A Multi-Objective Metaheuristic Approach Based Adaptive Clustering and Path Selection in IoT Enabled Wireless Sensor Networks

Source-location privacy (SLP) protection improves security in event monitoring wireless sensor networks (WSNs) for Internet of Things (IoT) applications. However, due to constrained energy resources, WSNs incur coverage holes that affect the level of SLP protection. Existing studies have ineffectively analysed the effects of coverage holes on SLP protection. Noting the limitation, this study investigated the coverage hole effects. Performance of various SLP routing protocols was evaluated based on the sensor node utilization ratio, attack success rate, end-to-end delay, and packet delivery ratio. Then, considering the challenge of limited battery power in IoT sensors, the performance gains and limitations of the protocols were identified. Simulation results demonstrate that the data dissemination SLP routing protocol (DIRP) outperforms other protocols. However, the performance of DIRP is significantly affected by coverage holes. The results suggest that for DIRP to be viable in IoT applications, the integration of distributed energy resources should be considered.

As a promising technology with increased adoption in recent years, Internet of Things (IoT) realizes ubiquitous interconnection of physical devices through internet, opening doors for building powerful industrial applications by leveraging the advances in sensor technology and wireless networks. IoT technologies can be viewed as enablers for smart manufacturing and Industry 4.0. This review paper focuses on applications of IoT in manufacturing, which is also known as Industrial Internet of Things IIoT. To that end, technologies relevant to the application of IoT in manufacturing, such as wireless sensor networks (WSNs), smart sensors, big data analytics, and cloud computing are discussed. A service oriented architecture (SOA) based four-layer model for realizing IoT applications in manufacturing is proposed. Finally, a review of the state of art of IoT applications in manufacturing including shop floor automation, predictive maintenance, energy aware manufacturing, and smart workers is presented with relevant industry use cases.

Wireless Sensor Networks (WSNs) are a component of various applications, including environmental monitoring and the Internet of Things (IoT). Energy efficiency is one of the significant issues in WSNs, since sensor nodes are usually battery-powered and have limited energy resources. The Enhanced Distributed Energy-Efficient Clustering (DEEC) protocol is one of the most common methods for improving energy efficiency and network lifespan by selecting cluster heads according to residual energy. Nevertheless, standard DEEC methods are limited in dynamic environments because of their fixed nature. This paper presents a novel modular implementation of the DEEC protocol for Wireless Sensor Networks, addressing the limitations of the standard DEEC in dynamic and heterogeneous environments. Unlike the typical DEEC protocol, the proposed approach incorporates realistic energy models, supports heterogeneous nodes, implements load balancing, and enables dynamic cluster head selection Numerical simulations in MATLAB® demonstrate that the improved DEEC protocol achieves a 133% longer stability period (first node death at 1166 rounds vs. 472 rounds), nearly doubles the network lifetime (4000 rounds vs. 2111 rounds), and significantly enhances energy efficiency compared to the standard DEEC. These results verify the effectiveness of the proposed enhancements, making the protocol a robust solution for modern WSN and IoT applications.

Wireless Sensor Networks (WSNs) have a wide range of applications across multiple platforms within the Internet of Things (IoT), yet face serious challenges like constrained resources, energy, and memory limitations. Current techniques often struggle to efficiently manage energy consumption, resulting in faster battery drain and reduced network lifetimes. Additionally, memory limitations in sensor nodes can affect data storage, further reducing the productivity and scalability of WSNs. To overcome these issues, this research work presents NSPL-HCS (Novel Smoothed Projected Landweber based Hybrid Compressive Sensing), a novel framework that integrates enhanced Particle Swarm Optimization and Grey Wolf Optimization with the compressive sensing technique. NSPL-HCS enhances essential WSN operations, including the creation of cluster, cluster head selection, data compression, data transmission, and data reconstruction. Compared with the available standard and optimized techniques in this context, NSPL-HCS achieves improvements in throughput, residual energy, alive nodes, first and half dead nodes and, largely in network lifetime. Simulation results validated with relevant test functions revealed the potential of NSPL-HCS, showing its competency to improve WSN performance simultaneously retaining reliability and feasibility, as a result, setting the path for wider implementation of WSN in a number of applications of IoT.

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.

In today’s world Internet of Things (IoT) is a system of interconnected devices through unique ID and will be able to transfer the data over a network. Wireless sensor network (WSN) have been helpful in IoT application for collection of data and processing of data to the sink node or end user. The limited sensor battery life and the lifetime of the network is a concern issue. Here improved LEACH protocol is used to solve this issue. It is a hierarchical protocol that uses Cluster head selection algorithm along with cache node selection for data transfer from sensor node to sink node. The cluster heads are selected on the basis of energy comparison with threshold energy. Data transfer form cluster head to cache node is done on the basis of minimum distance. The cache node memory replacement is done on the basis of priority. By implanting the desired algorithm, the network lifetime and energy efficiency of the network can be improved. The performance of proposed algorithm has been evaluated on the basis of standard parameters average residual energy, dead and alive nodes in network and throughput.

Wireless sensor network (WSN) communication has gathered a lot of attention of research scholars due to its various features such as high wireless data transmission. A large number of techniques have been developed till now in order to achieve an energy efficient network. The clustering and cluster head selection is the major and difficult task to perform in a network. LEACH serves as a basic for rest of the energy efficient clustering protocols. This study considers the LEACH-Mobile Fuzzy (LEACH-MF) as base for developing the proposed work. Fuzzy Inference System (FIS) with LEACH along with threshold based data transmission concept is developed in this work. The major objective of this work is to utilize the allotted energy to sensor nodes in an effective way. The proposed model is parted in two forms i.e. Modified Parameter-LEACH-MF (MP-LEACH-MF) and Limited Communication-LEACH-MF (LC-LEACH-MF). LC-LEACH-MF is a reactive protocol whereas the former one is periodic. In order to assure the performance efficiency of the proposed work, the parameters such as Packet Delivery Ratio (PDR), Last Node Dead (LND), Half Node Dead (HND), First Node Dead (FND), Energy Consumption of the network are evaluated and along with this a comparison analysis has been done with traditional LEACH, LEACH–Mobile (LEACH-M), LEACH-MF. After analyzing the obtained results it is concluded that the LC-LEACH-MF outnumbers the rest of the traditional energy efficient clustering techniques.

A novel method for optimizing energy consumption in wireless sensor network using genetic algorithm

Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

The internet of things (IoT) model connects physical devices to the virtual world and enables them to interact. It enables smart devices to communicate with other devices to exchange information. To link a wireless network or cloud network, it takes the help of several technologies such as radio frequency identification (RFID), wireless sensor network (WSN), near field communication (NFC), ZigBee, and others. The IoT requires a standard architecture and protocol stack to establish links between the devices. This chapter provides a brief introduction, pillars, the evolution, architecture, application of IoT, and issues related to IoT implementation in real life.

SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.