Low-energy Sensor Research Articles

A Novel Approach for Mobility-Aware Energy-Efficient Clustering-Based Routing Using EANN With GA Algorithm on WSNs

Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

IoT based Smart Parking System Using Bluetooth

This paper presents an IoT-based smart parking system utilizing Bluetooth technology to efficiently manage parking spaces in urban environments. The system integrates Bluetooth Low Energy (BLE) sensors installed in parking spots with a central server and a mobile application. BLE sensors detect the presence of vehicles and transmit this information to the server, which updates the availability status in real-time. Users can access the availability status via a mobile app, allowing them to locate and reserve parking spaces conveniently. The system improves parking efficiency, reduces congestion, and enhances the overall urban mobility experience. Keywords: 1.Arduino IDE program software. 2.Development board ESP8266. 3.Communication devices. 4.Sensors.

An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors’ lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network’s lifetime: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> ) how to balance energy consumption between mobile sensors using their mobility capabilities; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> ) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper provides pseudo code, mathematical expressions, performance evaluation, and results, which can serve as a practical implementation guide for practitioners.

Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture.

A significant technological transformation has recently occurred in the agriculture sector. Precision agriculture is one among those transformations that largely focus on the acquisition of the sensor data, identifying the insights, and summarizing the information for better decision-making that would enhance the resource usage efficiency, crop yield, and substantial quality of the yield resulting in better profitability, and sustainability of agricultural output. For continuous crop monitoring, the farmlands are connected with various sensors that must be robust in data acquisition and processing. The legibility of such sensors is an exceptionally challenging task, which needs energy-efficient models for handling the lifetime of the sensors. In the current study, the energy-aware software-defined network for precisely selecting the cluster head for communication with the base station and the neighboring low-energy sensors. The cluster head is initially chosen according to energy consumption, data transmission consumption, proximity measures, and latency measures. In the subsequent rounds, the node indexes are updated to select the optimal cluster head. The cluster fitness is assessed in each round to retain the cluster in the subsequent rounds. The network model's performance is assessed against network lifetime, throughput, and network processing latency. The experimental findings presented here show that the model outperforms the alternatives presented in this study.

(Invited) Potential of Silicon Oxide Films for Low-Cost and High-Performance Resistive Switching Devices

This paper reviews our experimental studies on the resistive switching behaviors of sputter-deposited Si oxide films. Theoretical simulations are also demonstrated to elucidate the physical mechanisms of resistive switching behaviors. These suggest the various potential applications for low-energy sensor network systems, neuromorphic computing systems and low-cost commercial electronic systems.

A multi-sensory real-time data transmission method with sustainable and robust 5G energy signals for smart cities

New generation technologies like Fifth Generation (5G) access technology have great potential to provide a sustainable Quality of Service (QoS) for Electric Vehicles (EVs) in smart cities. The environmental features of urban cities mean that signal transmission can be affected across communication networks, with packet delays, packet loss and higher error rates occurring during data exchange. There is a need for sustainable and robust low-energy signals for smart EV communication. The present paper proposes a novel method for continuous, robust video data transmission at low energy via multi-sensory-enabled 5G technology. The transceivers (transmitters and receivers) are designed with multiple low-energy sensors for detecting data availability at the receiver device. By applying a threshold level detection technique, data availability is identified dependent upon energy level and successive data exchange occurs between transceivers. The application of low-energy signal transmission reduces power usage during video data transmission and leads to a sustainable solution for intelligent EV applications. The performance of the proposed multi-sensory technique is tested and verified using various calibrated parameters considering key metrics including collision error, propagation error, sensing error and accuracy. The proposed model significantly improves accuracy, with higher sensing capabilities and lower delays and propagation errors compared to the existing buffer model.

Thermoelectric energy harvesting for wireless onboard rail condition monitoring

ABSTRACT A thermoelectric energy harvesting device is evaluated to power a bearing health monitoring system. Unlike wayside equipment, the new system is an onboard wireless solution utilizing accelerometer and temperature sensors to assess the bearing condition continuously. The harvesting system consists of two thermoelectric generator modules with aluminium heat sinks, a switching boost converter, a battery management circuit, and a lithium rechargeable battery. The performance of the harvester is validated on an AAR class bearing mounted on a laboratory tester, with load and speed simulating common freight routes of up to 896 miles. The energy harvested varies with operating conditions, and data is presented showing the effect of load and speed. Over a realistic route, the net energy harvested is more than double that needed to indefinitely power a Bluetooth Low Energy sensor. The critical design parameters are the ratio of open-circuit voltage to the temperature difference for the thermoelectric module, and the cold start voltage of the boost converter.

