Wireless Sensor Nodes Research Articles

Flag-type hybrid nanogenerator utilizing flapping wakes for consistent high performance over an ultra-broad wind speed range

Fluttering of regular flags and flapping of inverted flags in the wind serve as the foundational principles of flag-type nanogenerators (FNGs). However, FNGs relying on a single aerodynamic behavior exhibit significant power output only within a limited spectrum of wind speeds, posing a challenge to their robustness in scenarios with intensely fluctuating wind. In this paper, we propose a novel hybrid scheme aimed at harnessing the synergistic potential of two aerodynamic behaviors to enhance the performance of FNGs and broaden their operational wind speed ranges. A flag-type triboelectric-piezoelectric hybrid nanogenerator (FTPNG) is developed with the integration of flapping piezoelectric flags (PEFs) and a fluttering triboelectric flag (TEF). To overcome the limited operational wind speed range, flapping PEFs are configured in an array format, optimized through fluid-solid coupled simulations. The rear TEF leverages the fluttering motion of a polytetrafluoroethylene (PTFE) membrane, which intermittently contacts and separates from conductive textiles positioned on the inner surface of the baffles. A noteworthy feature is the innovative “back-to-back” design, which utilizes the flapping wakes generated by PEFs to intensify the fluttering of the PTFE membrane, resulting in a remarkable boost in power generation of up to 132 times and achieving a maximum peak power output of 5400 μW. The FTPNG offers consistent high performance, with an average output of exceeding 200 μW over an ultra-broad wind speed range of 4.7–14.6 m/s, while the complete operational range is 3.7–15 m/s. It also attains a considerable average power output of 850 μW at 7.8 m/s, marking a significant advancement compared to other FNGs. Finally, in demonstration tests, the FTPNG can light 252 LEDs and showcases the capabilities of PEF array and TEF to independently power a wireless sensor node (WSN), highlighting its significant potential for applications in the Internet of Things and various self-powered systems.

A Novel Hybridized Cluster-Based Geographical Opportunistic Routing Protocol for Effective Data Routing in Underwater Wireless Sensor Networks

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods.

Height-Fixed UAV Enabled Energy-Efficient Data Collection in RIS-Aided Wireless Sensor Networks

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Solar Powered IoT Sensors to Increase the Network Longevity

A wide variety of industries, such as the manufacturing business, the transportation industry, the environmental industry, and even the medical industry, are beginning to comprehend the significance of wireless sensor networks. This problem is being further amplified by the rapidly increasing use of devices connected to the Internet of Things across a wide variety of enterprises. Because of this, having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance, both of which are essential to the widespread adoption of the technology, the successful application of the technology, and the long-term viability of the technology. In this paper we enable a solar powered IoT node communication using energy management system. Having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance. The creation of energy-aware wireless sensor nodes has become increasingly feasible as a result of recent advancements in fields such as the manufacturing of a large number of highly efficient energy converters and the reduction of energy consumption in hardware, software, and communication protocols.

A Compact RF Energy Harvesting Wireless Sensor Node with an Energy Intensity Adaptive Management Algorithm.

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of -24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

Passive Internet of Events Enabled by Broadly Compatible Self-Powered Visualized Platform Toward Real-Time Surveillance.

Surveillance is an intricate challenge worldwide especially in those complicated environments such as nuclear plants, banks, crowded areas, barns, etc. Deploying self-powered wireless sensor nodes can increase the system's event detection capabilities by collecting environmental changes, while the incompatibility among components (energy harvesters, sensors, and wireless modules) limits their application. Here, a broadly compatible self-powered visualized platform (SPVP) is reported to construct a passive internet of events (IoE) network for surveillance systems. By encoding electric signals into reference and working LEDs, SPVP can visualize resistance change generated by commercial resistive sensors with a broad working range (<107 Ω) and the transmission distance is up to 30 meters. Visible light signals are captured by surveillance cameras and processed by the cloud to achieve real-time event monitoring and identification, which forms the passive IoE network. It is demonstrated that the passive-IoE-based surveillance system can detect intrusion, theft, fire alarm, and distress signals quickly (30ms) for 106 cycles. Moreover, the confidential information can be encrypted by SPVPs and accessed through a phone application. This universal scheme may have huge potential for the construction of safe and smart cities.

