Large Number Of IoT Devices Research Articles

Federated Reinforcement Learning-Based Resource Allocation for D2D-Aided Digital Twin Edge Networks in 6G Industrial IoT

The 6G is conceived to address the expected high level of requirements (such as ultra-high data transmission rate, support for the highest moving speed and seamless connection, etc.) in the next decade and beyond. In the context of 6G, a large number of Industrial Internet of Things (IIoT) devices may access the network, and thanks to the rapid development of artificial intelligence make smart manufacturing has the opportunity to be realized. However, a large number of IoT devices, the tremendous volume of data, the heterogeneous nature of devices, and the increasing concerns of privacy challenge the efficient management and quality of services in IIoT. To address these problems, in this article, a device-to-device (D2D) communication-aided digital twin edge network is proposed, where edge computing is introduced to bring computing and storage resources near to the end devices, and digital twin is utilized to fill the gap between physical and virtual space and D2D communication is applied to assist resource limited IoT devices to achieve normal communication. Moreover, digital twin-empowered federated reinforcement learning is leveraged to provide privacy awareness and decentralized resource allocation strategy training on D2D communication links to further improve network performance. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes

In recent years cybersecurity has become a major concern in the adaptation of smart applications. A secure and trusted mechanism can provide peace of mind for users, especially in smart homes where a large number of IoT devices are used. Artificial Neural Networks (ANN) provide promising results for detecting any security attacks on smart applications. However, due to the complex nature of the model used for this technique, it is not easy for common users to trust ANN-based security solutions. Also, the selection of the right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks. This paper proposes Multi-tiered ANN Model for Intrusion Detection (MAMID) that is a novel and scalable solution for optimal hyperparameter selection to detect security attacks with high accuracy. The explainability analysis of the predictions made by the model is also provided for establishing trust among users. The approach considers a subset of the dataset for quick, scalable and optimal selection of hyperparameters to reduce the overhead of the process of ANN architecture design. Using a very recent IoT dataset the proposed approach showed high performance for intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for binary, category, and subcategory classification of attacks. To the best of the authors' knowledge, no previous research work has been able to achieve attack detection at the subcategory level beyond 90%. • An optimal hyperparameter selection method is provided. • Our approach shows high performance for security attack detection. • The model's explainability is helpful for user understanding and trust building.

A Blockchain Based Secure IoT System Using Device Identity Management.

Sharing data securely and efficiently has been identified as an issue in IoT-based smart systems such as smart cities, smart agriculture, smart health, etc. A large number of IoT devices are used in these smart systems and they produce a large amount of data. IoT devices generally have limited storage and processing capabilities, and configuring any security techniques on these devices is a challenge. In this paper, we propose a novel device identity management approach for blockchain-based IoT systems that provides data security in two ways. Firstly, a lightweight time-based identification protocol that uses hub identification for validating data. Secondly, data storage is augmented with an effective blockchain application for providing easy access and immutability for data sharing among multiple parties. Our initial prototype implementation shows that: our identity management approach can be implemented in large scale settings, our system can be effectively implemented in blockchain platforms, and our performance evaluation result shows that the prototype fulfills system requirements adequately.

Fine-Grained Identification for Large-Scale IoT Devices: A Smart Probe-Scheduling Approach Based on Information Feedback

A large number of IoT devices access the Internet. While enriching our lives, IoT devices bring potential security risks. Device identification is one effective way to mitigate security risks and manage IoT assets. Typical identification algorithms generally separate data capture and target identification into two parts. As a result, it is inefficient and coarse-grained to evaluate the results only once the identification process is complete and then adjust the data capture strategy afterward. To solve this problem, we propose a fine-grained probe-scheduling approach based on information feedback. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate it on 53,000 IoT devices across various categories to show its wide applicability. The results indicate that our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and reduce the identification time by 55.96%.

