Sensor Network Applications Research Articles

In-Network Processing or Feature Compressive Sensing? Case Study of Structural Health Monitoring With Wireless Sensor Networks

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring (SHM), volcano tomography, and machine diagnosis, all the raw data in WSNs are required to be gathered to the sink where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large-scale raw data transmission while guaranteeing the global feature quality, there are two kinds of solutions: one is in-network processing, which generally needs to distribute the centralized computation of feature extraction into networks. Another solution is compressive sensing (CS) followed with the feature extraction (called feature CS in this article). An interesting question is: for in-network processing and feature CS, which kind of solutions is more cost efficient to accomplish the task of feature extraction? This question is seldom studied. To answer it, we take the case of SHM with WSNs along with the classic feature extraction algorithm, i.e., the Eigen-system realization algorithm (ERA), and appropriately design two novel routes for in-network processing and feature CS, respectively. Both theoretical analysis of the two solutions’transmission cost and numerous simulations have been conducted. Based on the comparison results, we summarize some guidelines on the solution choice for different kinds of WSNs for SHM. In addition, we find that, instead of guaranteeing the quality of raw data reconstructed, CS with guaranteeing the quality of feature extracted is usually more meaningful and cost efficient.

Anomaly detection for high‐dimensional dynamic data stream using stacked habituation autoencoder and union kernel density estimator

AbstractWith the rapid development of communication technology, various complex heterogeneous sensor network applications produce a large number of high‐dimensional dynamic data streams, which results in more difficult to anomaly detection than ever before. So, anomaly detection for high‐dimensional dynamic data streams is of a more and more challenging problem. This paper proposes a novel method for detecting anomalies in high‐dimensional dynamic data streams by utilizing several components. Firstly, it uses a stacked habituation autoencoder with habituation physiological mechanism to detect similarity anomalies more easily and capture feature relationships. Secondly, a union kernel density estimator with micro‐cluster is designed to improve online anomaly detection accuracy by estimating the data density. Lastly, candidate anomaly sets and a delayed processing approach are utilized to cope with conceptual drift and evolution in the data stream, allowing the system to adapt to changes in the data over time. Extensive experiments on four high‐dimensional dynamic data streams of the Internet of Things show that the proposed method is very effective.

Low-Profile and Wider-Angle Beam Tilting Parasitic Array Resonator Antenna with Optimized Deflected Ground Plane on FR-4 Substrate

A low-profile and wide-angle radiation pattern reconfigurable antenna is designed, analyzed, and fabricated for wireless sensor network (WSN) applications, which operate at a 2.5-GHz frequency. This work aims to minimize the number of switches and optimize the parasitic size and ground plane to achieve a steering angle of more than 30° using a low cost-high loss FR-4 substrate. The radiation pattern reconfigurability is achieved by introducing four parasitic elements surrounding a driven element. In this work, the single driven element is fed by a coaxial feed, while other parasitic elements are integrated with the RF switches on the FR-4 as the substrate with dimensions of 150 × 100 mm (1.67 × 2.5 λo). The RF switches of the parasitic elements are surface mounted on the substrate. By truncating and modifying the ground plane, the beam steering can be achieved at more than 30° on the xz plane. Additionally, the proposed antenna can attain an average tilt angle of more than 10° on the yz plane. The antenna is also capable of attaining other important results, such as a fractional bandwidth of 4% at 2.5 GHz and an average gain of 2.3 dBi for all configurations. By adopting the ON/OFF condition on the embedded RF switches, the beam steering can be controlled at a certain angle, thus increasing the tilting angle of the wireless sensor networks. With such a good performance, the proposed antenna has high potential to serve as a base station in WSN applications.

The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam.

Earth dams or embankments are susceptible to instability due to internal seepage, piping, and erosion, which can lead to catastrophic failure. Therefore, monitoring the seepage water level before the dam collapses is an important task for early warning of dam failure. Currently, there are hardly any monitoring methods that use wireless underground transmission to monitor the water content inside earth dams. Real-time monitoring of changes in the soil moisture content can more directly determine the water level of seepage. Wireless transmission of sensors buried underground requires signal transmission through the soil medium, which is more complex than traditional air transmission. Henceforth, this study establishes a wireless underground transmission sensor that overcomes the distance limitation of underground transmission through a hop network. A series of feasibility tests were conducted on the wireless underground transmission sensor, including peer-to-peer transmission tests, multi-hop underground transmission tests, power management tests, and soil moisture measurement tests. Finally, field seepage tests were conducted to apply wireless underground transmission sensors to monitor the internal seepage water level before an earth dam failure. The findings show that wireless underground transmission sensors can achieve the monitoring of seepage water levels inside earth dams. In addition, the results supersede those of a conventional water level gauge. This could be crucial in early warning systems during the era of climate change, which has caused unprecedented flooding events.

