Smart Wireless Sensor Networks Research Articles

Real-time spatial-temporal mapping and visualization of thermal comfort and HVAC control by integrating immersive augmented reality technologies and IoT-enabled wireless sensor networks: Towards immersive human-building interactions

Existing methods to assess and visualize thermal comfort mainly focused on either individual users (e.g., through wearables) or for specific locations where sensors are installed, rather than continuously for the entire indoor space. Also, most of the existing thermal comfort visualization methods and HVAC system control approaches rely on phones and mobile devices and lack a fully immersive/interactive experience and human-building interaction. Thus, this paper develops an immersive human-building interaction framework for real-time spatial-temporal mapping and visualization of indoor thermal comfort by integrating immersive augmented reality (AR) technologies and IoT-enabled smart wireless sensor networks (WSNs) (i.e., temperature and relative humidity data) to provide thermal comfort assessment for the entire space (rather than at localized/individual locations/points) and to enable users to interact with and adjust the HVAC systems from a distance using fully immersive and realistic environments. First, a smart WSN comprised of four IoT-enabled sensing stations was developed to collect real-time thermal comfort-related information (i.e., temperature and relative humidity). Second, an AR environment was created using the Unity engine, where virtual objects were overlayed into, and aligned with, the real-world environment. Third, the real-time data from the IoT-enabled WSN was integrated into the AR environment using various programming scripts that add multiple functionalities to the users, including controlling the HVAC system and visualizing the thermal comfort along the entire indoor space or room of interest through a spatial-temporal map. Fourth, the developed framework was validated and evaluated by 118 participants using a survey questionnaire. The findings showed that the graphical satisfaction achieved an overall average score of 4.75 out of 5, the user's sense of spatial presence was rated at an average score of 4.68 out of 5, the involvement aspects of the developed approach received an overall average score of 4.80 out of 5, and experienced realism was rated at an average score of 4.58 out of 5. Ultimately, this paper contributes to enhanced and intelligent human-building interaction and visualization through the use of emerging technologies by allowing the building users to control and interact with the HVAC systems using AR capabilities to improve the health and well-being of the building occupants.

Efficient Strategy to Exchange Road Messages Between Smart Vehicles and Wireless Sensor Networks in Hybrid Sensor and Vehicular Networks

The aim of the intelligent transport system (ITS) is the improvement of road safety. This system is based on intelligent vehicles composing a network called Vehicular Ad hoc NETwork (VANET). This network suffers from a disconnection problem due to its dynamic topology. Therefore, a framework of collaboration between the Wireless Sensor Network (WSN) and VANET called Hybr1id Sensor and Vehicular Networks (HSVN) has been proposed. In HSVN, the WSN can play the role of a relay between disconnected vehicles. This paper aims to propose a strategy allowing the exchange of messages between the two networks. That reduces the number of accidents and improves the management of road traffic. We can summarize our proposition in three essential points. First, an algorithm is proposed to decompose the vehicular network into clusters. This algorithm takes into account the mobile aspect of vehicles and the road model. Second, a data encoding model and a message model have been proposed to improve the quality of messages. That reduces the response time of drivers to a critical situation. Finally, an exchange algorithm is proposed to ensure the transmission of road messages between vehicles and sensors. Its principle of work is based on several scenarios defined relative to the network condition. Obtained results show an improvement in the delivery delays of road messages and the number of exchanged road messages between the vehicles.

Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things.

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 μm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 μm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT.

Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems

Data acquisition systems, such as Wireless Smart Sensor Networks (WSSNs) can increase the resilience of infrastructure by providing real-time monitoring and data collection of environmental parameters. Yet, sustainable energy supplies for sensor networks established in remote and inaccessible areas still present a challenge. Previously, researchers have attempted to address this difficulty by proposing different energy systems including solar energy harvesting, however, significant prolonged experimental data for the operation of extensive networks powered by solar energy has not been reported. This paper presents an original design and implementation of an energy system for a large WSSN and provides the sensors' power status data over a significant duration. A network of low-cost flood monitoring sensors, including twenty-six water level sensors, twenty rain gauges, and eight communication nodes were deployed and tested on summer and fall 2022 at six remote locations at the northern New Mexico Pueblo, Ohkay Owingeh. A thermometer and a humidity sensor were added to each communication node to record temperature and air's moisture level. In addition, a networked voltage monitoring system was deployed to observe the sensors energy status in real-time. The items of the WSSN are composed of two differing energy circuits suited for their energy demands. The sensors' energy circuits contain a photovoltaic panel, a lithium-polymer battery, a control device, and a DC-to-DC converter. Whereas the communication nodes contain another photovoltaic panel, a lead-acid battery, and a solar charging controller. The findings provide a perspective on the long-term field deployment of WSSNs consisting of low-cost sensors.

Linear Segmented Arc-Shaped Piezoelectric Branch Beam Energy Harvester for Ultra-Low Frequency Vibrations.

Piezoelectric energy harvesting systems have been drawing the attention of the research community over recent years due to their potential for recharging/replacing batteries embedded in low-power-consuming smart electronic devices and wireless sensor networks. However, conventional linear piezoelectric energy harvesters (PEH) are often not a viable solution in such advanced practices, as they suffer from a narrow operating bandwidth, having a single resonance peak present in the frequency spectrum and very low voltage generation, which limits their ability to function as a standalone energy harvester. Generally, the most common PEH is the conventional cantilever beam harvester (CBH) attached with a piezoelectric patch and a proof mass. This study investigated a novel multimode harvester design named the arc-shaped branch beam harvester (ASBBH), which combined the concepts of the curved beam and branch beam to improve the energy-harvesting capability of PEH in ultra-low-frequency applications, in particular, human motion. The key objectives of the study were to broaden the operating bandwidth and enhance the harvester's effectiveness in terms of voltage and power generation. The ASBBH was first studied using the finite element method (FEM) to understand the operating bandwidth of the harvester. Then, the ASBBH was experimentally assessed using a mechanical shaker and real-life human motion as excitation sources. It was found that ASBBH achieved six natural frequencies within the ultra-low frequency range (<10 Hz), in comparison with only one natural frequency achieved by CBH within the same frequency range. The proposed design significantly broadened the operating bandwidth, favouring ultra-low-frequency-based human motion applications. In addition, the proposed harvester achieved an average output power of 427 μW at its first resonance frequency under 0.5 g acceleration. The overall results of the study demonstrated that the ASBBH design can achieve a broader operating bandwidth and significantly higher effectiveness, in comparison with CBH.

Home Chimney Pinwheels (HCP) as Steh and Remote Monitoring for Smart Building IoT and WSN Applications

Smart, and ultra-low energy consuming Internet of Things (IoTs), wireless sensor networks (WSN), and autonomous devices are being deployed to smart buildings and cities, which require continuous power supply, whereas battery usage has accompanying environmental problems, coupled with additional maintenance cost. We present Home Chimney Pinwheels (HCP) as the Smart Turbine Energy Harvester (STEH) for wind; and Cloud-based remote monitoring of its output data. The HCP commonly serves as an external cap to home chimney exhaust outlets; they have very low inertia to wind; and are available on the rooftops of some buildings. Here, an electromagnetic converter adapted from a brushless DC motor was mechanically fastened to the circular base of an 18-blade HCP. In simulated wind, and rooftop experiments, an output voltage of 0.3 V to 16 V was realised for a wind speed between 0.6 to 16 km/h. This is sufficient to operate low-power IoT devices deployed around a smart city. The harvester was connected to a power management unit and its output data was remotely monitored via the IoT analytic Cloud platform "ThingSpeak" by means of LoRa transceivers, serving as sensors; while also obtaining supply from the harvester. The HCP can be a battery-less "stand-alone" low-cost STEH, with no grid connection, and can be installed as attachments to IoT or wireless sensors nodes in smart buildings and cities.

