Wireless Sensor Network Applications Research Articles

WSNs node localization algorithm based on multi-hop distance vector and error correction

Wireless sensor networks (WSNs) have broad application prospects in various industries, and node localization technology is the foundation of WSN applications. Recently, many range-free node localization algorithms have been proposed, but most of them suffer from low accuracy. In order to improve the localization accuracy, in this paper we proposed the node localization algorithm based on multi-hop distance vector and error correction (MDV-EC). In terms of distance estimation, firstly the MDV-EC algorithm calculates the neighbor distance according to node neighbor relationship, then estimates the distance between unknown node and anchor node in multi-hop manner, and finally calibrates the distance refer to distance correction coefficient. In view of similarity of localization errors of nodes in similar regions, an error correction scheme is also investigated, which corrects the node initial estimated locations of nodes refer to the localization error vector of nearby anchor node. Simulation results show that our proposed MDV-EC has better performance than the other two algorithms in terms of node localization accuracy, and the error correction scheme can effectively reduce the localization errors.

Effective proactive routing protocol using smart nodes system

Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires.

Modified Adaptive Mechanism for Optimising IEEE 802.15.4 WPANs for Wireless Sensor Networks

Different applications of wireless sensor networks (WSNs) have different expectations from the working of medium access control protocols. Some value reliability more than delay incurred while some demand a fair trade-off for the factors like: Throughput, bit error rate etc. This paper evaluates the performance of wireless personal area networks from 802.15.4 group for WSNs with modified algorithm which helps in reducing the medium access delay and delay in reaching of the packet from one end to another end. In this paper certain modifications to existing algorithm have been proposed for reducing the medium access delay and to reduce the number of packets dropped. The result comparisons on the performance parameters like: network output load, generated acknowledged traffic, media access delay, battery consumed and delay in packet transmission from one end to another end that the backoff number and exponent values used for transmission play vital role for improving the performance of WSNs as they directly affect the number of packets dropped, successfully acknowledged and medium access delay.

Research Trends on Greenhouse Engineering Using a Science Mapping Approach

Horticultural protected cultivation has spread throughout the world as it has proven to be extremely effective. In recent years, the greenhouse engineering research field has become one of the main research topics within greenhouse farming. The main objectives of the current study were to identify the major research topics and their trends during the last four decades by analyzing the co-occurrence network of keywords associated with greenhouse engineering publications. A total of 3804 pertinent documents published, in 1981–2021, were analyzed and discussed. China, the United States, Spain, Italy and the Netherlands have been the most active countries with more than 36% of the relevant literature. The keyword cluster analysis suggested the presence of five principal research topics: energy management and storage; monitoring and control of greenhouse climate parameters; automation of greenhouse operations through the internet of things (IoT) and wireless sensor network (WSN) applications; greenhouse covering materials and microclimate optimization in relation to plant growth; structural and functional design for improving greenhouse stability, ventilation and microclimate. Recent research trends are focused on real-time monitoring and automatic control systems based on the IoT and WSN technologies, multi-objective optimization approaches for greenhouse climate control, efficient artificial lighting and sustainable greenhouse crop cultivation using renewable energy.

A Systematic Review on the Energy Efficiency of Dynamic Clustering in a Heterogeneous Environment of Wireless Sensor Networks (WSNs)

There are a variety of applications for wireless sensor networks (WSNs), such as military, health monitoring systems, natural disasters, smartphones, and other surveillance systems. While the primary purpose of sensor nodes is to collect unattended data in hostile environments, many are placed in large numbers and operate independently. Due to limited capabilities, power is often limited. Therefore, these nodes are grouped into clusters to increase communication efficiency. In WSNs, two different routing protocols are possible: apartment and hierarchical or clustering protocols. Due to their significant role in minimizing energy consumption, hierarchical methods have become very popular in clustering. In cluster-based methods, nodes are organized into clusters, and the sensor node with the most resources is appointed as the cluster head (CH). In this paper, we present a Systematic Literature Review (SLR) explaining the difficulties in developing cluster-based methods, critical factors for clustering, and hierarchical clustering protocols. The most important factor of a routing protocol for WSN is the energy consumption and lifetime of a network. Focusing on energy consumption, different cluster-based methods were analyzed to determine which technology should be deployed by analyzing specific criteria to support the selection process. Additionally, the pros and cons of different protocols are listed with their relevance in specific scenarios. To identify these protocols, a systematic literature review was conducted using research studies published from 2010 to 2021, with 30 papers analyzed in the final phase. Based on the results of this SLR, several issues need to be further investigated with respect to the interaction of the potential technology with the Internet of Things (IoT) and Vehicular Ad-Hoc Networks (VANETs).

