The Industrial Internet of Things (IIoT) offers transformative potential but introduces critical security risks, including unauthorized access, data breaches, and privacy compromise. Hardware Security Modules (HSMs) have emerged as robust solutions to protect IIoT ecosystems by enabling secure cryptographic operations, providing tamperresistant hardware and creating trusted execution environments. This work presents the first comprehensive review of HSMs tailored for secure IIoT communications, addressing their architectural foundations, operational mechanisms, and deployment scenarios. It first outlines the IIoT security landscape and HSM deployment architectures, including cloud-based, edge-integrated, and distributed models. Next, cutting-edge HSM implementations are analyzed, emphasizing their effectiveness in authentication, secure communication protocols, and physical tamper resistance. It then explores attack surfaces and vulnerabilities, such as firmware exploits, logical flaws, and network-based threats, along with mitigation strategies. Case studies from smart manufacturing, energy grids, and logistics demonstrate practical HSM applications, while a comparative evaluation assesses commercial and open-source solutions based on performance, compliance, and scalability. Emerging trends such as AI-driven threat detection, post-quantum cryptography, and decentralized HSMs are also discussed. Finally, key challenges are highlighted, including latency in real-time systems, supply chain risks, and regulatory hurdles, and future directions for research and industry adoption are proposed. This work serves as a roadmap for securing IIoT deployments, offering actionable insights for researchers, practitioners, and policymakers.

Highly precise clock synchronization is an important aspect of the Industrial Internet of Things (IIoT) network because desynchronized clocks among nodes in IIoT can degrade system performance and even lead to system failure. IEEE 1588 Precision Time Protocol (PTP) is widely used in such time-sensitive networks. Resource efficiency and security have become the most important concerns in designing PTP for IIoT applications. PTP provides unified and high-precision time, whereas it is resource inefficient and insecure in its current form, particularly for resource-constrained IoT devices, such as battery powered sensing nodes. To this end, this paper aims to advance the existing PTP to improve security for IIoT networks without involving complex and power-consuming cryptographic algorithms. We study and analyze the potential cyber-attacks that can affect the security and synchronization of the PTP network. Considering the limitations of the PTP security defined by IEEE 1588 in its Annex K, we propose a security extension to the PTP algorithm. This security model covers the full PTP attack surface and allows the detection of attacks on all the PTP nodes in a timely manner. Along with the attack detection, we establish an attack mitigation model to mitigate the attack effects on Master PTP nodes. The proposed secure PTP model was evaluated under different network conditions and with varying important parameters. It was observed that newly introduced functions do not compromise synchronization accuracy. All the experimental evaluations demonstrate that the proposed approach is more secure and robust to cyber-attacks and does not affect the operation of PTP devices in all considered network configurations.

Recently, energy storage devices (ESDs) have been widely deployed to alleviate high ramp rates in microgrids (MGs), thereby enabling the large-scale penetration of renewable energy resources (RERs) into utility grids. However, there is a lack of general guidelines to assist design engineers promptly evaluate the techno-economic benefits of ESDs under various RERs, RERs-load demand combinations and different ESD combinations. A bi-level evaluation method is devised in this study to assess the techno-economic feasibility of deploying ESDs to control the ramp rate of an MG, and comprehensive case studies are performed to answer the following ques-tions: (i) How do the fluctuations in a single RER, hybrid RERs, and a hybrid RERs-load demand combination influence the techno-economic aspects of ESD?, (ii) What type of ESD should be considered to obtain the best techno-economic benefits? The results suggest that the total energy storage requirements for ramp rate control of the MG can be lowered by hybridizing the RERs at a specific location. Specifically, the power and energy ca-pacities can be reduced by up to 19.4 % and 57.6 %, respectively. The results also revealed that failing to account for fluctuations in load demand when calculating the ESD capacity may result in an underestimation of the power and energy capacities of 7.6 % and 14.3 %, respectively. Regarding the type of ESD, lead-acid batteries were found to be the most expensive option to alleviate the ramp rate of the MG. The results indicated that lithium-ion batteries are the most cost-effective option compared with other types of sole/hybrid combinations. Moreover, it was determined that the initial investment cost and lifetime of the ESD are critical factors in selecting the most suitable ESD/ hybrid ESD for ramp rate control of a MG. In addition, the results demonstrated that hybrid combinations are only economical for stricter ramp rate constraints.

The energy crisis has pushed Pakistan way down the economic rankings. More than 70% the total population resides in rural regions. The energy crisis has crippled the lifestyle of the rural class (RC) in many aspects. For rural Pakistan, the energy scarcity, a hurdle in sustainable development, can be eliminated by biogas. Almost every rural family is associated with agriculture and owns livestock animals, therefore self-sufficient in biogas plant's fuel. Fuel abundance and favourable climatic conditions make energy extraction from biomass facile and fruitful; still there are many reasons that RC is dumped in the energy crisis. In this paper, a survey is conducted in villages of Pakistan to discover the reasons due to which domestic biogas plants have not yet gained their due importance. Institutional, financial, awry planning and social barriers are highlighted at the grass root level in the development of biogas technology in rural Pakistan. In contrast, villages where biogas plants are present, the survey analysis revealed prosperity rendered through savings, reduction in diseases and workload. The paper concludes with an emphasis on the requirement of concrete and comprehensive policy that is capable to deal with the barriers in the route of biogas technology.

