Applications of Wireless Sensor Networks: Innovations and Future Trends

1. Introduction

Wireless Sensor Networks (WSNs) bridge the physical environment and digital computing systems [1,2]. Nowadays, WSNs have evolved from simple data collection tools into complex, intelligent ecosystems that form the backbone of the Internet of Things (IoT) and Industry 4.0 [3,4,5]. Consequently, they have an endlessly growing range of applications [6].

Issues related to energy conservation and the collection and transmission of data are typically included within the scientific research for WSNs [7,8,9,10,11,12]. However, in modern applications of WSNs, additional requirements include real-time processing, high security, and reliable connectivity [13,14,15,16,17,18]. Simultaneously, contemporary advances in several scientific domains, such as Artificial Intelligence (AI), Reconfigurable Intelligent Surfaces (RIS), wireless charging, physical layer enhancements, energy harvesting, and edge computing, may further enhance the capabilities of WSNs by redefining the boundaries of connectivity, energy efficiency, and operational autonomy [19,20,21,22,23,24,25,26,27,28,29,30,31].

This Special Issue gathers a diverse collection of 25 high-quality research articles whose subject matter ranges from foundational theoretical models to practical, real-world applications. The contributions included herein address critical challenges in next-generation networks.

2. Overview of This Special Issue

2.1. Application Domain Clusters

2.1.1. Industrial and Environmental Monitoring (Industry 4.0 and Smart Cities)

WSNs are the neural infrastructure of Industry 4.0, as they enable real-time data collection to create Cyber–Physical Systems (CPSs) and support the Industrial IoT (IIoT). Replacing traditional wired installations, WSNs offer the maximum flexibility, lower installation costs, and easy scalability that smart factories require. WSNs are used in various industrial applications, such as the monitoring of the vibration, temperature, and pressure of machinery to avoid sudden failures, the real-time location and status monitoring of raw materials or products, autonomous decision-making in the factory, and the detection of chemical leaks, pollution levels, and hazardous conditions for workers.

Similarly, WSNs form the backbone of Smart Cities. Specifically, they are used in numerous civil applications such as the monitoring of air quality, noise levels, and microclimatic changes, the detection of leaks in pipes, the control of drinking water quality in real time, waste management, the smart lighting of streets, and the smart management of traffic lights depending on pedestrian or vehicle movement.

• The Review Context:

Tsallis et al. (Contribution 1) focus on the reliability and readiness of Industrial Wireless Sensor and Actuator Networks (IWSANs) for Industry 4.0 applications. Their methodology follows the PRISMA 2020 guidelines in analyzing 60 primary studies. The main conclusions of this paper include the identification of a shift from reactive to predictive maintenance using AI/ML, and improvements in time synchronization. However, the paper also highlights a persistent gap in real-world validation, noting that most studies still rely on simulations rather than field deployments, which hinders the full adoption of wireless technologies in critical industrial control loops.

Mattar et al. (Contribution 2), in their review paper, examine how information spreads across complex, large-scale networks, and discuss the diffusion of information in the IoT networks, emphasizing the need for robust simulators for large-scale monitoring. The core problem addressed is the selection of appropriate algorithms and simulation tools for modeling diffusion in both terrestrial and satellite IoT. The main conclusions categorize diffusion strategies (e.g., epidemic, game-theoretic) and evaluate simulators like NS-3 and OMNeT++. The paper highlights that while terrestrial IoT diffusion is well-studied, satellite-based diffusion is an emerging field requiring new models that account for high mobility and dynamic network topologies.

• The Research Context:

Aftab et al. (Contribution 3) directly address the hardware reliability gap mentioned by Tsallis et al. (Contribution 1) by developing a passive dielectric temperature sensor capable of withstanding 700 °C and eliminating the battery failures common in harsh industrial environments. Specifically, this article addresses the problem of monitoring temperatures in harsh industrial environments, such as on rotating parts, where wired sensors or active battery-powered devices are prone to failure. The authors utilize a WSN methodology centered on wireless passive sensing using dielectric microwave resonators, specifically leveraging the temperature-dependent permittivity of dielectric materials to modulate the resonant frequency. The hardware setup involves a passive sensor tag interrogated by a radar-like reader unit. The main results demonstrate that the dielectric sensor provides high-resolution temperature sensing up to 700 °C, outperforming traditional SAW and LC sensors in terms of high-temperature stability and quality, thus making it a robust solution for industrial telemetry.

