Cloud Computing Leveraging Techno-Management Approaches for IoT-Based Decision Making

The integration of Blockchain technology into Internet of Things (IoT) ecosystems has emerged as a transformative approach to address critical security and privacy challenges in connected devices. This chapter explores the innovative convergence of Blockchain and IoT, focusing on the enhancement of data security, privacy, and trust through decentralized solutions. By leveraging the immutable and transparent nature of Blockchain, IoT networks can achieve secure, efficient, and automated data exchanges. The chapter delves into key Blockchain trends that are shaping IoT security, including decentralized identity management, smart contracts, and consensus mechanisms, while addressing the specific challenges of resource-constrained IoT devices. The use of advanced cryptographic techniques such as encryption, Zero-Knowledge Proofs, and homomorphic encryption ensures that sensitive data remains private and secure. The application of Blockchain in facilitating secure data sharing, device authentication, and access control was discussed, alongside the potential of permissioned blockchains to balance privacy with scalability. The chapter concludes by identifying the future directions for Blockchain and IoT innovations, emphasizing their role in the evolution of secure, autonomous, and scalable connected environments.

Incorporating software-defined networking (SDN) technology into Internet of Things (IoT) can improve network utilization. In this paper, we consider SDN-IoT networks, in which IoT devices use Zigbee protocol. A machine-learning- assisted minimum end-to-end delay (MaMED) routing scheme for IoT monitoring services is proposed; and this scheme requires no prior knowledge of input traffic. Under different SDN- IoT simulation scenarios, results show that our routing scheme outperforms the shortest path first (SPF) algorithm in terms of end-to-end delay (E2E) performances (e.g., average E2E delay and jitter), and gives more consistent delay performance when the input IoT traffic increases.

The internet is an optimal place to human as a producer or a consumer for sharing information until now. In the future, not only the human-generated information but also things will be connected into internet and these will evolve into internet of things(IoT) which can share the information thing-in-itself and its environment. Firstly, this paper proposes a service architecture which includes various devices for providing web base IoT services. Secondly, this paper also proposes a service platform which supports service orchestration and composition with device objectification and finally describes structure and functionalities of web of object gateway.

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability.

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) medical devices has revolutionized arrhythmia monitoring. However, the high computational and power demands of traditional Deep Learning (DL) models pose significant challenges for long-term, battery-operated smart electronics. Spiking Neural Networks (SNNs), inspired by the biological efficiency of the human brain, offer a promising solution. This paper reviews the intersection of SNNs, low-power IoT hardware, and biomedical signal processing. I examine the transition from frame-based to event-driven processing, and discuss the hardware–software co-design necessary for next-generation cardiac wearables.

Monitoring the internal conditions of spent nuclear fuel (SNF) dry-storage casks is essential for ensuring the long-term safety and integrity of radioactive waste containment. This study addresses the critical challenge of early detection of potential failures within sealed SNF casks by introducing a novel gas sensor based on microelectromechanical systems (MEMS) technology. The sensor is specifically designed to detect nitrogen concentration in helium, an important indicator of canister degradation, particularly cracks and leaks that may compromise the sealed environment. The proposed sensor utilizes an aluminium nitride (AlN) piezoelectric micromachined structure comprising weakly coupled bridge and cantilever resonators to measure changes in gas thermal conductivity. Electrothermal excitation revealed distinct vibration modes, including a fixed first global mode, a highly variable second global mode, and two instances of mode veering at defined voltage thresholds. Nitrogen sensitivity was quantitatively assessed using the dimensionless Frequency Localisation Factor (FLF), capturing the sensor’s responsiveness to gas composition changes. Experimental validation confirmed the sensor’s high sensitivity, strong linearity, and low power consumption, making it well-suited for integration into Internet of Things (IoT) platforms and wireless sensor networks. By combining the precision and scalability of MEMS technology with robust gas detection capabilities, this work provides a practical and effective solution for enhancing safety monitoring in SNF dry-storage systems.

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Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience.