The birth of electricity witnessed "the battle of currents" between AC and DC as a medium of power transfer.AC won the battle in the first place because of its ability to transform voltage levels.However, with the development of power electronic converters (PECs), DC is striking back.Most of the electronic loads in our conventional AC-based homes are DC by nature.Moreover, the modern concept of energy-efficient variable speed drive (VSD) based loads, i.e.DC-inverter based air-conditioners and refrigerators, require a DC link for their operation.The driving component of all such loads is the PEC.The operational efficiency of PECs depends on the loading which varies throughout the day.This paper presents a mathematical model based on a bottom-up approach to the comparative efficiency analysis of AC and DC distribution systems considering daily load variation.Two topologies are presented where AC and DC distribution systems are compared in terms of efficiency.The first topology (T1) defines a separate/independent converter for each load, whereas in the second topology (T2) loads of a particular class are lumped and driven by a single converter.The results present DC distribution better than AC distribution with an efficiency advantage of 2.28% and 1.57% for T1 and T2, respectively.

The industrial Internet of Things (IIoT) plays an important role in the industrial sector, where secure, scalable, and easily adopted technologies are being implemented for the smart industry. The traditional IIoT architectures are generally based on centralized architectures that are vulnerable to a single point of failure and to several cyber-attacks. Blockchain technology is frequently adopted in the modern industry because of its security and decentralization. This paper proposes a blockchain-based architecture that ensures secure and trustworthy industrial operations. A private and lightweight blockchain architecture is proposed to regulate access to valuable sensor and actuator data. To enhance the computational performance of the proposed architecture, real-time cryptographic algorithms are processed using a low-power ARM Cortex-M4 processor, and a highly scalable, fast, and energy-efficient consensus mechanism proof of authentication (PoAh) is deployed in the blockchain network. Extensive experiments and analysis proved the effectiveness of the proposed framework for smart industrial environments. Finally, we transform a conventional fruit processing plant into a secure and smart industrial platform by implementing the proposed architecture.

The development of spectral efficient solutions for internet of things (IoT) face challenges primarily due to the large-scale placement of an immense number of sensors and devices. Cognitive radio (CR) technology is considered as a potential solution to resolve the spectrum scarcity problems of IoT. Incorporation of CR in IoT encounters various challenges including fast response and efficient spectrum sensing even in low signal to noise ratio. In this study we integrate the basic functionalities of the both CR and IoT technology and present a five layered framework for CR enabled IoT. In addition to the framework we also proposed and develop a spectrum sensing algorithm for CR-based IoT architecture, meeting the efficiency and time sensitivity requirements. The proposed algorithm is more accurate, robust to noisy environment and four times faster than existing approaches. The developed algorithm is compared with existing blind spectrum sensing techniques in term of detection performance, optimization methods and computational complexity. Experimental evaluations with real wireless microphone signals demonstrate the effectiveness of the proposed scheme and show superiority over existing ones.

The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT) concepts in industrial sectors and applications. IIoT has several applications in smart homes, smart cities, smart grids, connected cars, and supply chain management. However, these systems are being more frequently targeted by cybercriminals. Deep learning and big data analytics have great potential in designing and developing robust security mechanisms for IIoT networks. In this paper, a novel hybrid deep random neural network (HDRaNN) for cyberattack detection in the IIoT is presented. The HDRaNN combines a deep random neural network and a multilayer perceptron with dropout regularization. The proposed technique is evaluated using two IIoT security-related datasets: (i) DS2OS and (ii) UNSW-NB15. The performance of the proposed scheme is analyzed through a number of performance metrics such as accuracy, precision, recall, F1 score, log loss, Region of Convergence (ROC), and Area Under the Curve (AUC). The HDRaNN classified 16 different types of cyberattacks using with higher accuracy of 98% and 99% for DS2OS and UNSW-NB15, respectively. To measure the effectiveness of the proposed scheme, the performance metrics are also compared with several state-of-the-art attack detection algorithms. The findings of HDRaNN proved its superior performance over other DL-based schemes. The deployment perspective of the proposed work is also highlighted in this work.

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

The blockchain has emerged as an innovative and powerful technology that shows the tremendous potential to enhance the smart industrial frameworks by providing encryption, immutable storage, and decentralization. In the past few years, several applications in the industrial Internet of Things (IIoT) have emerged and blockchain technologies gained great attention from academia and industry. To discover the great potential of blockchain technology for the IIoT, we present a comprehensive survey on security issues, blockchain architectures, and applications from the industrial perspective. This article starts with a comparison of exiting state‐of‐the‐art surveys of blockchain technologies for IoT/IIoT applications. A four‐layer reference architecture of IIoT is presented along with the functionalities and security issues of each layer. To address these challenges, we assess the potential of blockchain technology by considering the key characteristics, architectures, consensus algorithms, and implementation platforms. Furthermore, we also discussed some use cases of blockchain for the IIoT frameworks. Finally, this survey is concluded by highlighting some open issues and future research directions.

