Wireless Sensor Network System Research Articles

Hybrid packet routing algorithm based on ant colony system and tabu search in wireless sensor network

<span>Packet routing in wireless sensor network is one of the most crucial aspects as it controls the way packets move through sensor nodes with various capabilities to reach to the destination node. Inefficient routing process may lead to higher energy consumption, higher failure rate, and lower throughput. Metaheuristic algorithms have been some of the common approaches to solve these problems due to their adaptability with dynamic environment. This paper proposed a hybrid metaheuristics routing algorithm that hybridizes ant colony system and tabu search which focuses on exploitation and exploration mechanism while reducing the local optima. The proposed algorithm uses ant colony system technique to discover the best path for packet transmission by considering the energy level of each sensor node. Additionally, tabu search technique is applied to overcome the local optima problem by temporarily suspending the bad nodes and initiate backward movement with the aim to prevent the search agent from getting trapped in a blind alley. The proposed hybrid routing algorithm was evaluated against single and hybrid routing algorithms in terms of throughput, energy consumption, and energy efficiency. Experimental results showed that the proposed algorithm outperformed the other routing algorithms in terms of throughput, energy consumption, and energy efficiency.</span>

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.

An efficient, compact, wide-angle, wide-band, and polarization-insensitive metamaterial electromagnetic energy harvester

This paper introduces a metamaterial energy harvester that is compact, highly efficient, and capable of operating at wide angles. The proposed design has an outer ring resonator housing inverted T-shaped resonators, and it can operate at two distinct frequencies, 3.2 GHz and 5.4 GHz. The structure's impedance is carefully designed to align with that of free space, ensuring efficient capture of incident electromagnetic power with minimal reflection. This enables the resistor load to receive power in the most efficient manner. Based on the simulation findings, the proposed harvester exhibits a notably higher conversion efficiency of around 97 %. To ensure the accuracy and reliability of the simulation outcomes, we fabricated a 3x3 cell array of the proposed design and conducted experimental tests within an anechoic chamber. The simulation and experimental results exhibit a strong correlation. Existing metamaterial-based energy harvesting designs frequently confront size, absorption band, and polarization sensitivity limitations. Our compact design is distinguished by its ability to accomplish near-unity absorption and greater power conversion efficiencies at the desired frequency bands. This makes it an ideal option for energy harvesting systems for wireless sensor networks prioritizing efficiency and size.

Logistic Regression and Internet of Things Based Smart Irrigation to Predict Crops Water Need

Irrigation is crucial to agriculture. Due to advancements in technology, when we go outside or whenever crops need to be watered, we no longer need to rely on someone to perform irrigation. Many researchers have attempted to irrigate crops automatically, but accuracy, timeliness, and cost concerns are rarely addressed and given top priority. The proposed approach carries out autonomous watering, which results in smart irrigation, using a wireless sensor network, real-time sensors, and irrigation system control. By using this method, waste is decreased and the necessary water is kept in the container. Instead than just evaluating soil moisture, automated irrigation takes into account the type of crop, the weather, and soil moisture to determine whether the crops need to be watered. By taking into account the aforementioned three factors, a machine learning technique called logistic regression is utilized to estimate the need for water. The logistic regression model, which is based on the values of the three parameters given, forecasts the watering needs of the plants using an arduino-based IoT circuitry. The strategy outlined is advantageous in terms of accuracy, timeliness, and cost. The results of the proposed model has proven its betterment in performance and the amount of automation over the existing irrigation systems. The IoT and machine learning combined model is useful from the point of view of its accuracy, cost and timeliness in predicting water needs for crops as well as full automation is enabled in the irrigation system with this approach.

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study.

An intelligent sleep-awake energy management system for wireless sensor network

An intelligent sleep-awake energy management system for wireless sensor network

Safety Evaluation Method of Tunnel Construction System in Rechargeable Wireless Sensor Networks

In the aspect of constructing the comprehensive assessment and early warning model of typical risk of tunnel construction, this paper introduces fuzzy mathematics theory to quantify the typical risk factors of tunnel construction. On the basis of the constant weight of the warning index determined by Analytic Hierarchy Process (AHP), the variable weight calculation is added to establish a set of fuzzy hierarchical comprehensive assessment and early warning model of typical tunnel construction risks with variable weight, which further improves the accuracy and reliability of risk early warning. The cluster-based Rechargeable Wireless Sensor Networks (RWSN) on-demand charging method designed in this paper sends charging requests by cluster, and the Mobile Charger (MC) considers the distance from the requesting node as the priority criterion. Through the simulation, it can be seen that the algorithm proposed in this paper reduces the MC working energy consumption and improves the network performance. The dynamic risk index of construction process is obtained. The experimental results show that this method can track and analyze the dynamic risks in the construction process timely and accurately. By cooperating with the specific construction process, the probability of risk accidents is reduced and the safety of the project is ensured.