Decentralized fault-tolerant controller based on cooperative smart-wireless sensors in large-scale buildings

The internet of things, based on smart-wireless sensor networks, is more and more used in buildings to monitor and improve their performance in terms of thermal comfort and power consumption. However, a detailed knowledge of the thermal and energy behaviour of the monitored smart building requires frequent use of these objects. Despite the advancement of current technologies today, these smart sensors or actuators are still prone to problems such as loss of communication or low battery. In the presence of this type of failures, we propose in this paper to generate virtual signals by the cooperation of faulty and non-faulty distributed sensors to optimize locally the performance of each heating, ventilation, and air conditioning system according to thermal comfort and energy-efficiency conditions. Thus, we design a decentralized cooperative fault-controller for internet of things networks to overcome this kind of problem and ensure optimal occupant comfort and energy consumption. We give some conditions to verify the feasibility of the technique and the stabilizability of this class of controllers. We then check its effectiveness by applying it to control the heating systems of a student residence located in the north of France.

Improvement of salp swarm algorithm (SSA2) for intelligent fault detection in smart wireless sensor networks

This paper highlighted on a new improvement of the salp swarm algorithm (SSA) that is taken from the behavior of salp fishes. This shows a promising result, however it suffered from some drawbacks that limited its ability. The proposed improvement of SSA is called SSA 2. In this paper the design system where the ZigBee protocol received temperature, humidity and gas from the sensors. The base station collects received data and sends it to platform in order to process these data via SSA2. This system enables the ability to monitor and track any fault that may be occurring. A new term has been developed to make the algorithm more targeted in finding the optimal answer. Moreover, the calculation results and simulation results in Matlab shows great enhancement in the accuracy and precision of the results at very fast speed for convergence compared with the original salp swarm algorithm (SSA).

Communication optimisation of smart agriculture wireless sensor network based on improved ant colony algorithm

Communication optimisation of smart agriculture wireless sensor network based on improved ant colony algorithm

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

Smart Irrigation System Deploying PSoC and Wireless Sensor Network

Traditional agricultural systems require huge amount of power for field watering. This paper deals with designing of a smart irrigation system, that helps farmers water their agricultural fields using innovative technology. There is no need to frequently apply water across entire fields. Instead, they can use the minimum quantities required and target very specific areas. To increase the productivity, the newer technology is more helpful. The important factors of agricultural sectors are temperature, water and fertilizer management. On survey it is found that water management is an important issue, hence the research work is focused on the water and moisture management of soil. In last decades advancements in technology is tremendous. Programmable System-on-chip (PSoC) integrate configurable analog and digital blocks which makes it different from traditional microcontrollers and is a miniaturized device. It combines the architectures of FPGA and ASIC. Deploying the advanced features of PSoC it is proposed to design and develop the smart irrigation system based on PSoC and Wireless Communication Technology. To achieve the desired goal, the data from the soil moisture sensor is processed and the data is calibrated in real units. After that it is displayed on LCD and using wireless technology same is transmitted towards the base station. The smart NRF24L01 transceiver operates on IEEE 802.15.4 Industrial Scientific and Medical (ISM) Band. Moreover, the base station of Wireless Sensor Network is designed to demonstrate collected information in user friendly format.

A Virtual Soil Moisture Sensor for Smart Farming Using Deep Learning

Precision farming technologies refer to a set of cutting-edge tools and strategies implemented to optimize the management of the plantation. Smart meter devices, IoT technologies, and Wireless Sensor Networks are only a few examples of the innovative systems increasingly employed from an Agriculture 4.0 point of view. Recent literature has paid close attention to the role of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in helping farmers and improving soil productivity. In this regard, this paper presents the design of a Wireless Sensor Network based on low-cost, low-power PV-supplied sensor nodes able to acquire data regarding environmental conditions and soil parameters. Among all the implemented sensors, the most critical is the soil moisture sensors because of many issues related to cost, installation, reliability, and calibration. Thus, this paper proposes a deep learning approach based on Long Short-Term Memory (LSTM) networks to provide a virtual soil moisture sensor using only the data acquired by the other transducer installed on the node. Performances estimation of the virtual sensors and an in-depth comparison with other learning-based approaches have been presented in this paper to validate the effectiveness of the proposed soft sensing approach.