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Network lifetime and energy constraint are the main issues for the application of wireless sensor networks. Sensor nodes spend more energy in the communication process and affect the network lifetime. Clustering is the technique for choosing the optimal cluster head from the clusters. LEACH-C is the clustering protocol in WSN. BFA is applied in the LEACH-C protocol to form the optimal clusters. This optimization obtains more steps in the tumbling process and reaches the global optimum solution very slowly. This method directly affects the network lifetime. The above limitations are overcome by introducing the hybrid approach of bacteria foraging algorithm by integrating the PSO, and DE is applied in LEACH-C algorithm for finding the optimal cluster head. The best foraging solution is utilized in the chemotactic behavior of the bacterium by using PSO and DE algorithms. The proposed methodology increases by 66% and 77% of the alive nodes when compared to FA and BFPSO.

Efficiency of integration between sensor networks and clouds

Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption.

Observing the different optimizations in communication protocols for more efficient algorithms in Wireless Sensor Networks

The different optimization performed for data exchange between the nodes of a wireless sensor network is necessary to reduce as much as possible the energy consumption. The battery constrain is the main challenge in this kind of network implementation. The protocol used for data communication from the source the sensing node toward the sink needs to bring an improvement. It will allow a longer lifetime duration for the network by keeping the nodes alive for a longer time. A longer the lifetime of the network allows its usability in different environment where the monitoring of different conditions maybe a necessity. But lately the Wireless Sensor Networks are used as well for tracking purposes like finding the nurses and doctors in a hospital centre. The data exchange and choices of the next sensor node to pass the data in the new algorithms apply the social behaviour of the birds, bees, ants in their search for food where those are in bunches or colonies. Also, within Wireless Sensor Networks application the nodes communicate by mimicking their social behaviour within the algorithm used for the data exchange, with the only purpose that data transmission and communication with the other nodes might not drain the battery.

Fractal Loaded Circular Patch Antenna for Super Wide Band Operation in THZ Frequency Region

This work describes the design and analysis of a 22 circular micro-strip patch antenna array on a FR-4 epoxy substrate. The thickness of the substrate is 1.6 mm, and it is used in a variety of applications, including RADAR and radio frequency identification. The task involves creating a 22 circular microstrip patch antenna array that resonates at 2.4GHz and simulating it for use in Wireless Sensor Network (WSN) applications. An FR-4 Epoxy substrate is used to create a circular patch antenna array, which is then supplied using microstrip feed lines. A manufactured antenna has a 1.187GHz resonance. The analysis of antenna parameters includes gain, return loss, VSWR, directivity, reflection coefficient, and radiation pattern. In comparison to a 2x2 rectangular microstrip patch antenna, a 2x2 circular microstrip patch antenna generates better gain and less return loss. It is more helpful for WSN applications than a rectangular array due to its small size and improved directivity.

IFAA: An Intelligent Framework Aware Algorithm to Determine the Boundary of Area under Attack in Military Surveillance and Reconnaissance WSN

Wireless sensor networks (WSNs) have proven effective in military applications of surveillance and reconnaissance. Sensors capable of detecting pressure, temperature, movement and presence of specific chemicals are deployed in such applications. Traditionally, sensor data is collected and transferred to a centralized high-capacity node or control station. Analysis of data is carried out at such centralized facilities. Information or intelligence gathered from sensor data after analysis is used to generate control and management commands that are relayed back to sensor nodes. The situation is analogous to an actual wartime scenario where soldiers who are on the field are equivalent to the sensors. Soldiers observe and sense the situation and communicate their observations to the decision maker who is stationed in the control tent. On gathering field information, the decision maker analyses the data and arrives at his decision which is again communicated to the soldiers on the field. Soldiers as well as sensors are not placed illogically or randomly but intentionally and strategically. Observations made on the field ultimately affect how the soldiers or sensors continue to function. Intelligence gained on the field ultimately gets used on the field itself. Our attempt is to observe, analyze and apply intelligence on the field itself. This work proposes an intelligent algorithm that is aware of the sensor network topology, analyses sensor data within the network and uses the network framework to arrive at usable intelligence. Locally generated intelligence avoids communication to and from the command/control and adds value to military surveillance and reconnaissance applications of WSN. Intelligent sensor management allows us to use just the necessary number of sensors while saving resources on otherwise redundant expenditure. In the present work we have designed and applied a dynamic boundary computation algorithm to determine the boundary of the area under attack. We have compared the results of simulation experiments incorporating the proposed algorithm against a control experiment without the algorithm.

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

Research on a Machine Learning-Based Method for Assessing the Safety State of Historic Buildings.

