IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

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.

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

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 .

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 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).

Internet of Things (IoT) sensor networks comprise a set of sensors and actuators to observe the physical environment. As the sensors are battery powered, the reduction in energy consumption plays a vital role to achieve energy efficiency and maximized network lifetime. To achieve, this chapter introduces a new energy-efficient clustering using metaheuristic quasi-oppositional krill herd (QOKHC) algorithm for IoT sensor networks. The proposed QOKHC algorithm incorporates the concept of quasi-oppositional-based learning in the krill herd algorithm to increase the convergence rate. The presented QOKHC algorithm has the capability of selecting the cluster heads properly and organizes clusters in the network. The efficiency of the QOKHC-based clustering technique has been assessed and the results are examined under diverse measures: residual energy, network lifetime, alive node analysis, and the number of packets transmitted to base station. The obtained simulation outcome stated the superior energy efficiency performance of the QOKHC technique over the compared methods.

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.

In the future environment of ubiquitous electric Internet of Things, smart home system will further utilize the Internet of Things to endow common household electrical equipment with good perception, decision-making and adaptability. In order to improve their intelligent degree, through real-time access to household load information to make a reasonable power consumption strategy in time, to minimize energy waste and reduce the cost of electricity for users. The authors propose a design scheme of Internet of Things platform based on the analysis of customers' electricity consumption behavior. Firstly, the authors studied the power consumption habits of different customers, then Logistic regression and probability density mathematical models for different customers have been established. A small volume of 86 boxes of sensors were designed, and the gateway of multi-protocol access for edge computing was added to complete the design of energy analysis platform. This scheme could solve the inconvenience caused by the large size of traditional sensors and the inaccuracy of traditional energy saving calculation. Because of the single communication protocol, the gateway can not be fully connected to electrical equipment, and other problems have been solved. The experiment of practical office buildings show that the proposed system is reliable and feasible, and has certain application and reference value in the field of intelligent buildings.

Sensors and actuators play a vital role in realizing the Internet of Things (IoT) applications. The data transmission capability from edge devices to servers and vice-versa enables IoT applications to be more realistic and effective. However, one of the major challenges to IoT applications is power management at the edge devices. The power consumption of the edge devices depends on the amount of data transferred through the network in a defined interval. Data size and its applicability to the receiving device are controllable parameters, and consequently, energy efficiency can be improved. This work aims to establish an IoT architecture using a message queuing telemetry transport (MQTT) protocol that effectively handles the data transmission in the network. It also provides a solution for the elimination of data redundancy; hence, the network was effectively utilized, and the energy consumption was reduced in the edge devices. The proposed architecture was demonstrated with a potentiometer as a data generator and MQTT as a message broker. Further, the transmitted data was received at the edge devices and its performance was analyzed. Based on the results, it was verified that the client was able to receive the data without any loss in terms of precision. Data redundancy was avoided by disabling the data transmission whenever the nodeMCU identifies the constant sensor value. This retains the power at the edge devices and data transmission devices.

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.

The development of the Internet of Things (IoT) technology has achieved remarkable results in recent years. A large number of sensors and machinery are connected, and combined with the Internet, to achieve intelligent management and operation. However, the traditional centralized IoT data management solution inevitably encounters challenges of data trust, security, sustainability, and user privacy is difficult to guarantee. This paper takes the intelligent transportation sensor network as an example and proposes a blockchain-based Internet of Vehicles (IoV) data secure sharing scheme (called IoVChain), which implements automatic registration, rapid authentication, and reliable sharing method of IoV data through smart contract. The smart contract performs homomorphic encryption and zero-knowledge proof processing on the sensitive part of the data, and this part exists in the form of ciphertext on the blockchain. We adopt PBFT consensus mechanism to ensure the consistency of the entire network ledger, and all the IoV data processing and usage procedure is stored in the Merkle-tree-based block, which cannot be tampered with. Compared with the centralized IoV data management solution, the IoVChain scheme avoids the risk of single point of failure, and keeps the IoV data credible, available, and tamper-resistant under the premise of privacy and security, so that the data can be traced when needed. We have implemented the IoVChain scheme based on the consortium blockchain for trusted and secure sharing of IoV data. Finally, analysis and evaluation show that the proposed IoVChain scheme is feasible, safe, and extensible for the secure sharing of IoV data.

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.

With the continuous development of power Internet of Things and smart grid technology, the potential value of power big data is also being continuously explored. Based on power big data, this paper introduces the technical system of power big data in detail. Through classifying users, it determines the influencing factors of users' electricity consumption behavior and common research methods for analyzing users' electricity consumption behavior.

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.

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.