A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks.

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems.

Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network

Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data.

Developing a new encryption algorithm for images transmitted through WSN systems

Wireless sensor networks (WSNs) have up until now faced many challenges because of their open, wide-ranging, and resource-limited nature, including security, efficiency, and energy consumption. In the military system, it is essential to provide high-level security to the significant data over wireless network which is proved to be unreliable public communications. To solve the above problem, high level of security with minimum complexity should be applied to be adequate to limited capabilities of transmission system. This paper presents a new algorithm named (SRS) for encrypting transmitted military images to keep them from getting hacked or broken by WSN. The SRS algorithm is designed to be lightweight, fast, and secure, taking into consideration the limited capabilities of the transmission system. It is implemented as a public key cryptosystem specifically designed for image encryption. The algorithm consists of two parts: encryption and decryption. The proposed system suggested some equations and calculations that are applied to the plain and coded images after being transmitted over the WSN. The results of testing the simulation model demonstrate the effectiveness of the system by evaluation using various metrics such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Overall, the paper presents a new encryption algorithm, SRS, specifically designed for securing military images transmitted over wireless sensor networks. The algorithm aims to provide a balance between security, efficiency, and energy consumption, considering the resource-limited nature of WSNs. The simulation results indicate the improvement of the proposed system by 13 %, 10 %, and 55 % in packet delivery ratio (PDR), throughput, and dropping ratio, respectively, and it shows that the suggested SRS method increased execution time by 67 % compared to RSA based algorithm

Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm.

Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people's productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network's energy consumption by 26.8% and prolonging the network's life cycle.

Challenges in human centric intelligent systems for wireless sensor networks: A state of art

AbstractHuman centric computing is a technique that is gaining more attention nowadays and integrates innovative processing methods for analyzing extensive data collection. Human centric intelligent systems focus on handling the interactions between customers, companies, communities and systems of computing to represent social and institutional concepts effectively. However, this interaction between the companies and customers will lead to various privacy challenges in human centric intelligent systems. This paper briefly studies cyber‐physical systems and various privacy challenges in human centric intelligent systems. In parallel, this article enunciates the human‐centric pipe water monitoring system framework in detail. It also discusses the smart body area network (SBAN) as a typical example of space cooperation. SBAN employs low‐power wireless devices in a compact form that can be incorporated inside the human body for health monitoring. This study examines the challenges of privacy, propagation, and trust assessment that human‐centric systems face.

Collaboration and Connection through Video Games

To make the wireless sensor network used for intelligent workshop products tracking system, firstly, the moving target tracking technology of wireless sensor networks was introduced and the target positioning technique based on ranging was emphatically analyzed, which laid the technical foundation for the construction of moving target tracking system of wireless sensor network. Then, the extended Kalman filter algorithm with less calculation and high tracking accuracy and implementation steps of untraced Kalman algorithm were analyzed and summarized.

New approach to monitoring a wastewater irrigation system controlled by the artificial neural network (ANN)

In this paper, a cost-effective and scalable wireless sensor network (WSN) system has been developed, targeting monitoring and observation applications. The system specifically emphasises wastewater control and management in the context of irrigation practises. An artificial neural network (ANN) technique, incorporating the Well Node approach, has been employed to process a one-year dataset collected from sensor nodes in an actual wastewater treatment station in Morocco. Thus, the acquired data include a range of parameters, such as electrical conductivity (EC), total dissolved solids (TDS), chlorine levels (Cl), nitrogen content (N), hydrogen carbonate concentrations (HCO3), and pH values. The proposed hybrid system incorporates the Kohonen self-regulating characteristic map for data validation and reconstruction, along with multilayer perceptrons for sample prediction. The selected ANN demonstrates impressive performance, with recognition rates exceeding 95% and an F1 score of approximately 96.81% in terms of wastewater reusability. In particular, the system exhibits robustness in handling uncertainties, deviations, and low information content in input data, allowing for informed decisions on usage or reprocessing through its self-learning capabilities. The study highlights the adaptability of the WSN system to different farming requirements and geographical areas, contributing to improved resource management, enhanced water utilization efficiency, and protection of plants, soil, and the environment. The research findings advance the understanding of ANN architecture and its unique modifications for effective wastewater control. The results also emphasise the effectiveness of the system during the adaptation and implementation phases, offering valuable insights for algorithmic improvements.

