Sensor Transmission Research Articles

Transmission power allocation for remote estimation with multi-packet reception capabilities

In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e.g. with the latest wireless technologies such as 5G and WiFi. To this end we introduce a general model where packet arrival probabilities are determined based on the received Signal-to-Interference-and-Noise Ratio and with two different receivers design schemes, one implementing standard multi-packet reception technique and one implementing Successive Interference Cancellation decoding algorithm in addition. Then we cast the power allocation problem as an optimization task where the mean error covariance at the remote estimator is minimized, while penalizing the mean transmission power consumption. For the infinite-horizon problem we show the existence of a stationary optimal policy, while for the finite-horizon case we derive some structural properties under the special scenario where the overall system to be estimated can be seen as a set of independent subsystems. Numerical simulations illustrate the improvement given by the proposed receivers, especially when SIC is used, over orthogonal schemes that schedule only one sensor transmission at a time in order to avoid interference.

Nodes clustering and multi-hop routing protocol optimization using hybrid chimp optimization and hunger games search algorithms for sustainable energy efficient underwater wireless sensor networks

Clustering and routing processes in underwater wireless sensor networks (UWSNs) are challenging tasks in the underwater environment due to the multiplicity of sensor nodes, transmission bandwidth, and limited energy resources. In order to address the shortcomings mentioned above, this paper proposes a novel hybrid Chimp Optimization and Hunger Games Search (ChOA-HGS) algorithms for clustering and multi-hop routing optimization in UWSNs. In this approach, first, the ChOA is used to choose cluster heads and efficiently structure clusters. Then, the HGS-based routing procedure is used to determine the network’s best pathways. The proposed approach combines the advantages of clustering and routing, resulting in optimal network lifetime and energy efficiency. The proposed ChOA-HGS is validated using a variety of measures after it is simulated using three different scenarios. In order to evaluate the performance of the ChOA-HGS, results are compared to PSO, MPSO, IPSO-GWO, TEEN, and LEACH. The results show that the ChOA-HGS outperformed other benchmarks in terms of lifetime and energy consumption.

Te4+/Bi3+ Co-Doped Double Perovskites with Tunable Dual-Emission for Contactless Light Sensor, Encrypted Information Transmission and White Light Emitting Diodes

• The energy transfer between Bi 3+ and Te 4+ is realized in Cs 2 ZrCl 6 perovskite. • Bi 3+ /Te 4+ co-doped perovskite can realize color hue evolution from blue to yellow. • High quality WLEDs can be fabricated by Cs 2 ZrCl 6 : Bi 3+ , Te 4+ and UV LED chip. • The phosphor can be exploited in visual light sensor and information transmission. Stable and high-efficiency inorganic lead-free double perovskites have aroused great attention for their application in photoluminescence field. However, most of the double perovskite materials with single dopant cannot realize multi-mode excitation, tunable or full-spectrum emission, which lead to the rare applications in contactless light sensor, encrypted information transmission and white light emitting diodes. Herein, inspired by a traditional methodology of co-doping rare-earth, ns 2 type ions (Bi 3+ and Te 4+ ) co-doped lead-free perovskite Cs 2 ZrCl 6 is proposed and developed. Comparing to the single dopant, Bi 3+ /Te 4+ co-doped perovskites can exhibit high sensitivity to the excitation wavelength and then emit greatly adjustable emission spectra from ∼457 to 577 nm, which corresponds to the color hue evolution from blue to yellow. Meanwhile, the high resonance energy transfer efficiency process from triplet STEs state of Bi 3+ to Te 4+ is also proved by employing the excitation/emission spectra and the transient fluorescence spectrum. Based on the efficient energy transfer process, high quality white light (CIE = (0.330, 0.347), CCT = 5608 K, Ra = 85.1), covering the entire visible region, can be obtained under ultraviolet light excitation. These features guarantee its potential application in contactless light sensor, encrypted information transmission and single-phase white light emitting diodes. The above results show that the methodology of co-doping ns 2 type ions in Cs 2 ZrCl 6 will open up new opportunities to the multifunctional application field.