Grid-Based Routing Model for Energy Efficient and Secure Data Transmission in WSN for Smart Building Applications

Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.

Implementation of modular MPPT algorithm for energy harvesting embedded and IoT applications

The establishment of the latest IoT systems available today such as smart cities, smart buildings, and smart homes and wireless sensor networks (WSNs) are let the main design restriction on the inadequate supply of battery power. Hence proposing a solar-based photovoltaic (PV) system which is designed DC-DC buck-boost converter with an improved modular maximum power point tracking (MPPT) algorithm. The output voltage depends on the inductor, capacitor values, metal oxide semiconductor field effect transistor (MOSFET) switching frequency, and duty cycle. This paper focuses on the design and simulation of min ripple current/voltage and improved efficiency at PV array output, to store DC power. The stored DC power will be used for smart IoT systems. From the simulation results, the current ripples are observed to be minimized from 0.062 A to 0.02 A maintaining the duty cycle at 61.09 for switching frequencies ranges from 300 kHz to 10 MHz at the input voltage 48 V and the output voltage in buck mode 24 V, boost mode 100 V by maintaining constant 99.7 efficiencies. The improvised approach is compared to various existed techniques. It is noticed that the results are more useful for the self-powered Embedded &amp;amp; Internet of Things systems.

Design and FPGA Implementation of Intelligent Fault Detection in Smart Wireless Sensor Networks

In this paper, both the design and hardware of Fault Detection (FD) in Wireless Sensor Network (WSN) was implemented using FPGA NI myRIO kit, wireless temperature sensors network with small size, low cost, and low power consumption. Work data processing was performed using pattern recognition methods to detect residual generation. LabVIEW software environment was employed for system performance. In this paper. The design of the hardware circuit NI myRIO kit received temperature from the sensors. The examined system showed an ability to monitor and track any fault or fire that may occur; based on the results, if collected data is exceeded predetermined threshold, then the system is responding, a direct connection is using WIFI to process this data by LabVIEW.

New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation

Water quality may be affected by aspects such as pollution from industries, agricultural fertilizers and pesticides, and waste produced by humans. This contamination can affect the produce of the fields irrigated by untreated water. Therefore, it is necessary to add a treatment process in irrigation systems. In this paper, an architecture, communication protocol, and a data analysis algorithm for a wastewater treatment system intended for irrigation are presented. Our system includes a smart group-based wireless sensor network that is able to detect high salinity levels and pollution stains, such as oil spills. When contamination is detected, the water is led into auxiliary canals that perform the biosorption process to treat the water and dump it back into the main canal. Simulations were performed to assess the amount of data stored on the secure digital (SD) card, the consumed bandwidth, and the energy consumption of our proposal. The results show the system has a low bandwidth consumption with a maximum of 2.58 kbps for the setting of two daily data transmissions of the node in the last auxiliary canal. Furthermore, it can sustain the energy consumption in adverse conditions, where the node with the highest energy consumption reaches the lowest energy value of 12,320 mW/h.

Applying a triboelectric nanogenerator by using facial mask for flexible touch sensor

With the development of artificial intelligence, research on the touch sensor has become a popular object. Here, we proposed a novel triboelectric nanogenerator based on the facial mask (FM-TENG) for harvesting mechanical energy, and it can also serve as a flexible touch sensor. A series of triboelectricity experiments show that facial mask has a strong ability to lose electrons. The FM-TENG can generate good performance, as evidenced by peak short-circuit current (Isc), open-circuit voltage (Voc), and power density values of 37.48 μA, 1037 V, and 727.78 μW/cm2, respectively. Commercial electronics, including an electronic calculator, and a total of 150 high-power light-emitting diodes (LEDs), were easily driven by the FM-TENG. Additionally, the facial mask is harmless material for human skin, and thus it is smeared on the skin to form a self-powered flexible touch sensor to distinguish the different states of fingers. As a new-efficient method for developing low-cost bio-mechanical energy harvesters and self-powered touch sensors, we also foresee that an alert touch sensing system represents tremendous progress in the smart wireless sensor networks.