Particle swarm optimization based artificial neural network (PSO-ANN) model for effective k-barrier count intrusion detection system in WSN

Wireless Sensor Networks (WSN) offers an extensive array of possibilities and finds application to nearly every wake of human lifetime. Two of the methods are intrusion detection and monitoring in dangerous environments. Generally, WSN are extremely vulnerable to various kinds of network attacks, lifespan of whole network is reduced by interfering with data transmission and communication operations. A novel approach, provides accurate measurement of several obstacles to achieve rapid identification and avoidance of disruptions. In order to more accurately estimate k-barrier number for efficient intrusion detection and prevention, a Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) is developed. The region's size, sensor's detecting field, area where sensor transmits its signals, several sensors constitute the four important features utilized for learning and evaluating PSO-ANN model. The extraction of the different attributes is achieved with the help of simulation through Monte Carlo. It has been inferred from the research that the approach is accurate in predicting number of invasions, correlation coefficient, root mean square error, accuracy, precision are achieved to be excellent in contrast to the remaining approaches.

Wireless Sensor Network with Blockchain Technology using Elliptic Curve Discrete Logarithm Problem and its Applications

Wireless sensor network (WSN), a type of communication system, is normally deployed into the unattended environment where the intended user can get access to the network. The sensor nodes collect data from this environment. If the data are valuable and confidential, then security measures are needed to protect them from the unauthorized access. This situation requires an access control protocol (ACP) in the design of sensor network because of sensor nodes which are vulnerable to various malicious attacks during the authentication and key establishment and the new node addition phase. Nowadays, wireless sensor networks are being widely applied in many fields of human life such as civil and military applications. Although WSNs can bring a lot of benefits and conveniences. However, when applying the WSNs in the real world we have to face many challenges such as security, storage due to its centralized server/client model. Therefore, it is necessary to apply the distributed model in the WSNs system. One of the newest distributed systems today is Blockchain (BC). Blockchain is a decentralized technology that can help the computation and management processes as well as security in WSNs. In this paper, we propose a secured ACP for such WSN. This protocol is based on Elliptic Curve Discrete Log Problem (ECDLP) and double trapdoor chameleon hash function which secures the WSN from malicious attacks such as node masquerading attack, replay attack, man-in-the-middle attack, and forgery attacks. Proposed ACP has a special feature known as session key security. Also, the proposed ACP is more efficient as it requires only one modular multiplication during the initialization phase. This article provides an overview of Blockchain integration in WSN with highlighting the benefits and challenges of applying this technology to WSN. We can conclude that using Blockchain technology to solve the problem of security and distributed storage for WSN can be an effective approach. It could pave the way for new research directions and distributed applications.

Research on Indoor Wireless Positioning Method based on Center-of-mass Algorithm

Indoor mobile positioning is widely used in shopping malls, energy saving in buildings, medical care and emergency rescue. Range-free algorithms typically use the connectivity of a wireless sensor network system to estimate the location of target nodes, typically through Dv-hop, the prime method, ap-proximate point-in-triangulation test, or APIT for short. -free algorithms can be obtained by changing the distribution density of reference nodes or by changing the blind node communication radius to obtain higher accuracy. Due to constraints such as complex indoor environments, current indoor positioning methods are often less accurate and more expensive. To address these issues, this paper improves on the traditional center-of-mass algorithm by using a six-point center-of-mass algorithm for indoor positioning to further improve the accuracy of the indoor positioning algorithm. To this end, the following work is accomplished in this paper: 1. introduced the algorithms currently used for indoor positioning and their shortcomings, laying the theoretical foundation for the improved approach proposed in the following paper. 2. introduced the basic working principle of the center-of-mass algorithm and proposed an improved system using the six-point center-of-mass algorithm. 3. established the best possible model through experiments, then input test data and evaluated it based on expert judgement. The experimental results show that the model proposed in this study has excellent accuracy in indoor wireless positioning.

