Search Results (145)

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

Wireless sensor networks (WSNs) have attracted significant attention due to their widespread applications in various fields such as environmental monitoring, agriculture, intelligent transportation, and healthcare. In these networks, the power cost of a sensor node is closely related to the radius of its coverage area, following a nonlinear relationship where power increases as the coverage radius grows according to an attenuation factor. This means that increasing the coverage radius of a sensor leads to a corresponding increase in its power cost. Consequently, minimizing the total power cost of the network while all clients are served has become a crucial research topic. The power cover problem focuses on adjusting the power levels of sensors to serve all clients while minimizing the total power cost. This survey focuses on the power cover problem and its related variants in WSNs. Specifically, it introduces nonlinear integer programming formulations for the power cover problem and its related variants, all within the specified sensor setting. It also provides a comprehensive overview of the power cover problem and its variants under both specified and unspecified sensor settings, summarizes existing results and approximation algorithms, and outlines potential directions for future research. Full article

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

This study proposes an entropy-weighted multi-view collaborative fusion algorithm to address key challenges in agricultural Wireless Sensor Network (WSN) monitoring systems, including high redundancy in multi-modal data, low energy efficiency, and poor cross-parameter adaptability of traditional fusion methods. A fuzzy clustering framework based on principal property selection is established to enable dynamic compression of multi-source heterogeneous data at cluster head nodes. The algorithm innovatively distinguishes between principal and secondary properties based on their contributions to clustering. Clustering is performed using principal properties, allowing data from nodes with similar values to be fused into unified categories, thereby enhancing the reliability of environmental information. Experimental results show that, compared to existing agricultural WSN data fusion algorithms, the proposed method reduces fusion error by an average of 5.6%, lowers the computational complexity of the original multi-view algorithm, and is more suitable for small-sized, low-capacity sensor nodes. Moreover, it has better adaptability to multiple agricultural parameters, reduces network energy consumption, and improves computational accuracy. Full article

LoRa, as a leading LPWAN technology, plays a pivotal role in enabling long-range, low-power wireless communication, especially in agricultural IoT applications. This study examines the propagation characteristics of 433 MHz LoRa signals in maize fields, focusing on signal attenuation, RSSI, SNR, and packet loss under dense crop conditions. Field experiments were conducted in Wuwei, Gansu Province, with validation tests in Tongliao, Inner Mongolia. The effects of transmitter and receiver antenna heights on signal quality and propagation distance were systematically analyzed. Results show a consistent improvement in signal quality and range with increased antenna height. Path loss models were developed using regression analysis, achieving high predictive accuracy (R² > 0.9). Validation confirmed the models’ reliability, offering valuable insights for deploying wireless sensor networks (WSNs) in agriculture. Future research will integrate machine learning for dynamic modeling and explore variations across crop growth stages. Full article

Smart agriculture is transforming traditional farming by integrating advanced sensor-based systems, intelligent control technologies, and sustainable energy solutions to meet the growing global demand for food while reducing environmental impact. This review presents a comprehensive analysis of recent innovations in smart agriculture, focusing on the deployment of IoT-based sensors, wireless communication protocols, energy-harvesting methods, and automated irrigation and fertilization systems. Furthermore, the paper explores the role of artificial intelligence (AI), machine learning (ML), computer vision, and big data analytics in monitoring and managing key agricultural parameters such as crop health, pest and disease detection, soil conditions, and water usage. Special attention is given to decision-support systems, precision agriculture techniques, and the application of remote and proximal sensing technologies like hyperspectral imaging, thermal imaging, and NDVI-based indices. By evaluating the benefits, limitations, and emerging trends of these technologies, this review aims to provide insights into how smart agriculture can enhance productivity, resource efficiency, and sustainability in modern farming systems. The findings serve as a valuable reference for researchers, practitioners, and policymakers working towards sustainable agricultural innovation. Full article

This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based on user-defined scenarios. The proposed model is evaluated using a scenario based on a medium-sized agricultural field. The Simple Additive Weighting (SAW) method is used to assign scores to the candidate algorithm pairs by weighting the scenario-specific criteria according to their significance in the decision-making process. To further refine the selection, mean shift clustering is utilized to group and identify the highest scored candidates. The resulting model is versatile and adaptable, enabling WSNs to be configured according to specific operational needs. The provided pseudocode elucidates the model workflow and aids in an effective implementation. The presented model establishes a solid foundation for the development of guided self-configuring context-aware WSNs capable of dynamically adapting to a wide range of application requirements. Full article

Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management was investigated. The study aimed to identify key research trends and technological developments in the field. Using VOSviewer (CWTS, Leiden, The Netherlands) for bibliometric mapping, the influential research clusters were identified. The analysis revealed a significant rise in scholarly interest, with peak activity between 2020 and 2022, and a shift towards interdisciplinary and applied research. Additionally, the content analysis revealed prevalent agricultural applications, frequently employed microcontroller units (MCUs), widely used sensors, and trends in communication technologies such as the increasing adoption of low-power, scalable communication protocols for real-time data acquisition. This study not only offers a comprehensive overview of the current status of IoT integration in smart irrigation but also highlights the technological advancements. Future research directions include integrating IoT with emerging technologies such as artificial intelligence, edge computing, and blockchain to enhance decision-support systems and predictive irrigation strategies. By examining the transformative potential of IoT, this study provides valuable insights for researchers and practitioners seeking to enhance agricultural productivity, optimize resource use, and improve sustainability. Full article

