Wireless Sensor Network System Research Articles

Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires

Around a third of the world’s surface area is covered with forests. This is about 4–5 billion hectares of land. We are losing an additional 3 million hectares of forest cover each year than we did in 2001. Hence, researchers worldwide have been working on the identification and prevention of forest fires more aggressively. The wireless sensor network technology for forest fire detection was established as a result of the shortcomings of standard forest fire detection techniques in real time. This paper proposes an energy-aware Internet of Things (IoT) based Wireless Sensor Network (WSN) model with an efficient clustering and routing technique for forest fire detection. The proposed model is named Energy Efficient Routing Protocol (EERP). The model decreases the energy utilization in sensor nodes by minimizing idle listening in cluster heads. EERP also minimizes the transmission of redundant data by allowing only the sensor nodes close to an event to report the event. Furthermore, the model ensures that sensor nodes with low energy levels do not become cluster heads. EERP uses multi-hop routes to send data from source nodes to the Base Station. The performance of EERP is compared with that of some existing Medium Access Control (MAC) protocols in various scenarios. The simulation results prove that the proposed model reduces energy consumption in sensor nodes significantly.

A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.

Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

Research on fire source location and early warning algorithm based on sensor network

In the construction and development of modern society, the form of urban buildings presents the characteristics of diversification, although different types of building structure for social residents' life and production has brought convenient conditions, but also caused more safety risks, the building internal fire safety technology requirements are increasingly high. In this context, the traditional fire detection technology has been unable to meet the needs of building fire prevention, so scholars from various countries in the practice of exploration, integrated the use of network technology and intelligent algorithm to build an automatic fire alarm system, which can accurately detect and effectively control the fire in the early stage, can truly realize the prevention oriented development requirements. In this paper, based on the research status of building fire source location early warning technology in the new era, according to the basic principle of wireless sensor network system and fire location DV-hop algorithm, based on the multi-sensor data fusion technology to create a fire source early warning system. The final experimental results show that this algorithm can ensure the accuracy of fire warning positioning and improve the scientific and accurate decision making of the system.

Design and Development of Intrusion Detection System for Wireless Sensor Network

Design and Development of Intrusion Detection System for Wireless Sensor Network

Energy-efficient output feedback control mechanism based on an event-driven discrete time-invariant system in wireless sensor networks

Energy-efficient output feedback control mechanism based on an event-driven discrete time-invariant system in wireless sensor networks

Reduce the Spread Risk of COVID-19 based on Clinical Fusion Data and Monitoring System in Wireless Sensor Network

The expression “COVID-19” has been the fiercest but most trending Google search since it first appeared in November 2019. Due to advances in mobile technology and sensors, Healthcare systems based on the Internet of Things are conceivable. Instead of the traditional reactive healthcare systems, these new healthcare systems can be proactive and preventive. This paper suggested a framework for real-time suspect detection based on the Internet of Things. In the early phases of predicting COVID-19, the framework evaluates the existence of the virus by extracting health variables obtained in real-time from sensors and other IoT devices, in order to better understand the behavior of the virus by collecting symptom data of COVID-19, In this paper, four machine learning models (Random Forest, Decision Tree, K-Nearest Neural Network, and Artificial Neural Network) are proposed, these data and applied as a machine learning model to obtain high diagnostic accuracy, however, it is noted that there is a problem when collecting clinical fusion data that is scarce and unbalanced, so a dataset augmented by Generative Adversarial Network (GAN) was used. Several algorithms achieved high levels of accuracy (ACC), including Random Forest (99%), and Decision Tree (99%), K-Nearest Neighbour (98%), and Artificial Neural Network (99%). These results show the ability of GANs to generate data and their ability to provide relevant data to efficiently manage Covid-19 and reduce the risk of its spread through accurate diagnosis of patients and informing health authorities of suspected cases.

Energy-efficient output feedback control mechanism based on an event-driven discrete time-invariant system in wireless sensor networks

Wireless sensor networks promote intelligent transportation, intelligent medicine and environmental monitoring, but the limited power of sensor nodes limits the development of these applications. In order to effectively save data transmission energy in wireless sensor networks, an event-driven output feedback control mechanism with discrete time invariant system is proposed. Firstly, the transmission strategy is derived by using approximate quadratic function. And the proposed transmission mechanism can be used to balance the actuator and wireless transmission rates. Secondly, the output feedback gain matrix is designed, the upper bound of the approximate quadratic function is calculated, and the corresponding output feedback controller is designed. Finally, the simulation results show that the energy consumption per data transmission is only 50 nJ/b. The proposed transmission mechanism can extend the battery life of sensor nodes by about 20% by comparing with the traditional periodic transmission mechanism, which verifies the validity of the theoretical results.