Yu and Kim (Contribution 4) focus on the hardware efficiency of smart meters in the IoT. The core problem is the high-power consumption and complexity of digital signal processing in ultrasonic flowmeters. Their methodology introduces a novel signal processing architecture using a peak detector-based envelope detection method to calculate the Time-of-Flight (ToF) of ultrasonic signals, replacing complex Hilbert transforms. The main results show that this approach drastically reduces computational complexity (number of multiplications/additions) while maintaining the measurement accuracy required for commercial flowmeters. This enables longer battery life for smart water/gas meters in WSNs.

Sabando-Bravo et al. (Contribution 5) and Yukawa et al. (Contribution 6) provide the field validation requested by Tsallis et al. (Contribution 1).

Specifically, Sabando-Bravo et al. (Contribution 5) focus on the monitoring of CO₂ levels in air. The WSN methodology combines simulation tools with a real-world deployment of nodes equipped with MQ-135 gas sensors using LoRaWAN and ESP32 hardware for long-range, low-power communication. Also, this study evaluates node placement and packet loss to optimize coverage.

On the other hand, Yukawa et al. (Contribution 6) address disaster management, specifically the monitoring of river and reservoir levels to prevent flooding. The core problem is the risk and inefficiency of manual visual checks during storms. Their methodology employs a WSN of camera nodes combined with computer vision algorithms that automatically read the physical scale on water level gauges (converting images to numerical data). The main results demonstrate that the system can accurately recognize and transmit water level data remotely. This system reduces human risk during typhoons and provides continuous, reliable data for flood control and water resource management.

2.1.2. Healthcare and Public Safety

Also, WSNs transform critical human-centric sectors such as healthcare and public safety.

Specifically, Wireless Sensor Networks (WSNs) in healthcare are the basis for the smart, continuous, and remote monitoring of patient health (Smart Healthcare). They consist of tiny, low-power devices (sensors) placed on the patient’s body (wearable/in-body), which collect vital data and send it in real time to doctors. They are typically used for the continuous monitoring of elderly people or patients with chronic diseases in their homes, the in-hospital monitoring of patients, and drug management (smart pillboxes).

Similarly, WSNs play a crucial role in public safety and disaster management in various processes, for example chemical, biological, radiological, or nuclear (CBRN) monitoring, fire detection, earthquake and tsunami early warning, flood monitoring, survivor locating firefighter protection, infrastructure monitoring and smart structural health monitoring.

• The Review Context:

Othman et al. (Contribution 7) highlight e-health as a primary driver for 6G, requiring ultra-reliable low-latency communication (uRLLC).

• The Research Context:

As mentioned above, Sabando-Bravo et al. (Contribution 5) address the public health and environmental challenge of monitoring urban air quality. Their study demonstrates that simulation results closely mirror real-world performance, providing a scalable model for smart city environmental monitoring. Their main conclusion is that the proposed low-cost sensor network is viable for identifying pollution hotspots in urban areas.

Olatinwo et al. (Contribution 8) focus on the application of Wireless Body Area Networks (WBANs) for mental health monitoring. The core problem is accurately diagnosing mental disorders using large, complex physiological datasets on resource-constrained devices. Their methodology uses deep learning combined with dimensionality reduction techniques to process signals collected from IoT sensors. The main results indicate that the proposed model achieves high classification accuracy for various mental states while significantly reducing computational complexity. This makes it feasible to deploy advanced diagnostic AI on edge devices within a WBAN ecosystem.

Shao et al. (Contribution 9) explore the intersection of WSNs and biomedicine, specifically the Internet of Bio-Nano Things (IoBNT). The core problem is detecting vascular stenosis (narrowing of blood vessels) non-invasively. Their methodology uses Molecular Communication (MC) modeling, where nanomachines release molecules into the bloodstream, and the flow characteristics are analyzed to detect obstructions. The main results demonstrate through simulation that the received molecular signal intensity and delay spread vary distinctly with the degree of stenosis. This establishes a theoretical basis for using bio-compatible nanosensors to diagnose cardiovascular diseases early via the IoT.