Device for Simultaneous Wind and Raindrop Energy Harvesting Operating on the Surface of Plant Leaves

Soft (bio)hybrid robotics aims at interfacing living beings with artificial technology. It was recently demonstrated that plant leaves coupled with artificial leaves of selected materials and tailored mechanics can convert wind-driven leaf fluttering into electricity. Here, we significantly advance this technology by establishing the additional opportunity to convert kinetic energy from raindrops hitting the upper surface of the artificial leaf into electricity. To achieve this, we integrated an extra electrification layer and exposed electrodes on the free upper surface of the wind energy harvesting leaf that allow to produce a significant current when droplets land and spread on the device. Single water drops create voltage and current peaks of over 40V and 15µA and can directly power 11 LEDs. The same structure has the additional capability to harvest wind energy using leaf oscillations. This shows that environment-responsive biohybrid technologies can be tailored to produce electricity in challenging settings, such as on plants under motion and exposed to rain. The devices have the potential for multisource energy harvesting and as self-powered sensors for environmental monitoring, pointing at applications in wireless sensor networks (WSNs), the internet of things (IoT), smart agriculture, and smart forestry.

A Study of the Fully On-Chip Inductor Coils for 30 MHz Power Regulation Applications in Energy Harvesting, Sensor Networks, and IoT Scenarios

A Study of the Fully On-Chip Inductor Coils for 30 MHz Power Regulation Applications in Energy Harvesting, Sensor Networks, and IoT Scenarios

Design and Analysis of 1x4 and 1x8 Circular Patch Microstrip Antenna Array for IWSN Application

A 1x4 and 1x8 circular micro strip patch antenna array operating at 2.4GHz are simulated for Industrial Wireless Sensor Network (IWSN) applications using CST studio suite and MATLAB based simulator software. The circular patch antenna designed on a FR4 substrate with dielectric constant of 4.4 and a substrate height of 1.6mm is fed using inset-fed technique. Several antenna characteristics such as return loss, radiation pattern, bandwidth, directivity, antenna gain, radiation efficiency isanalyzed. The compact size and higher directivity help to improve the network performance by alleviating contention, increasing communication range, and mitigating interference of the system. Hence, the advantages gained, make the system more useful for IWSN applications as achieved in this research

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

Charging Protocol for Partially Rechargeable Mobile Sensor Networks.

Wireless sensor networks (WSNs) have wide applicability in services used in daily life. However, for such networks, limited energy is a critical issue. The efficiency of a deployed sensor network may be subject to energy supply. Wireless rechargeable sensor networks have recently been proposed and discussed. Most related studies have involved applying static rechargeable sensors to an entire rechargeable environment or having mobile chargers patrol the environment to charge sensors within it. For partially rechargeable environments, improving the recharge efficiency and extending the lifetime of WSNs are considerable challenges. Scientists have devoted attention to energy transmission technologies and mobile sensor network (MSN) applications. In this paper, we propose a flexible charging protocol in which energy can be transmitted from certain energy supply regions to other regions in an MSN. Mobile rechargeable sensors are deployed to monitor the environment. To share energy in a certain region, the sensors move to replenish their energy and transmit energy to sensors outside the energy supply region. The efficiency of the proposed protocol is also discussed in the context of various situations. The evaluation results suggest that the flexible protocol is more efficient than other charging protocols in several situations.

A Context-Adaptive Hybrid MAC Scheme for Provisioning QoS and Energy Savings in Delay Sensitive Wireless Sensor Network Application

A Context-Adaptive Hybrid MAC Scheme for Provisioning QoS and Energy Savings in Delay Sensitive Wireless Sensor Network Application

A Survey of Secure Time Synchronization

Today, the use of wireless sensor networks has grown rapidly; however, wireless sensor networks are prone to receiving cyber-physical attacks. Time synchronization is a fundamental requirement for protocols in wired and wireless sensor network applications; hence, secure time synchronization is also crucial. This paper presents an introduction to time synchronization, including the concepts, challenges, and requirements of time synchronization protocols. The scope of the paper includes both software- and hardware-based protocols. Then, different time synchronization methods are analyzed. Moreover, research progress in secure time synchronization is reviewed. The survey also discusses the weaknesses of current secure time synchronization protocols and provides suggestions for future research directions. This survey aims to highlight research progress and trends in time synchronization and secure time synchronization.