Smart-Systems for Precision Agriculture, Environmental Protection and Healthcare

Appearance of innovative tools of informatics in the world is determined by development of information-communication technology, microelectronics, sensor and biosensor technologies. Spreading of these technologies on precision agriculture, environmental protection and health care gives opportunity to create smart fields, gardens, greenhouses, forests and parks, and also smart health monitors, which estimate the health state of person as in rehabilitation, so in emergency situations. Results of research of authors in the development of new information technologies and creation of main components of the smart systems for different purposes on this base are shows in this paper. Main requirements for knowledge bank for precision agriculture and ecological monitoring are defined. Main principles of creating knowledge bank on base of requirements are proposed. The typical smart system models and their development stages are considered. Structure of wireless multilevel networks for the estimation of state of biological objects of different origin and their nodes are described. Developed sensor networks and smart systems for agriculture are considered. Structure of proposed smart system for agriculture and ecological monitoring is given. Approaches for chlorophyll fluorescence induction curves analysis are studied. Results of network testing for the estimation of the autonomous work time of network nodes and possible errors are given. Wireless sensor network and smart system for remote medical monitoring are described. Diagnostic smart systems for estimation of quality of life are considered. Medical communicator, computer device on base of tablet computer, was used for their development. The short form of Survey Instrument (SF-36) for life quality estimation and abnormal uterine bleeding questionnaire, which was developed in State Scientific Institution "Center for Innovative Medical Technologies of the National Academy of Sciences of Ukraine", were embedded in communicator. STEPS questionnaire which was designed for epidemiological monitoring of the prevalence of noncommunicable diseases and their risk factors in the target subpopulations could be added to communicator. Development of smart systems for estimation of quality of life and prevalence of noncommunicable diseases was made in cooperation with R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology NAS of Ukraine (department of research management and innovation), Chebotarev Institute of Gerontology of the National Academy of Medical Sciences of Ukraine, Center for Innovative Medical Technologies of the National Academy of Sciences of the Ukraine. Keywords: wireless sensor network, precision agriculture, chlorophyll fluorescence induction, express-diagnostics of plant state, quality of life, SF-36.

Recovery of a salinized tomato field in a coastal polder after the 2016 Kumamoto Earthquake in Japan

After the 2016 Kumamoto Earthquake (Kumamoto earthquake sequence) of maximum magnitude M7.3 occurred, agricultural fields and crops were significantly damaged by soil liquefaction in lowland polders, which had shallow saline groundwater. A desalinization treatment was required to recover agricultural production in polders in Kumamoto. To understand the soil salinity levels in the root zone, we installed multiple-point measurements using the wireless sensor network system (WSNS) for real-time monitoring of soil moisture (θ ) and bulk soil EC (ECb) in a cherry tomato greenhouse. Pore-water electrical conductivity (ECw) can be estimated as an indicator of the field's salinity level simultaneously. First, we found rapid increases in θ and ECw where soil liquefaction and sand boils occurred. Through soil survey, we confirmed the existence of a large channel beneath one of the liquefaction plots, suggesting considerably high spatial heterogeneity of soil salinity across the greenhouse. Secondly, the high salinity level could not be controlled by drip irrigation at all. Finally, 1-week flooded leaching treatments were carried out; the electrical resistivity tomography (ERT) survey indicated that the salinity level decreased significantly except around the channel plot. The combination of the flooded leaching and natural rainfall was more effective for salt leaching in macropores and channels. Our analysis using the WSNS highlighted three management strategies that may help reduce a polder's salinity level.

A free-floating structure triboelectric nanogenerator based on natural wool ball for offshore wind turbine environmental monitoring

The marine distributed wireless sensor network system can promote the development of offshore wind turbine environmental monitoring. However, traditional ocean sensors and large power generation equipment are hard to meet the requirements of distributed sensor networks. Hence, we proposed a free-floating structure triboelectric nanogenerator (FF-TENG) based on the all-in-one ocean energy harvesting and sensing device concept for offshore wind turbine environmental monitoring. The FF-TENG device follows the integrated structure design of the sphere and floating body, which brings high wave energy capture efficiency and high wave sensing sensitivity. In this design, the wool ball plays the role of the triboelectric material owning to excellent positive triboelectricity. And the lightweight characteristic of the wool ball endows the FF-TENG device with fine wave-following properties in the ocean environment. In rolling working mode, when the motion amplitude and frequency are 5 cm and 4.5 Hz, the instantaneous open-circuit voltage (Voc) and short-circuit current (Isc) of a single FF-TENG can arrive at 462 V and 15.5 μA, respectively. The transferred charge (Qsc) of a single FF-TENG can reach 107 nC. According to the results, the FF-TENG device can achieve a 4.26 times energy harvesting effect compared with nine-independent spherical TENG. Moreover, the Isc of FF-TENG represents a nicely linear relationship with the swing angle (minimum frequency: 0.2 Hz), which indicates that the FF-TENG can monitor the change of wave frequency and amplitude. This research provides a new path for all-in-one ocean energy harvesting and sensing device in offshore wind turbine environmental monitoring.