Internet Of Things Edge Research Articles

Failure-Aware and Delay-Predicted Multipath Virtual Queue Scheduling for Multimedia Transmission in Edge IoT

The spread of Edge Internet of Things (IoT) radically changes our lifestyle. However, the multimedia services in edge IoT are still stuck by inefficiency. The dynamic typologies perplex the transmission of massive real-time data. To solve this problem, multipath transmission control protocol (MPTCP) which has a natural advantage in transmission robustness and bandwidth aggregation is becoming a good choice. In this paper, failure-aware and delay-predicted multipath virtual queue scheduling (FD-MVQS) is proposed to optimize the MPTCP performance in edge IoT. FD-MVQS constructs a two-plane cooperative scheduling system. In the control plane, the transmission failure estimation and chaos theory-based arrival delay prediction methods are introduced to provide the foundation for prescheduling. In the data plane, the multipath virtual queue scheduling is designed to allocate segments to different subflows. Simulation results showed that the proposed FD-MVQS performed better than standard and typical multipath transmission solutions in throughput, delay, and segment disorder.

Advanced centralized and distributed SVM models over different IoT levels for edge layer intelligence and control

In this era, internet-of-things (IoT) deal with billions of edge devices potentially connected to each other. Maximum applications built on these edge devices generate a massive amount of online data and also require real-time computation and decision making with low latency (e.g., robotics/ drones, self-driving cars, smart IoT, electronics/ wearable devices). To suffice the requirement, the future generation intelligent edge devices need to be capable of computing complex machine learning algorithms on live data in real-time. Considering different layers of IoT and distributed computing concept, this paper suggests three different operational models where the ML algorithm will be executed in a distributed manner between the edge and cloud layer of IoT so that the edge node can take a decision in real-time. The three models are; model 1: training and prediction both will be done locally by the edge, model 2: training at the server and decision making at the edge node, and model 3: distributed training and distributed decision making at the edge level with global shared knowledge and security. All three models have been tested using support vector machine using thirteen diverse datasets to profile their performance in terms of both training and inference time. A comparative study between the computational performance of the edge and cloud nodes is also presented here. Through the simulated experiments using the different datasets, it is observed that, the edge node inference time is approximately ten times faster than cloud time for all tested datasets for each proposed model. At the same time, the model 2 training time is approximately nine times faster than model 1 and eleven times faster than model 3.

IMSC-EIoTD: Identity Management and Secure Communication for Edge IoT Devices.

The Internet of things (IoT) will accommodate several billions of devices to the Internet to enhance human society as well as to improve the quality of living. A huge number of sensors, actuators, gateways, servers, and related end-user applications will be connected to the Internet. All these entities require identities to communicate with each other. The communicating devices may have mobility and currently, the only main identity solution is IP based identity management which is not suitable for the authentication and authorization of the heterogeneous IoT devices. Sometimes devices and applications need to communicate in real-time to make decisions within very short times. Most of the recently proposed solutions for identity management are cloud-based. Those cloud-based identity management solutions are not feasible for heterogeneous IoT devices. In this paper, we have proposed an edge-fog based decentralized identity management and authentication solution for IoT devices (IoTD) and edge IoT gateways (EIoTG). We have also presented a secure communication protocol for communication between edge IoT devices and edge IoT gateways. The proposed security protocols are verified using Scyther formal verification tool, which is a popular tool for automated verification of security protocols. The proposed model is specified using the PROMELA language. SPIN model checker is used to confirm the specification of the proposed model. The results show different message flows without any error.

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things.

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor–critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

Self-Powered 6LoWPAN Sensor Node for Green IoT Edge Devices

In this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report data wirelessly and powered perpetually using clean energy. The developed edge device utilizes solar energy harvesting mechanism through photovoltaic (PV) module, this approach will avoid periodical battery replacement and hence, the energy supplied to the sensor mode is not limited anymore. The implemented testbed is based on open-source hardware and software platforms while the simulation environment is based on MATLAB/SIMULINK 2019a. The effects of temperature and solar irradiance on the performance of the developed approach are examined in order to confirm the leverage of the proposed methodology scheme. The lifetime of the developed green IoT device is predicted based on the device’s activities, current consumption, and energy storage capacity. The obtained results showed that the battery lifetime is extended by 38-49% when the edge device runs on an independent power source.