This work presents a lightweight, FPGA-based hardware implementation for polynomial multiplication, which is the major bottleneck in the NTRU public-key cryptographic scheme. NTRU is a quantum-resilient, lattice-based key exchange cryptosystem, and is currently a finalist in the ongoing National Institute of Standards and Technology post-quantum cryptography standardization. It is challenging to fit these quantum-resilient schemes into Internet of Things (IoT) sensor nodes due to the strict resource constraints (smaller area, less memory, and lower energy budgets) and the limited computational capabilities in embedded devices. We undertake this compact implementation for polynomial multiplication with two motivations: 1) constant-time implementation ensuring inherent security against timing side-channel attacks and 2) optimized hardware consumption to make it suitable for IoT applications. A single-step multiplexer-based iterative architecture is proposed to achieve both goals simultaneously. Compared to the architectures presented in the literature, our proposed work eliminates the utilization of a modular arithmetic unit and replaces it with the correct selection of input followed by an accumulator, which can help to save substantial device resources. Experimental results with an FPGA show that our proposed architecture achieves an area reduction of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.86 \times $ </tex-math></inline-formula> and the throughput increase up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.23 \times $ </tex-math></inline-formula> compared to the state-of-the-art implementation strategies, providing comparable latency along with an inherent-timing attack resilience that is absent in several NTRU hardware implementation schemes.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The rapid advancements in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) are transforming the landscape of agriculture, creating opportunities for smarter, more sustainable farming practices. This book, AI, ML, and IoT for Smart Agriculture: Innovations in Crop Yield, Resource Optimization, and Sustainable Farming Practices, explores the integration of cutting-edge technologies to address the challenges faced by modern agriculture. With the global population continuously rising, the demand for increased agricultural productivity, resource efficiency, and sustainability has never been more pressing. AI and ML offer powerful tools for analyzing vast amounts of agricultural data, driving precision farming practices that can optimize crop yield, improve pest management, and predict climate impacts. IoT-enabled devices provide real-time monitoring and automation capabilities, enhancing resource management and reducing wastage. Through comprehensive research, case studies, and expert insights, this book highlights the transformative potential of these technologies in shaping the future of agriculture. It offers practical guidance for stakeholders, from farmers to policymakers, looking to harness the power of AI, ML, and IoT in achieving sustainable, profitable, and environmentally responsible farming. This resource aims to inspire innovation and promote a holistic approach to the challenges and opportunities of modern agriculture.

The Internet of Things (IoT) enable the IoT to sense and respond using the power of computing to autonomously come up with the best solutions for any industry today. However, Internet of Things have vulnerabilities since it can be hacked by cybercriminals. The cybercriminals know where the IoT vulnerabilities are, such as unsecured update mechanisms and malware (Malicious Software) to attack the IoT devices. The recently posted IoT-23 dataset based on several IoT devices such as Philips Hue, Amazon Echo devices and Somfy door lock were used for machine learning classification algorithms and data mining techniques with training and testing for predictive modelling of a variety of malware attacks like Distributed Denial of Service (DDoS), Command and Control (C&C) and various IoT botnets like Mirai and Okiru. This paper aims to develop predictive modeling that will predict malicious software to protect IoT and reduce vulnerabilities by using machine learning and data mining techniques. We collected, analyzed and processed benign and several of malicious software in IoT network traffic. Malware prediction is crucial in maintaining IoT devices’ safety and security from cybercriminals’ activities. Furthermore, the Principal Component Analysis (PCA) method was applied to determine the important features of IoT-23. In addition, this study compared with previous studies that used the IoT-23 dataset in terms of accuracy rate and other metrics. Experiments show that Random Forest (RF) classifier achieved the predictive model produced classification accuracy 0.9714% as well as predict 8754 samples with various types of malware and obtained 0.9644% of Area Under Curve (AUC) which outperforms several bassline machine learning classification models.

In the dynamic contemporary business environment, the efficient optimization of organizational operations is crucial for companies to maintain competitiveness and secure enduring success. To achieve this goal, organizations can leverage a range of elements including human resource management, the Internet of Things (IoT), technology, time management, employee training, development, and customer relationship management. Enhancing operations through these factors offers numerous benefits such as increased productivity, cost efficiency, better decision-making, work–life balance, heightened satisfaction among employees and customers, boosted revenue, improved competitiveness, and sustained success. This research employed a blended research methodology, encompassing quantitative surveys and qualitative interviews, to explore the effective application of these elements in optimizing organizational operations. Additionally, an artificial neural network (ANN) model was utilized to deepen the understanding of the relationships between key parameters and their impacts on organizational outcomes like productivity, efficiency, and competitiveness. The results indicated that technology had the most significant impact at 76.28%, underscoring the substantial influence of new technologies on organizational performance. Moreover, factors like human resource management, employee training and development, and customer relationship management also played significant roles in optimizing operations. The study identified various challenges to implementation, such as resistance to change among employees, lack of technical expertise, integration issues with legacy systems, and incomplete data, along with best practices to overcome these hurdles including regular performance evaluations, robust security measures, and personalized customer experiences. By adopting a holistic approach that integrates internal and external factors, this study offers valuable insights for organizations seeking to improve their operations, enhance productivity, and achieve their goals more efficiently. The findings emphasize the importance of a multifaceted strategy that harnesses technological advancements and efficient human resource management practices to propel organizational success in today’s fast-paced business landscape. Further research on the intricate interactions between these factors can provide additional guidance for organizations striving to enhance their performance and secure long-term competitive advantages.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.