Joint Optimization of Altitude and Beamwidth for UAV-Powered Wireless Sensor Networks

In this paper, we consider an unmanned aerial vehicle (UAV)-powered wireless sensor network (WSN) in which a UAV transfers wireless energy to ground sensor (GS) nodes using a directional antenna, and the GS nodes use the harvested energy to transmit sensing information back to the UAV. Considering the tradeoff aspect in adjusting the antenna beamwidth and altitude of the UAV, we jointly optimize the altitude and beamwidth to collect sensing data from all GSs as quickly as possible. The numerical analysis verifies the existence of unique altitude and beamwidth values that minimize the data collection time. The simulation result indicates that the use of a directional antenna and the joint optimization of altitude and beamwidth are essential for maximizing the performance of UAV-powered WSN systems.

Combined Dual-Prediction Based Data Fusion and Enhanced Leak Detection and Isolation Method for WSN Pipeline Monitoring System

In a Wireless Sensor Networks (WSN) based fluid pipeline leak monitoring system, numerous sensors are deployed along the pipeline networks. A great amount of measurements are continuously transmitted from the sensor nodes to their corresponding sink nodes. The energy consumed on data transmission dominates the power depletion of a WSN system. To reduce the amount of data transmission and prolong the lifetime of WSN, in this paper, a Combined Dual-Prediction based Data Fusion (CDPDF) method is proposed. Transmissions are only triggered if the measurement is substantially different from the predicted value. Furthermore, unlike existing methods which establish the predictor by merely considering the measurements from a single sensor, the proposed CDPDF learns and updates the predictor by integrating measurements from multiple neighboring sensors, hence the spatial cross-correlation is taken into account and the prediction accuracy is significantly improved. In this paper, an Enhanced Leak Detection and Isolation (EnLDI) method is also proposed in which several important parameters, such as the friction factor and the pressure wave propagation speed, can be online updated, resulting in improvement of the leak localization accuracy. Experimental case studies are conducted. By employing the proposed CDPDF and EnLDI methods in pipeline networks monitoring, the accuracy of leak isolation is significantly increased with reduced data transmission demands. Note to Practitioners—This work delivers a hybrid scheme that combines machine learning based data fusion and transmission, with model-based leak detection and isolation. The work is motivated by the problem of high energy consumption on data transmission and poor leak diagnosis accuracy in WSN based pipeline networks monitoring system. To reduce the energy consumed during frequent transmissions among sensor nodes, in this paper, a machine learning based data fusion method is proposed which can eliminate most of the redundant transmissions. Among the investigated schemes, the Extreme Learning Machine (ELM) based predictor can not only achieve satisfactory prediction accuracy but also has low computational cost, hence it can be easily implemented in most of the embedded micro-controller systems in practice. At the base station of a WSN, in the leak diagnosis phase, traditional model-based methods employ the fixed model parameters which should be adjustable in different pressure and flow conditions etc. In this paper, an online model parameter estimation procedure is introduced and incorporated in the scheme designed to estimate the leak size and location, thus, the leak localization accuracy is significantly improved. Moreover, the algorithmic procedures, mathematical expressions, evaluation process and results are also provided for practical implementation.

Sistem Pemantauan dan Kendali Kelembapan Udara Pada Budi Daya Bunga Anggrek Berbasis Internet of Things

