Wireless Sensor Network System Research Articles

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.

A novel cluster heads election method based on group sparse TSK fuzzy system for energy-efficient wireless sensor network

Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND).

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

In positioning systems in wireless sensor networks, the accuracy of localization is often affected by signal distortion or attenuation caused by environmental factors, especially in indoor environments. Although using a combination of K-Nearest Neighbor (KNN) algorithm and fingerprinting matching can reduce positioning errors due to poor signal quality, the improvement in accuracy by increasing the number of reference points and K values is not significant. This paper proposes a Differential Evolution-based KNN (DE-KNN) method to overcome the performance limitations of the KNN algorithm and enhance indoor area positioning accuracy in WSNs. The DE-KNN method aims to improve the accuracy and stability of indoor positioning in wireless sensor networks. According to the simulation results, in a simple indoor environment with four reference points, when the sensors are deployed in both fixed and random arrangements, the positioning accuracy was improved by 29.09% and 30.20%, respectively, compared to using the KNN algorithm alone. In a complex indoor environment with four reference points, the positioning accuracy was increased by 32.24% and 33.72%, respectively. When the number of reference points increased to five, in a simple environment, the accuracy improvement for both fixed and random deployment was 20.70% and 26.01%, respectively. In a complex environment, the accuracy improvement was 23.88% and 27.99% for fixed and random deployment, respectively.

Vehicle Tracking System for Small Scale Areas Using Wireless Sensor Network

A real-time vehicle tracking system is designed for specific covering zones based on wireless sensor network (WSN) technology. In this designed WSN infrastructure the location of the vehicles is tracked using a Neo-6MV2 UBLOX GPS module which is placed on the vehicles. Furthermore, the Enhanced ShockBurst protocol is employed to transmit wirelessly acquired location data to a master/center (destination) node using the NRF24101+PA LNA modules. The location information of the vehicle is graphically represented for a security center using an ADO.net-based desktop application. It is used to record the vehicle location information at certain time intervals (minutes) in the MSSQL database. This system intimates the system administrator about the long ques/waiting time of vehicles outside the premeditated area. After the successful deployment of the proposed WSN system, it has been substantiated that the proposed system can be used as an alternative to other wireless network infrastructures like WIFI or GSM for the small-scale areas vehicle tracking.

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop.

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.

Sensor Network Solutions for Aircraft Route Scheduling and Parking Allocation with Localization and Synchronization

The Wireless Sensor Network (WSN) is the modernized version of the sensor networking, earlier the concerned networking system used to be wired. The wireless and modified features have been able to increase the efficiencies of the networking in routing scheduling of aircraft and allocating the parking. This new approach to the WSN system is not much different from the typical sensor networks framework that contains sensors, a communication system, and a controller. Instead of a communication system, a wireless protocol is applied within the sensor network. The smart system of parking has effectively implied due to its hugely innovative as well as in-ground sensors. This can monitor individual spaces of parking and able to relay the status of occupancy to the smart-sport gateways. Then, this can send the live status data to the platform of the smart cloud. This entire process enables the real-time parking data to be accessed and observed on multiple devices.

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm.

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network's status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs' capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning based Approach

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning based Approach

Stochastic Machine Learning Based Attacks Detection System in Wireless Sensor Networks

Stochastic Machine Learning Based Attacks Detection System in Wireless Sensor Networks

Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector.

The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware-software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical-chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees' colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies' health) that the beekeeper will appraise as a consequence of their use.

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

T-Slot Antennas-Embedded ZigBee Wireless Sensor Network System for IoT-Enabled Monitoring and Control Systems

This research proposes a 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system, consisting of an IoT gateway board and a sensor node board, for Internet of Things (IoT) applications. Simulations were first carried out to optimize the parameters for the T-shaped slot patch antenna. The prototypes of the IoT gateway and sensor node boards were subsequently fabricated and measurements undertaken. The measured impedance matching (|S11|), bandwidth, and gain of the proposed ZigBee sensor network system were -18 dB, 15.38%, and 1.722 dBi, respectively. Furthermore, the ZigBee IoT-based monitoring and control schemes based on the ZigBee wireless sensor network system were set up and experiments carried out in an enclosed area for the monitoring scheme and in an open area for the control scheme. The experimental results revealed that the proposed IoT-enabled 2.4 GHz ZigBee sensor network system with embedded T-slot patch antennas could efficiently be utilized in IoT-based monitoring and control systems. In essence, the novelty of this research lies in the integration of the IoT and ZigBee sensor network technologies to store data in a cloud server in a real-time fashion, as opposed to in the microcontroller memory which is common in conventional ZigBee systems. In addition, the data stored in the cloud server are retrievable and viewable via the Blynk application on smartphone, rendering the proposed 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system operationally suitable for IoT-based monitoring and control systems.

A Study on Energy-Efficient Wireless Sensor Network based on Machine Learning Techniques

Wireless sensor networks (WSNs) are advantageous when there is no existing infrastructure (such as in military applications, emergency relief efforts, etc.) and it is necessary to develop a network at a low cost. A predetermined routing protocol or intrusion detection system is not available to Wireless Sensor Networks because they are dynamic by nature and need separating the network's nodes to do this. Because nodes in the majority of WSN applications are mobile and rely on battery capacity and the availability of restricted resources, energy consumption is an important research area for carrying out a variety of activities in WSNs. Self-learning algorithms that function without scripting or human involvement can be effectively used to report this problem depending on the applications need. This study investigated different ML-based WSN systems and exploring the ML techniques for energy efficiency along with some open issues.

Design and development of a continuous water quality monitoring buoy for health monitoring of river Ganga

Real-time monitoring of water quality in the river Ganga and other Indian rivers is crucial to determining its suitability for drinking and other usages across the seasons and round the clock. For this, a structurally strong and hydrostatically stable floating observation center is required to house all the sensors and related equipment. This paper explains the design process for such a buoy platform that can house an array of water quality sensors powered by hybrid energy harvesting systems. Sensors are connected to a wireless sensor network (WSN) system that transfers data to a web-based platform, where we can monitor and analyze our data for the purpose of hazard prediction. Computational analysis has been carried out for the observatory body to ascertain its structural integrity and hydrostatic stability at small and large angles of inclination. The buoy design is based on various requirements specific to Indian rivers at different locations from the mid-course to the confluence. It is important that the system be modular and portable for use in a constantly changing river/water environment. A full-scale functional prototype has been developed, and field testing has been carried out to bring out the efficacy of the proposed system. Also, the WSN system collected real-time water quality data that have been validated with laboratory-based experiments. The establishment of a network of low-cost river/water health monitoring system will further initiate the large-scale data collection and help create digital twins of the Indian rivers.

Improved DV-Hop model based on the application of variable scenarios

This work proposes an improved DV-Hop model based on function analysis and simulation parameter determination (named FuncDV-Hop) to address the problems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step function segmentation experiment, weight function strategy with equivalent points, and maximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distance estimation, and least square calculation of the DV-Hop model. The experiment is designed using the control variable method. The total number of nodes, the ratio of beacon nodes, the communication radius, the number of beacon nodes, and the number of unknown nodes are designed as variables to control the experiment. Finally, the experiments of two stages of simulation parameter determination and integration optimization are carried out. The simulation positioning error of all scenarios is reduced by 0.0630–0.2472, and the positioning accuracy is increased by 23.81%–75.85%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios compared with the other models, the localization error is reduced by more than 0.1, and the optimization rate is increased by more than 10% in the record parameters of the existing wireless sensor network system.