Kalina et al. (Contribution 10) address the safety risks and lack of real-time data for firefighters operating in hazardous environments. The core problem is maintaining situational awareness regarding both the fire environment and the physiological state of the firefighters. The WSN methodology utilizes a “system of systems” approach called OFMS, integrating wearable sensors (heart rate, temperature) and environmental sensors communicating via LoRa (Long Range radio) and MQTT (Message Queuing Telemetry Transport) protocols to a central dashboard. Actually, combining LoRa with MQTT creates an efficient bridge for IoT networks. LoRa handles long-range, low-power wireless communication among remote sensors and a gateway, while MQTT acts as the lightweight publish–subscribe protocol to route that data from the gateway to local servers or cloud dashboards. The main conclusion is that the system successfully provides command centers with real-time, actionable data, enabling proactive decision-making that enhances personnel safety and operational efficiency during fire incidents.

Minhas et al. (Contribution 11) also focus on public safety. Specifically, in order to address the high mortality rate of motorcyclists due to delayed medical assistance after accidents, they propose a smart helmet. Specifically, it is a wearable IoT safety device that integrates a vibration sensor, a piezoelectric sensor, a GPS module, and a GSM/GPRS module into a microcontroller-based smart helmet. The main results demonstrate that the helmet successfully detects impacts and falls, immediately generating an SMS alert with precise GPS coordinates to emergency contacts. The system offers a cost-effective, automated solution to reduce response times and potentially save lives in traffic accidents.

2.1.3. Precision Agriculture (Agriculture 4.0)

Precision Agriculture (PA/Agriculture 4.0) aims to optimize crop yields while minimizing resource usage and environmental impact. Wireless Sensor Networks (WSNs) are the backbone of PA because they allow producers to collect real-time data regarding microclimate, soil, and crops, thus transforming traditional agriculture into a data-driven process. Specifically, they are used for soil and irrigation management, nutrients (NPK) monitoring, microclimate monitoring, frost forecast, leaf wetness monitoring, and pest detection.

• The Review Context:

Othman et al. (Contribution 7) list precision agriculture as a key “ultra-high data density” use case for 6G, requiring massive connectivity and long battery life.

• The Research Context:

Barrile et al. (Contribution 12) implement a “system of systems” for Agriculture 4.0, integrating atmospheric simulation with local LoRa sensors to optimize crop yield, validating the connectivity models discussed in 6G reviews. The WSN scheme employed involves a network of soil and atmospheric sensors (measuring moisture, leaf wetness, pH, etc.) communicating via LoRa technology, integrated with Geographic Information Systems (GIS) and remote sensing data. The main results indicate that the system successfully provides real-time environmental monitoring, allowing farmers to adjust irrigation and treatments dynamically. This integration leads to sustainable agricultural practices, reduced resource waste, and maintained productivity, validating the “Agriculture 4.0” paradigm.

Makni et al. (Contribution 13), within the scope of precision agriculture, focus on the non-invasive measurement of plant water stress. The core problem is detecting water deficits early without damaging the plant tissue. The authors develop specific optical and electrical impedance sensors for non-invasive plant water status monitoring, providing the granular hardware data inputs necessary for the high-level agricultural digital twins described in the reviews. The proposed methodology involves developing a custom WSN node equipped with two sensor types, an infrared spectroscopy sensor (optical) and a bio-impedance sensor (electrical), to measure leaf water content. The main results validate that the optical sensor outputs correlate strongly with actual leaf water content. This allows for continuous, real-time monitoring of crop health, enabling precise irrigation automation and reducing water waste in agricultural settings.

