuracy with rigorous privacy guarantees and great communication efficiency. In particular, we develop a sampling-based intermittent communication strategy via a PID (proportional, integral, and derivative) controller on the cloud server to adaptively reduce the communication frequency. In addition, we design a budget allocation mechanism to balance the tradeoff between model accuracy and privacy loss. Then, we develop PCFed+, an enhanced variant for PCFed, with further consideration of infinite data streams on edge servers. Extensive experiments demonstrate that both PCFed and PCFed+ can significantly outperform existing schemes, in terms of communication efficiency, privacy protection, and model accuracy.

The implementation and performance evaluation for a smart robot with edge computing algorithms

PurposeApart from, the smart edge computing (EC) robot (SECR) provides the tools to manage Internet of things (IoT) services in the edge landscape by means of real-world test-bed designed in ECR. Eventually, based on the results from two experiments held in little constrained condition, such as the maximum data size is 2GB, the performance of the proposed techniques demonstrate the effectiveness, scalability and performance efficiency of the proposed IoT model.Design/methodology/approachCertainly, the proposed SECR is trying primarily to take over other traditional static robots in a centralized or distributed cloud environment. One aspect of representation of the proposed edge computing algorithms is due to challenge to slow down the consumption of time which happened in an artificial intelligence (AI) robot system. Thus, the developed SECR trained by tiny machine learning (TinyML) techniques to develop a decentralized and dynamic software environment.FindingsSpecifically, the waste time of SECR has actually slowed down when it is embedded with Edge Computing devices in the demonstration of data transmission within different paths. The TinyML is applied to train with image data sets for generating a framework running in the SECR for the recognition which has also proved with a second complete experiment.Originality/valueThe work presented in this paper is the first research effort, and which is focusing on resource allocation and dynamic path selection for edge computing. The developed platform using a decoupled resource management model that manages the allocation of micro node resources independent of the service provisioning performed at the cloud and manager nodes. Besides, the algorithm of the edge computing management is established with different path and pass large data to cloud and receive it. In this work which considered the SECR framework is able to perform the same function as that supports to the multi-dimensional scaling (MDS).

A Low-power Programmable Machine Learning Hardware Accelerator Design for Intelligent Edge Devices

With the advent of the machine learning and IoT, many low-power edge devices, such as wearable devices with various sensors, are used for machine learning–based intelligent applications, such as healthcare or motion recognition. While these applications are becoming more complex to provide high-quality services, the performance of conventional low-power edge devices with extremely limited hardware resources is insufficient to support the emerging intelligent applications. We designed a hardware accelerator, called an Intelligence Boost Engine (IBE), for low-power smart edge devices to enable the real-time processing of emerging intelligent applications with energy efficiency and limited programmability. The measurement results confirm that the proposed IBE can reduce the power consumption of the edge node device by 75% and achieve performance improvement in processing the kernel operations of applications such as motion recognition by 69.9 times.

Learning-Based Joint QoE Optimization for Adaptive Video Streaming Based on Smart Edge

The latest increase in HTTP-based adaptive video streaming over the Internet enables a growing number of clients to compete for a shared bottleneck bandwidth. This competition may affect users&#x2019; Quality of Experience (QoE) negatively, especially in terms of fairness and stability. This paper presents <i>Flex-Steward</i>, a solution that performs multi-client joint QoE optimization for adaptive video streaming during bottleneck bandwidth sharing. Joint QoE optimization refers to improving QoE fairness among clients with various video devices and availing from differentiated services with different priorities. Flex-Steward deploys an adaptive bitrate delivery algorithm based on Neural Networks (NN) and reinforcement learning at the network edge. It relies on a trained NN model to make appropriate bitrate recommendations in terms of video chunks to be requested by clients sharing the same bottleneck bandwidth. Flex-Steward is assessed in comparison with alternative state-of-the-art algorithms under different network conditions using a real-life prototype. Results show how Flex-Steward reduces the unfairness in terms of joint QoE optimization with between 10.9&#x0025; and 41.7&#x0025;.

