Use Of Wireless Sensor Networks Research Articles

An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs

Wireless sensor networks consist of many restrictive sensor nodes with limited abilities, including limited power, low bandwidth and battery, small storage space, and limited computational capacity. Sensor nodes produce massive amounts of data that are then collected and transferred to the sink via single or multihop pathways. Since the nodes’ abilities are limited, ineffective data transmission across the nodes makes the network unstable due to the rising data transmission delay and the high consumption of energy. Furthermore, sink location and sensor-to-sink routing significantly impact network performance. Although there are suggested solutions for this challenge, they suffer from low-lifetime networks, high energy consumption, and data transmission delay. Based on these constrained capacities, clustering is a promising technique for reducing the energy use of wireless sensor networks, thus improving their performance. This paper models the problem of multiple sink deployment and sensor-to-sink routing using the clustering technique to extend the lifetime of wireless sensor networks. The proposed model determines the sink placements and the most effective way to transmit data from sensor nodes to the sink. First, we propose an improved ant clustering algorithm to group nodes, and we select the cluster head based on the chance of picking factor. Second, we assign nodes to sinks that are designated as data collectors. Third, we provide optimal paths for nodes to relay the data to the sink by maximizing the network’s lifetime and improving data flow. The results of simulation on a real network dataset demonstrate that our proposal outperforms the existing state-of-the-art approaches in terms of energy consumption, network lifetime, data transmission delay, and scalability.

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks.

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

Detecting anomalous sensor readings in wireless networks for remote area

In recent years, the use of wireless sensor networks has become increasingly popular in remote areas for various applications, including environmental monitoring and surveillance. However, wireless sensor networks are often vulnerable to anomalous sensor readings, which can be caused by various factors, such as hardware malfunctions, environmental changes, and interference from other sources. Anomalous sensor readings can lead to false alarms, reduced data quality, and decreased network reliability. To address this challenge, this paper presents a study of anomalous sensor reading detection in wireless networks for remote areas. Our approach is based on machine learning algorithms and involves the use of a clustering algorithm to identify normal patterns in the sensor readings, and a classifier to detect readings that deviate from these patterns. We evaluate the performance of our approach using a dataset of real-world sensor readings and compare it to several benchmark methods. The results of our study demonstrate that our approach outperforms the benchmark methods in terms of accuracy and robustness, and that it is capable of effectively detecting anomalous sensor readings. Furthermore, our approach has the advantage of being scalable and easily adaptable to different types of sensor readings and applications. In conclusion, our approach provides a promising solution for the detection of anomalous sensor readings in wireless networks for remote areas. The results of this study have the potential to improve the reliability and performance of wireless sensor networks and support the development of more effective and efficient monitoring systems for remote areas. We hope that this work will inspire further research in the field and contribute to the development of advanced techniques for anomalous sensor reading detection.

An adaptive transformer model for anomaly detection in wireless sensor networks in real-time

Defense, environmental monitoring, healthcare, home automation, and other fields are just a few of the many that make use of wireless sensor networks. There are sensor nodes in these networks that are sent out into the field to collect data and relay it back to the network's hub. The data streams may be infused with anomalous data due to interferences, malfunctioning nodes, etc. The security of the network depends on the prompt identification of any irregularities. Due to noise, missing data, and the dynamic nature of the network, identifying and detecting abnormal data is difficult.This study introduces a dynamic context-capturing deep learning model for sensor data anomaly detection, constructed as a Transformer with a spatio-temporal attention mechanism. The proposed model is trained and tested with a public dataset built from real-time sensor data captured in a water treatment plant. With an accuracy of 0.09142, F1 of 0.9109, and precision of 0.9191, the STA-Tran model outperforms the best existing methods.

