Wireless Sensor Network System Research Articles

Reliability and Sensitivity Analysis of Wireless Sensor Network Using a Continuous-Time Markov Process

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.

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

Implementation of optimal routing in heterogeneous wireless sensor network with multi‐channel Media Access Control protocol using Enhanced Henry Gas Solubility Optimizer

SummaryThe majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low‐Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy‐saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self‐healing, scalability, self‐reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi‐channel MAC protocol with IEEE 802.15.4.

Environmental noise monitoring using distributed hierarchical wireless acoustic sensor network

Acoustic noise pollution is one of many problems people face as cities grow. Long-term noise exposure can result in a series of physical and mental health diseases that are highly harmful to foetuses and newborns. Hence, many IoT-based wireless sensor network systems have been proposed for automated monitoring for long-term operation. However, these systems suffer from weaknesses in functionality, power consumption, costs, and scalability, which hinder large-scale deployment. In this study, we propose a distributed hierarchical wireless acoustic sensor network for environmental noise monitoring to do sound classification and A-weighted sound-pressure-level measurement to address the shortcomings of existing systems. A series of tests and comparisons are performed in diagnosing the performance with respect to recording continuity, packet loss, recording quality, accuracy on A-weighted sound pressure level calculations, and costs. Results show that this proposed network structure is feasible as a part of hardware implementation in a large-scale, low-cost, and high-scalable environmental noise monitoring system to classify sound.

Design and experimental validation of a compact dual-band metamaterial perfect absorber for electromagnetic energy harvesting applications

This article introduces, characterizes, and experimentally validates an innovative design for a compact-sized metamaterial (MM) perfect absorber (PA) for electromagnetic (EM) energy harvesting (EH) applications. The absorber design, comprised of three octagonal ring resonators made of annealed copper, incorporates split gaps at 45-degree inclinations within the uppermost and middle resonators. A split strip line connects the inner octagonal ring resonators, while the split gaps of the outermost and inner rings are filled with a 50 Ω resistive load. This structure is made on a Rogers RT5880 substrate, and the back side of the proposed design is entirely coated with annealed copper. The proposed absorber achieves precise impedance matching with free space, facilitating efficient absorption and redirecting EM power toward the resistive loads. The absorber demonstrates absorption peaks of 99.98 % at 2.4 GHz and 99.99 % at 4.9 GHz. In addition, the efficiency of absorption is assessed for different incident (θ) and polarization angles (ϕ) in both the Transverse Electric (TE) and Transverse Magnetic (TM) modes. Simulated harvesting efficiencies of 95.33 % at 2.4 GHz and 95.99 % at 4.9 GHz are recorded. An experimental validation is performed using a 3 × 3 array that measures 30 × 30 mm. The tests are carried out in an anechoic chamber. The measured harvesting efficiencies strongly correlate with the simulated results, indicating the reliability of the proposed design. This absorber's efficiency and compact size make it an excellent option for EH systems in wireless sensor networks (WSNs).

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

An event-triggered synchronization strategy for high-precise clock systems with switching topologies

The clock synchronization is a key technology for the reliability of wireless sensor networks (WSN). To reduce the communication burden and address the challenge of clock synchronization for high-precise clock systems in WSN with switching topologies, this paper proposes an event-triggered synchronization strategy for high-precise clock systems. Firstly, based on the concept of offset and skew, the model of whole clock systems is investigated and an event-triggered strategy is proposed for high-precise clock systems with switching topologies. Then, a synchronization strategy for clock systems is presented to achieve clock synchronization with the convergence of clock offset and clock skew. The sufficient condition for the synchronization of high-precise clock systems with switching topologies is obtained. Afterwards, the optimal event-triggered weighting matrices and controller gains are calculated by converting the clock synchronization problem as a solvable optimization problem. Finally, the performance of the proposed clock synchronization method is evaluated by simulations.