2.2. Techniques and Algorithms

2.2.1. Edge Computing and AI/Machine Learning

Edge computing is a critical architectural evolution for WSNs. Traditionally, sensor nodes collect data and sent it all to the central cloud for processing. With edge computing, processing, filtering, and decision-making are done at the “edge” of the network (locally), i.e., at the sensors themselves or at nearby network gateways (edge gateways). However, this traditional architecture faces significant problems, which edge solves. Specifically, WSN-to-cloud communication is associated with high energy consumption because wireless data transmission (especially via 4G/5G/Wi-Fi) consumes most of the energy in a sensor node. By processing data locally, only the most important data are sent to the Cloud, extending battery lifetime. Additionally, in critical applications, waiting for a response from the cloud can be fatal. Edge allows for real-time response. Also, bandwidth saving is accomplished by edge computing because the transmission of redundant data is avoided. Likewise, autonomous operation is achieved, because if the connection to the Internet is lost, the local WSN network continues to operate and make decisions autonomously. At the same time, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in WSNs transforms networks from simple data collectors to intelligent, self-governing systems. The application of AI/ML in WSNs can be used to optimize the operation of the network itself and to intelligently analyze the data collected. The combined use of edge computing and AI/ML is represented by TinyML, which enables highly compressed models to be executed directly on the microcontrollers of the sensor nodes themselves. This architecture offers zero latency, superior privacy, and high energy saving.

• The Review Context:

While Tsallis et al. (Contribution 1) note a trend toward AI-driven fault tolerance and security in industrial networks, Tahir and Parasuraman (Contribution 14) focus on the convergence of robotics and edge computing. Specifically, the goal of “edge robotics” is to overcome the latency and processing limitations of cloud robotics. Their methodology involves a comprehensive survey of literature, categorizing approaches by their primary objective (computational offloading, SLAM, navigation). The main conclusions identify edge computing as a critical enabler of time-sensitive robotic tasks, allowing multi-robot systems to share data and process AI algorithms locally with low latency. The paper identifies open challenges in security, seamless handover between edge nodes, and energy optimization for battery-powered robots.

• The Research Context:

Shaikh and Mouftah (Contribution 15) aim to optimize operations of Unmanned Aerial Vehicles (UAVs) through Intelligent Edge Computing (IEC) and Dynamic Wireless Charging (DWC). Specifically, the article addresses the limited flight time and energy constraints of UAVs in Smart Cities. The authors apply IEC to UAV charging and propose a three-layer architecture where edge servers handle trip planning and charging reservations. In this way, they directly address the latency issues in robotic networks raised by Tahir and Parasuraman (Contribution 14). Their methodology proposes a system where UAVs utilize 6G communication to coordinate with charging infrastructure (laser beaming or inductive) and edge servers for trip planning. The main results show that the system’s dynamic arrival management algorithm reduces UAV wait times at charging stations by 1.5 to 5 min and maintains a charging efficiency of over 91%. This framework significantly enhances the operational endurance and autonomy of UAV fleets.

Khatami et al. (Contribution 16) tackle the dual challenges of energy scarcity and eavesdropping security risks in WSNs and demonstrate how AI can manage the complex, dynamic channel conditions in 6G, a challenge highlighted in Othman’s review (Contribution 7). The goals include optimizing Energy Harvesting (EH) and the Secrecy Rate (SR). Their methodology employs a double Reconfigurable Intelligent Surface (RIS) architecture managed by a Fuzzy Deep Reinforcement Learning (FDRL) algorithm with LSTM networks to dynamically adjust phase shifts and reflection coefficients. The main results show that the proposed framework improves energy efficiency by 35.4% and the secrecy rate by 29.7% compared to benchmark methods. It effectively balances the trade-off between energy harvesting and secure communication in dynamic wireless environments.

Megzari et al. (Contribution 17) address the Critical Node Detection Problem (CNDP) in WSNs, which refers to the identification of specific nodes whose failure would disconnect the network. The problem is NP-hard and computationally expensive for large networks. The authors apply a Discrete Particle Swarm Optimization (DPSO) algorithm to the Critical Node Detection Problem (CNDP). The specific DPSO algorithm is applied along with a dynamic neighborhood topology and a specific position update equation to search for these nodes efficiently. This algorithmic approach solves the network robustness issues relevant to the diffusion models discussed by Mattar et al. (Contribution 2). The main results show that the proposed dynamic PSO approach finds better-quality solutions (more effective critical node sets) than standard star-topology PSO and other heuristics, providing a tool with which to analyze and improve WSN robustness.

2.2.2. Security and Cryptography

Security and cryptography in WSNs are one of the greatest challenges in modern computing. Due to their nature, WSN nodes are often placed in hostile or unprotected environments and have extremely restricted memory, computing power, and energy. This makes it impossible to implement traditional, heavy cryptographic algorithms. A secure WSN network must ensure confidentiality, integrity, authentication, and availability.