Continuous Monitoring of Train Parameters Using IoT Sensor and Edge Computing

The speed and the overall loading of a train are of great importance in determining whether the entire train is running in a safe and stable manner. We present here our work in developing a wireless rail monitoring system using IoT (Internet-of-Things) sensors and edge computing to provide accurate real-time running state parameters of trains continuously over time. By placing a wireless MEMS-based sensing system (overall size of 50×50×17 mm) on a rail to collect the vibration information of the rail under excitation by the wheels of passing trains, and by using edge computing technique to analyze these captured vibration data, we calculated the state parameters of the vehicle with the microprocessor integrated with the MEMS sensors. Then, the final analysis results were uploaded to a cloud server via Narrow Band Internet-of-Things (NB-IoT) networks. The entire system consists of a solar power module, a cloud server, and an IoT sensor. Compared with traditional train state detection systems (i.e., computer vision or IR detectors) this system has the advantages of low cost, self-powering, and no line occupation. We have demonstrated that 24-hour real-time wireless monitoring without occupation of track resources is feasible using this system, which greatly improves the efficiency and quality of railway track detection. We conducted field experiments on the Datong-Qinhuangdao Railway line using our system. Experimental results showed that the absolute error in speed is 0.2 Km/h and no error in carriages quantity detection. It is expected to play a key role in the real-time monitoring of railways and trains in the future.

Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach

Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.

Tracking Pandemics: A MEC-Enabled IoT Ecosystem with Learning Capability

The COVID-19 pandemic has resulted in unprecedented challenges to global society and the healthcare system in particular. The main objective of this article is to introduce an end-to-end Internet of Things (IoT) ecosystem for healthcare that uses an open source hardware and interoperable IoT standard for eHealth monitoring in general, and COVID-19 symptoms (e.g., fever, coughing, and fatigue) in particular. The system is designed to monitor the physical conditions of human subjects and send the data to a hierarchical multi-access edge computing (MEC) framework. Such a system is expected to be cognizant, taskable (i.e., tasks can be assigned to any computing process in the system), and adaptable. To this end, we demonstrate how a learning method can be introduced in the ecosystem to achieve taskability and efficiency. Specifically, the proposed system utilizes a shared representation learning process to extract actionable information from large volumes of high-dimensional data obtained from IoT edge devices. These edge devices are enabled with tri-sensors for real-time monitoring of COVID-19 symptoms. The feasibility of the proposed system is evaluated by testing real datasets.

Multi-agent reinforcement learning for resource allocation in IoT networks with edge computing

To support popular Internet of Things (IoT) applications such as virtual reality and mobile games, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency and distributed data processing. However, it is challenging for multiple users to offload their computation tasks because they are competing for spectrum and computation as well as Radio Access Technologies (RAT) resources. In this paper, we investigate computation offloading mechanism of multiple selfish users with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Each user is a learning agent observing its local network environment to learn optimal decisions on either local computing or edge computing with a goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other users. Since users' decisions are coupling at the gateway, we define the reward function of each user by considering the aggregated effect of other users. Therefore, a multi-agent reinforcement learning framework is developed to solve the game with the proposed Independent Learners based Multi-Agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway. Finally, compared with the other three benchmark algorithms, it has better system cost performance and achieves distributed computation offloading.