Design and development of a compact, wide-angle metamaterial electromagnetic energy harvester with multiband functionality and polarization-insensitive features

This article presents a compact, wide-angle, polarization-insensitive metamaterial harvester that can efficiently harvest electromagnetic (EM) energy in the S, C, X, and Ku bands. The harvester's unit cell consists of a split ring resonator, two strip lines, and two split strip lines, giving it a total size of (10 × 10) mm2. Each split gap is filled with a 50 Ω resistive load. The input impedance of the harvester is precisely designed to match that of free space, allowing for efficient absorption of EM power and appropriate redirection towards the resistive loads. The harvester's performance is also evaluated for various polarization and incident angles, considering the Transverse Electric and Transverse Magnetic modes. The simulation results reveal that the proposed harvester exhibits a notably greater conversion efficiency of around > 95%. The simulation outcomes were carefully validated through experimental tests conducted in an anechoic chamber using a 3 × 3 cell array of the proposed design. This ensured the accuracy and reliability of the results. The strong correlation between the experimental data and the full-wave simulations strongly supports the effectiveness of the proposed harvester. Therefore, the demonstrated efficiency and compact size make it a perfect fit for energy harvesting systems in wireless sensor networks.

Performance analysis of capacitive soil moisture, temperature sensors and their applications at farmer's field.

This study aims to assess the effectiveness of PCB-based capacitive soil moisture sensors for local field conditions. The electrical scheme of designed sensors has been presented in this study. The PCB-based capacitive soil moisture sensors are calibrated using a linear equation developed between analog values of capacitive sensors and soil moisture content measured from the gravimetric method. The performance of the designed soil moisture sensors was assessed at five different locations at varying depths (i.e., 15cm, 30cm, and 45cm). The calibration results indicated a positive correlation between the soil moisture content and measurement frequency of the sensor for wheat crop, with R2 values of 0.72, 0.83, and 0.83 for 15cm, 30cm, and 45cm depths, respectively. Results reveal that 85% of the sensors accurately detected the patterns in soil moisture fluctuations during the cropping period. The designed capacitive sensors demonstrated a maximum relative error of 5.87% for 45cm depth. However, the relative error remained below 5% for the 15cm and 30cm soil depths. For the sugarcane crop, R2 values vary from 0.66 to 0.82, with the highest relative error of 5.22% at a 15cm depth. These sensors offer a highly cost-effective solution for farmers, with the entire wireless sensor network system including one sensor node, three soil moisture sensors, and one soil temperature sensor, which is priced at approximately $150, making it a practical and affordable option for widespread adoption.

A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks

Recently, Sahoo et al. introduced a three-factor authentication scheme for Wireless Sensor Networks (WSNs) based on an elliptic curve cryptosystem. Nonetheless, upon closer examination, we have identified critical vulnerabilities in their scheme, including susceptibility to user impersonation, gateway impersonation, sensor node impersonation attacks, and a breach in the three-factor security aspect. Further, the scheme fails to withstand offline sensor node identity guessing attacks, man-in-the-middle attacks, and known session-specific temporary information attacks. Intending to elevate both security and efficiency, we propose a novel three-factor authentication scheme that capitalizes on the strengths of a fuzzy extractor and a cryptographic one-way hash function. The proposed scheme’s security has been rigorously assessed using the SCYTHER tool, confirming its validity under the real-or-random (ROR) model. Moreover, a heuristic analysis exemplifies that the scheme effectively withstands various known cryptographic attacks. Consequently, the performance comparisons establish the superiority of our scheme over related approaches in terms of security and efficiency. Additionally, its suitability for WSNs is evident due to the minimal overhead on the sensor nodes, making it a highly promising solution for real-world implementation.

Detection and mitigation of vampire attacks with secure routing in WSN using weighted RNN and optimal path selection

In Wireless Sensor Networks (WSNs), one of the most significant threats is vampire attacks in sensor nodes. These attacks are marked by malicious behaviors within sensor nodes, often exploiting vulnerabilities inherent in routing protocols. These attacks can disrupt the connectivity of the network and significantly impact the energy resources. However, these intermediate nodes can introduce security vulnerabilities, making network security in WSN is challenging task. To address this issue, a novel deep learning-based vampire attack detection model is proposed. The developed deep learning-based vampire attack detection model is performed by following steps like data collection, attack detection, mitigation, and optimal path selection. Initially, the data attributes for all sensor nodes in the WSN system are collected. Further, the vampire attack detection is carried out by a Weighted Recurrent Neural Network (WRNN), here the weight values are optimized using Enhanced Golf Optimization Algorithm (EGOA). The detected vampire nodes are effectively separated based on different characteristics of nodes like node broadcast count, node energy, and node Packet Received Ratio (PRR). The attack mitigation process is executed by the separation of the vampire nodes from the network, the remaining nodes are considered for the routing process. The optimal paths are chosen by the proposed EGOA. Finally, the result of the suggested vampire attack detection model is compared with the conventional techniques in terms of various evaluation indices.