• The Review Context:

Tsallis et al. (Contribution 1) identify cybersecurity vulnerabilities, specifically the lack of trust and resource constraints in Industrial IoT, as a major barrier to adoption.

• The Research Context:

Srour et al. (Contribution 18) and Hamdi et al. (Contribution 19) provide lightweight security solutions suitable for resource-constrained nodes, while Haroon and Li (contribution 20) attack the problem from the hardware level.

Specifically, Srour et al. (Contribution 18) tackle the trade-off between strong security and low computational power in Wireless Actuator/Sensor Networks (WA/SNs). The core problem is that traditional encryption is often too heavy for sensor nodes. Their methodology proposes a hybrid algorithm that first decomposes data using Discrete Wavelet Transform (DWT) and then encrypts only the critical parts using 2D Chaos Maps (Logistic and Baker maps). The main results prove that this selective encryption reduces computational complexity by over 50% while passing rigorous security tests (histogram analysis, entropy, correlation). A power-efficient security solution for multimedia data in resource-constrained networks is proposed.

Hamdi et al. (Contribution 19) focus on the security and integrity of multimedia data transmission, particularly solving the problem of transmitting hidden data (steganography) within audio signals over noisy wireless channels. Their methodology involves embedding encrypted text into audio files and transmitting them using Orthogonal Frequency Division Multiplexing (OFDM) combined with various modulation schemes (BPSK, QPSK) and convolutional coding to combat channel fading. The main results show that the proposed model achieves high imperceptibility (high SNR) and robust-ness. The system successfully extracts the hidden secret text with low Bit Error Rates (BER) even under challenging channel conditions like Rayleigh fading, ensuring secure data exchange.

Haroon and Li (Contribution 20) address the problem of high computational and power costs associated with cryptographic operations in resource-constrained WSN nodes. Their goal is to identify efficient hardware architectures for security. Their methodology focuses on designing a specific hardware component: a digit-serial modular polynomial multiplier in the Galois Field, which is essential for Elliptic Curve Cryptography (ECC). The main results demonstrate that the proposed multiplier architecture significantly reduces area usage and power consumption compared to existing designs. This efficiency makes it highly suitable for implementation in low-cost FPGA or ASIC hardware for secure IoT and WSN devices.

2.3. Hardware and Sensors

2.3.1. Reconfigurable Intelligent Surfaces and Beamforming

A Reconfigurable Intelligent Surface (RIS) is a two-dimensional artificial structure (meta-surface), which consists of a large number of passive, low-cost meta-elements. Each element can be controlled individually by an integrated controller in such a way that the elements change the phase, amplitude, or polarization of the radio waves that hit the surface before reflecting them. Beamforming is a signal processing technique used in systems with multiple antennas for the directional transmission or reception of signals. Instead of the signal being emitted uniformly in all directions (as in a classic antenna), beamforming concentrates the energy into a narrow beam aimed directly at the user’s device.

The integration of RIS and beamforming in WSNs is a revolutionary approach. It transforms the passive propagation environment into a controllable component of the system, solving the two biggest problems of WSNs, namely, limited node energy and signal loss due to obstacles.

• The Review Context:

Waduge et al. (Contribution 21) address the severe physical limitations of Underwater Wireless Communication (UWC), such as attenuation and turbulence. The goal is the application of Reconfigurable Intelligent Surfaces (RIS) to the underwater domain. Their methodology surveys state-of-the-art research on both acoustic-RIS and optical-RIS, analyzing channel modeling, hard-ware, and beamforming techniques. The main conclusions suggest that RIS can effectively mitigate underwater signal fading and blockage (“shadow zones”). The authors propose that future hybrid systems combining acoustic and optical RIS with energy harvesting could realize self-sustainable, high-bandwidth underwater networks for 6G and IoUT.

• The Research Context:

Smida et al. (Contribution 22) address signal degradation in Wireless Sensor and Actuator Networks (WSANs) caused by channel estimation errors. The goal is the improvement of transmission reliability in scattered environments. Their methodology introduces a Robust Distributed Collaborative Beamforming (RDCB) technique, utilizing statistical information about node locations and scattering (monochromatic and polychromatic) to adjust beam weights. The main results show that the proposed RDCB solutions significantly improve the Signal-to-Noise Ratio (SNR) and maintain robust connectivity even when node positions are not perfectly known. This enhances the spectral and power efficiency of dual-hop transmissions in 5G and IoT networks.