Historic and protected buildings are increasingly valued due to their valuable historical and cultural value. The assessment of the safety state of historic buildings has received more attention. Emerging machine learning algorithms, with their excellent computational performance, provide new ideas and new means to solve practical problems in various fields. Therefore, this paper proposes a method for assessing the safety state of historic buildings based on machine learning techniques. Firstly, based on the analysis of the characteristics of historical buildings and common security problems, the application of wireless sensor networks to the security monitoring of historical buildings is proposed in order to improve the automation of monitoring. Then, in order to improve the accuracy of the assessment, a combination of kernel canonical correlation analysis (KCCA) and support vector machine (SVM) is used to establish the security monitoring model. The experimental results show that by choosing a suitable KCCA function, the redundant features of the data can be reduced while the comprehensiveness of the building structure identification features can be retained, thus effectively improving the prediction accuracy of the SVM. The KCCA-SVM model can accurately predict the physical quantities such as relative structural displacement of historical buildings with good reliability.

A deep learning approach to predict the number of [formula omitted]-barriers for intrusion detection over a circular region using wireless sensor networks

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at border areas and in the defence establishments. The border areas are stretched over hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of a few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that can identify and detect the enemy as soon as it comes within the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, transmission range of sensors, and number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

Low-Delay Opportunistic Routing with Reducing Overhead in Asynchronous Duty-Cycled Wireless Sensor Networks

The Internet of Things (IoT) and cloud computing are well developed and applied in different services. Among these services, some of them take latency and overhead as the metrics to evaluate the quality of services. This paper introduces how an opportunistic routing (OR) protocol reduces latency and overhead when it is applied in wireless sensor network (WSN) application among lightweight devices. OR mitigates the delay problem and balances the energy consumption and delay of the nodes in WSNs. However, OR, with the nature of broadcasting, can easily cause heavy overhead such as redundant data and control packets in the forwarding process. In the current solution, the number of candidates (potential forwarders) is not limited by the dynamics of forwarding process. Also, the overhead of establishing the candidate sets is high due to global information calculation. Therefore, a low-delay OR protocol with reducing overhead which designs the dynamic candidate area (DCA) to establish candidate sets is first proposed in this paper. The main work is as follows. (a) The duty cycle of a node is adjusted according to the distance between the node and sink node in the initialized network. (b) A method to establish dynamic candidate sets is proposed based on the adaptive duty cycle. Before and during the forwarding process, the dynamic candidate area is adjusted in time. (c) According to the feature of the candidate area, the corresponding routing metric is proposed to complete the cooperative communication among candidates. Through further theoretical analysis and simulations, the results indicate that this protocol achieves better performance in terms of transmission delay, data and control overhead, and network lifetime compared to the state-of-the-art solutions.

Asynchronous Collaborative Localization System for Large-Capacity Sensor Networks

With the widespread application of wireless sensor networks, localization issue has attracted much attention. It is a major challenge for many sensor network tasks to locate a large number of asynchronous nodes in an unknown environment without external references. In this article, we present an asynchronous collaborative localization system (ACLS) to address the localization challenge for large-capacity sensor networks (LCSNs). ACLS exploits a hierarchical architecture, under which the wireless sensor nodes in the network are divided into parent nodes that can communicate with each other through wireless signals and child nodes that can only passively receive signals. Specific protocols and nonlinear distance estimators for this broadcast communication ranging technique are proposed. These characteristics are verified through theoretical analyses to have a strong suppression effect on local clock errors and are not sensitive to measurement noise. The simulation experiments further illustrate that the proposed ACLS can achieve high-rate and high-precision ranging and localization for asynchronous LCSN without preinstalled infrastructures.

Empirical Analysis of Extended QX-MAC for IOT-Based WSNS

The Internet of Things (IoT) connects our world in more ways than we imagine. Wireless sensor network (WSN) technology is at the core of implementing IoT architectures. Although WSN applications give us enormous opportunities, their deployment is challenging because of the energy constraint in sensor nodes. The primary design objective of WSNs is therefore to maximize energy efficiency. Enhancing network quality of service (QoS), such as latency, is another crucial factor, particularly for different delay-sensitive applications. Medium access control (MAC) protocols are of paramount importance to achieve these targets. Over the years, several duty-cycled MAC protocols were proposed. Among them, the strobed preamble approach introduced in X-MAC has gained much interest in IoT field because of its several theoretical advantages. However, X-MAC is highly efficient only under light traffic. Under heavy traffic, X-MAC incurs high per-packet overhead and extra delay. In addition, X-MAC has several design flaws that can significantly degrade network performance. In this paper, we point out some specific malfunctions in the original X-MAC design and propose alternatives to reduce their impact. We present an energy-efficient, traffic-adaptive MAC protocol called QX-MAC that addresses the foreseen shortcomings in X-MAC. QX-MAC integrates Q-learning and the more bit scheme to enable the nodes to adapt the active period and duty cycle in accordance with incoming traffic. Finally, the performance of QX-MAC is thoroughly analyzed compared with other reference protocols to validate its efficacy. Our QX-MAC simulation results demonstrate substantial improvements in overall network performance in terms of energy consumption, packet loss, delay, or throughput.