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor Contact Tracing in Epidemic Control

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users’ privacy and security.

Dysregulation of Angiopoietin-like-4 Associated with Hyperlipidemia-induced Renal Injury by AMPK/ACC Pathway.

Angiopoietin-like protein 4 (Angptl4) is a glycoprotein that is involved in regulating lipid metabolism, which has been indicated as a link between hypertriglyceridemia and albuminuria in glomerulonephropathy. Deregulated lipid metabolism is increasingly recognized as an important risk factor of glomerulonephropathy. This study aimed to investigate the Angptl4 expression in renal tissue and podocyte under hyperlipidemia conditions and explore the potential molecular mechanisms. The role of Angptl4 in hyperlipidemia-induced glomerular disease and the detailed underlying mechanisms are unclear. This study sought new insights into this issue. We measured Angptl4 levels in the plasma and urine from patients with hyperlipidemia and healthy people. Rats were fed a high fat diet (HFD) to induce dyslipidemia model and the human podocytes were stimulated by palmitic acid as in vivo and in vitro experiments. The podocytes injury and the Angptl4 level in renal tissues were evaluated. Furthermore, the mechanism of Angptl4 on podocytes injury was investigated. The urinary Angptl4 level was gradually upregulated in both patients with hyperlipidaemia and high fat-diet-induced rats. HFD rats showed increased 24 h urinary protein and glomerular tuft area at week 12. The levels of nephrin and WT-1 were down-regulated, but the Angptl4 levels were markedly upregulated on the glomerular of rats on HFD. In the human podocytes, lipid accumulation accompanied by increases of Angptl4, but the expression of nephrin, WT-1, p-AMPKα and p-ACC was decreased after palmitic acid treatment. However, this injury effect was mediated by the aminoimidazole-4-carboxamide-1β-D-ribofuranoside (AICAR), activator of the low energy sensor AMPK/ACC signaling. This study was the first of its kind to show that podocyte damage induced by dyslipidemia could be associated with upregulated Angptl4 and that patients with hyperlipidemia might have relatively high urinary Angptl4 expression. The dysregulation of Angptl4 in the podocytes under hyperlipidemia is possibly carried out through AMPK/ACC signaling pathway.

Bluetooth Low Energy-based Indoor Localization using Artificial Intelligence

Bluetooth is one of the several technologies to cater to indoor localization. It has the lowest power consumption and good accuracy performance. In the world of IoT, data from sensors and software help in giving meaning to physical objects connected to the internet. &#x0D; This paper uses data gathered using Bluetooth Low-Energy sensors in predicting an agent's location in an indoor environment.&#x0D; We propose a Bluetooth-based model that is divided into two parts: a Convolutional Neural Network(CNN) that trains on data transformed into images and ideas from Game Theory that uses the Markov Decision Process(MDP) to determine the exact location of the agent. The data to image transformation uses the Image Generator for Tabular Data (IGTD) algorithm, which considers the Euclidean distances between the access points in creating the images.&#x0D; The results show that the CNN trains well on transformed images and offers a solid approach to determining every beacon used for Bluetooth-based indoor localization. After a beacon is found, MDP finds the optimal policy to locate the access point under which the agent lies.

High-Reliability and Low-Energy Sensor Sharing in Vehicle Platoon Based on Multihop Millimeter-Wave Communication

Sensor sharing is an important ability for vehicle platoon to reduce vehicle costs and improve driving safety. However, sensing data with a large amount of information is difficult to be shared by using traditional communication methods. Applying millimeter-wave communication can improve sensor sharing capabilities of vehicles, but millimeter wave has large propagation loss and is easy to be blocked by obstacles. Therefore, how to apply millimeter-wave communication to improve sensor sharing capabilities in vehicle platoon is still an open issue. To this end, we present a stochastic game approach and propose two distributed algorithms to achieve high-reliability and low-energy sensor sharing. First, we model the multihop millimeter-wave sensor sharing of the vehicle platoon as an optimization problem of reducing energy consumption while ensuring communication reliability, and then based on the characteristics of vehicle communication, we transform the optimization problem into a multiagent stochastic game. Second, by utilizing the characteristic that the immediate cost has nothing to do with the transfer function of the game, we prove that this stochastic game has equilibrium by proving that each subgame has a Nash equilibrium. Third, we propose an equilibrium-guided multiagent distributed (EGMD) algorithm to obtain the optimal action of each vehicle in the platoon, and then based on the analysis of the limitation of the approach, we present a multiagent distributed cooperation (MDC) algorithm to further reduce the system energy consumption. Finally, through simulation experiments, we evaluate a variety of approaches and demonstrate that our proposed methods can significantly reduce energy consumption while ensuring communication reliability.