Orchid cultivation has been widely carried out by orchid agribusiness in Indonesia, even though there has been a lot of orchid cultivation but there are still obstacles in terms of supplies or maintenance. One of them is water sprinkling and fertilizing the orchid plants which are still done manually. Manual watering is watering done by orchid farmers so it requires a lot of effort and time and the amount of water that is sprinkled is not the same. If the water is poured too much or too little, it can cause rot or dryness of the plant roots so that the plants can die quickly. In the current technological era, watering can be done automatically by utilizing Internet of Things (IoT) technology and implementing the Wireless Sensor Network (WSN) system. NodeMCU ESP32 is used to control all hardware and software components. In this system there are 2 sensor nodes and 1 controller node. Users can control manually or automatically through the website interface. From the results of the implementation and testing it can be concluded that the system is able to provide information on conditions of temperature, air humidity, humidity of the planting medium, water level, liquid fertilizer height and water pH. The system is also capable of running automatic and manual systems, namely controlling and providing on/off condition information on water pumps, fertilizer pumps, faucet 1, faucet 2, faucet 3, fan 1, fan 2 and fan 3 on the website. The effect of the automatic system on the orchid plants was very good, because on the 41st day, the orchid plants using the automatic system experienced faster growth of new shoots, compared to using the manual system. The average delay time for the entire system is 4.5 seconds.

Elongate the network lifetime using Adaptive Network-Based Fuzzy Inference System in wireless sensor networks

Elongate the network lifetime using Adaptive Network-Based Fuzzy Inference System in wireless sensor networks

Dynamic Online Trajectory Planning for a UAV-Enabled Data Collection System

Due to the maneuverability and flexibility of unmanned aerial vehicles (UAVs), the UAV-enabled data collection systems for wireless sensor networks (WSN) have received widespread attention. However, the state-of-the-art UAV trajectory designs mainly focus on static environments, which are not applicable in the practical scenarios considered in this work, e.g., mobile nodes, decommissioning of existing nodes, and new emergency nodes. This article proposes a two-level deep reinforcement learning (DRL) framework to solve this challenge. The first-level deep neural network (DNN) is applied to model the dynamic changing environment. In the second level, we employ a deep Q-learning network to plan a trajectory online according to the environment features from the first level DNN. Besides, online trajectory planning is performed by a low-power UAV edge computing platform. To enable online planning on the power-constraint UAV edge-computing platform, all networks adopt a lightweight low-complexity optimization design. According to simulation results, the proposed system achieves higher data acquisition success rates when compared to existing state-of-the-art methods. We also perform field tests on the proposed UAV edge computing platform, which also demonstrates high data acquisition performance.

Improving Health WSN with Distinguished Cloud Computing for Multi-Sensor Analytics

In the twenty-first century, automation has become more prevalent in large part due to the Internet of Things (IoT). The integration of IoT, Wireless Sensor Networks (WSNs) and health monitoring system with multi-cloud environment can optimize a lot of parameters like bandwidth, communication protocols and redundancy of the sensor network. The proposed architecture is introduced keeping in mind the current health infrastructure and monitoring system with different types of network framework. We have also discussed about machine to machine protocols for health monitoring with common entities linked with building communication protocols. The sensors data analytics carried out can sustain multi-level computation is the idea behind this research prospect to give greater depth about direct applicability in multi cloud sensor communication protocols. The offered prototype, when modelled with the unique communication system, produces a more efficient model than the standard way, with a system that is 98 percent more accurate.

An Enhanced User Authentication and Key Agreement Scheme for Wireless Sensor Networks Tailored for IoT.

A security protocol for wireless transmission is essential to defend sensitive information from malicious enemies by providing a variety of facilities such as privacy of the user's information, secure session key, associated authentication, and user-repeal facility when a person's authorizations are suddenly disclosed. Singh et al. proposed an improved user authentication and key agreement system for wireless sensor networks (WSNs). Authors are sure that their protocol is secure from various attacks. Here, we find several security pitfalls in their scheme, such as an offline password-guessing attack, failure to protect the session key, and a man-in-the-middle attack. To remove the identified pitfalls found in Singh et al.'s scheme, we design an enhanced authentication scheme for WSNs tailored for IoT. We prove the reliability of our proposed protocol using the real or random (RoR) model. We also evaluate the proposed scheme with the associated schemes and show its superior efficacy as compared to its counterparts.

