Blockchain-Driven Trusted Data Sharing With Privacy Protection in IoT Sensor Network

Internet of Things (IoT) sensor networks comprise diminutive sensor units primarily designed for monitoring phenomena within a designated area. However, reaching the complete potential of this kind of network is extremely difficult due to several challenges, including the fact that the data transmitted by the sensor nodes contains a large amount of duplicates. Data aggregation can be employed to address this issue in routing packets from nodes that send data to the base station (BS). In this study, a novel, hybrid data aggregation framework for IoT sensor networks is proposed by integrating Self-Organizing Maps (SOMs) with the Firefly Optimization Algorithm (FOA). The core motivation for this integration is to address persistent challenges in IoT sensor networks, chiefly energy efficiency, network longevity, and the reliability of data transmission. By combining the adaptive, unsupervised clustering capabilities of SOMs with the robust, multi-objective optimization properties of the FOA, the method aims to achieve more intelligent, adaptive, and practical solutions for real-world IoT systems. This work presents an innovative framework that synergistically leverages the strengths of FOA and SOM, offering a new methodology that addresses key challenges in scalable and energy-efficient IoT sensor network clustering. The suggested algorithm's validity has been verified using an experimental analysis performed in MATLAB. Experimental results show the proposed method extends network lifetime by 15% and reduces energy consumption by 10% compared to FOA, SOM, and LEACH benchmarks. A notable classification rate was attained after implementing and testing the proposed method using the Intel Berkeley Research Lab dataset.

Monitoring environmental conditions can be done effectively with the help of the Internet of Things (IOT) sensor network. Massive data generated by IOT sensor networks presents technological hurdles in terms of storage, processing, and querying. A Hadoop cloud platform is suggested as a fix for the issue. The data processing platform makes it possible for one node's work to be shared with others employing the time and workload genetic algorithm (TWLGA), which lowers the risk of software and hardware compatibility while simultaneously increasing the efficiency of a single node. For the experiment, a Hadoop cluster platform employing the TWLGA scheduling algorithm is built, and its performance is assessed. The outcomes demonstrate that processing huge volumes of data from the IOT sensor network is acceptable for the Hadoop cloud platform .

An energy efficient authentication scheme for cluster-based wireless IoT sensor networks

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.

This study investigates the deployment of artificial intelligence (AI)–supported structural health monitoring (SHM) systems for in-service bridges using Internet of Things (IoT) sensor networks, a rapidly advancing domain that merges cutting-edge sensing, communication, and data analytics to improve infrastructure safety and durability. Traditional bridge inspections and early wired SHM systems have faced persistent limitations, including manual data interpretation, latency in damage detection, and scalability barriers due to cost and complexity. Recent breakthroughs in wireless IoT technologies, multi-modal sensor fusion, and AI-based analytics now offer the potential for continuous, automated, and highly reliable monitoring of structural integrity across diverse bridge typologies and environmental conditions. To critically synthesize the state of knowledge, this systematic review analyzed 146 peer-reviewed papers published between 2000 and 2022 spanning civil engineering, computer science, and information systems. The review explored key dimensions: (a) the historical evolution and objectives of SHM in bridge engineering; (b) IoT sensor modalities and network architectures enabling large-scale monitoring; (c) AI techniques for damage classification, anomaly detection, and signal feature engineering; (d) system reliability and data integrity strategies including calibration, drift compensation, and cybersecurity; (e) deployment challenges and scalability considerations across steel and concrete bridges; (f) comparative field case studies and lessons from global smart infrastructure programs; and (g) emerging research directions such as digital twins, blockchain data provenance, climate-resilient AI, and hybrid human–AI decision systems. The findings indicate that AI-enhanced SHM significantly improves predictive damage detection, reduces false alarms, and supports timely maintenance decisions, especially when combined with multi-sensor IoT networks and robust data governance. However, challenges remain in standardization, model retraining under concept drift, cost–benefit justification for large-scale deployment, and regulatory acceptance of AI-informed safety decisions. The review also highlights knowledge gaps around extreme climate resilience, secure and scalable data management, and human oversight frameworks for trustworthy AI. By consolidating insights across 146 studies, this work provides an integrated roadmap for researchers, engineers, and policymakers aiming to advance the next generation of smart, resilient, and cost-effective bridge monitoring systems.

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).