A fuzzy based approach to enhance the lifespan of sensor network

The use of wireless sensor networks (WSNs) for data collection is widespread. The resource constraint is an important factor in WSN communications design. The issue arises naturally in WSNs as a result of uneven energy consumption caused by multi-hop routing and dynamic network models, which substantially affects network lifetime. The nodes are dispersed over distant sensing areas and are powered by finite or limited energy batteries that are difficult to replace. The energy of nodes is reduced as a result of changes in network topology or the network’s lifespan and the main intention of this research is to figure out how to make sensor networks last longer. The suggested study work focuses on a specific routing strategy for WSNs that employs the AO-star algorithm with a Fuzzy approach and link stability for extending the network lifetime. The technique chooses the optimum routing path by the sensing point to the receiving node based on how much energy is consumed, the smallest number of nodes with the shortest latency, and lower transmission loads with higher throughput. To compare the proposed strategy’s efficiency in energy consumption balancing and network lifespan enhancement, the proposed technique may achieve a 30% longer average network lifetime than the A-star algorithm.

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.

An IOT-Based System for Agriculture Monitoring

A system is developed for agricultural field monitoring in IoT-based smart farming with the aid of sensors like light, humidity, temperature, soil moisture, etc. Farmers can keep an eye on the state of their fields from anywhere. Smart farming that is IoT-based is significantly more efficient than traditional farming. Agriculture has been practiced for centuries and is regarded as both a science and an art of plant cultivation. In today's world, agriculture must also maintain with the advancement of technology. IoT is crucial to smart agriculture. The necessary data regarding farm areas is provided by Internet of Things (IoT) sensors. The key benefit of IoT is the ability to monitor agriculture through the use of wireless sensor networks, which collect data from numerous sensors put at various locations and transmit it via wireless protocol. NodeMCU is the IoT system that powers smart agriculture. It has a DC motor, moisture sensor, temperature sensor, and humidity sensor. This system begins to measure the level of moisture and humidity. The sensors are used to detect the water level, and the system automatically begins watering if the level is below the range. The sensor works according to the change in temperature level. IoT also displays humidity and moisture level information along with date and time. You can also modify the temperature based on the crops that are grown.

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review.

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

Protecting Source Location Privacy in IoT-Enabled Wireless Sensor Networks: The Case of Multiple Assets

A major limitation to the use of wireless sensor networks (WSNs) in asset monitoring applications is security and privacy, particularly the privacy of source location information. In this article, we develop two phantom routing-based solutions to provide source location privacy for the case of multisource/asset scenario—the case that has received very little attention in the literature. The idea of phantom routing is to relay the packets to a distant node in a random fashion to obfuscate the traffic flows and confuse the attacker. The first technique phantom routing-based backward random walk (PRBRW) uses a combination of backward random walk (RW) and a greedy forwarding approach to route the packets to the base station (BS). Although the first approach has better performance improvements in terms of capture ratio and safety period it hampers the lifetime of the network and has a poor entropy metric. To better this problem, an improved phantom routing scheme phantom routing-based L-path RW (PRLPRW) is proposed. The second technique has three phases: 1) pure RW; 2) L-walk; and 3) greedy walk. This technique performs well in terms of capture ratio, safety period, and entropy metrics. The improvement in network lifetime is 10-folds and entropy is 477-folds when compared with PRBRW. The performance is evaluated using the developed analytical models and compared with the baseline protection-less scheme shortest path routing (SPR). It is observed that PRBRW and PRLPRW, respectively, have 60- and 73-fold improvements in terms of capture ratio when compared with SPR, whereas existing phantom routing-based pure RW and forward RW techniques, respectively, have only 54- and 34-fold improvements.

IoT-Enhanced Early Warning System for Forest Fire Detection

Everyone is aware that the forest is regarded as one of the most important and necessary resources, and that forest fires pose a constant threat to biological systems and environmental factors. The necessity to detect forest fires as quickly as possible is urgent because the identification of forest fires has become a crucial issue in the pre-suppression phase. This literary work has made a strong case for the skilled use of wireless sensor networks as a plausible explanation for the cause of forest fires. To complete the solution process, the proposed system relies on a variety of sensors that are attached to it as well as data from wireless communication. A small satellite transmits these sensor data in the system to a ground station where they are examined. The data from wireless sensor reticulation used in the discourse plan is dependent on the earlier detection of forest fire.