Reconfigurable Microarchitecture-Based PMDC Prototype Development for IoT Edge Computing Utilization

in this article, a Reconfigurable Microarchi- tecture-based PMDC prototype is developed with the help of dynamic pipeline stages. The Reconfigurable Microarchitecture is intended for the Internet of Things (IoT) edge level computing utilization purposes. This microarchitecture is implemented using a reconfigurable computing system with the help of an FPGA board. The reconfigurable microarchitecture is used as a feedback circuit of the PMDC motor to come out with a developed speed control prototype that can be sufficiently integrated with IoT technology. The number of pipeline stages will dynamically change as per user speed requirements. Several methods of speed measurement are tested using different pipeline structures to find out an appropriate method for the IoT edge level. Resource utilization, throughput and latency for those methods are analyzed. Additionally, the reconfigurable microarchitecture gives dynamic power consumption which more suitable for IoT edge level computations. The Real-Time behavior of the developed prototype is carried out with a pseudo-random binary sequence (PRBS) test for its system identification, a particle swarm optimization (PSO) technique for its optimal controller design and edge level computations for its IoT integration procedure.

Machine Learning Attacks and Countermeasures for PUF-Based IoT Edge Node Security

The Internet of things (IoT) ecosystem has grown exponentially with the convergence of various technologies such as deep learning, sensor systems, and advances in computing platforms. With such a highly pervasive nature of “smart” devices, the nature of data being collected and processed can be increasingly private and require safeguards to ensure the data’s integrity and security. Physically unclonable functions (PUFs) have emerged as a lightweight, viable security protocol in the Internet of Things (IoT) framework. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. In this work, we show that knowledge of the underlying PUF structure is unnecessary to clone a PUF. We tackle the problem of cloning PUF-based edge nodes in different settings such as unencrypted, encrypted, and obfuscated challenges in an IoT framework. We present a novel non-invasive, architecture-independent, machine learning attack for robust PUF designs and can handle encryption and obfuscation-based security measures on the transmitted challenge response pairs (CRPs). We show that the proposed framework can successfully clone different PUF architectures, including those encrypted using two (2) different encryption protocols in DES and AES and with varying degrees of obfuscation. We also show that the proposed approach outperforms a two-stage brute force attack model. Finally, we offer a machine learning-based countermeasure, a discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

CREAT: Blockchain-Assisted Compression Algorithm of Federated Learning for Content Caching in Edge Computing

Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes’ data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.

Intelligent Internet of Things Enabled Edge System for Smart Healthcare

Internet of Things (IoT) has provided a new horizon to explore numerous application deployment in the pervasive domain. However, due to power, delay and centralized issues of existing IoT-cloud scenario, novel interventions are getting outperformed in many ways. Edge computing has come up with new aspect to mainly minimize the packet transmission delay between the end user device to the remote cloud. Thus, a new way of delay mitigation has been possible. In this study, we conglomerate both the IoT and edge computing to facilitate a proof-of-concept application to application toward serving an e-healthcare scenario. We deploy our application on top of the newly released IoTSim-Edge simulator to diminish the dependency over the physical use of IoT device, sensor and actuators and increase the time to marketize the prototype. In this study, we propose a novel architecture that deals with the e-healthcare application development by using pulse rate monitoring sensor. We also develop activity diagram, class relationship diagram, and configure the engine of the system. We investigate the application against the shrinking factor of the IoT edge, mean execution time, and overall energy consumption by the novel IoT-edgelet modules.

Industrial Cyber-Physical Systems-Based Cloud IoT Edge for Federated Heterogeneous Distillation

Deep convoloutional networks have been widely deployed in modern cyber-physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this article, we propose the heterogeneous brain storming (HBS) method for object recognition tasks in real-world Internet of Things (IoT) scenarios. Our method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new “brain storming” mechanism and optimizable temperature parameters. In our comparison experiments, this HBS method outperformed multiple state-of-the-art single-model compression methods, as well as the newest multinetwork knowledge distillation methods with both homogeneous and heterogeneous classifiers. The ablation experiment results proved that the trainable temperature parameter into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of authors' knowledge, this is the first IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks.

Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines

In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

Network reliability evaluation for a distributed network with edge computing

Reliability and speed are crucial for ensuring robustness in a computer network, which is used for sharing information and resources within a business. The two most recent and important developments in the field of computing are cloud computing and edge computing. In this study, a modern distributed network comprising computers, internet of things (IoT), edge servers, and cloud servers for data transmission was investigated. A distributed network can be modeled as a graph with nodes and arcs, in which each arc represents a transmission line, and each node represents an IoT device, an edge server, or a cloud server. The data are transmitted through these transmission lines which are combined with numerous physical lines such as fiber cables. Therefore, each transmission line has several possible capacities and the capacity of each arc in the distributed network can be regarded as a multistate. Such the distributed network is called a multi-state distributed network (MDN). In an MDN, the data produced by IoT devices are transmitted to the edge servers. After being processed and compressed by the edge servers, the data are then transmitted to the cloud servers for advance computing. The purpose of this study is to first construct a model of a network in order to elucidate the flow relationship among the IoT devices, edge servers, and cloud servers and subsequently develop an algorithm to evaluate network reliability, which is the probability of the data being successfully processed by the MDN.

A Generic IoT Architecture for Ubiquitous Context-Aware Learning

Ubiquitous learning environments move learners out of a classroom and into the real world where learners engage in experiential and tangible learning involving instrumented physical things. Learners use peer-to-peer networks connecting learners, teachers, and a host of learning “things,” such as instrumented pieces of art, flower pots, and even buildings. A key component of such systems is wireless-enabled edge devices augmented with various types of sensors to represent the state of physical things and their context. This article presents a novel generic architecture for context-aware ubiquitous learning systems based on the Internet of Things (IoT) computing paradigm. A commonly used IoT edge device was used to implement four variants of the proposed architecture. The variants were based on advanced message queuing protocol, constrained application protocol (CoAP), message queue telemetry transport, and extensible messaging and presence protocol. All variants provided more than adequate performance for implementing ubiquitous learning scenarios. As expected, CoAP was generally more resource-efficient. However, practical differences in performance were so small that the choice of which architectural variant to use may depend on nonfunctional requirements such as reliability, security, maximum payload size, and ease of implementation.

ECCbAP: A secure ECC-based authentication protocol for IoT edge devices

Despite continuous efforts, designing both a resource-efficient and secure authentication protocol for Internet of Things (IoT) edge devices is still a great challenge for the industry. To address these concerns, in this paper, we present a new and more efficient method of providing secure communication between IoT edge devices and cloud servers, using a secure Elliptic Curve Cryptography (ECC)-based authentication protocol — ECCbAP. First, we evaluate four existing secure authentication protocols and analyze their security level against traceability and man-in-the-middle attacks and prove their vulnerabilities. Next, we propose our new scheme and examine its security via both informal and formal methods, e.g. BAN logic and Scyther tool, against well-known IoT threats. The results show that not only does ECCbAP provides a greater security compared with other protocols evaluated in this paper, but it is also more resource-efficient, which renders it more appropriate for the constrained environment like Radio Frequency Identification (RFID) tags or Bluetooth Low Energy (BLE) sensors.

High-throughput area-delay-efficient systolic multiplier over GF(2m) for a class of trinomials

The paradigm of Edge Computing in Internet of Things (IoT) demands high-throughput implementations of IoT edge devices. Security algorithms used in these devices heavily use GF(2m) multiplications. Systolic structures for GF(2m) multipliers can offer high throughput rates along with other features such as regularity, concurrency, and modularity. Many systolic architectures for GF(2m) multiplier were proposed in the literature to achieve low area and time complexities. In this paper, we propose a high-throughput and low area-delay complexity systolic structure for the hardware realization of polynomial basis multiplication in GF(2m) for a class of trinomials. The complexity analysis shows that the proposed architecture results in achieving high throughput and less area-delay complexity compared with the existing polynomial basis multipliers in the literature. When compared with the best available multipliers, the multiplier realized using the proposed architecture achieves throughput improvement by 42% and reduction in area-delay product by 7% for m=409. Application specific integrated circuit (ASIC) implementations of the proposed multiplier and the best available multipliers confirm that the proposed multiplier outperforms the other multipliers. Hence, the proposed high-throughput area-delay-efficient multiplier can be employed in IoT edge devices.