Smart Sensor Networks Research Articles

Low-Voltage IGZO Field-Effect Ultraviolet Photodiode

Abstract In the era of Internet of Things (IoTs), an energy-efficient ultraviolet (UV) photodetector (PD) is highly desirable considering the massive usage scenarios such as environmental sterilization, fire alarm and corona discharge monitoring. So far, common self-powered UV PDs are mainly based on metal-semiconductor hetero-structures or p–n heterojunctions, where the limited intrinsic built-in electric field restricts further enhancement of the photoresponsivity. In this work, an extremely low-voltage field-effect UV PD is proposed using a gate-drain shorted amorphous IGZO (a-IGZO) thin film transistor (TFT) architecture. A combined investigation of the experimental measurements and technology computer-aided design (TCAD) simulations suggests that the reverse current (I R) of field-effect diode (FED) is highly related with the threshold voltage (V th) of the parental TFT, implying an enhancement-mode TFT is preferable to fabricate the field-effect UV PD with low dark current. Driven by a low bias of −0.1 V, decent UV response has been realized including large UV/visible (R 300/R 550) rejection ratio (1.9 × 103), low dark current (1.15 × 10−12 A) as well as high photo-to-dark current ratio (PDCR, ∼ 103) and responsivity (1.89 A/W). This field-effect photodiode provides a new platform to construct UV PDs with well-balanced photoresponse performance at a low bias, which is attractive for designs of large-scale smart sensor networks with high energy efficiency.

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach.

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.

Distributed set-membership estimation for automated straddle carriers using smart sensors

Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton’s second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses.

Update to ttprocessing: the R-package to handle the TreeTalker monitoring data

Managing forest ecosystems at global scale is a pressing challenge for resource managers and it needs to address important questions on continuous monitoring. Advance in information technology and in the use of multifunctional devices based on Internet of Things (IoT) technology, for real time observation of biological and physical variable of trees is getting more and more common in modern forestry. Among the different smart sensor networks, the Tree Talkers® devices are modular multiparameter devices designed to monitor tree physiological parameters (e.g. sap flux, growth) and surrounding microenvironmental parameters (e.g. temperature, humidity). There is a dedicated R package for handling the data produced by this monitoring system: ttprocessing, that has been recently updated, with several improvements in the data processing pipelines, the implementation of the soil water content equations of the TT-soil monitoring device and the inclusion of some basic outlier detection functionalities. In this Technical note, after a summary of the structure of the ttprocessing R package, we describe the new and update version. The updates include: a refinement of the methodology to compute the sap-flux, code for compatibility TT+ devices of version 2.0 and 3.4, that previously were not covered, code for processing the TT-soil data, a device of the Tree Talker system that monitors soil moisture and temperature. Finally, several utility functions have been added: outlier removal and functions for the technical management of the system, thus increasing its usability and making it a more comprehensive tool.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network.

This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.

IOT IV Bag Monitoring and Termination System

Abstract: This paper introduces an innovative IoT-based Intravenous (IV) Bag Monitoring and Termination System designed to address critical challenges in traditional IV therapy. The proposed system leverages advanced sensors and connectivity to monitor IV bag parameters in real-time, ensuring accurate and timely administration of medical fluids. The system comprises a network of smart sensors integrated into IV bags, capable of continuously measuring fluid volume, infusion rate, and temperature. These parameters are wirelessly transmitted to a centralized control unit, providing healthcare professionals with comprehensive and up-to-the-minute data. The IoT IV Bag Monitoring and Termination System offers an intelligent alert mechanism to notify healthcare providers of any deviations from prescribed values, facilitating proactive intervention and minimizing the risk of complications. This ensures patient safety by preventing over-infusion or under-infusion scenarios. The proposed system not only improves the precision of IV therapy but also streamlines healthcare workflows, reducing the burden on medical staff and enhancing overall patient care.

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.

Ultra-Low Power IoT Solution based on WSSN for Monitoring Infrastructures

In recent years, one of the emerging technologies that have had big impact on the field of research is Wireless Smart Sensors Networks (WSSN) given its diversity of features and applications. The accelerating growth in IoT for data communication, for sensing and monitoring applications (ranging from biomedical to environmental targets) has originated a high demand not only for low-power autonomous devices but also for ultra- low cost combining single chip radio with baseband signal and digital data processing [1]. Target applications, such as Smart Grids, Cyber-Physical Systems, Body Area Networks, Internet of Things (IoT) and high-density environmental sensing networks, will be massively available if it is removed the requirement of explicit human action for energy recharge. A third concern for the deployment of large numbers wireless sensor networks is the cost of each individual remote device.

College English Flipped Classroom Teaching System Based on Smart Sensor Network

This study aims at the autonomy dimension of students in the college English flipped classroom based on the help of a sensor network to investigate and explore the status quo in each stage of the college English flipped classroom, analyse the shortcomings, and propose teaching suggestions. The college English curriculum reform has been in effect for more than 10 years since it started in 2004. Under the foreshadowing of information-assisted teaching, the flipped classroom teaching model also influences the reform of the Chinese traditional teaching model. The research conducted a one-semester flipped classroom teaching of college English in the subjects' classes. The study analyses the data using SPSS software for correlation analysis and finds that the correlation coefficient (r value) between the total score of autonomy and English performance is 0.904, the sig value is <0.05, and the two component values are highly correlated. Student autonomy is not only important in the English flipped classroom but also has a significant impact on students' performance.