Khatami et al. (Contribution 16) practically apply a “Double RIS” architecture to enhance energy harvesting and secrecy rates, offering a concrete implementation of the theoretical benefits of RIS outlined in the reviews.

2.3.2. LoRa/LoRaWAN and Signal Propagation

In WSNs, the choice of LoRa/LoRaWAN as the physical layer and MAC technology solves the classic problem of limited range of traditional WSNs (e.g., ZigBee, Bluetooth). However, the signal propagation in a LoRa-WSN is dramatically affected by the deployment environment of the sensor nodes. For instance, the low height of the nodes’ antennas increases diffraction and ground reflection losses. Also, the presence of obstacles reduces the received signal strength. Furthermore, reflections in enclosed or densely populated areas cause temporal dispersion of the signal.

• The Review Context:

Mattar et al. (Contribution 2) critique various network simulators (like NS-3 and OMNeT++) used to model diffusion and connectivity in the IoT, noting the need for accurate physical layer parameters.

• The Research Context:

Lorincz et al. [23,24] provide the empirical data needed to calibrate the simulators discussed by Mattar et al. (Contribution 2).

Specifically, Contribution 23 focuses on the reliability of LoRaWAN connectivity in complex indoor environments. The core problem is the variability of signal quality (RSSI and SNR) due to walls and obstructions. Their methodology involves an extensive measurement campaign using LoRa end-devices transmitting measuring packets with varying Spreading Factors (SF7–SF12), bandwidths, and transmission powers within a multi-floor building. The main conclusions reveal that while higher Spreading Factors improve coverage and packet delivery ratios, they drastically increase time on air. The study provides empirical data showing how specific indoor obstructions degrade signal quality, offering guidelines for indoor LoRa network planning.

On the other hand, Contribution 24 addresses the difficulty of predicting battery life for IoT devices. The goal is to create accurate mathematical models for the energy consumption of LoRaWAN end-devices. Their methodology uses multiple linear regression analysis on data collected from real hardware, correlating energy consumption with transmission parameters like Spreading Factor (SF), transmission power, and payload size. The main results provide a set of highly accurate regression equations (with high R-squared values) that allow network designers to estimate the energy usage for different LoRa configurations. This enables better battery life planning and optimization for long-term IoT deployments.

2.3.3. UAVs and Wireless Charging

• The Review Context:

Othman et al. (Review) [8] highlight UAVs as a key enabler of 6G coverage but note energy endurance as a critical bottleneck.

• The Research Context:

Shaikh and Mouftah (Contribution 15) and Banimelhem and Hamad (Contribution 25) offer specific architectural solutions to the energy endurance limitations of UAVs, cited in the reviews.

Shaikh and Mouftah (Contribution 15) propose a novel charging protocol for UAVs by integrating Dynamic Wireless Charging (DWC) to enable efficient, on-the-go energy replenishment, Intelligent Edge Computing (IEC) for local data processing to reduce latency and optimize energy management, 6G networks for real-time Vehicle-to-Infrastructure (V2I) communication, and a novel dynamic arrival management algorithm to minimize UAV wait times.

Banimelhem and Hamad (Contribution 25) tackle the critical issue of energy limitation and node death in Wireless Rechargeable Sensor Networks (WRSNs). The authors propose a “proactive charging” methodology whereby the Base Station (BS) virtually divides the network into hexagonal cells and predicts energy needs, directing a Mobile Charger (MC) to cell centers rather than waiting for individual on-demand requests. The main conclusion is that this proactive approach significantly reduces charging latency and prevents node energy depletion more effectively than traditional on-demand or periodic schemes. By eliminating the need for nodes to send energy requests, the system conserves communication energy and ensures continuous network operation.

3. Conclusions

This editorial, discussing 25 high-quality scientific articles related to wireless sensor and actuator networks, underscores the evolving research landscape in the domain of WSNs. By integrating theoretical algorithmic frameworks with practical hardware deployments, these studies drive the development of more resilient, efficient, and intelligent networks. The editors trust that this compilation will stimulate subsequent innovation across an expanding spectrum of WSN applications.