Sensor Network Applications Research Articles

Fruchterman–Reingold Hexagon Empowered Node Deployment in Wireless Sensor Network Application

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

Cotton-assisted dual rotor-stator triboelectric nanogenerator for real-time monitoring of crop growth environment

Employing clean and sustainable wind energy to build a sensor system is of critical importance for the currently thriving smart agriculture and the goals of carbon peaking and carbon neutrality, as an essential driving force for rural revitalization. Triboelectric nanogenerators (TENGs) exhibit broad applications in sensor networks as power sources owing to the advantages of irregular and distributed energy harvesting. However, TENGs suffer from the problems of limited average power density and durability, which motivate us to design the soft-contact flower-bud array cotton-based TENG (SFC-TENG) with the rotation of a double rotor-stator structure for harvesting wind energy. The adopted cellulose-rich cotton-assisted soft-contact and double rotor-stator structure with multiple contact areas endow SFC-TENG with excellent performance in triboelectrification, which is beneficial for reducing frictional resistance and prolonging the lifetime. The assembled TENG device reveals excellent output performances with high open-circuit voltage of 2500 V, short circuit current of 85 μA, and maximum output power of 80 mW. Especially, high durability is achieved with no significant degradation of output performance after 320,000 cycles. The prominent properties confirm that the SFC-TENG is feasible for powering smart farming in night lighting, pH detection, and temperature/humidity monitoring. This work provides an effective strategy combining design of the double rotor-stator structure with the flexible contact of cotton, which is of great inspiration for the exploitation of high-performance TENGs for smart agriculture application.

High-speed long-range multihop underwater wireless optical communication convergent with free-space optical system for optical internet of underwater things and underwater optical wireless sensor network applications

We propose a multihop underwater wireless optical communication (UWOC) convergent with free-space optical (FSO) system for an optical internet of underwater things (O-IoUT) and underwater optical wireless sensor network (UOWSN) applications. A closed-form expression of outage probability was derived for the proposed system using the cumulative distribution functions gamma–gamma and hypertangent log-normal for FSO link and UWOC link, respectively. The outage performance of the proposed multihop UWOC convergent with FSO system was analyzed over various oceanic water types (clear ocean, coastal ocean, and turbid harbor) and different FSO weather conditions (clear air, haze, drizzle, and light fog). The results also depict the end-to-end system performance for different pointing errors (strong, moderate, and weak) and varying the number of hops. The maximum link range of ∼2.5 km, which includes 2 km of FSO link and 450 m of multihop UWOC link (nine hops with each of 50 m clear ocean), is considered.

Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach

Wireless sensor network has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. In this paper, we propose a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work, and confirm the accuracy of the proposed MAC protocol strategy is higher than the existing work.

Study of Wireless Sensor Network Based on Optical Communication: Research Challenges and Current Results

With the rapid developments of commercial demands, a majority of advanced researches have been investigated for the applications of underwater wireless sensor (WSN) networks. Recently optical communication has been considered for underwater wireless sensor network. An experimental set-up for testing optical communication underwater has been provided and designed in present papers to maximize the energy coupled from these displacements to the transduction mechanism that converts the mechanical energy into electrical. The true case has been considered by measuring diffuse attenuation coefficients in different seas. One stand out potential optical communication method, Visible Light Communication (VLC) has been talked and several communication methods are compared from many points of view, for example attenuation in salt water. The evaluation of modulation techniques for underwater wireless optical communications has been displayed, and further how the data collection and storage with an underwater WSN is introduced. In this paper current researches for an (UWSN) based on optical communication are studied, in particular the potential VLC method and comparisons of VLC with other optical communication approaches. Underwater challenges would be analyzed by comparing a sort of communication methods, applied in underwater. Future work will be developed at last.