Monitoring of Macadamia Nut Planting Environment Based on Smart Sensor Technology

Smart sensor network technology integrates sensors, embedded computing, modern networks, wireless communication and distributed information processing technologies, and is currently a frontier hot research field in the world. The application of wireless sensor network to the greenhouse environment monitoring system to realize self-organizing network, wireless data transmission and intelligent control is also of great significance to the realization of agricultural modernization. The purpose of this paper is to study the monitoring of macadamia nut planting environment based on smart sensor technology. Since the greenhouse environment monitoring system has very high requirements for power consumption, cost and stability, this article selects ZigBee wireless sensor network technology with low power consumption, low cost, high stability, etc. to build a set of monitoring temperature and humidity Greenhouse wireless smart sensor network model. According to the actual requirements of greenhouse environment monitoring, this article proposes a new type of ZigBee technology-based greenhouse macadamia nut planting environment monitoring system solution, and gives the overall structure of the system. Considering many aspects such as cost and power consumption, this article stores, displays, analyzes and processes the acquired data. The functions of the system meet the design requirements. The system realizes the collection of greenhouse parameters and the data display and processing functions, and realizes the video monitoring of the greenhouse. Experimental studies have shown that in the actual greenhouse environment, due to a large number of crops blocking, it is bound to produce certain obstacles to signal transmission, but because of the large number of nodes, the distance between the node and its parent node will not exceed 25m, which also ensures The data collected by sensor nodes can be safely and error-freely transmitted to the coordinator node in a multi-hop manner.

Statistical Approach for Vibration-Based Damage Localization in Civil Infrastructures Using Smart Sensor Networks

One of the most discussed aspects of vibration-based structural health monitoring (SHM) is how to link identified parameters with structural health conditions. To this aim, several damage indexes have been proposed in the relevant literature based on typical assumptions of the operational modal analysis (OMA), such as stationary excitation and unlimited vibration record. Wireless smart sensor networks based on low-power electronic components are becoming increasingly popular among SHM specialists. However, such solutions are not able to deal with long data series due to energy and computational constraints. The decentralization of processing tasks has been shown to mitigate these issues. Nevertheless, traditional damage indicators might not be suitable for onboard computations. In this paper, a robust damage index is proposed based on a damage sensitive feature computed in a decentralized fashion, suitable for smart wireless sensing solutions. The proposed method is tested on a numerical benchmark and on a real case study, namely the S101 bridge in Austria, a prestressed concrete bridge that has been artificially damaged for research purposes. The results obtained show the potential of the proposed method to monitor the conditions of civil infrastructures.

An integrated circuit to null standby using energy provided by MEMS sensors

The advent of smart measurement systems and innovative wireless sensor networks evinces the necessity to develop novel solutions for conditioning circuits to be used in autonomous or quasi autonomous measurement systems and sensing nodes. The main problem these systems still face is the question of how to supply the nodes in a cost-effective way, considering that, very often, a battery is required, and consequently, the maintenance labor and cost to replace or recharge it may be high. The main target is to increase battery lifetime by decreasing unnecessary energy consumption as much as possible. In this context, several solutions, including energy harvesters, have already been proposed. One of the main solutions is the reduction of power consumption by the measurement device while in standby, which, in most cases, represents a significant amount of the total power dissipation. To this end, authors have already addressed a zero-energy standby solution able to supply the power requested by the measurement equipment only when the appliance is turned on. In this paper, we present an integrated circuit solution suitable to be used with MEMS scale transducers. The validation and the characterisation of the system will be shown to demonstrate the suitability of the proposed method.