Sparse Blind Demixing for Low-Latency Wireless Random Access with Massive Connectivity

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

Grant-free random access has the potential to sup-port massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a key issue that needs to be tackled. The existing methods for JADCE usually suffer from one of the following limitations: high computational complexity, ineffective in inducing sparsity, and incapable of handling complex matrix estimation. To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas. Specifically, the JADCE problem is formulated as a group-sparse-matrix estimation problem, which is regularized by non-convex minimax concave penalty (MCP). This problem can be iteratively solved by using the proximal operator method, based on which we develop a unfolding neural network structure by parameterizing the algorithmic iterations. By further exploiting the coupling structure among the training parameters as well as the analytical computation, we develop two additional unfolding structures to reduce the training complexity. We prove that the proposed algorithm achieves a linear convergence rate. Results show that our proposed three unfolding structures not only achieve a faster convergence rate but also obtain a higher estimation accuracy than the baseline methods.

A new technology known as the Internet of Things (IoT) uses several sensor devices and communication protocols. By implementing cutting‐edge and modern equipment, people use IoT to make their lives easier. Home automation is one of them, and it works with actuators and sensors. However, increasing the number of devices in the IoT network could degrade the Quality of Service (QoS). Therefore, an appropriate framework in software and hardware can improve the Quality of Experience (QoE) and QoS for all users. One of the critical QoS measures in IoT is called Ultra Reliability and Low Latency Communication (URLLC). URLLC is essential in the IoT network released from the third Generation Partnership Project (3GPP) cellular. However, a systematic and comprehensive investigation of the practical procedures for URLLC in IoT needs to be done. This paper comprehensively investigates the existing methodologies in this subject. All the chosen techniques are separated into four categories to obtain a complete picture of the topic: structure‐based, diversity‐based, metaheuristic algorithm‐based, and channel state information. In this paper, we also investigate more benefits and drawbacks of other QoS when URLLC is applied in the IoT network. This paper highlights the challenges of URLLC in IoT networks and describes future open issues in detail to provide an efficient way for researchers in this field.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

A fuzzy based Co-Operative Blackmailing Attack detection scheme for Edge Computing nodes in MANET-IOT environment

Background. With the proliferation of Internet of Things (IoT) networks in the digital age, the risk of cyberattacks, especially DDoS attacks, is also increasing. IoT devices such as smart refrigerators, thermostats, webcams and other Internet-connected home appliances are being targeted by attackers who can use them as part of a botnet to conduct DDoS attacks. These devices often have inadequate network security and are rarely updated, making them vulnerable. DDoS attacks can result in significant losses such as lost revenue, reputational damage and costs to restore services. So, the vulnerability of IoT networks to DDoS attacks and the need to develop effective protection measures is a pressing issue. Objective. The purpose of the paper is to analyse software that supports DDoS attacks in IoT networks. Provide general recommendations to help improve approaches to defence measures in IoT networks against DDoS-enabled malware. Methods. Five main DDoS attack models are considered: agent-handler model, reflexive model, IRC-based model, web-based model, and P2P-based model. Three most dangerous software capable of DDoS attacks on IoT networks are analyzed: Mirai, XOR.DDoS and Linux.Hydra. Results. There are many models and methods of DDoS attacks on IoT networks. The most dangerous are Mirai, XOR.DDoS and Linux.Hydra. Each of these software has its own specific characteristics and methods of carrying out attacks. The study also showed that there are several effective measures to counter these attacks, including setting strong passwords, regularly updating software, setting up traffic filters and restricting network access. Conclusions. Key aspects of DDoS attacks, their models and process steps are considered. The paper focuses on the three most dangerous software used to conduct such attacks and provides recommendations on how to counteract them.

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks.

Ensemble classification using traffic flow metrics to predict distributed denial of service scope in the Internet of Things (IoT) networks

Ubiquitous sensing enabled networks have transformed the living chores into a modern living standard in which each move is automated. The dependency of humans on IoT is increasing every day, so the IoT networks must be efficient and secure. Secure communication in the Internet of Things (IoT) networks is a hotly debated research issue. Cryptography is one of the solutions for secure data transmission. In IoT networks, designing a cryptography algorithm is a major challenge for resource constraint environment. So, when we talk about cryptography in IoT, this term transforms into “lightweight cryptography.” This paper presents a review of lightweight cryptography algorithms in the form of a stream cipher, block cipher, symmetric encryption, and asymmetric encryption.