Sistem Monitoring Tekanan Pada Pipa Air Menggunakan Arduino Uno Pada Jaringan Lora 920-923 Mhz

In everyday life water is one of the components that is very important and becomes a basic need for human life and other living things. Of course, when flowing water to all corners there are obstacles such as leaks in water pipes that can be caused by air pressure. These leaks can cause the flowing water to get dirty or even not flow all the way to the destination. Therefore, this study will be created a pressure monitoring system on water pipes that will use pressure transmitter sensors that serve to read the pressure on water pipes using LoRaWAN data communication, this communication is used because the device can communicate up to a distance of 5 to 15 kilometers. To connect or send data from the sensor to LoRa requires an Arduino uno that will process the data from the sensor to be used. To store data using the Antares platform. This research is useful for monitoring pressure on water pipes and minimizing leaks in pipes. By using the method of calculating linear regression equations obtained a very accurate value for water pressure readings on pressure sensors that are 98.9%, while the results of testing the values received signal strength indicator (RSSI), signal noise radio (SNR) and packet loss are interconnected, in the value of RSSI and SNR the smaller the packet loss the more data is lost. The use of Spreading Factor values can also be taken into account if you want a longer range with a small packet loss result.

A Sensor-aware Offloading Model for IoT Edge Computing

In IoT sensor network environment, offloading is an important factor that affects all design objectives. Since massive amounts of data are collected every second to the gateway and so immediate processing is difficult, offloading is critical to quickly eliminate worthless data in advance. Similar sensor data are continuously generated except in abnormal situations such as sudden changes and failure events. Therefore, the amount of data processing and frequency of data transmission can be greatly reduced by classifying, filtering, and compressing the data. In addition, more meaningful IoT context can be analyzed by combining multiple sensor data, since the sensor values generated by each sensor has its own characteristics. The previous offloading techniques mainly focused on minimizing latency without using data context and data resizing. Therefore, a new filtering technique is required to enhance the offloading efficiency through precision control using sensor context patterns. This paper proposes a new sensor-aware context offloading model called SCOM to support efficient data filtering services for the edge-based IoT environment. The architecture of SCOM consists of three layers of sensor context, pattern context and transmission context. SCOM exploits context-aware stream pattern matching using general string matching based on slide window for sensor stream offloading. Experiments show that the performance gain of SCOM reaches to 14.8% with respect to the operation throughput. Since proposed data layering and pattern-based offloading scheme can improve the sensor data filtering performance in edge gateways, it can be used for IoT sensor monitoring applications.

Transmission scheduling for privacy-optimal encryption against eavesdropping attacks on remote state estimation

In remote state estimation systems, the state being estimated may be wiretapped by eavesdropping communications from the sensor to the remote estimator. A straightforward remedy for protecting the state privacy is to encrypt all the sensory data before transmission, which however may not be affordable for an energy-constrained sensor. In this paper, we address the optimal sensor transmission scheduling problem in order to protect the state privacy in an energy-efficient manner. The sensor can choose either to transmit data in plaintext or encrypted text, or to stay in silence without transmitting. We formulate the problem as the one of maximizing the eavesdropper’s estimation error of the system state, while guaranteeing a certain level of the remote estimation performance under a given energy budget for the sensor in the finite time horizon. Then, we transform the problem into an optimal scheduling problem under any fixed transmission times and an energy allocation problem, and propose algorithms to solve each of them. We theoretically prove that, provided that a certain level of remote estimation performance is guaranteed, the optimal sensor schedule is to group instants of encrypted transmissions and silent states as tighter as possible and the optimal energy allocation tends to arrange more energy to transmit encrypted text. Furthermore, we extend our results over finite time horizon to infinite periodic time horizon. We construct a set of monotonic sequences to approximate the estimator’s performance constraint. Then, we design an algorithm to obtain the suboptimal schedule Finally, numerical results are presented to demonstrate the performance of the proposed methods.

Optimal power allocation for harvesting based wireless control systems: To listen or not to listen to channel information

This paper studies a wireless sensor system, where packeted information is transmitted to a receiver over a fading channel. The sensor's transmission energy can be chosen within a given interval. To obtain the current channel gain information, the sensor may listen to a pilot signal from the receiver and estimate the channel, which requires energy. When the sensor does not listen to the pilot signal, the transmission energy has to be estimated solely based on other information like the channel gain distribution and past channel information. The sensor is equipped with an energy harvester and a rechargeable battery to temporarily store the harvested energy. The paper derives the optimal energy allocation policy, shows structural properties of the optimal solution and derives a heuristic suboptimal energy allocation policy. The performance of the optimal policy is compared with several other simple policies through numerical examples.

Radix-Partition-Based Over-the-Air Aggregation and Low-Complexity State Estimation for IoT Systems Over Wireless Fading Channels

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines.