The Challenges that Frustrate the Deployment and use of Wireless Sensor Networks for Oil Pipeline Monitoring in the Niger Delta Region of Nigeria

The Challenges that Frustrate the Deployment and use of Wireless Sensor Networks for Oil Pipeline Monitoring in the Niger Delta Region of Nigeria

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.

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.

A weighted Markov-clustering routing protocol for optimizing energy use in wireless sensor networks

Interest has increased in wireless sensor networks (WSNs) in fields such as healthcare, industrial control and environmental monitoring. More recently, WSNs have been widely deployed in Internet of Things (IoT) based applications. They are considered as an essential part of IoT networks. The expanded use of WSNs raises daunting technical challenges, not the least of which is sensor battery energy conservation. The design of efficient sensor clustering strategies to reduce energy consumption by data transmission throughout the WSN has become crucial. Several applications of WSNs induce a random deployment of sensors such as the monitoring of conflict zones, the study of natural phenomena in hostile zones or rescue operations. In this context, energy conservation should be ensured according to the non-uniformity of the resulting sensor distribution in different areas of the network. In this study, a novel weighted Markov clustering protocol that considers sensor abundance for cluster-head and queried sensor selections is presented. The new protocol aims to decrease intra-cluster energy consumption by reducing the sending of redundant data in sensor-dense regions. In addition, it attempts to prolong sensor-sparse regions lifetime by limiting the number of queried sensors. This protocol combines a Markov clustering of sensors with a sensor weighting based on residual energy and sensor abundance in the network. The proposed protocol is a significant improvement of an existing unweighted Markov clustering protocol. The unweighted Markov clustering protocol is based on sensor residual energy and sensor location without taking into account the sensor abundance in different areas of the network. Simulations affirm that the new protocol handles more appropriately the non-uniformity of sensor distribution and enhance the durability of wireless sensor networks. Indeed, simulation results show that the proposed clustering protocol outperforms its unweighted ancestor and other well-known clustering protocols in terms of energy conservation and network lifetime. The number of expired sensors and the average dissipated energy are reduced, whereas, the average sensor lifetime is prolonged compared to the unweighted ancestor, or HEED, LEACH, PEGASIS, and TEEN.

An Ultra-wideband Dielectric Resonator Antenna for WSN based IoT Applications in Agriculture

Background: Agriculture sector is one of the prime and widely spread sectors. So to make it autonomous and increase its yield, we require a major technological improvement. The only solution to make advancement is with the use of wireless sensor networks. Internet of Things in this field is used to provide connectivity to all real-time sensors and to collect that data in computer-based systems without human involvement. Objective: IoT based system is used to monitor physical and environmental conditions of the agriculture field through a network of wireless sensor. Here, a novel ultra-wideband Dielectric Resonator antenna is designed that is used in Wi-Fi for transmission of data received from sensors. The antenna designed should be easy to fabricate and compact in size and should provide high data rates. The complete designed system should be reliable and cost effective one. Method: A proposed IoT based system monitors physical and environmental conditions using a wireless sensor network consisting of power supply, soil moisture sensor (FC-28), humidity sensor (LM-35), temperature sensor (HR-202), water level sensor, ARM 7 processor, Liquid Crystal Display (LCD), Relay, motor and Wi-Fi module that is installed at remote locations and connected to the main system comprises of a novel ultra-wideband Dielectric Resonator antenna. Results: The designed WSN based IoT system for agriculture application monitors temperature, humidity, soil moisture, and water level in the field. For Wi-Fi module implementation ultra-wideband inverted sigmoid shaped DRA is designed that provides an impedance bandwidth of 36.46 % at 6.226 GHz (5.51 - 7.78 GHz). The designed antenna provides a peak gain of 5.44 dB at a resonant frequency of 6.226 GHz. Conclusion: The proposed IoT based system is used to monitors physical and environmental conditions like soil moisture, humidity, temperature and water level and sends the data through Wi-Fi module comprising of an ultra-wideband Dielectric Resonator antenna. The designed antenna is compact and can be easily fabricated using printed circuit board technology. The complete system is cost-effective and can be easily implemented.