General Sensor Network application approach

The paper describes the general approach for Sensor Networks and deals with principled components of Sensor Networks, architecture as well and opportunities for the implementation of current and new technologies. The paper also illustrates an example of the application of the EN 12830:2018 standard.

Improved 3D localization algorithm for large scale wireless sensor networks

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

Enhancing Parameters of LEACH Protocol for Efficient Routing in Wireless Sensor Networks

The increasing popularity of wireless sensor networks can be attributed to their unique characteristics, which include high density, low power consumption, software programmability, and long-term accuracy. This paper presents a thorough analysis of key parameters for sensor network applications, including network lifetime, packet delivery ratio, energy-efficient transmission, and dead node ratio. This paper examines the effectiveness of clustering in wireless sensor networks for achieving efficiency. The research focuses on the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol and proposes an enhanced algorithm that improves network lifetime, throughput, and the number of living nodes. This paper evaluates the performance of the improved LEACH protocol and compares it to other protocols currently in use. The results indicate that the proposed method significantly improves the performance of the protocol, making it a promising method for efficient routing in wireless sensor networks. This study is an important contribution to the field of wireless sensor networks and can aid in the creation of more efficient and effective protocols for various sensor network applications.

Analysis of Life Time Improvement Energy Efficient Risk-Free Protocol (ERP) for Data Transmission in Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have come to be seen as the fundamental architecture that paved the way for the development of the Internet of Things (IoT). However, a challenging problem develops when WSNs are connected to the IoT because of their nodes' high energy consumption and low network lifetime. As a result, the core concepts of WSNs include energy limits in sensor nodes, sensor data exchange, and routing protocols. The enhanced smart-energy-efficient routing protocol (ESEERP) presented in this study addresses the aforementioned shortcomings by extending the network's lifespan and enhancing connectivity. Since the Internet of Things (IoT) demands a significant amount of energy for sensing, processing, and data transport, the IoT application in wireless sensor networks (WSNs) poses substantial issues in maintaining network durability. As a result, multiple traditional algorithms incorporate different optimization techniques to improve the performance of WSN networks. We utilize IPv4 in wireless sensor networks to transmit data (packets), however, this strategy prevents users from accessing big amounts of data over long distances since it may need a lot of power to transmit large amounts of data using IPv4. The Internet of Things (IoT) can be used to solve this problem because it connects small battery-operated devices like sensors as well as consumer electronics and home appliances. One of the most often utilised wireless methods for sensor network connectivity is the IEEE 802.11 standard.

An Efficient Distributed MAC Protocol for Underwater Acoustic Sensor Networks

Many marine scientific and industrial applications in underwater acoustic sensor networks (UASNs) are characterized by dynamic-high load and dense deployment. In such UASNs, the channel reservation mechanism is devised to deal with channel utilization degradation. Unfortunately, channel reservation would lead to an increase in end-to-end delay and a decrease in throughput due to long propagation delay in UASNs. To reduce end-to-end delay and improve throughput, we proposed an efficient distributed medium access control (ED-MAC) protocol for UASNs. ED-MAC reduces the impact of long propagation delays on channel reservation by removing the channel reservation confirmation process and improving channel reservation efficiency. First, the optimized time slot length for the next time frame is obtained based on the data packet number estimate and secondary transmission data packet number, which could reduce idle waiting time. Then, the channel reservation confirmation process is removed by distributed channel reservation mechanism, and efficient channel reservation is achieved through channel reservation packet collision-free transmission and channel multiplexing. Meanwhile, channel reservation is performed before packet generation. Thus, the data packet waiting delay is reduced. Finally, a distributed data packet time slot schedule scheme is proposed to achieve collision avoidance between different data packets, as well as data packets and channel reservation packets. The performance analysis and simulation results show that our proposed ED-MAC could efficiently reduce end-to-end delay and improve throughput in dynamic-high-load and dense UASNs. Simulation results show that ED-MAC can achieve a maximum 98.7% reduction in average end-to-end delay and a maximum 61.1% improvement in throughput.