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

SummaryIn recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)‐based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant‐sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5‐fold cross‐validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

Industrial functional safety assessment for WSN using QoS metrics

Wireless Sensor Networks are increasingly getting deployed for the safety use cases in industrial applications. While several research papers discuss about the Quality & Reliability improvement techniques in WSN systems to achieve minimal delay, higher node life, optimal routing etc., very limited work is witnessed on assessment of safety integrity levels of WSN systems. In this paper we tried to bridge this gap by bringing out a QoS metric-based safety integrity assessment for the end-to-end industrial Wireless Sensor Network (WSN) system. To identify relevant QoS metrics for monitoring the safety integrity levels, we also bring out a 4-step mapping methodology to link the QoS metrics and communication defenses/safety mechanisms. This mapping approach is expected to serve the network safety design engineers. Finally, a simulation case example is discussed to illustrate safety integrity assessment and we conclude by bringing out future research opportunities to improve safety integrity levels of industrial WSN systems.

Edge Controller Placement for Next Generation Wireless Sensor Networks

Nowadays, Fog architecture or Edge architecture is becoming a popular research trend to distribute a substantial amount of computing resources, data processing and resource management at the extreme edge of the wireless sensor networks (WSNs). Industrial communication is a research track in next generation wireless sensor networks for the fourth revolution in the industrial process. Adopting fog architecture into Industrial communication systems is a promising technology within sensor networks architecture. With Software Defined Network (SDN) architecture, in this paper, we address edge controller placement as an optimization problem with the objective of more robustness while minimizing the delay of network management and the associated synchronization overhead. The optimization problem is provided and modelled as submodular function. Two algorithms are provided to find the optimal solution using a real wireless network to get more realistic results. Greedy Algorithm and Connectivity Ranking Algorithm are provided. Greedy algorithm outperforms connectivity ranking algorithm to find the optimum balance between the different metrics. Also, based on the network operator preference, the number of edge controllers to be placed will be provided. This research paper plays a great role in standardization of softwarization into Industrial communication systems for next generation wireless sensor networks.

Hybrid of WOA-ABC and proposed CNN for intrusion detection system in wireless sensor networks

Wireless sensor networks (i.e., WSNs) are self-configured and infrastructure-less wireless networks that monitor environmental or physical factors, such as sound, temperature, vibration, motion, pressure, or pollutants, and collaborate with the base station for analysis of their data. A sink, also known as a base station, serves as a link between customers and the network. However, WSNs have various challenges such as high energy consumption, high bandwidth demand, quality of service, compression techniques, data processing, and intrusion. To avoid intrusion, an intrusion detection system is the best solution. It investigates the network by gathering enough information from each node and detecting abnormal sensor node behavior. The intrusion detection system is primarily divided into two steps: feature extraction/selection and classification. In this work, we introduced a new intrusion detection system based on a deep learning network. For feature selection, a proposed hybrid WOA-ABC algorithm is used, and a proposed CNN architecture is used for classification. In WSN there are various attacks available mainly DoS attack, Sybil attack, black hole attack, wormhole attack, etc. In this paper, the NSLKDD dataset is used for intrusion detection. In the NSLKDD dataset, numerous attacks available but those attacks come under 4 types of attacks. There are Dos, UR2, R2L, and probe. So, in this work mainly we concentrate to classify these 4 types of attacks. Compared to the existing method our proposed architecture gives the best performance result in terms of execution time, detection rate, accuracy, and false alarm rate. When compared to existing approaches, our proposed mechanism minimizes execution time by 76.54 %.

Implementation of Wireless Sensor Network (WSN) for Earthquake Detection

The current earthquake monitoring system uses a seismometer that can capture seismic vibrations very well but is expensive, heavy, and difficult to launch. Therefore, earthquake monitoring stations can only be launched in a few places in small numbers. This study aims to implement a Wireless Sensor Network (WSN) system for earthquake monitoring. The WSN system has advantages in cost, size, and ease of launch, so it is very appropriate to be used for this purpose. An earthquake detection sensor system has been designed in this study using a vibration sensor and a piezoelectric sensor. When an earthquake occurs, the resulting shock will trigger the vibration sensor and activate the sensor node. The shock data is then captured by the piezo sensor and processed by the microcontroller using Fast Fourier Transform (FFT) to determine the frequency value of the shock. The data is then sent to a gateway via a sensor network and uploaded to the Cayenne monitoring website. Operators can then view the data on the website. Three sensor nodes are implemented in this study. The test is done by placing those sensor nodes together in random positions. A shock is then given to the three sensor nodes, and the resulting data is then observed. The results show that the three sensors can detect, retrieve, process, and send shock data to the Cayenne monitoring website.