Wireless sensor networks (WSNs) enable large-scale data collection across wide areas but face significant challenges, including limited energy resources, unbalanced energy consumption, and inefficient data transmission, leading to reduced network lifetime and poor reliability. To address these issues, this paper proposes an energy-efficient clustering scheme that integrates unmanned aerial vehicle (UAV)-assisted data collection and wireless power transfer (WPT) to enhance WSN performance in smart irrigation applications. The proposed scheme divides the network into two regions: a central circular area containing standard nodes and an outer region divided into four clusters housing advanced nodes. It dynamically adjusts cluster formation, optimizes cluster head (CH) selection based on residual energy, minimizes transmission hops, and ensures the shortest possible UAV path, thereby reducing energy consumption. Additionally, UAV-based WPT provides continuous energy replenishment, mitigating the hotspot problem and extending network lifetime. Extensive simulations demonstrate that the proposed scheme reduces energy consumption by up to 13.16%, enhances data collection efficiency by 8.84%, and extends network lifetime by more than 9.6% compared to Yoon’s scheme. Additionally, the proposed scheme achieves 26% higher residual energy and a 43% increase in throughput over Yoon’s scheme. Moreover, in smart irrigation applications, the proposed scheme reduces water consumption by approximately 20%, demonstrating its effectiveness for sustainable precision agriculture. Full article

Creating a suitable growing environment is necessary to ensure good plant growth in a plant factory, which requires wireless sensor networks (WSNs) to monitor the environment in real time. However, existing WSN clustered routing methods hardly take into account the network unreliability caused by varying link quality among nodes, resulting in reduced stability and accuracy of environmental monitoring. This study proposes a wireless sensor network system strategy for improving network reliability in large-scale reliable wireless sensor networks suitable for plant factory scenarios. Firstly, a hybrid wireless sensor network was designed and built based on Wi-Fi and ZigBee communication protocols. Secondly, a nonlinear link quality prediction model for plant factory scenarios was developed using a function fitting method, taking into account the interference and attenuation caused by the dense concentration of agricultural facilities and plants in plant factories on the wireless signal propagation. Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. The results indicated that the coefficient of determination (R²) for the prediction model was 0.9962. The impact of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Under the optimal node deployment, the number of nodes was 33, and the network coverage rate (CR) reached 97.512%. Compared with the traditional clustered routing method, the wireless sensor network designed in this study is more applicable to the field of plant factories; it further enhances data transmission effectiveness and link quality, improves the reliability of the network, and realizes the load balancing of the internal transmission of the network, which in turn ensures the accuracy of environmental monitoring and the stability of the system. Full article

The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands. Full article

Due to the increasing demand for agricultural production and the depletion of natural resources, the rational and efficient use of resources in agriculture becomes essential. Thus, Agriculture 4.0 or precision agriculture (PA) was born, which leverages advanced technologies such as Geographic Information Systems (GIS), Artificial Intelligence (AI), sensors and remote sensing techniques to optimize agricultural practices. This study focuses on an innovative approach integrating data from different sources, within a GIS platform, including data from an experimental atmospheric simulator and from a wireless sensor network, to identify the most suitable areas for future crops. In addition, we also calculate the optimal path of a drone for crop monitoring and for a farm machine for agricultural operations, improving efficiency and sustainability in relation to agricultural practices and applications. Expected and obtained results of the conducted study in a specific area of Reggio Calabria (Italy) include increased accuracy in agricultural planning, reduced resource and pesticide use, as well as increased yields and more sustainable management of natural resources. Full article

Wireless sensor networks deployed in energy-constrained environments face critical challenges relating to sustainability and protection. This paper introduces an innovative blockchain-powered safe energy-swapping protocol that enables sensor nodes to voluntarily and securely trade excess energy, optimizing usage and prolonging lifespan. Unlike traditional centralized management schemes, the proposed approach leverages blockchain technology to generate an open, immutable ledger for transactions, guaranteeing integrity, visibility, and resistance to manipulation. Employing smart contracts and a lightweight Proof-of-Stake consensus mechanism, computational and power costs are minimized, making it suitable for WSNs with limited assets. The system is built using NS-3 to simulate node behavior, energy usage, and network dynamics, while Python manages the blockchain architecture, cryptographic security, and trading algorithms. Sensor nodes checked their power levels and broadcast requests when energy fell under a predefined threshold. Neighboring nodes with surplus power responded with offers, and intelligent contracts facilitated secure exchanges recorded on the blockchain. The Proof-of-Stake-based consensus process ensured efficient and secure validation of transactions without the energy-intensive need for Proof-of-Work schemes. The simulation results indicated that the proposed approach reduces wastage and significantly boosts network resilience by allowing nodes to remain operational longer. A 20% increase in lifespan is observed compared to traditional methods while maintaining low communication overhead and ensuring secure, tamper-proof trading of energy. This solution provides a scalable, safe, and energy-efficient answer for next-generation WSNs, especially in applications like smart cities, precision agriculture, and environmental monitoring, where autonomy of energy is paramount. Full article

Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devices on farms essential. The development of smart sensors that utilize artificial intelligence (AI) algorithms for advanced edge computations only intensifies this need. Moreover, once data are collected, farmers frequently find it challenging to apply them effectively, especially in alignment with agroecological principles. In this context, this paper introduces an energy-efficient platform for embedded AI sensors that leverages the LoRaWAN network, along with a knowledge-based system to aid farmers in decision-making rooted in sensor data and agroecological practices. This paper focuses on the deployment of an end-to-end IoT platform that integrates a wireless sensor network (WSN), embedded AI, and a knowledge base. Full article