Real-time water quality monitoring for distribution networks in IoT environment

Water quality has always been a significant concern worldwide as a large portion of accessible water is either contaminated or polluted, which can spread serious diseases like dysentery, diarrhoea and cholera. Before consumption, the water quality should be tested to reduce the risk of infection. In real-time applications, the traditional approach for water quality monitoring is not appropriate, as on-site water sample collection is often a cost-intensive and time-consuming process. This paper introduces a real-time assessment of water quality parameters in distribution systems employing Raspberry Pi and Arduino development boards. The parameters were chosen based on the different categories identified by the Central Pollution and Control Board, Government of India. An Arduino development board was used at the sensing node for water quality sensor interfacing, data acquisition, and transmission to the wireless sensor network via Zigbee. Raspberry Pi was used at the server to collect data and upload data on the cloud platform. The 'Thingspeak' cloud platform was used for IoT implementation. The results were validated with the reference instrument.

SnO2 nanomaterial coating micro-fiber interferometer for ammonia concentration measurement

This paper provides a quick and simple method for detecting ammonia gas concentration by coating microfiber interferometer (MFI) with SnO2 sensitive material. The MFI sensor is fabricated by the method of fiber fusion and taper. Then the nanomaterial sensitive to ammonia is prepared by hydrothermal method and coated in the sensing area. When the sensor is in contact with ammonia the SnO2 nanomaterial can adsorb ammonia molecules, which changes the refractive index of the MFI surface and causes the sensor transmission spectrum to shift. The real-time sensing of ammonia concentration can be realized by judging the magnitude of spectral shift. The sensor unit has only optical signal, simple structure, low cost and high sensitivity. The sensor can effectively detect the concentration of ammonia within the range of 0 ppm-10,476 ppm, and the sensor has a sensitivity of 0.58 pm/ppm. The experiment show that SnO2 nanomaterial have high sensitivity to ammonia gas, and the interference sensor coated with SnO2 nanomaterial has good repeatability and selectivity, so it has a broad application prospect in gas sensing in the future.

Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels

The convergence performance of wireless networked iterative learning control systems is affected by additive noises, random delays, and data dropouts in both sensor‐to‐controller and controller‐to‐actuator channels. In order to guarantee the convergence performance of such systems, an input filter is designed in front of actuators to estimate the controller updated input under the effect of those uncertainties. Specifically, a P‐type learning controller is considered firstly, and then a model is developed to describe the transmission of sensor measured output data and controller updated input data under the mixed effect of those uncertainties. On the basis of state augmentation, the two developed data transmission processes are further combined with the controller learning process to build a unified filtering model. According to this filtering model and the projection theory, an optimal filter in linear minimum variance sense is designed at the actuator side to estimate the controller updated input in iteration domain. The convergence performance of the norm of filtering error covariance matrix is proved theoretically, which means driven by this accurately estimated input, the convergence performance of networked systems adopting the P‐type learning controller is guaranteed. Finally, some numerical results are given to illustrate the effectiveness of the proposed method.

Deep Learning on Synthesized Sensor Characteristics and Transmission Spectra Enabling MEMS-Based Spectroscopic Gas Analysis beyond the Fourier Transform Limit

Miniaturized Fourier transform infrared spectrometers serve emerging market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than bench-top instrumentation, especially for the spectral resolution. However, higher spectral resolution is needed for better identification of the composition of materials. This article presents a convolutional neural network (CNN) for 3X resolution enhancement of the measured infrared gas spectra using a Fourier transform infrared (FTIR) spectrometer beyond the transform limit. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network is trained using synthetic transmission spectra of complex gas mixtures and simulated sensor non-idealities such as baseline drifts and non-uniform signal-to-noise ratio. Ten gases that are relevant to the natural and bio gas industry are considered whose mixtures suffer from overlapped features in the mid-infrared spectral range of 2000–4000 cm−1. The network results are presented for both synthetic and experimentally measured spectra using both bench-top and miniaturized MEMS spectrometers, improving the resolution from 60 cm−1 to 20 cm−1 with a mean square error down to 2.4×10−3 in the transmission spectra. The technique supports selective spectral analysis based on miniaturized MEMS spectrometers.

