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Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles. Full article

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML’s potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment. Full article

A novel prototype based on the combination of a multi-junction, high-efficiency photovoltaic (PV) module and a supercapacitor (SC) able to self-power a wireless sensor node (WSN) for outdoor air quality monitoring has been developed and tested. A PV module with about an 8 cm² active area made of eight GaAs-based triple-junction solar cells with a nominal 29% efficiency was assembled and characterized under terrestrial clear-sky conditions. Energy is stored in a 4000 F/4.2 V supercapacitor with high energy capacity and a virtually infinite lifetime (10⁴ cycles). The node power consumption was tailored to the typical power consumption of miniaturized, low-consumption NDIR CO₂ sensors relying on an LED as the IR source. The charge/discharge cycles of the supercapacitor connected to the triple-junction PV module were measured under illumination with a Sun Simulator device at selected radiation intensities and different node duty cycles. Tests of the miniaturized prototype in different illumination conditions outdoors were carried out. A model was developed from the test outcomes to predict the maximum number of sensor samplings and data transmissions tolerated by the node, thus optimizing the WSN operating conditions to ensure its self-powering for years of outdoor deployment. The results show the self-powering ability of the WSN node over different insolation periods throughout the year, demonstrating its operation for a virtually unlimited lifetime without the need for battery substitution. Full article

A remarkably high growth has been observed in the uses of wireless sensor networks (WSNs), due to their momentous potential in various applications, namely the health sector, smart agriculture, safety systems, environmental monitoring, military operations, and many more. It is quite important that a WSN must have high reliability along with the least MTTF. This paper introduces a continuous-time Markov process, which is a special case of stochastic process, based on modeling of a wireless sensor network for analyzing the various reliability indices of the same. The modeling has been conducted by considering the different components, including the sensing unit, transceiver, microcontroller, power supply, standby power supply unit, and their failures/repairs, which may occur during their functioning. The study uncovered different important assessment parameters like reliability, components-wise reliability, MTTF, and sensitivity analysis. The critical components of a WSN are identified by incorporating the concept of sensitivity analysis. The outcomes emphasize that the proposed model will be ideal for understanding different reliability indices of WSNs and guiding researchers and potential users in developing a more robust wireless sensor network system. Full article

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs. Full article

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network’s lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network. Full article

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6–120 m with the existing works. Full article

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the “FloatingBlue” architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE’s low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, “FloatingBlue” demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight “FloatingBlue” as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches. Full article

Due to their inherent openness, wireless sensor networks (WSNs) are vulnerable to eavesdropping attacks. Addressing the issue of secure Internet Key Exchange (IKE) in the absence of reliable third parties like CA/PKI (Certificate Authority/Public Key Infrastructure) in WSNs, a novel key synchronization method named NDPCS-KS is proposed in the paper. Firstly, through an initial negotiation process, both ends of the main channels generate the same initial key seeds using the Channel State Information (CSI). Subsequently, negotiation keys and a negative database (NDB) are synchronously generated at the two ends based on the initial key seeds. Then, in a second-negotiation process, the NDB is employed to filter the negotiation keys to obtain the keys for encryption. NDPCS-KS reduced the risk of information leakage, since the keys are not directly transmitted over the network, and the eavesdroppers cannot acquire the initial key seeds because of the physical isolation of their eavesdropping channels and the main channels. Furthermore, due to the NP-hard problem of reversing the NDB, even if an attacker obtains the NDB, deducing the initial key seeds is computationally infeasible. Therefore, it becomes exceedingly difficult for attackers to generate legitimate encryption keys without the NDB or initial key seeds. Moreover, a lightweight anti-replay and identity verification mechanism is designed to deal with replay attacks or forgery attacks. Experimental results show that NDPCS-KS has less time overhead and stronger randomness in key generation compared with other methods, and it can effectively counter replay, forgery, and tampering attacks. Full article