Towards People Crowd Detection Using Wireless Sensor Networks

Objective: The objective of this study was to examine and propose the use of wireless sensor networks for people crowd detection in resource constrained environments such as developing economies.
 Methodology: A systematic review was carried out on current technological trends and application of Wireless Sensor Networks (WSNs) in crowd detection. For this study, focus was on WSN implementation in developing economies, where infrastructure is underdeveloped and people crowds are dynamic and spontaneous. Based on a requirement analysis and knowledge of the inherent challenges of WSNs, a WSN implementation for people crowd detection was proposed.
 Findings: Most studies in crowd detection using WSNs, have been in the area of non-people crowds. However issues critical to deployment of WSNs for people crowd detection in developing countries include: the uncontrollable nature of people crowds, under developed physical infrastructure and the inherent challenges of power, computational capacity and broadcast communication characterizing WSNs. Achieving people crowd detection using WSNs therefore, calls for special attention.
 Recommendation: To ensure effective people crowd detection, requires taking into consideration connectivity, scalability, performance, security, accuracy and resource utilization of WSNs.

Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things.

With the rapid development of the economy and society, the low efficiency and high loss of the traditional power grid can no longer meet the growing social demand, and the power grid market is facing a reform. Smart grid, as a next-generation power system, it can effectively improve the performance of traditional power grids. The ubiquitous power Internet of Things (UPIOT) replaces the traditional grids with efficient, safe, reliable, and flexible new grids, improves the utilization efficiency of the grid, reduces the loss of the power grid in the transmission process, and can meet the needs of different types of markets and users. As an advanced information acquisition and processing technology, wireless sensor networks have been widely used in medical, industrial, agricultural, commercial, and public management fields. It is an important means to promote future economic development and build a harmonious society. In the power system, wireless sensor network technology can be widely used in many fields such as line fault location, real-time monitoring, smart meter reading, and relay protection. In this paper, the basic concepts and overall architecture of ubiquitous power Internet of Things are summarized. Then, we summarize the research status of the wireless sensor network in smart grid, including power equipment, line monitoring, smart grid wireless automatic meter reading, distribution network relay protection, power assets life-cycle management, power grid fault location, and power grid fault diagnosis. In view of the technical characteristics of wireless sensor networks, combined with the production links of power systems, the application framework of wireless sensor network technology in the power systems is constructed. The application of wireless sensor networks is prospected from the aspects of network development of relay protection, application research of smart substation, application research of power grid catastrophe, security protection of power system, and deep-seated ubiquitous power Internet of Things.

EESR: Energy efficient sector-based routing protocol for reliable data communication in UWSNs

There is a remarkable need of advancement in data communication for application of sensor and ad-hoc networks for reliable and secure delivery of data. Due to the dynamic behavior of the network, it is an emerging demand to achieve reliability in data communication. The existence of different network environment features and diverse dynamic topology leads to the need of protocol design for efficient and reliable delivery of data in underwater acoustic sensor networks. The paper proposes energy efficient and reliable protocol design for routing data packets in Underwater Wireless Sensor Networks environment using sector based forwarding mechanism. The sector based forwarding mechanism helps in routing the reliable data in an efficient and secure manner The proposal suggests to model the network in such a way that the entire network is divided into sectors to optimize the hop count that further leads towards the energy efficient delivery of data. The mechanism of data forwarding is done vertically by maintaining sector wise sequence through pivot node which is considered as sector head. The proposed sector-based network topology mechanism is useful in identifying the number of hops used in communication along with enhancement in energy for effective data communication. The experiment performed and its consecutive results suggests that the proposed protocol enriches the energy utilization of the network. The enhancement of data communication reliability in Underwater Wireless Sensor Networks environment is managed by the mechanism of reduced hop count.

Optimized deep learning‐based intrusion detection for wireless sensor networks

SummaryThe technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature‐inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.

Selection of Cluster Head in Wireless Sensor Network using Convolution Neural Network Algorithm

Network lifetime enhancement is one of the primary goals for all the Wireless Sensor Network (WSN) applications. Clustering process helps the WSNs to improve its lifetime by forming a group with nearby sensor nodes. A primary node called cluster head is elected from the group for managing the data transmission from the other clustering groups to the basic sensor nodes. Also, the cluster heads are placed to transfer the signal collected from its sensor nodes to the base stations. Identifying an optimum node for making it as a cluster head is a challengeable process for many WSN applications. The proposed work is employed with a neural network algorithm for electing a cluster head by analyzing the residual energy, base station distance and neighbor sensor counts. The experimental work carried out in the paper indicates a betterment on survival node time and energy consumption.