A Cloud-Edge-Smart IoT Architecture for Speeding Up the Deployment of Neural Network Models with Transfer Learning Techniques

Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different neural network models to a large number of IoT devices. In this paper, a cloud-edge smart IoT architecture for speeding up the deployment of neural network models with transfer learning techniques is proposed. A new model deployment and update mechanism based on the share weight characteristic of transfer learning is proposed to address the model deployment issues associated with the significant number of IoT devices. The proposed mechanism compares the feature weight and parameter difference between the old and new models whenever a new model is trained. With the proposed mechanism, the neural network model can be updated on IoT devices with just a small quantity of data sent. Utilizing the proposed collaborative edge computing platform, we demonstrate a significant reduction in network bandwidth transmission and an improved deployment speed of neural network models. Subsequently, the service quality of smart IoT applications can be enhanced.

Guest Editorial Special Issue on Communications and Computing for Green Industrial IoT and Smart Grids

With this special issue, the guest editorial team, Drs. Melike Erol-Kantarci, George C. Alexandropoulos, Peter Han Joo Chong, Andrea M. Tonello and Yan Zhang have prepared a selection of excellent articles on novel energy-efficient communication and computing techniques for Industrial IoT (IIoT) and smart grids. The goal of this special issue is to advance and promote significant technological advances in the field. IIoT covers the broad domain of smart grid, smart manufacturing, intelligent transportation and smart cities, and it refers to the combination of IoT technology with big data coming from intelligent processes in those domains. Energy-efficient transmission and processing of this data targets automation, efficiency, and productivity increase. In particular, smart grid can be considered as a typical IIoT example, since it comprises an industrial setting with a large number of IoT devices. Within Green Industrial IoT, smart grid communications, use state-of-the-art communication technologies towards ensuring the reduction of energy consumption, optimal operation of the smart grid, Advanced Metering Infrastructure (AMI), as well as coordination between the different smart grid components from generation to distribution and consumption.

A lightweight verifiable trust based data collection approach for sensor–cloud systems

A Lightweight Verifiable Trust based Data Collection (LVT-DC) approach is proposed to obtain credible data for mobile vehicles network, in which, a large number of IoT devices are deployed in smart city, and Mobile Vehicles (MVs) collect data from IoT devices and report data to cloud to construct various applications. First, some IoT devices are selected as core-IoT device whose data more than others. Then, Unmanned Aerial Vehicles (UAV) delivers a short verification code to each core-IoT devices and collects its data, and it embeds verification code into the data when the MVs collect it in order for cloud to check. Last, a verifiable trust inference method is proposed which includes two types of trust calculations. (a) Direct trust: MVs If the reported data cannot recover the verification code or is inconsistent with the UAV collection result, reduce the trust of the MVs, (b) Trust inference: If the data of a non-core-IoT devices reported by a MVs is inconsistent with the trusted MVs, then reduce its trust. After a large number of experimental results, the LVT-DC approach can quickly and accurately identify the credibility of the data collector and ensure credible data collection.

Optimal replacement model for the physical component of safety critical smart-world CPSs

Nowadays CPSs have drawn an upsurge of interests for their enormous potential towards the next generation smart systems where safety is a critical issue. In CPS, large number of IoT devices (sensors, actuators, etc.) are deployed to collect data to support safety critical smart-world CPS infrastructures such as smart city, smart health, smart manufacturing, and smart transportation. The degradation of physical components of safety critical smart-world CPSs would deteriorate the performance of the smart system and lead to loss of human life with significant damage to properties. Hence, planned preventive maintenance replacement of physical components of the system is vital to extend the lifetime of the system, to reduce maintenance cost and to avoid risks that may cause major harm to life and property. In this study, we focus on cost effective preventive replacement strategy that recovers failure of physical components of safety critical smart-world CPSs by mainly considering the deterioration state of the physical component on the availability of the system. In view of the degraded physical component of CPSs, we demonstrate the effects of involving maintenance actions during the deterioration state of physical components of CPSs on its availability. Since timely preventive replacement is crucial to support continuous and effective system operation, we compute the optimum time of the constant-interval of preventive replacement strategy for the physical components of CPSs. In this study, we use Weibull distribution as it is a generalized failure model that has been widely used for process equipment life data analyses. At last, the effects of the shape and the scale parameters on the optimal preventive replacement interval of the physical component are also demonstrated.