Energy efficient greedy tree based algorithm for data aggregation in wireless sensor network

Secure data aggregation is essential in wireless sensor networks for lowering the amount of data transmitted and extending the lifetime of the network. The foundation underlying significantly greater industrial internet of things applications is primarily wireless sensor nodes. Sensors that have already been integrated within can be used to sense data in any form of real-time IoT application. In real-time physical surroundings, sensors utilize as little power as feasible to conduct operations including sensing, communicating, and processing data. Many investigations are being carried out to improve sensor node energy efficiency and network lifetime. To save energy, more attention must be paid to the clustering and routing aspects of communication. In this paper, we introduce Energy Efficient Greedy Tree based Data Aggregation (EE–GTDA) algorithm for efficient data aggregation with increased reliability and reduced energy consumption. It is a two-fold homogeneous technique that supervises safe energy-efficient connectivity and data aggregation with the greedy tree based solution that emphasizes multi-objective function. These methodologies are based on minimizing sensor energy consumption to maximize network lifetime simultaneously decreasing communication overhead. A trade-off between energy and safety is accomplished in order to increase efficient energy consumption with a higher packet delivery ratio.

Design development and testing of traffic induced wind based artificial tree type hybrid energy harvester for wireless sensor nodes

With the rapid development of the urban system, the structural health monitoring (SHM) of high-rise buildings, flyovers, and bridges have attracted researchers’ attention. Currently, wireless sensor nodes (WSNs) are used for the SHM, however, WSNs use batteries as power sources which have limited shelf life. To attain the self-sustain long-term operation of WSNs, ambient energy from their surrounding can be harvested for their power requirements. In this research, a traffic induced wind based artificial tree type hybrid energy harvester is designed, developed, and tested. The developed prototype is able to harvest traffic induced wind. The prototype consists of multiple piezoelectric leaves and two electromagnetic energy harvesters (EM-EH) - short beam EM-EH and long beam EM-EH. Each EM-EH is composed of a cantilever beam, a permanent magnet, an airfoil, and a planar coil. Test results reveals that at a maximum windspeed of 10 m/s, the prototype can harvest 1.60 μW from a single piezoelectric leaf, 53 μW from a short beam EM-EH and 50 μW from a long beam EM-EH at optimum load. This artificial tree type hybrid energy harvester will help in the deployment of WSNs and in the beautification of the urban system.

Energy and relevance-aware adaptive monitoring method for wireless sensor nodes with hard energy constraints

Traditional dynamic energy management methods optimize the energy usage in wireless sensor nodes adjusting their behavior to the operating conditions. However, this comes at the cost of losing the predictability in the operation of the sensor nodes. This loss of predictability is particularly problematic for the battery life, as it determines when the nodes need to be serviced. In this paper, we propose an energy and relevance-aware monitoring method, which leverages the principles of self-awareness to address this challenge. On one hand, the relevance-aware behavior optimizes how the monitoring efforts are allocated to maximize the monitoring accuracy; while on the other hand, the power-aware behavior adjusts the overall energy consumption of the node to achieve the target battery life. The proposed method is able to balance both behaviors so as to achieve the target battery life, at the same time is able to exploit variations in the collected data to maximize the monitoring accuracy. Furthermore, the proposed method coordinates two different adaptive schemes, a dynamic sampling period scheme, and a dual prediction scheme, to adjust the behavior of the sensor node. The evaluation results show that the proposed method consistently meets its battery lifetime goal, even when the operating conditions are artificially changed, and is able to improve the mean square error of the collected signal by up to 20% with respect to the same method with the relevance-aware behavior disabled, and of up to 16% with respect the same algorithm with just the adaptive sampling period or the dual prediction scheme enabled. Consequently showing the ability of the proposed method of making appropriate decisions to balance the competing interest of its two behaviors and coordinate the two adaptive schemes to improve their performance.