In this paper, we proposed a novel method to solve the coverage control problem of sensor networks in the Internet of Things (IoT). The coverage control is an important index to evaluate the performance of network services. Ensuring the quality of network services, it is mainly to maximize the coverage of the network and minimize the energy consumption at the same time for the purpose of extending the network life cycle effect. Because of the overlay redundancy, it adopts the sleeping scheduling mechanism of nodes. The optimal solution is obtained after utilizing the coverage rate and the node sleep rate as the optimization objective function. Particle swarm optimization (PSO) is a group intelligent optimization algorithm. In practical applications, PSO often convergence in the local optimal solution prematurely. In order to balance the global search ability and convergence speed of PSO, We have improved the PSO based on the resampling technique, named resampled PSO (RPSO). The RPSO can not only maintain the diversity of the population, which can avoid premature convergence of the algorithm to some extent, but also ensure that each particle is active, reducing the calculation of redundancy, thereby improving the efficiency of the algorithm. The experimental results show that the RPSO can deal with complex multipeak optimization problem efficiently and reliably. Then the RPSO is used to solve the coverage control problem of sensor networks in IoT and has a great performance.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

Internet of things (IoT) is a network of connected devices that work together and exchange information. In IoT, things or devices means any object with its own IP address that is able to connect to a network and can send and receive using internet. Examples of IoT devices are computers, laptops, smart phones, and objects that are operational with chips to collect and correspond data over a network. The range of internet of things devices is huge. Consumers use smart phones to correspond with IoT devices.

Coronavirus disease (COVID 19) is the dangerous pandemic that scares the world from November 2019 affects more than 50% of the world population. Internet of things (IoT) is coming as a survivor to manage the pandemic situation because of its controlling and monitoring capability without human intervention. In this paper, Secure IoT integrated withwireless sensor network (WSN) for COVID 19 is implemented to monitor the infected patient's health condition. The sensor node's monitored data are encrypted using the different existing encryption algorithms to secure the health care data. Triad parallel advanced encryption standard (AES) algorithmis novel encryption algorithm proposed to encrypt the data in the internet server that improves the security of the stored data and the parallel processing reduces the execution time of the algorithm in insignificant amount. In this proposed work, the performance evaluation of the network in different encryption algorithms are carried out.

The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage. Today, this large volume of IoMT generated medical data is stored in the centralized storage system. However, centralization of patient sensitive information leads to a single point of failure, privacy, and security concern. To address these issues, we propose a smart contracts enabled consortium blockchain network. We integrated interplanetary file systems (IPFS) cluster node where smart contracts are deployed at the initial stage for authentication of patient’s and medical devices, the same cluster layer is also proposed as a distributed data storage layer for device-generated data after authentication and these data are securely transmitted over the consortium blockchain. The IPFS cluster node ensures the security and authentication of the devices and it also provides secure storage management in IoMT enabled healthcare system. The consortium network enables the privacy of data owing to hash-based storage in a block of IoMT enabled healthcare network.

This study aims to explore how big data and the Internet of Things can improve the economic benefits of enterprises in online marketing. We first introduced the concept and development trend of big data, Internet of Things, and Internet marketing and then collected a large number of empirical data through the design and implementation of questionnaire survey. Our data analysis results show that the level of use of big data and the Internet of Things has a significant positive impact on business sales. However, there are also some problems and challenges in the implementation of big data and the Internet of Things, such as resource and technology limitations, and legal and ethical issues such as data security and privacy protection. Therefore, we suggest that enterprises should reasonably plan the use of big data and the Internet of Things according to their own resources and capabilities, while constantly upgrading their own technical level, focusing on data security and privacy protection, and tracking the latest technology and market trends. In addition, the government and industry organizations should also formulate and improve relevant policies and standards to support the development of big data and the Internet of Things and protect the interests of consumers. In general, big data and the Internet of Things provide new opportunities and challenges for enterprise online marketing, which need further research and discussion.

The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into “smart” devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G’s high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices.

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

This book chapter explores the intersection of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), highlighting their transformative impact on various industries and applications. As IoT devices generate massive amounts of real-time data, AI and ML algorithms provide the analytical power necessary to derive actionable insights, automate processes, and enhance decision-making. The chapter covers key AI and ML techniques, such as supervised and unsupervised learning, reinforcement learning, and deep learning, and discusses their integration into IoT systems for improved efficiency, scalability, and real-time performance. Specific use cases, including smart agriculture, industrial IoT, and healthcare, are examined to demonstrate the practical applications of these technologies. Furthermore, the chapter addresses the challenges in data processing, security, and privacy that arise from IoT and AI integration, as well as the future potential of these systems in driving innovation across sectors. This comprehensive analysis serves as a foundational resource for understanding the synergistic relationship between AI, ML, and IoT in creating intelligent, connected systems.