Designing Arduino-Based Sluice Control System With Self-Check Sensor Feature

The use of Arduino-Based floodgates aims to address the issue of flooding. The goal of this research was to create a system that could monitor water levels and control floodgatesUltrasonic sensor was used to measure water level, as the water level becomes the indicator to open and close the floodgates. The ultrasonic sensor is mounted on the dam and measures the distance between the water’s surface and the sensor transmitter. The results of the measurement of the water level are used to control the floodgates. Sensor measurement results are not always valid, given the age of the sensor and the terrain around the dam is quite challenging. The self-check sensor feature is introduced in this study as a way to overcome detection faults in the system, where ultrasonic sensors can perform self-monitoring by relying solely on their neighbours. In general, the process is carried out in four stages, starting from the stage of reading the sensor, the stage transmitting and receiving data from the Arduino, the stage of detecting sensor’s value, the stage of displaying the sensor values. At the stage of displaying the sensor value, there are four water level conditions, normal, waspada, siaga and bahaya. According to the test results, incorporating self-check sensors into the system enables for more efficient Arduino-based sluice control systems, as well as the possibility of detecting malfunctions caused by sensor damage

Transmission scheduling for multi-process multi-sensor remote estimation via approximate dynamic programming

In this paper, we consider a remote estimation problem where multiple dynamical systems are observed by smart sensors, which transmit their local estimates to a remote estimator over channels prone to packet losses. Unlike previous works, we allow multiple sensors to transmit simultaneously even though they can cause interference, thanks to the multi-packet reception capability at the remote estimator. In this setting, the remote estimator can decode multiple sensor transmissions (successful packet arrivals) as long as their signal-to-interference-and-noise ratios (SINR) are above a certain threshold. In this setting, we address the problem of optimal sensor transmission scheduling by minimizing a finite horizon discounted expected estimation error covariance cost across all systems at the remote estimator, subject to an average transmission cost. While this problem can be posed as a stochastic control problem, the optimal solution requires solving a Bellman equation for a dynamic programming (DP) problem, the complexity of which scales exponentially with the number of systems being measured and their state dimensions. In this paper, we resort to a novel Least Squares Temporal Difference (LSTD) Approximate Dynamic Programming (ADP) based approach to approximating the value function. More specifically, an off-policy based LSTD approach, named in short Enhanced-Exploration Greedy LSTD (EG-LSTD), is proposed. We discuss the convergence analysis of the EG-LSTD algorithm and its implementation. A Python based program is developed to implement and analyse the different aspects of the proposed method. Simulation examples are presented to support the results of the proposed approach both for the exact DP and ADP cases.

High-sensitivity biosensor for simultaneous detection of cancer and diabetes using photonic crystal microstructure

In this study, we propose a refractive index-based sensor to detect skin cancer (Basal, HeLa, MDA-MB-231) through human blood. It also can detect diabetes through human tear fluid based on photonic crystals (PhC) at the same time. The proposed PhC composed of silicon rods in the air bed arranged in a hexagonal lattice, forms the fundamental structure, and two tubes are used to place the cancerous or diabetic samples for measurement. The sensor's transmission characteristics are simulated and analyzed by solving Maxwell's electromagnetic equations using the finite-difference time-domain method for samples under study. The diagnosis of three types of cancer and diabetes is based on changing the samples' refractive index by applying the laser source centered at 1550 nm. Our results demonstrate that the proposed structure's quality factor and sensitivity can be adjusted by changing the sensor's geometry. They reveal that the transmission power is between 91–100%, depending on the sample. The sensitivity range is also between 1294 and 3080 nm/RIU. The maximum figure of merit is about 1550.11 RIU−1 with a detection range of 31 × 10–6 RIU. The small biosensor area of 61.56 µm2 makes it suitable for various applications in compact photonic integrated circuits.

Closed-Loop Position Control of a Self-Sensing 3-DoF Origami Module With Pneumatic Actuators

This letter presents closed-loop position control of a pneumatically actuated modular robotic platform “pneumagami” that can be stacked to enlarge work and design space for wearable applications. The module is a 3 degrees of freedom (DoF) parallel robot with two rotational and one translational motion, which is actuated by three antagonistic pneumatic pouch motor pairs attached to three leg joints. To control the pouch motors, we utilize miniature proportional valves. As for the sensing, we introduce a novel embedded resistive sensor mechanism utilizing rotary-to-translational transmission. The sensor's transmission is modeled and verified by experiments. Furthermore, we study analytic forward and inverse kinematic models of the pneumagami module. Utilizing the models, we design a closed-loop feedback controller to track two different trajectories. The experimental results show that the module follows the desired trajectories successfully. Thus, we report that the proposed pneumagami modules can be utilized for achieving a controllable robotic third arm with higher DoFs and range of motion (RoM) when connected in series.