The Internet of Things (IoT) enables smart settings, which help human pursuits. Although the IoT has increased economic opportunities and made numerous human conveniences possible, it has also made it easier for intruders or attackers to take advantage of the technology by either attacking it or avoiding it. Therefore, the primary concerns for IoT networks are security and privacy. For Internet of Things (IoT) networks, several intrusion detection systems (IDS) have been developed thus far using various optimization techniques methods. But as data dimensionality has increased, the search space has grown significantly, offering difficult problems for optimization techniques like swarm optimization using particles (PSO). To overcome these obstacles, this work suggests an approach for feature selection dubbed enhanced to increase the sticky binary dynamic sticky binary particle swarm optimization's searchability, and particle swarm optimization (IDSBPSO) was developed. It introduced a method for decrease of the dynamic search space and various dynamic parameters (SBPSO). To identify malicious data flow in IoT networks, an IDS was developed using this methodology. The IoTID20 and UNSW-NB15 IoT network datasets were used to assess the proposed model. It was found that, even with less features, IDSBPSO typically attained accuracy that was either higher or comparable. Moreover, as compared to traditional IDSBPSO and PSO-based feature selection methods dramatically reduced computational cost and prediction time.

Internet of Things is coming to critical missions such as battlefield, border patrol, search and rescue, critical structure monitoring and surveillance, etc. Internet of Things (IoT) in Critical Missions or Internet of Mission-Critical Things (IoMCT) is propelled by the convergence of sensing, communication, computing, and control. To support IoMCT, the mission-critical networks will need to be flexible and interactive, and still work despite limited bandwidth, intermittent connectivity and with a large number of devices on the network. The focus of IoMCT is to improve surveillance utilizing a network, not fusion of disparate sensor products. Such adaptation, management, and re-organization of information sources, devices, and networks must be accomplished almost entirely autonomously, in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services.

The Underwater Internet of Things (UW-IoT) is a novel class of Internet of Things (IoT) that enables extend the terrestrial internet to the marine environment. However, as the underwater acoustic channel poses extremely challenging difficulties for underwater IoT communication and networking, the performance of the underwater IoT networks lags far behind the terrestrial internet. Due to the expense of conducting experiments in real underwater systems many researches have been endowed with only simulation model and analysis. This paper presents the design and implementation of an underwater IoT network test platform in order to evaluate and analyze the performance of underwater communication and network protocol in physical shallow water environment. The design of this IoT network platform considers the application background of environment parameter acquisition in marine environment stereoscopic monitoring. The software and hardware implementation of the IoT platform are introduced. Sea test result shows that the platform is effective in evaluating the performance of underwater IoT network.

The advancements in the field of Information and Communication Technology (ICT) have led to the development of the Internet of Things (IoT), where ordinary things (e.g. smart meters, sensors etc.) can be connected and controlled over the internet. However, an IoT network is vulnerable to many malicious network attacks due to its inherent properties like limited computational abilities, heterogeneity, massive connectivity, etc. This paper presents a feature selection technique, which uses an amalgamation of LASSO regression and correlation based techniques, to find appropriate features for IoT network attack detection. The effectiveness of the proposed mechanism was evaluated on benchmark IoT network datasets using different machine learning techniques. The results revealed that the Gradient Boosting ensemble learning model achieved a maximum attack detection accuracy of 99.98%, and outperformed the other studied models.

The blockchain network architecture is a promising technology for constructing highly secure Internet of Things (IoT) networks. IoT networks typically consist of many sensors and actuators. Blockchain network technology can be applied to secure control robots in smart factories or for reliable drone delivery in smart cities. The distributed ledger and data block validation across blockchain networks guarantee the ultimate data security. However, the current blockchain technology is restricted in terms of its overall deployment across IoT networks. A general permissionless blockchain technology typically targets high-performance network nodes with sufficient computing power and memory space. A blockchain node with less computing power and memory, such as an IoT sensor or actuator, cannot employ blockchain technology as a fully functional node. A lightweight blockchain provides practical blockchain availability to IoT networks. We propose an operational advance to develop a lightweight blockchain for IoT networks. The opportunistic block validation optimizes the block validation process. It measures the network vulnerability based on the node reputation, block validation degree, number of fraudulent messages, and network stability. The reinforcement learning mechanism employed in the opportunistic block validation determines whether block validation is to be performed. Two separately developed reinforcement learning methods can increase the processing performance while maintain the transaction data integrity. In addition, the proposed blockchain technology is easily implementable because it adopts a Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed block validation mechanism.