To explore the potential of plant trace elements as indicators in the search for concealed deposits within the W–Sn polymetallic mining area of Shizhuyuan, Hunan Province, this study focused on the geochemical characterization of 21 trace elements, including Ag, As, B, Bi, Cd, Mo, Ni, Pb, and U, in the stem and leaf tissues of three predominant plants in the area. A total of 126 plant samples were collected, covering an area of about 10 km², and analyzed using ICP-MS. The best indicator plants and sampling sites were selected using multiple indicators, including the biological absorption coefficient (X_BAC), the enrichment coefficient (K_NJ), and the contrast coefficient (K_CD). The results showed that plant leaf tissues represent the most effective sampling components for phyto-geochemical surveys in this region. Dicranopteris dichotoma exhibited markedly pronounced geochemical anomalies of Ag (0.137 µg/g), As (86.12 µg/g), Mo (0.963 µg/g), Pb (15.4 µg/g), Sb (2.03 µg/g), and Se (0.547 µg/g) and demonstrated superior absorption capabilities for Ni, Sn, Sb, Pb, and Bi in the soil, with X_BAC values of 12.0, 54.2, 23.3, 2.9, and 83.9, respectively. R-type cluster analysis and factor analysis identified four distinct mineralization element combinations: (1) Sn–As, (2) Ag–Cu–Mo, (3) Pb, and (4) Bi–Sb–Se. Consequently, D. dichotoma is a viable indicator plant for the phyto-geochemical detection of concealed Ag, Bi, Mo, Pb, Sb, Se, and Sn mineralization in mining areas. The results demonstrate that using phyto-geochemical methods for mineral prospecting is feasible and has significant application value in the Shizhuyuan mining area, which is characterized by dense vegetation and complex geological conditions. Full article

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio. Full article

Various works propose solutions addressing the sustainability of IoT technologies to reduce their energy consumption, especially in the domain of wireless sensor networks. The diversity of applications, as well as the variability of their long-term constraints, forces them to dynamically adapt the network through time. Accordingly, this study formalizes the SADHoA-WSN framework to tackle the reconfiguration process. This proposal is a dynamic Holonic Control Architecture, linking the physical network evolution to the decisions made by a virtual multi-agent control system. The potential of such an approach is demonstrated by applying this framework to the energy optimization of communicating materials, i.e., materials equipped with inner wireless sensor nodes. The first implemented components of SADHoA-WSN and their related experimental results validate it as a promising energy-efficient dynamic methodology. This work lays the groundwork for optimized energy control in IoT networks. Full article

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error. Full article

In this manuscript, we present in detail the design and implementation of the hardware and software to produce a standalone wireless sensor node, called SensorQ system, for the detection of a toxic chemical agent. The proposed wireless sensor node prototype is composed of a micro-controller unit (MCU), a radio frequency (RF) transceiver, a dual-band antenna, a rechargeable battery, a voltage regulator, and four integrated sensing devices, all of them integrated in a package with final dimensions and weight of 200 × 80 × 60 mm and 0.422 kg, respectively. The proposed SensorQ prototype operates using the Long-Range (LoRa) wireless communication protocol at 2.4 GHz, with a sensor head implemented on a hetero-core fiber optic structure supporting the surface plasmon resonance (SPR) phenomenon with a sensing section (L = 10 mm) coated with titanium/gold/titanium and a chemically sensitive material (zinc oxide) for the detection of Di-Methyl Methyl Phosphonate (DMMP) vapor in the air, a simulant of the toxic nerve agent Sarin. The transmitted spectra with respect to different concentrations of DMMP vapor in the air were recorded, and then the transmitted power for these concentrations was calculated at a wavelength of 750 nm. The experimental results indicate the feasibility of detecting DMMP vapor in air using the proposed optical sensor head, with DMMP concentrations in the air of 10, 150, and 150 ppm in this proof of concept. We expect that the sensor and wireless sensor node presented herein are promising candidates for integration into a wireless sensor network (WSN) for chemical warfare agent (CWA) detection and contaminated site monitoring without exposure of armed forces. Full article

This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions. Full article