Wake galloping piezoelectric-electromagnetic hybrid ocean wave energy harvesting with oscillating water column

On-site continuous and energy-autonomous wireless sensor nodes are highly desirable for the sustainable monitoring of the ocean environment. In this work, a wake galloping piezoelectric-electromagnetic hybrid energy harvesting technique is developed to take off electric power from ocean wave energy. This mechanism is composed of an air chamber intake for the oscillating water column (OWC) and a piezoelectric-electromagnetic hybrid wave energy harvester in a vortex tunnel. Theoretical models and multi-physics coupled simulations are derived and established to systematically investigate the performance of the proposed harvester and the feasibility of harvesting wave energy is verified with a series of wave flume and wind vortex tunnel experiments. The results show that when the wave frequency is 0.4 Hz (the period is 2.5 s), the air pressure of the chamber is 140.12 Pa. Hence, the air is exhaled with the maximal flowing speed of 17.85 m/s in the wind vortex tunnel. For comparison, It is evaluated by the simulation model with air pressure results of 142.86 Pa and wind speed at 18.36 m/s which agree well with the theoretical values. The output voltages of the piezoelectric and electromagnetic parts are calculated with the theory and simulation as 20.34 V and 0.258 V, respectively. Furthermore, the power of the fabricated prototype piezoelectric-electromagnetic hybrid energy harvester achieves 3.76 mW and 1.33 mW, respectively, at the wind speed of 18 m/s while it is set up from the downstream of the bluff body with a windward width of 34 mm, and the space distance ratio is about 4.5. Therefore, the test and computed results provide the wave-WG-PEEH for ocean wave energy harvesting to supply the perspective of maritime low-power wireless sensors.

Internet of Things-based sustainable environment management for large indoor facilities.

Due to global warming and climate change, the poultry industry is heavily impacted, especially the broiler industry, due to the sensitive immune system of broiler chickens. However, the continuous monitoring and controlling of the farm's environmental parameters can help to curtail the negative impacts of the environment on chickens' health, leading to increased meat production. This article presents smart solutions to such issues, which are practically implemented, and have low production and operational costs. In this article, an Internet of Things (IoT) based environmental parameters monitoring has been demonstrated for the poultry farmhouse. This system enables the collection and visualization of crucially sensed data automatically and reliably, and at a low cost to efficiently manage and operate a poultry farm. The proposed IoT-based remote monitoring system collects and visualizes environmental parameters, such as air temperature, relative humidity (RH), oxygen level (O2), carbon dioxide (CO2), carbon monoxide (CO), and ammonia (NH3) gas concentrations. The wireless sensor nodes have been designed and deployed for efficient data collection of the essential environmental parameters that are key for monitoring and decision-making process. The hardware is implemented and deployed successfully at a site within the control shed of the poultry farmhouse. The results revealed important findings related to the environmental conditions within the poultry farm. The temperature inside the control sheds remained within the desired range throughout the monitoring period, with daily average values ranging from 32 °C to 34 °C. The RH showed slight variations monitoring period, ranging from 65% to 75%, with a daily average of 70%. The O2 concentration exhibited an average value of 17% to 18.5% throughout the monitoring period. The CO2 levels showed occasional increases, reaching a maximum value of 1,100 ppm. However, this value was below the maximum permissible level of 2,500 ppm, indicating that the ventilation system was effective in maintaining acceptable CO2 levels within the control sheds. The NH3 gas concentration remained consistently low throughout the duration, with an average value of 50 parts per million (ppm).

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.