MMEDD: Multithreading Model for an Efficient Data Delivery in wireless sensor networks

Nowadays, the use of Wireless Sensor Networks (WSNs) is increasingly growing as they allow a large number of applications. In a large scale sensor network, communication among sensors is achieved by using a multihop communication. However, since the sensor is limited by its resources, sensors' operating systems are developed in order to optimize the management of these resources, especially the power consumption. Therefore, the hybrid operating system Contiki uses a low consumption layer called Rime which allows sensors to perform multihop sending with a low energy cost. This is favored by the implementation of lightweight processes called protothreads. These processes have a good efficiency/consumption ratio for monolithic tasks, but the management of several tasks remains a problem. In order to enable multitasking, Contiki provides to users a preemptive multithreading module that allows the management of multiple threads. However, it usually causes greater energy wastage. To improve multithreading in sensor networks, a Multithreading Model for an Efficient Data Delivery (MMEDD) using protothreads is proposed in this paper. Intensive experiments have been conducted on COOJA simulator that is integrated in Contiki. The results show that MMEDD provides better ratio message reception rate/energy consumption than other architectures.

Addressing the Directionality Challenge through RSSI-Based Multilateration Technique, to Localize Nodes in Underwater WSNs by Using Magneto-Inductive Communication

The deployment and efficient use of wireless sensor networks (WSNs) in underwater and underground environments persists to be a difficult task. In addition, the localization of a sensor Rx node in WSNs is an important aspect for the successful communication with the aforementioned environments. To overcome the limitations of electromagnetic, acoustic, and optical communication in underwater and underground wireless sensor networks (UWSNs), magneto-inductive (MI) communication technology emerged as a promising alternative for usage in UWSNs with a wide range of applications. To make the magneto-inductive underwater wireless sensor networks (MI-UWSNs) more efficient, recently, various research studies focused on the optimization of the physical layer, MAC layer, and routing layer, but none of them has taken into account the effect of directionality. Despite the directionality issue posed by the physical nature of a magnetic field, the unique qualities of MI communication open up a gateway for several applications. The directionality issue of MI sensors is a critical challenge that must be taken into account while developing any WSN protocol or localization algorithm. This paper highlights and discusses the severity and impact of the directionality issue in designing a localization algorithm for magneto-inductive wireless sensor networks (MI-WSNs). A received signal strength indicator (RSSI)-based multilateration localization algorithm is presented in this paper, where a minimum of 2 and maximum of 10 anchor Tx nodes are used to estimate the position of the sensor Rx nodes, which are deployed randomly in a 15 m × 15 m simulation environment. This RSSI-based multilateration technique is the most suitable option that can be used to quantify the impact of directionality on the localization of a sensor Rx node.

Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network

The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered by batteries. Since the sensor is powered by batteries, replacing and charging the battery after its depletion are challenging. Manual battery replacement is hampered by geographical restrictions, which results in significant reduction of wireless sensor network performance and longevity. As a result, this study addresses the energy-constrained wireless sensor networks by creating a technological model to a heterogeneous clustered wireless sensor network in an outdoor application using solar-enabled energy harvesting photovoltaic cells. Due to lack of energy, this effort was employed to overcome the challenges of relatively restricted processing performance and limited radio frequency transmission bandwidth. The program was created to efficiently utilize the energy produced from solar panels and charge the batteries in a variety of ways. The algorithm also controls the discharging process. According to the results of extensive investigation and experimentation, the sensor is continuously operated even when there is no solar light. The batteries are charged on bright day and discharged at night and on overcast days. The algorithm is used to govern the energy supplement to rechargeable lithium-ion polymer batteries as well as a load (sensor). In the worst scenario (no solar light/cloudy, and sensors reporting data every 30 minutes), the present cluster head sensor with 6 ordinary nodes in the cluster lasts just 16.6667 days. But the life duration of the newly designed model algorithm has been raised to 54.16667 days. The normal sensor node’s life duration has also been enhanced from 50 to 91.66667 days.