ARCADIS: Asynchronous Remote Control-Flow Attestation of Distributed IoT Services

Remote attestation (RA) is a security mechanism that verifies the trustworthiness of remote IoT devices. Traditional RA protocols aim to detect the presence of malicious code in the static memory of a device. In the IoT domain, RA research is currently following two main directions: Dynamic RA and Swarm RA. Dynamic RA schemes intend to detect runtime attacks that hijack the control-flow execution of a running program without injecting new malicious code into the memory. On the other hand, swarm RA protocols focus on attesting efficiently and effectively a large number of IoT devices. However, existing RA protocols do not perform dynamic attestation in asynchronous IoT networks. This paper proposes a RA protocol for Asynchronous Remote Control-Flow Attestation of Distributed IoT Services (ARCADIS). This protocol extends the state-of-the-art by detecting IoT devices that have (directly or indirectly) been maliciously influenced by runtime attacks on asynchronous distributed IoT services. The protocol has been simulated for Wismote sensors in the Contiki emulator. The conducted experiments confirm the feasibility of ARCADIS and demonstrate its practicality for small IoT networks.

Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This article presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

Effective mitigation against IoTs using super materials for distributed denial of service attacks in cloud computing

Cloud Computing technology has a key role to play in growing IT services. However, attacks can put the availability of the IT services in danger. One such deadliest attack is IoT based Distributed Denial of Service (DDoS) attacks. The use of super materials like Graphene, Carbon nanotubes, Molybdenum disulfide (MoS2) etc. are revolutionizing the manufacturing of new Internet of Things (IoTs). These super materials are flexible, light weight, high performing and thus allows to expand the range of IoTs. Moreover, the growth of vulnerable IoT devices are increasing at an alarming rate. The bot herders take the control of these vulnerable IoT devices and launch massive DDoS attacks against the cloud systems. This leads to unavailability of the services and economic or reputation losses. A mirai botnet exploited large number of IoT devices and successfully executed the largest DDoS attack of this kind. Hence, it is very important to mitigate these attacks and lower its impact. There are various captcha based strategies (like text based, audio based, video based etc.) for blocking the traffic generated by bots towards the victim. These blocking techniques for mitigation are inflexible, expensive and time consuming. In this paper we propose a novel approach based on gesture verification for effective mitigation of DDoS attacks. The clients are challenged with captcha based on gesture verification. The IoT devices are not capable of making gestures for passing the challenge. Gestures can be simply drawing a circle or a line etc. The IoT devices generating attack are blocked and legitimate users pass the challenge and thus access the services. The proposed method is fast and is less expensive than the state-of-the-art methods.

Towards secure and efficient energy trading in IIoT-enabled energy internet: A blockchain approach

Nowadays, with the rapid development and wide application of Internet of Things technology, Industrial Internet of Things (IIoT) has become an essential part of industrial systems. A large number of IoT devices has enabled the diversification and decentralization of energy systems, and the Energy Internet makes it possible to interconnect distributed energy sources and consumers. However, as the number of entities involved in the energy transactions grows and the forms of transactions diversify, the traditional centralized energy trading model faces the problems such as insufficient scalability, vulnerabilities to cyber-attack, and low processing efficiency. To address these challenges, we propose BC-ETS, a secure and efficient BlockChain based Energy Trading Scheme. In BC-ETS, the energy trading model is divided into two levels, which can not only protect privacy but also achieve the balance between power supply and demand. Additionally, to adapt to the relatively weak computing power of the underlying IoT equipment in energy internet, a credibility-based equity proof mechanism is designed to greatly improve the system availability. The analysis shows that BC-ETS can meet the security requirements and has a better performance compared with other similar energy trading schemes.