A Survey on MAC-Based Physical Layer Security over Wireless Sensor Network

Physical layer security for wireless sensor networks (WSNs) is a laborious and highly critical issue in the world. Wireless sensor networks have great importance in civil and military fields or applications. Security of data/information through wireless medium remains a challenge. The data that we transmit wirelessly has increased the speed of transmission rate. In physical layer security, the data transfer between source and destination is not confidential, and thus the user has privacy issues, which is why improving the security of wireless sensor networks is a prime concern. The loss of physical security causes a great threat to a network. We have various techniques to resolve these issues, such as interference, noise, fading in the communications, etc. In this paper we have surveyed the different parameters of a security design model to highlight the vulnerabilities. Further we have discussed the various attacks on different layers of the TCP/IP model along with their mitigation techniques. We also elaborated on the applications of WSNs in healthcare, military information integration, oil and gas. Finally, we have proposed a solution to enhance the security of WSNs by adopting the alpha method and handshake mechanism with encryption and decryption.

Study of Wireless Sensor Networks Topological Protocol and Its Applications

In this paper we studied about the wireless sensor networks and topology protocols and applications of wireless sensor networks (WSN). Many applications of RFID and Wireless Sensors Networks (WSN) in diverse areas including intelligent transport vehicles, defense, environmental monitoring and forecasting such as air pollution monitoring, structural health monitoring, intelligent home, and warehouse management were offered [1-4]. The integration infrastructure of WSN and RFID system with a network was dedicated. Furthermore, the architecture framework of Electronic Production Code (EPC) global sensor network was proposed aiming to establish the infrastructure of global network which is able to aggregate various data [6]. However, RFID and WSN integration was developed by [7]. This research strives to elaborate accurate traceability of different objects by means of RFID which are not conveniently identifiable by applying ordinary sensors. Notwithstanding the fact that the condition of items cannot be monitored by RFID, the environmental condition as well as condition of items can be achieved by sensor nodes. To address this issue, both RFID system and WSN are applied together to overcome some difficulties in industrial environment.

Applications of wireless sensor networks to improve occupational safety and health in underground mines

Introduction: The very complex and hazardous environment of underground mines may significantly contribute to occupational fatalities and injuries. Deploying wireless sensor network (WSN) technology has the potential to improve safety and health monitoring of miners and operators. However, the application of WSN in the industry is not fully understood and current research themes in this area are fragmented. Thus, there is a need for a comprehensive review that directly explores the contribution of WSNs to occupational safety and health (OSH) in underground mines. Method: This study aims to conduct a systematic literature review on the existing applications of WSNs for improving OSH in the underground mining industry to pinpoint innovative research themes and their main achievements, reveal gaps and shortcomings in the literature, recommend avenues for future scholarly works, and propose potential safety interventions. The major contribution of this review is to provide researchers and practitioners with a holistic understanding of the integration of WSN applications into underground mine safety and health management. Results: The review results have been categorized and discussed under three predominant categories including location monitoring and tracking, physiological and body kinematics monitoring, and environmental monitoring. Finally, seven major directions for future research and practical interventions have been identified based on the existing research gaps including: (1) further applications of WSNs for underground mining OSH management; (2) application of WSNs from research to real-world practice; (3) big data analytics and management; (4) deploying multiple WSNs-based monitoring systems; (5) integration of WSNs with other communication systems; (6) adapting WSNs to the Internet of Things (IoT) infrastructure; and (7) autonomous WSNs.

A distributed self‐organising node deployment algorithm for mobile sensor networks

SummaryProviding the appropriate coverage is quite essential for the effective functioning of many applications in Wireless Sensor Networks. Therefore, the efficiency of node deployment algorithms to supply the requested coverage is of high significance. In this paper, inspired by the equilibrium of molecules, a novel node deployment algorithm, called Smart Self‐organising Node Deployment (SSND), is proposed to provide maximum coverage. Despite other proposed algorithms, which provide coverage based on the collective movement of nodes with massive energy consumption, SSND moves one sensor in every neighbourhood at each step to reduce the sensor nodes' movement and hence the energy consumption. The chosen sensor nodes at each time step are distributedly determined by an eligibility function to reduce the non‐essential movements while improving the accuracy of the reported locations of neighbours. Our extensive simulation study shows that SSND can achieve up to 30%coverage improvement compared to those of other algorithms in most scenarios and provides an adequate trade‐off between coverage and energy consumption.