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box.

Wireless sensor networks (WSNs) are attracting increasing research interest due to their ability to monitor large areas independently. Their reliability is a crucial issue, as it is influenced by hardware, data, and energy-related factors such as loading conditions, signal attenuation, and battery lifetime. Proper selection of sensor node positions is essential to maximise system reliability during the development of products equipped with WSNs. For this purpose, this paper presents an approach to estimate WSN system reliability during the development phase based on the analysis of measurements, using strain measurements in finite element (FE) models as an example. The approach involves dividing the part under consideration into regions with similar strains using a region growing algorithm (RGA). The WSN configuration is then analysed for reliability based on data paths and measurement redundancy resulting from the sensor positions in the identified measuring regions. This methodology was tested on an exemplary WSN configuration at an aircraft wing box under bending load and found to effectively estimate the hardware perspective on system reliability. Therefore, the methodology and algorithm show potential for optimising sensor node positions to achieve better reliability results.

Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks

Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches.

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach.

Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost's hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively.

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Trust management systems in Wireless Sensor Networks

The article begins by offering a comprehensive review of the current research landscape concerning trust management mechanisms. It elucidates the foundational concepts behind trust management mechanisms, subsequently detailing various attack models and the inherent vulnerabilities they exploit. A significant portion of the discussion delves into the primary computational methodologies employed in trust management. These encompass a range of techniques such as Bayesian statistics, subjective logic, fuzzy logic, D-s evidence theory, entropy theory, cloud theory, hierarchical analysis, fog computing, and machine learning. From this foundational understanding, the piece transitions to outline the challenges poised to shape the evolution of trust management mechanisms. This section not only emphasizes the hurdles currently faced by researchers and practitioners but also attempts to forecast the potential obstacles of the future. In culmination, the article encapsulates both the current state of research and the anticipated directions that promise to steer the trajectory of trust management mechanisms in forthcoming years. This holistic perspective aims to provide readers with a clear roadmap of the field's progression, emphasizing both its achievements and the milestones yet to be attained.

A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems.

The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs.

DBlock-RLB: An energy efficient framework for intelligent routing and trading based load balancing in SDWSN environment

Wireless Sensor Networks (WSNs) is an emerging field that enables them to work in complex scenarios with enough flexibility. The WSN systems are improved in terms of network flexibility by integrating Software Defined Network (SDN) to obtain SDWSN environment. However, security and energy efficiency are the major issues in the SDWSN environment which leads to low network lifetime. To resolve the existing issues, we proposed DAG-based BLOCKchain-Routing and Load Balancing Model (DBLOCK-RLB). The proposed approach performs routing and load balancing in the SDWSN environment in an energy efficient manner with high security. The SDWSN environment is composed of four major planes as data plane, switch plane, control plane, and application plane. The sensor nodes in the data plane are authenticated priory to ensure legitimacy and reduce unwanted network traffics by adopting Camellia Encryption Algorithm (CAE) which provides secret key to the user by considering several metrics. The authenticated sensor nodes are clustered to reduce energy consumption using Adaptive Threshold based Network Partitioning in which the circular SDWSN environment is split into equal parts and optimal Cluster Head selection is done by considering clustering metrics. From the clustered network, the CH is responsible for intelligent routing. For intelligent routing, we utilize Dual Agent-Twin Delayed Deep Deterministic Policy Gradient (DA-TD3) in which one agent for optimal forwarder selection and another agent for optimal route selection. After routing, the load balancing is done to reduce the network overhead and scalability issues. The proposed work performs trading based load balancing by utilizing Stackelberg Game Model in which all the local controllers are considered as followers and global controller is considered as leaders. All the transactions are stored in the DAG blockchain to reduce the scalability and Proof-of Authentication (PoAH) used to verify the transactions. The proposed work is simulated using NS-3.26 simulation tool and compared with several existing works by validation metrics like throughput at 2.55(mbps), latency at 27.1(ms), packet delivery ratio at 91.5(%), network Lifetime at 546.5(s), and energy consumption at 14.5 (J). The results show that the proposed work outperforms better than the existing works.