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.

Reducing GPS Impreciseness by Odometer Sensor Reading to Improve Positioning Accuracy

Positioning accuracy is becoming more and more important as autonomous vehi-cle technology develops. The focus of this paper is on an onboard smart mobile de-vice’s feature to improve positioning accuracy, based on experimentally acquired GPS and odometer sensor data. A simplificative odometer reading based approach is applied instead of using more advanced smart device sensors (e.g., accelerometer, gy-roscope). Numerous driving tests were performed and analyzed to collect sufficient dynamic travel data. Traveled distances between two consecutive positions from GPS data are computed and correlated with a vehicle’s speed profile between the same two positions. To calculate distance more precisely speed values from GPS are corrected with odometer sensor reading. The results revealed an average increase in accuracy of 20%. The developed model can be incorporated with a smart device’s other low-energy sensors. Using the smart device sensors, the developed model can be extended to acquire a more accurate positioning.

An epidemic model to analyze the dynamics of malware propagation in rechargeable wireless sensor network

Wireless sensor network (WSN) comprises thousand sensor nodes, which exchange data from each other. Sensor network faces different types of problem such as energy constraint as well as malware attacks. The malware (virus, malicious codes, Trajan horse, worm, etc.) targets sensor node of WSN straightforwardly owing to constraint of resources. The spreading behavior of the malware is similar to the virus spreading in susceptible population. Therefore, prevention method is needed against malware attack in WSN. In this paper, we proposed an epidemic based model which describes the propagation dynamics of malware and depletion of sensor nodes’ energy due to malware attack. Security issue along with energy depletion of sensor node is addressed. The method of low energy sensor nodes charging is considered. The proposed model with consideration of low energy is SILRD (Susceptible-Infectious–Low Energy–Recovered–Dead) model. The charging method of sensor nodes with low energy is incorporated. To describe the dynamics of malware spreading and effect of charging constructed a set of differential equations. The deterrence technique of malware dissemination in WSN is explained. And the effect of charging on WSN is also discussed. The basic reproduction number of the model is also calculated which is used in the analysis of prominence of malware spreading. The points of equilibrium of the system have obtained for malware-free and endemic. And stability condition of WSN and related theorems are discussed in details. The proposed model has been substantiated by simulation results.

A Bending Passive RFID Tag as a Sensor for High-Temperature Exposure

This paper introduces a prototype of a low-energy high-temperature exposure sensor, which is a temperature-sensitive passive UHF RFID tag that bends forward when exposed to warm air. This “Bending Tag” design is based on a simple dipole antenna fabricated from an electro-textile material. The antenna has a 3D-printed substrate, which is constructed from a commercial Thermo Reactive Filament that gets soft when exposed to 50°C for 30 seconds, causing the tag to bend forward and curve. The sensor tag initially has a read range of more than 6 meters throughout the global UHF RFID frequency band. After bending, there is a significant decrease in the read range (to around 2–3 meters), which is caused by the changed backscattered power of the sensor tag. In an office environment, the backscattered power changes from −36 dBm to −43 dBm. The change in a sensor tag-reference tag system as dP% is approximately 70%. Based on these initial results, our bending tag can be further developed to work as a cost-effective low-energy sensor for monitoring high-temperature exposure.

Using distributed ledger technology to democratize neural network training

Artificial Intelligence has regained research interest, primarily because of big data. Internet expansion, social networks and online sensors led to the generation of an enormous amount of information daily. This unprecedented data availability boosted Machine Learning. A research area that has greatly benefited from this fact is Deep Neural Networks. Nowadays many use cases require huge models with millions of parameters and big data are proven to be essential to their proper training. The scientific community has proposed several methods to generate more accurate models. Usually, these methods need high performance infrastructure, which limits their applicability to large organizations and institutions that have the required funds. Another source of concern is privacy; anyone using the leased processing power of a remote data center, must trust another entity with their data. Unfortunately, in many cases sensitive data were leaked, either for financial exploitation or due to security issues. However, there is a lack of research studies when it comes to open communities of individuals with commodity hardware, who wish to join forces in a way that is non-binding and without the need for a central authority. Our work on LEARNAE attempts to fill this gap, by creating a way of providing training in Artificial Neural Networks, featuring decentralization, data ownership and fault tolerance. This article adds some important pieces to the puzzle: It studies the resilience of LEARNAE when dealing with network disruptions and proposes a novel way of embedding low-energy sensors that reside in the Internet of Things domain, retaining at the same time the established distributed philosophy.