Computational intelligence paradigms for automata based secured energy efficient routing in wireless sensor network - A technical survey

Wireless Sensor Network (WSN) has been deployed in several areas of interest for controlling the region, automation of mundane tasks due to features such as smart sensor nodes, low cost, and small-scale factors. Earlier, the sensory units were costly and deficient in the computational and communicational capabilities which were overcome by sensing, processing, storing, and forwarding the data powered by a battery. There predominates diverse applications which affects the low-cost solutions of WSNs in numerous specialties such as observing patients in health care, target exposure, and tracking purposes, examining the atmosphere and climate, home applications, contributing protection for vehicular networks Owing to the diversity of the applications of Wireless Sensor Network, it needs to impose varying design, implementation, and performance requirements on the Wireless Sensor Network to have deep knowledge about the characteristics of WSNs. The pervasiveness of designing and optimizing WSN appeared to be a surplus to several application fields which influenced countless researchers to pay attention to several issues such as routing, mobility of nodes, coverage, and security. In recent years, designing of WSN becomes the leading domain for many researchers. A WSN is bounded with a collection of devices that are associated in the network to communicate the information collected from the field through the wireless links that have been established in the network. The data thus collected is transmitted through multiple nodes and also, the data is communicated to other networks through the gateway. During the design and deployment of WSNs, more attention is required at various levels like routing, coverage, and security. A complete system could be designed so that it could resolve the problems such as energy exhaustion, network lifetime, throughput, end-to-end time, routing, and intrusion detection could also monitor and keep control of the network environment. Therefore, this proposed technical research survey work has been carried out by devising an automaton system that can learn, monitor and control the dynamic behaviour of the network environment as well as can obtain an optimal route using Computational Intelligence Techniques. Besides, it detects the malicious activities that occur in the network using Hybrid Intrusion Detection System (IDS) model to enhance the throughput, lifetime of the network, utilization of the energy, end-to-end delay, accuracy, detection rate, computational time and recall rate.

PSO-Based Target Localization and Tracking in Wireless Sensor Networks

Research of target localization and tracking is always a remarkable problem in the application of wireless sensor networks (WSNs) technology. There are many kinds of research and applications of target localization and tracking, such as Angle of Arrival (AOA), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The target localization accuracy for TOA, TDOA, and AOA is better than RSS. However, the required devices in the TOA, TDOA, and AOA are more expensive than RSS. In addition, the computational complexity of TOA, TDOA, and AOA is also more complicated than RSS. This paper uses a particle swarm optimization (PSO) algorithm with the received signal strength index (RSSI) channel model for indoor target localization and tracking. The performance of eight different method combinations of random or regular points, fixed or adaptive weights, and the region segmentation method (RSM) proposed in this paper for target localization and tracking is investigated for the number of particles in the PSO algorithm with 12, 24, 52, 72, and 100. The simulation results show that the proposed RSM method can reduce the number of particles used in the PSO algorithm and improve the speed of positioning and tracking without affecting the accuracy of target localization and tracking. The total average localization time for target localization and tracking with the RSM method can be reduced by 48.95% and 34.14%, respectively, and the average accuracy of target tracking reaches up to 93.09%.

Secure Internet of Things based hybrid optimization techniques for optimal centroid routing protocol in wireless sensor network

SummaryThe recent survey depicts internet growth has been reached to billion of people and Internet of Things (IoT) as another milestone. IoT is wired or wireless communication technologies to establish a communication channel between devices and services available over the Internet. The wireless sensor networks (WSNs) is one of the most essential technologies assisted in IoT for real‐time applications. The energy efficiency is a major issue in IoT and it becomes more complex due to large scalability and the WSNs cannot be applied directly to the IoT. Moreover, routing is a very challenging aspect that takes place in such platform because of it is intrinsic properties. In this article, we propose an optimal centroid‐based routing protocol (OCRP) for WSN assisted IoT based on hybrid optimization techniques, which aim is to enhance the energy efficiency and network lifetime. IoT network is composed by serving nodes and end users. In OCRP, a multi‐objective swarm optimization (MSO) algorithm is used to perform the clustering, which reduce the chaotic in nature of energy consumption. Then, multiservice queue based ant optimization algorithm is proposed for next neighborhood selection for inter‐cluster routing. Finally, our OCRP protocol is experimented with different simulation in network simulator (NS‐2) tool with wireless body sensor network (WBSN) application. The simulation results show the effectiveness of OCRP protocol in terms of energy consumption, average data transmission rate and network lifetime.

Energy Optimization of Wireless Sensor Embedded Cloud Computing Data Monitoring System in 6G Environment.

With the construction and development of modern and smart cities, people's lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.