Hierarchical Energy Harvesting Aware Adaptive Fuzzy Routing with Data Compression for Energy Harvesting WSN

In present scenario of the world moving towards the smart systems. Smart systems and applications are developed using sensor nodes for data collection, data aggregation and decision making process. Wireless sensor nodes are configured in the physical environment for communications between the devices and the user. The number of limitations affecting the performance of wireless sensor based application. The various factors affecting the performance of the WSN based applications, one the major factor is energy efficiency of the sensors. Sensor nodes are battery powered devices and the sensors are dropping their energy during the transmissions. So we need a solution to overcome the energy efficiency issue of WSN based applications. In this paper, we proposed a methodology called Hierarchical energy harvesting adaptive fuzzy routing algorithm with uniform data quality compression to manage the energy efficiency of the sensor nodes. It uses a new energy management framework and allocating the energy budget to sensors and reduces the consumption of each node. The existing energy harvesting approaches only concentrate on find the best path and forward the data to base station. The performance evaluation shows the effective use of proposed method in WSN based applications.

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).

Blocking intrusion logic using optimized multi-head convolution in wireless sensor network

Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively.

Communication performance optimization of coal mine goaf LoRa AD hoc network sensor system based on tree topology

Real-time accurate monitoring of goaf gas, temperature, and other parameters is of key significance for the prevention and control of spontaneous coal combustion in the goaf. In this paper, a tree network and communication protocol for LoRa technology are proposed to solve the problems of small communication coverage and short endurance of the traditional goaf wireless monitoring system. In this paper, a geometric topology structure is designed for wireless sensor nodes to find relay nodes through the path loss model, and an energy-saving optimization algorithm routing scheme is designed. In addition, LoRa AD hoc network experiment platform and upper computer system are developed and designed. Through numerical analysis and experimental results in coal mines, it is proved that the proposed method significantly reduces the energy consumption of the whole network. The coverage radius is increased by 300% compared with the traditional star wireless sensor system, which makes the endurance time and communication quality have higher reliability.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

Development a Software Defined Network (SDN) with Internet of Things (IoT) Security for Medical Issues

Because providing billions of objects with network connectivity, the Internet of Things (IoT) enables the collection and transfer of real-time data for intelligent applications. Therefore, IoT enables remote access and control of connected devices when there is a sufficient network infrastructure. Additionally, the introduction of software-defined networking (SDN) presents capabilities that allow internet providers and users to control and connect network equipment wirelessly, even as enabling a global perspective on the network, that has previously become a soaring interest area due to its extensive use for various applications and systems, including wireless sensor nodes, medical equipment, delicate home sensor systems, and some other connected IoT devices. In order to create worldwide connectivity between the Internet of Things (IoT) and based on the SDN architecture in the medical contexts, this paper's contribution is to outline some relevant directions. Additionally, we provide a model based on software defined network principles that depicts interactions between a group of people each of whom have a Nano network within their bodies and the medical services via the local network of a medical institution. For everybody electrical engineers to data engineers the requirement to integrate everything in a global setting is a significant problem. As a result, the cloud is useful for handling the instantaneous sharing of information. In terms of health care, the effort is also stated in terms of IoT architecture and services. IoT's current prospects for the healthcare sector are quite promising. Due to its capacity for sensing and measuring, it is also highly well-liked. From the smallest sensor to the massive amounts of data gathered, this revolution is completely changing how we view healthcare.

Relative Localization of Wireless Sensor Nodes by Using the RSSI and ToA based distance estimations

Wireless sensor network is a popular area for both academic research and commercial applications. A wireless sensor network is made up of several sensor nodes connected over various wireless infrastructures such as Wi-Fi, Bluetooth, ZigBee, or WiMAX. Determining the relative or absolute position of the sensor nodes is essential information for many applications. In this work, we present a novel method for estimating the position of sensor nodes using the Received Signal Strength Indicator and Time of Arrival metrics of the preferred wireless communication infrastructure. Localization results based on both of the metrics and comparison of them with respect to distance between the nodes are presented, and a novel combined method using both the RSSI and ToA based distance estimations is presented. The proposed method estimates the position of the WSN using both methods, but the result of a single method is preferred depending on the distance between the nodes since within the first 5 m. The RSSI based method is superior to ToA and for farther distances ToA outperforms RSSI. The measurement results show that the combined method reduces the estimation error and performs better than both methods it is based on.

Power control algorithm for wireless sensor nodes based on energy prediction

Power control algorithm for wireless sensor nodes based on energy prediction