Data reconstruction applications for IoT air pollution sensor networks using graph signal processing

The analysis of sensor networks for air pollution monitoring is challenging. Recent studies have demonstrated the ability to reconstruct the network measurements with graphs derived from the acquired data, thus describing the complex relationships between the sensors that compose the network. In this work, we propose a graph-based data reconstruction framework that can be used to carry out different post-processing applications that arise in real-world low-cost sensor deployments for air pollution monitoring. This data reconstruction framework first describes the relationships between the different network sensors by means of a graph learned from the measured data, and then a signal reconstruction model is superimposed to reconstruct sensor data. This methodology allows reconstructing sensor data to carry out missing value imputation, signal reconstruction at points where there are no physical sensors (virtual sensing), or data fusion. The results, using real data taken with an air pollution monitoring network including reference stations and low-cost sensors, show how this framework performs well for these applications compared to estimates obtained from individual sensors and task-specific state-of-the-art methods that are not able to deal with this necessary range of applications. In short, the results show the potential of using graphs, whose topology is based on the measurements taken, to calibrate and reconstruct signals in heterogeneous networks of low-cost air pollution sensors for a wide variety of applications.

Based on mesh sensor network: design and implementation of security monitoring system with Bluetooth technology

One of the most <span>critical aspects to consider in wireless sensor networks is security, particularly in internet of things (IoT) implementations. Sensor network applications had risen in the past 5 years since these networks have been used in various parts of life (smart residential and commercial buildings, medical, and agriculture). In this study, we provide a novel network of sensors based on the Bluetooth network that may be used to protect commercial buildings. The Bluetooth type HC 06 was chosen since it has a low energy consumption and a communication range of 100 meters. Such security network includes motion sensors and control cameras that are controlled by an Arduino Nano microcontroller. The motion sensor's primary characteristics are solely applicable to humans, and the Arduino Nano is an open-source microcontroller. The key benefit of this research is that it demonstrates how to create a low-cost Bluetooth sensor security network with limited storage space for control movies.</span>

Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network

Target tracking is the greatest important applications in Wireless Sensor Networks (WSNs). The wireless sensor network applications have been increasing since the IoT has been established. Most of the applications have various kind of sensors to transmit the information from one source to another. The basic operation of a wireless sensor network is to sense the data, collect the data and transmit the data from time to time whenever the base station requires the data for evaluation. Improving the reliability, performance for the collection of the data is the main role of the wireless sensor device. Moreover, the objective of the wireless sensor network device is to minimize the latency and improve the energy efficiency in order to provide more reliability is a major performance metric for provisioning WSNs. In this paper, we have presented an Energy Efficient Low Latency Routing (EELLR) design for Target Tracking (TT) Applications of Wireless Sensor Network. This model provides reliability and has a better performance in terms of communication overhead, energy efficiency and packet processing latency reduction when compared with the existing routing-based models.

Development of mobile application for wireless sensor networks for efficient irrigation water management

The evolution of smart phones has necessitated the development of mobile applications designed to perform a wide variety of functions. In the field of agriculture, mobile applications are currently used to monitor environmental parameters such as ambient temperature, humidity, soil moisture, water level, among others. A mobile application intended to monitor irrigation-related parameters and to control solenoid valves for irrigation automation was developed in this study. The mobile application was written using Flutter software development kit, and the Dart programming language. The mobile application communicates with the cloud server using a REST API written in JavaScript. The data acquired from the cloud server are presented as the current sensor reading and graphs. On the other hand, the mobile application controls the solenoid valves by sending designated bytes of data to the cloud server. The mobile application developed in this study was designed to be integrated with both low-cost sensors and the Smartmesh IP sensors to enable real-time monitoring and data visualization, and facilitate irrigation scheduling and manual irrigation control. The mobile application developed in this study may be used for efficient irrigation water management of upland crop production systems and for agricultural modernization in the Philippines and other developing countries.