Quasi-distributed fiber-optic in-situ monitoring technology for large-scale measurement of soil water content and its application

Accurate acquisition of in-situ soil water content is of great significance for mastering the water content distribution of unsaturated soil and understanding soil engineering properties. The actively heated fiber-optic (AHFO) method has recently become a research focus because of the capability to realize distributed and real-time measurement of soil water content. However, most reported AHFO systems have been coupled to distributed temperature sensing (DTS) which does not allow for remote measurements with high spatial resolution. In this paper, a new AHFO method based on the quasi-distributed fiber Bragg grating (AHFO-FBG) technique is proposed for the large-scale in-situ measurement of soil water content. An AHFO-FBG in-situ monitoring system is developed which consists of a sensor module, heating module, data acquisition and transmission module, and data processing module. The established system is applied to a loess slope in Yan'an City, Shaanxi Province, China to monitor its water content distribution in real time. The effectiveness of the monitoring system is verified and the water content distribution of the monitored loess slope are analysed. The in-situ measurement results indicate that in the spatial dimension, the water content in the top and bottom of the slope is low, and that near the scarp is high; in the time dimension, the water content in winter is low while that in autumn and spring is high. The monitored results also suggest that soil water evaporation in winter can significantly enhance the spatial distribution difference of soil water content.

基于环形电容检测传感器的实时漏播监测播种机

In order to improve the quality of corn sowing and fertilizer utilization, reduce labor costs, this paper aimed at the traditional tillage fertilizer machine to improve. The seeding and fertilizing machine with adjustable flow is designed by integrating particle fertilizer sensor and CAN bus transmission technology. It is mainly composed of seed and fertilizer discharging mechanism, ditch and soil covering mechanism, and leakage seeding and blockage monitoring system. Fertilizer drop signal can be obtained in real time by detecting the flow sensor outside the fertilizer discharge pipe. The speed of the stepper motor can be adjusted after signal processing, which can also realize the alarm of missing sowing and blocking light, and realize the precise variable sowing. The field experiment results show that the maximum relative error of seed leakage rate monitoring is 3.75%, and the alarm accuracy of fertilizer clogging is high. This machine can reduce the intensity of manual operation, and the quality of operation can be effectively monitored and controlled, can effectively reduce the production cost, has better practicability and economy.

An analytical study of (Smart Tennis Sensor) technical data and its relationship to the serving accuracy of wheelchair tennis players.

The study aimed to describe and analyze the technical data (Smart Tennis Sensor) for transmission accuracy in wheelchair tennis players and to identify the relationship between the technology data (Smart Tennis Sensor) and transmission accuracy in wheelchair tennis players. The researchers used the descriptive method of analysis and associative relationships . The research sample was selected from players with a local classification registered with the Wheelchair Tennis Association for the year (2020) in the intentional manner of (8 players) representing (25%) of the research community with a weight (63.8+1.26) kg, a height (171.2+1.09) meters, and an age (29.7+1.39) years. The researchers used the statistical bag (SPSS) to obtain the results of the research through: arithmetic mean, standard deviation, percentage law, torsion coefficient, and simple correlation coefficient. The results of the research showed a correlation between Smart Tennis Sensor data and transmission accuracy in wheelchair tennis players .The researchers recommended the adoption of this technique during training for wheelchair tennis players as this information gives instant feedback to the variables of each stroke and thus guide the player to the best performance

Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization

Accurate prediction of traffic status in real time is critical for advanced traffic management and travel navigation guidance. There are many attempts to predict short-term traffic flows using various deep learning algorithms. Most existing prediction models are only tested on spatiotemporal data assuming no missing data entries. However, this ideal situation rarely exists in real world due to sensor or network transmission failure. Missing data is a nonnegligible problem. Previous studies either remove time series with missing entries or impute missing data before building prediction models. The former may cause insufficient data for model training, while the latter adds extra computational burden and the imputation accuracy has direct impacts on the prediction performance. In this study, we propose an online framework that can make spatiotemporal predictions based on raw incomplete data and impute possible missing values at the same time. We design a novel spatial and temporal regularized matrix factorization model, namely LSTM-GL-ReMF, as the key component of the framework. The Long Short-term Memory (LSTM) model is chosen as the temporal regularizer to capture temporal dependency in time series data and the Graph Laplacian (GL) serves as the spatial regularizer to utilize spatial correlations among network sensors to enhance prediction and imputation performance. The proposed framework integrating with the LSTM-GL-ReMF model are tested and compared with other state-of-the-art matrix factorization models and deep learning models on three uni-variate and multi-variate spatiotemporal traffic datasets. The experimental results show our approach has a robust and accurate performance in terms of prediction and imputation accuracy under various data missing scenarios.