Simultaneous Power Feedback and Maximum Efficiency Point Tracking for Miniaturized RF Wireless Power Transfer Systems.

Near-field interfaces with miniaturized coil systems and low output power levels, such as applied in biomedical sensor systems, can suffer from severe efficiency degradation due to dynamic impedance mismatches, reducing battery life of the power transmitter unit and requiring to increase the level of electromagnetic emission. Moreover, the stability of weakly-coupled power transfer systems is generally limited by transient changes in coil alignment and load power consumption. Hence, a central research question in the domain of wireless power transfer is how to realize an adaptive impedance matching system under the constraints of a simultaneous power feedback to increase the system’s efficiency and stability, while maintaining circuit characteristics such as small size, low power consumption and fast reaction times. This paper presents a novel approach based on a two-stage control loop implemented in the primary-side reader unit, which uses a digital PI controller to maintain the rectifier output voltage for power feedback and an on-top perturb-and-observe controller configuring the setpoint of the voltage controller to maximize efficiency. The paper mathematically analyzes the AC and DC transfer characteristics of a resonant inductive link to design the reactive AC matching network, the digital voltage controller and ultimately the DC-domain impedance matching algorithm. It was found that static reactive L networks result in suitable efficiency levels for coils with sufficiently high quality factor even without adaptive tuning of operational frequency or reactive components. Furthermore, the regulated output voltage of the rectifier is a direct measure of the DC load impedance when using a regular DC/DC converter to supply the load circuits, so that this quantity can be tuned to maximize efficiency. A prototype implementation demonstrates the algorithms in a 40.68 MHz inductive link with load power levels from 10 to 100 mW and tuning time constants of 300 ms, while allowing for a simplified receiver with a footprint smaller than 200 mm2 and a self-consumption below 1 mW. Hence, the presented concepts enable adaptive impedance matching with favorable characteristics for low-energy sensor systems, i.e., minimized footprint, power level and reaction time.

SatProbe: Low-Energy and Fast Indoor/Outdoor Detection via Satellite Existence Sensing

Indoor-outdoor (IO) detection provides very useful hints for a mobile device to perform context-aware services. To that end, GPS presents a viable solution by relating a device's IO status with its positioning performance, which depends on the device's exposure to the open sky. This approach, however, is prohibitively expensive in terms of energy consumption and response time. Recent work has thus been focused on exploiting low-energy sensors such as light, cellular, and magnetic sensors to infer the IO status indirectly, at the cost of reduced adaptability or manual labeling effort. In this article, we propose a new method to address these problems. Our method, called SatProbe, reverts to the GPS approach for its directness and robustness, but avoids its drawback by extracting only the number of visible satellites from the raw GPS data, instead of going through extensive computation to obtain a final position. This metric provides a clear indicator of the IO status, yet can be obtained with great efficiency. Experiments on 79 raw GPS traces with 2595 detection points across a variety of environments show that SatProbe produces a 14.2 percent improvement in detection accuracy, with a 98.8 percent reduction in both energy use and detection time, in comparison with the standard GPS method.

Planning for tourist urban evacuation routes: A framework for improving the data collection and evacuation processes

Tourism is one of the largest growing industries worldwide. As the number of tourists is rapidly increasing, so too are tourist safety concerns. The increasing frequency of natural disasters along with the growth of urban areas makes it even more complex to address the resilience of tourists during such events. This article proposes a framework for collecting information about tourist locations and flows within urban areas and how to use this information for more efficient and safe evacuation routing. We define population behavior models that can be obtained from gathering empirical data and categorize them into three groups. We review the different evacuation scenarios (divided into sudden and predictable scenarios) and the types of information needed in each case. Further, we discuss the complexity of monitoring and forecasting tourists’ movements in the long term and for short-term predictions including the available data sources for doing so. The data gathering and tourist behavior are explained with examples from Kyoto, Japan, a major tourist attraction and a location that is prone to disasters. Finally, technological solutions for better guidance during the evacuation process of the population are discussed, including low-tech ones and advanced options such as websites, apps and Bluetooth Low Energy sensors, where the last one is demonstrated by a navigation experiment in a 3D environment.

Container-Based Virtualization for Bluetooth Low Energy Sensor Devices in Internet of Things Applications

Container-Based Virtualization for Bluetooth Low Energy Sensor Devices in Internet of Things Applications