Convolutional Echo‐State Network with Random Memristors for Spatiotemporal Signal Classification

The unprecedented development of Internet of Things results in the explosion of spatiotemporal signals generated by smart edge devices, leading to a surge of interest in real‐time learning of such data. This imposes a big challenge to conventional digital hardware because of physically separated memory and processing units and the transistor scaling limit. Memristors are deemed a solution for efficient and portable deep learning. However, their ionic resistive switching incurs large programming stochasticity and energy, compromising their advantages in real‐time learning spatiotemporal signals. To address the aforementioned issues, we propose a novel hardware–software codesign. Hardware‐wise, the stochasticity in memristor programming is leveraged to produce random matrices for efficient in‐memory computing. Software‐wise, random convolutional‐pooling architectures are integrated with echo‐state networks that compute with the hardware random matrices and make real‐time learning affordable. The synergy of the hardware and software not only improves the performance over conventional echo‐state networks, that is, 90.94% and 91.67% (compared to baselines 88.33% and 62.50%), but also retains 187.79× and 93.66× improvement of energy efficiency compared to the digital alternatives on the representative Human Activity Recognition Using Smartphones (HAR) and CRICKET datasets, respectively. These advantages make random convolutional echo‐state network (RCESN) a promising solution for the future smart edge hardware.

SmartDefense: A distributed deep defense against DDoS attacks with edge computing

The growing number of IoT edge devices have inflicted a change in the cyber-attack space. The DDoS attacks, in particular, have significantly increased in magnitude and intensity. Of the existing DDoS solutions, while the destination-based defense mechanisms incur high false positives due to the seemingly legitimate nature of the attack traffic, defense mechanisms implemented at the source alone do not suffice due to the lack of visibility into ongoing DDoS attacks. This paper proposes a distributed DDoS detection and mitigation framework, SmartDefense, based on edge computing approaches towards detecting and mitigating DDoS attacks at and near the source. By mitigating the DDoS attacks near the source, SmartDefense significantly reduces unnecessary bandwidth otherwise consumed by DDoS traffic going from residential edge networks to the ISP edge network.Furthermore, SmartDefense demonstrates how ISPs can detect botnet devices in their customer’s network by having smart edge devices pass attributes that are processed by the botnet detection engine at the provider’s edge. The evaluation of this work shows that SmartDefense can improve the detection and mitigation rate, with over 90% of DDoS traffic caught at the source and over 97.5% of remaining DDoS traffic caught at the provider’s edge. Our experiments also demonstrate how using a botnet detection engine can further reduce the DDoS traffic by up to 51.95% by facilitating ISPs to detect bot devices in their customers’ edge network.

Smart-3DM: Data-driven decision making using smart edge computing in hetero-crowdsensing environment

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm for Mobile Crowdsensing (MCS) environments. In a given Area of Interest (AoI), the sensing process is performed based on task requirements, which usually ask for a specific quality of the sensing outcome. In this work, a two-stage Data-Driven Decision-making Mechanism using smart edge computing (Smart-3DM) is proposed. It advocates the use of smart edge to better fulfill the data-related task requirements. Depending on the type of data to be collected, the minimum quality of the data required, and the heuristics to apply for each type of crowdsensing service, the smart edge orchestrates the selection of workers in MEC. Our approach relies on (a) smart-edge deployment: where a cluster-based distributed architecture using smart edge nodes is considered. Here, two entities are defined: the main edge node (MEN) and the local edge nodes (LENs); and (b) data management offloading where a two-layer re-selection strategy that considers data type and context-awareness is adopted, to reduce data computation complexity and to increase data quality while meeting the task target. The proposed Smart-3DM is evaluated using a real-life dataset and is compared to one-stage local and global approaches. The overall results show that by using two-stage re-selection strategies, better performance with lower processing power (CPU), less Storage(RAM), and improved execution time is achieved, when compared to the benchmarks.

Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments

There has been an enormous increase in information flow and communication data due to the rapid rise in the number of Internet of Everything (IoE) devices and the development of cutting-edge technologies such as the rollout of the Sixth Generation (6G) network. The rising and inevitable off-loading requirements of IoE devices have resulted in an unprecedented increase in the reliance on edge and cloud paradigms. However, such a reliance on far-end technologies to access already scarce resources can often result in increased latency and unstable connection issues due to limited bandwidth. In this paper, we investigate the solution for such a stringent network design by presenting a conceptual cloud architecture based on key components such as resource allocation, scheduling and task off-loading for IoE devices. The IoE devices utilise a scheduler to access resources from nearby higher resourced IoE devices for their task computation, where the scheduler allocates incoming requests according to the availability of resources within a cluster of devices or to other devices in nearby clusters. Motivated by these design characteristics, we propose a design of a novel Main Task Off-loading Scheduling Algorithm (MTOSA) for efficient task allocation and dissemination. We present a theoretical analysis of five different scheduling policies namely Round Robin (RR), Strongest Channel (SC), Max Rate (MR), Proportional Fair (PF) and Priority Base (PB) scheduling to find an optimal technique for task off-loading in futuristic networks. Furthermore, we compare the performance of these five scheduling policies with the two existing scheduling policies from the literature. It is shown through various experiments that the proposed MTOSA algorithm performs better when compared with the existing schemes for different performance parameters.

Computationally efficient topology optimization of scale-free IoT networks

The malicious attacks in the scale-free Internet of Things (IoT) networks create a serious threat for the functionality of nodes. During the malicious attacks, the removal of high degree nodes greatly affects the connectivity of the remaining nodes in the networks. Therefore, ensuring the maximum connectivity among the nodes is an important part of the topology optimization. A good scale-free network has the ability to maintain the functionality of the nodes even if some of them are removed from the network. Thus, designing a robust network to support the nodes’ functionality is the aim of topology optimization in the scale-free networks. Moreover, the computational complexity of an optimization process increases the cost of the network. Therefore, in this paper, the main objective is to reduce the computational cost of the network with the aim of constructing a robust network topology. Thus, four solutions are presented to reduce the computational cost of the network. First, a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the standard Random Edge Swap Mechanism (RESM). Second, a threshold based node removal method is introduced to reduce the operation of the edge swap mechanism when an objective function converges at a point. Third, multiple attacks are performed in the network to find the correlation between the measures, which are degree, betweenness and closeness centralities. Fourth, based on the third solution, a Heat Map Centrality (HMC) is used that finds the set of most important nodes from the network. The HMC damages the network by utilizing the information of two positively correlated measures. It helps to provide a good attack strategy for robust optimization. The simulation results demonstrate the efficacy of the proposed SESM mechanism. It outperforms the existing RESM mechanism by almost 4% better network robustness and 10% less number of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost of the network.

Guest Editorial: IEEE TC Special Issue On Smart Edge Computing and IoT

The papers in this special section focus on smart edge computing and the Internet of Things (IoT). The evolution of the (IoT) is changing the nature of edge-computing devices. Availability of novel sensor interfaces, efficient digital low power processors, and high-bandwidth low-power communication protocols have generated a perfect storm within the IoT ecosystem. Next generation IoT end-nodes have to support, in place, an increasing range of functionality: multi-sensory data processing and analysis, complex systems control strategies, and, ultimately, artificial intelligence. These new capabilities will enable disruptive innovation in wearable and implantable biomedical devices, autonomous insect-sized drones, miniaturized devices for environmental sensing and continuous monitoring of buildings, industrial machinery, power grids. As a result, we witness a paradigm shift towards computationally demanding tasks on tiny form-factor devices at extreme energy efficiency.