Internet of Things Adoption in the Manufacturing Sector: A Conceptual Model from a Multi-Theoretical Perspective

The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its dominance in global markets by using a vast network of smart sensors; nevertheless, IoT technology adoption in the MS remains in the early phase. This research aims to define the antecedents that affect IoT adoption in the MS and propose a conceptual model to explain the adoption intention. Based on an extensive literature review, the proposed model was constructed by three main antecedents: perceived value, perceived benefits, and perceived challenges, and 11 related variables. The model development used a multi-theoretical perspective by integrating three theories: the value-based adoption model, the diffusion of innovation theory, and the technology–organization–environment framework. This study provides decision-makers with valuable insight that promotes IoT adoption in MS and enriches the literature with a new perspective that encourages more studies on IoT adoption in organizations.

Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Smart Sensor Networks for Industrial IoT Applications

Smart Sensor Networks (SSNs) are an indispensable part of the Industrial Internet of Things (IIoT), which seeks to improve efficiency, productivity, and safety in different industrial applications. SSNs consist of a large number of sensors, regularly deployed in a wireless ad-hoc network, which communicates with each other and with other devices, such as gateways and servers. Nevertheless, the building of SSNs in IIoT environments encounters many challenges, such as trust management, data reliability, privacy, and security. These challenges necessitate proposing novel solutions and protocols, to provide a reliable, secure, and efficient SSN. To this end, this study presents a novel DL system that can effectively discriminate between normal traffics and malicious traffic in SSNs. A convolutional feature extractor is developed to learn important discriminative features necessary for the early detection of security threats in SSNs. Then, an improved LSTM (ILSTM) is presented to model the temporal dynamics of the SSNs flows, which helps model long interdependency between traffic samples. A focal loss function is applied to deal with the imbalance between class samples. Experimental analysis is performed on an open-source SSN security dataset, named WSN-DS, the findings demonstrated the competitive advantages of our system over the prevailing solutions.

Design of Urban Sculpture Artwork Pattern on Account of Smart Sensor Network from the Artistic Perspective

<p>As a combination of the three elements of modern information technology, smart sensor network is a brand-new information collection and management technology. The smart sensor network is composed of a large number of smart sensor nodes with low energy consumption. In order to improve the performance of pattern design, this paper proposes an urban sculpture artwork pattern design framework based on intelligent sensor network. It uses intelligent sensor network technology to build design models and design patterns for urban sculptures. Based on the introduction of intelligent sensor network technology, various computer software and pattern texture calculation methods are used to process the urban sculpture pattern in the design process, and the construction of the urban sculpture pattern model is completed through the construction process of the urban sculpture pattern intelligent sensor network mode. Experimental results show that the average design time of urban sculpture patterns based on smart sensor network technology is 0.42 s, the average resolution is 7200 pixels, and the average dot pitch is 0.27mm. Compared with traditional 3D technology pattern design methods, the design performance of urban sculpture patterns based on intelligent sensor network technology is better, and it meets the design requirements of urban sculpture patterns.</p> <p> </p>

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

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).

WSN-Based Smart Landslide Monitoring Device

In this paper, we present a smart monitoring wireless sensor networking node device, referred to as ‘SMARTMODE’. The indigenously developed SMARTMODE is energy-efficient, context-adaptive, reliable, and accurate. We have custom-designed it to monitor landslide movement and causative parameters and to ensure the system’s longevity, reliability and robustness in harsh environmental conditions. When in sleep mode, the SMARTMODE is designed in such a way that it consumes less power (i.e., 0.034 mA at 4.2 V) while is still able to detect minor slope variations. This is achieved by enabling low-power interrupt-based sensing in the SMARTMODE. The SMARTMODE is also capable of automatically varying its data acquisition frequency based on the severity of the environmental context, thereby making it context-adaptive. Furthermore, we designed the SMARTMODE in such a way that it can detect and filter faults such as noise and outliers in the acquired sensor’s data. Additionally, to enhance the reliability, SMARTMODE comprises of energy harvesting and backup approach to deal with power outage issues. This approach enables automatic recharging of SMARTMODE’s battery even during nighttime to ensure reliable and continuous landslide monitoring. Finally, we evaluated the performance of the proposed SMARTMODE by deploying it in an actual landslide location.

Keynote lecture. Debris-flow detection for early warning purposes: Recent advances, open problems, and future challenges

The mitigation of risk caused by debris flows is increasingly pursued by means of non-structural measures, including early warning systems (EWSs). Nowadays, EWSs are becoming attractive thanks to their flexibility and due to the new paradigm of smart sensor networks, proposed as a tool to monitor and gather intelligence from the surrounding environment. Also, an increasing number of extreme meteorological events is expected due to climatic changes, resulting in a consequent growing risk in areas considered safe so far. Although the technological development of detection systems based on low-cost sensor networks has recently spurred a great deal of interest, very few success stories exist of EWSs operational for long periods and trusted by local authorities. In this work, I present an overview on the recent advances, open problems, and future challenges in the field of detection of debris flows for early warning purposes, with a special attention to the European Alps. I discuss (i) the uncertainties related to the use of rainfall thresholds and their possible improvement based on field observations in the source areas, (ii) the new opportunities that seismo-acoustic sensors open in terms of warning performances and lead time, (iii) the problematic interaction of EWSs with structural mitigation measures, and (iv) the old but still actual problem of responsibility in issuing an alarm. Finally, I debate the “information paradox” that can contribute limiting the adoption of EWSs in future and the possible benefits of communication and dissemination.

Retracted: The Impact of Smart Sensor Networks on Improving the International Competitiveness of China’s Digital Economy Industry in the Context of the Smart Era

Retracted: The Impact of Smart Sensor Networks on Improving the International Competitiveness of China’s Digital Economy Industry in the Context of the Smart Era