Precise time synchronization becomes a vital constituent due to the rigorous needs of several time-sensitive applications.The clock synchronization protocol is one of the fundamental factors that can define the quality of communication.Our study starts with a brief discussion on the application domain of precise time synchronization and comes with an in-depth study of the synchronization with the main focus on the IEEE 1588 Precision Time Protocol (PTP).We have compared the well-known synchronization techniques and conclude that the PTP is the most appropriate answer to robust clock synchronization though challenges are there that requires thoughtful efforts and modification in the current version.The working mechanism and main components of the PTP network are discussed.We have established a testbench using commercially available devices and development boards to evaluate the PTP performance under different configurations.Major sources of synchronization error and other aspects contributing to precision are examined.This paper discussed numerous approaches that could enhance the performance of the PTP protocol.Structures for PTP based wireless clock synchronization required by advanced applications has also been discussed.In the end, paper focuses on the main industrial application areas in which PTP plays an important role, including WLAN, optical data centers, Smart grid, IEC 61850, etc.We conclude the paper by identifying the future trends and research directions for PTP based clock synchronization.

Air pollution is the foremost concern especially in modern cities, because of its noteworthy influences on the community health, and the global economy. The significance of the air quality statistics makes the highly accurate real-time monitoring systems vital. The partial data access, high cost and the non-scalability of conventional air monitoring system enforce the researchers to develop future air pollution monitoring system employing advance technologies such as internet of things (IoT), wireless sensor network (WSN) and the low-cost ambient sensors. This paper presents a short but comprehensive review of these air pollution monitoring systems (APMS), their enabling technologies and protocols. We categorize the recent work into two main classes as static and mobile air monitoring systems based on the carriers of the sensing node and the deployment strategies. Sub categories include, portable monitoring devices, community supported approaches, WSN based systems, and IoT supported approaches. Broad performance assessments and the comparison among these categories were performed with respect to their architecture and the incorporated tools. Furthermore, the present study explored the issues, groundwork, and the methodology of designing a real-time air pollution monitoring system. Finally, the review deliberated the limits of the existing works and summaries the objectives that need to attain in the future air monitoring systems to make them more accurate and realistic.

The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), malicious operation, malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.

Industrial Internet of Things (IIoT) has arisen as an emerging trend in the industrial sector. Millions of sensors present in IIoT networks generate a massive amount of data that can open the doors for several cyber-attacks. An intrusion detection system (IDS) monitors real-time internet traffic and identify the behavior and type of network attacks. In this paper, we presented a deep random neural (DRaNN) based scheme for intrusion detection in IIoT. The proposed scheme is evaluated by using a new generation IIoT security dataset UNSW-NB15. Experimental results prove that the proposed model successfully classified nine different types of attacks with a low false-positive rate and great accuracy of 99.54%. To validate the feasibility of the proposed scheme, experimental results are also compared with state-of-the-art deep learning-based intrusion detection schemes. The proposed model achieved a higher attack detection rate of 99.41%.

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

Principal component (PC) algorithm has recently been shown as a very accurate blind detection technique in comparison with other covariance-based detection algorithms. However, it also has a higher complexity owing to the computation of the eigenvectors. We propose a low-complexity Lanczos principal component (LPC) algorithm that utilizes Lanczos iterative method to compute the eigenvectors. In comparison with the PC algorithm, the proposed LPC algorithm offers significant reduction in complexity while giving a similar detection performance. Low-complexity LPC algorithm allows for the use of larger sized covariance matrix that further improves the detection performance. Maximum-minimum eigenvalue (MME) algorithm is also included in the comparison and it gives an inferior performance as compared to both PC and LPC algorithm. All the algorithms were tested with experimental data while using universal software radio peripheral (USRP) testbed that was controlled by GNU radio software.

In the recent years spatial spectrum sensing become a promising approach due to the convergence of almost all wireless standards to incorporate spatial dimensions and use of multiple antennas at both transmitter and receiver. Keeping in consideration such wireless environment, we proposed a spectrum sensing algorithm based on principal component (PC) of spatially received signals. The proposed algorithm is analyzed under SISO (single input single output), SIMO (single input multiple output) and MIMO (multiple input multiple output) (employing stream multiplexing and Alamouti space time coding) scenario. Performance comparison was done by receiver operating curve (ROC) with other proposed algorithms in literature i.e. Maximum minimum Eigen value (MME). No prior information about the channel or primary user's (PU) signal is assumed. Simulations show the improved performance when info about spatial diversity of PU is incorporated in the proposed PCA. All the algorithms were tested using experimental data while using USRP (universal software radio peripheral) test bed that was controlled by GNU radio software.