Blockchain based privacy-preserving software updates with proof-of-delivery for Internet of Things

A large number of IoT devices are connected via the Internet. However, most of these IoT devices are generally not perfect-by-design even have security weaknesses or vulnerabilities. Thus, it is essential to update these IoT devices securely, patching their vulnerabilities and protecting the safety of the involved users. Existing studies deliver secure and reliable updates based on blockchain network which serves as the transmission network. However, these approaches could compromise users privacy when updating the IoT devices.In this paper, we propose a new blockchain based privacy-preserving software update protocol, which delivers secure and reliable updates with an incentive mechanism while protects the privacy of involved users. A vendor delivers the updates and makes a commitment by using smart contract to provide financial incentive to the transmission nodes who deliver the updates to its IoT devices. A transmission node can get financial incentive by providing a proof-of-delivery. In order to obtain the proof-of-delivery, the transmission node uses double authentication preventing signature (DAPS) to carry out fair exchange. Specifically, the transmission node uses the DAPS to exchange an attribute-based signature (ABS) of one IoT device. Then, it uses the ABS as proof-of-delivery to receive financial incentives. Generally, to generate an ABS, the IoT device has to execute complex computations which is intolerable for resource limited devices. We propose a concrete outsourced attribute-based signature (OABS) scheme to overcome the weakness. Then, we prove the security of the proposed OABS and the protocol. Finally, we implement smart contract in Solidity to demonstrate the validity of the proposed protocol.

Programmable intelligent spaces for Industry 4.0: Indoor visual localization driving attocell networks

AbstractReal‐time and mission‐critical applications for Industry 4.0 demand fast and reliable communication. Therefore, knowing devices' location is essential, but GPS is of little use indoors, whereas electromagnetic impairments and interferences demand new approaches to ensure reliability. The challenges include real‐time feedback with end‐to‐end (E2E) low latency; high data density due to large number of IoT devices per area; and smaller communication cells, which increases the handover frequency and complexity. To tackle these issues, we introduce a programmable intelligent space (PIS) to deploy attocells, enable E2E programmability, and provide a precise computer vision localization system and networking programmability based on software‐defined networking. To validate our approach, experiments were conducted, controlling a mobile robot through a trajectory. We demonstrate the need for higher camera frame rate to achieve tighter precision, evaluating different trade‐offs on localization, bandwidth, and latency. Results have shown that PIS wireless attocell handover achieves seamlessly mobile communication, delivering packets within the deadline window, with similar performance to a no handover baseline.

A Security Architecture for 5G Networks

5G networks will provide opportunities for the creation of new services, for new business models, and for new players to enter the mobile market. The networks will support efficient and cost-effective launch of a multitude of services, tailored for different vertical markets having varying service and security requirements, and involving a large number of actors. Key technology concepts are network slicing and network softwarization, including network function virtualization and software-defined networking. The presented security architecture builds upon concepts from the 3G and 4G security architectures but extends and enhances them to cover the new 5G environment. It comprises a toolbox for security relevant modeling of the systems, a set of security design principles, and a set of security functions and mechanisms to implement the security controls needed to achieve stated security objectives. In a smart city use case setting, we illustrate its utility; we examine the high-level security aspects stemming from the deployment of a large number of IoT devices and network softwarization.

The Random Access Procedure in Long Term Evolution Networks for the Internet of Things

Network connectivity is a key issue in the realization of IoT, and LTE cellular technology is the most promising option for the provisioning of such connectivity. However, in LTE networks, a large number of IoT devices trying to access the medium can overload the RAN. In this article, we review the LTE random access procedure and its support for IoT applications. We also assess the performance of the RAN overload control schemes proposed by 3GPP, taking into consideration the interaction between the random access procedure and packet downlink control channel resource allocation.