Probabilistic based Optimal Node Localization in Wireless Sensor Networks

Localization is one of the most important technologies for many applications in wireless sensor networks (WSNs). Node localization is the process of discovering the exact location of the node. If the number of nodes and network size increase, it becomes very arduous to localize the nodes whose result leads to complexity and path loss. In this paper, we proposed an approach called probabilistic based optimal node localization to obtain the location of node in the WSNs. This approach provides an enhanced channel pathloss model by capturing the features of the additive noise in WSN. In addition, the complexity has been minimized by discovering a lower bound of the non-convex function. The problem of non-convex optimization and subsequent nonlinear is solved with the help of relaxation to achieve a sub-optimal solution. Simulation results show that our proposed localization approach has got better performance for considered scenario settings.

Tuning of dual frequency resonance analysis of circular and rectangular patch UWB antenna used for wireless sensor networks

In our modern digital world, communication plays a most important role in research of new designs and novel findings. The proposed analysis is based on Ultra-wide band microstrip patch antenna with two methods of circular and rectangular patch. The design analysis and comparison of various antenna parameters are used for wireless sensor networks. The V-shaped slot is introduced on top of the circular patch and the horizontal T-shaped slot on rectangular patch using jeans substrate material, the design gives good resonance frequency of 9.5GHz and 7.7GHz for wireless communication networks. At resonance the receiver of UWB antenna has a frequency sensing element and the antenna parameters were analyzed using High Frequency Structural Simulator. From the analysis, gain of an antenna, VSWR, return loss as well as radiation pattern, Specific Absorption Rate SAR values are in the acceptable range. The VSWR value of both rectangular and circular patch is 1.05, return loss below -11dB determined in order to avoid surface wave propagation, the specific absorption rate SAR value should be below 2W and gain of each antenna consists of positive value. Hence the antenna meets the requirement suitable for wireless sensor network application.

IMPERIALIST COMPETITIVE ALGORITHM FOR INCREASING THE LIFETIME OF WIRELESS SENSOR NETWORK

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm.

An Anchor Node Selection Scheme for Improving RSS-Based Localization in Wireless Sensor Network

Accurate location information is essential for various emerging applications in wireless sensor network (WSN). In order to improve localization accuracy, it is of paramount importance to reduce the effects of noisy distance measurements. This paper proposes an anchor node selection scheme for Received Signal Strength- (RSS-) based localization in WSN. In the proposed approach, the nodes are sorted firstly to select anchor nodes reasonably, and to further reduce the influence of range error, the weight is assigned to each selected anchor node. Finally, an effective modified cuckoo search algorithm is used to compute the coordinates of unknown nodes. Extensive experiments are conducted to study the effects of anchor node ratio, ranging error factor and node density on the localization accuracy performance of the proposed method. The experimental results demonstrate that the proposed method performs better in improving localization accuracy compared with the localization technique without special anchor selection scheme which selects all anchors’ information received and the localization technique selecting nearest anchors.

Retraction Note to: Localization approach of FLC and ANFIS technique for critical applications in wireless sensor networks

Retraction Note to: Localization approach of FLC and ANFIS technique for critical applications in wireless sensor networks

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

The Internet of Things (IoT) makes it conceivable to communicate among distinctive things. The use of IoT in the farming industry is critical for increasing utility. Smart agricultural practices may boost crop yield while also creating more output with the same amount of input. The majority of farmers, however, are still unaware of the most recent technologies and procedures. In this study, a revolutionary wireless mobile robot based on the Internet of Things (IoT) is created and installed to perform a variety of outdoor tasks. The benefits of this work include more accurate and efficient data, as well as a reduction in manpower. This research has applications in agriculture, arrival, and water division. Keen agrarian frameworks have been built up in different parts of the world utilising the Internet of Things (IoT) and remote sensor systems. One of the branches that springs to intellect in this respect is exactness cultivating. Numerous analysts have made checking and robotization frameworks for different cultivating capacities. Information collection and transmission between IoT gadgets set in ranches will be basic utilising WSN. The Kalman Filter (KF) is used with expectation investigation within the proposed method to get high-quality information free of commotion and exchange it with cluster-based WSNs. The quality of information utilised for examination is progressed as a result of this strategy, and information transport overhead within the wireless sensor network application is decreased. A decision tree is used for forecast analytics decision making for trim surrender expectation, trim classification, soil classification, climate expectation, and trim malady expectation. IoT components integrated with IoT cloud are coordinates in proposed framework to supply keen arrangement for edit development observing to clients.