Multi-channel Sensor Networks Research Articles

The application of a multi-channel sensor network to decompose the local and background sources and quantify their contributions

The construction site is one of the typical sources of anthropogenic dust and profoundly responsible for generating a high concentration of particles in the surrounding air. To assess the detailed attribution of construction dust to the environment, it is necessary to distinguish the emissions and underlying pollution levels from the measurements. This study demonstrated the feasibility of using multi-channel particulate matter (PM) sensors to measure the concentration of PM in different sizes at a near-source site in real time. A total of five sensors were placed at different locations throughout the construction site for the intensive air quality monitoring from April 12 to April 26, 2021, during the extensive construction activities. Meanwhile, iterative discrete wavelet transform (IDWT) was used to decompose pollutant time series into different components based on low/high frequency filtering. Overall, baseline component was closely related to regional background concentration, showing mild diurnal variation, while the emission component showed a significant increase during working hours from 8:00 to 18:00, especially for coarse particles. The baseline and long-period emission components were mainly from the rapid accumulation of fine particles, but when the particle size was larger than 2.5 μm, the short-period emissions still increased rapidly. The correlation matrix clustering supports the conclusion that the different components were mainly determined by three potential sources. Our results may help to understand the profiles of different-sized PM in construction sites and identify potential source attributions.

Real-time fire detection system based on dynamic time warping of multichannel sensor networks

Fires are considered one of the most devastating accidents that could occur within a building. However, existing fire detection systems result in late fire detection or produce a high false alarm rate due to the complex patterns and temporal dynamics found in sensor signals that come from the various types of fires. This paper develops a novel fire detection algorithm that is based on multiple sensors in different locations to provide reliable real-time fire monitoring. The proposed fire detection algorithm takes into account both current and past sensor measurements and evaluates the similarity of sensor signals based on a dynamic time warping distance measure. Moreover, the proposed algorithm adaptively selects the sensors that are critical in detecting a fire in an early stage based on the developed k-out-of-P fire voting rule, which effectively combines decision rules from P multichannel sensor signals. A case study was performed using a real-life fire dataset obtained from the National Institute of Standards and Technology to demonstrate the effectiveness of the proposed approach.

Detection and Separation of Eeg Artifacts Using Wavelet Transform

Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

Fully Automated Multi-Resolution Channels and Multithreaded Spectrum Allocation Protocol for IoT Based Sensor Nets

Internet-of-Things (IoT)-based sensor networks have gained unprecedented popularity in recent years and they become crucial for supporting high data rate real-time applications. For efficient data transmission within IoT networks, it is necessary that each IoT node learns and adapts itself to recent time/spectral characteristics of channels to maximize the throughput and perform channel swapping wherever required. Many researchers have proposed channel allocation and channel quality measurement protocols within multichannel sensor networks. However, to the best of our knowledge, there is no literature available that proposes an automated and adaptive protocol that can learn and adapt according to changing channel characteristics in IoT network for achieving maximum data transmission and throughput. Therefore, this paper proposes a fully automated self-learning and adaptive protocol which can automatically transmit multiuser data by efficiently utilizing channel time/spectral characteristics. The proposed protocol is unique as it learns and adapts itself to the increasing network density based upon the network metrics. It also allows each node within IoT network to automatically detect the neighboring channel attributes so that they can swap channels to achieve maximum data transfer. This is accomplished by continuously extracting distinct features from the network topology. After extracting these features, the proposed protocol efficiently selects the best channel for an incoming node, provides the best channel utilization based upon its time/spectral attributes, and detects and allocates the unused spectrum of neighboring channels through multistage Gaussian radial basis function and multilayer perceptron-based nonlinear support vector machines classification model. Simulation results demonstrate the supremacy of the proposed protocol in terms of throughput, successful reporting probability, average blocking probability, fairness, and classification accuracy.

Multi-channel random decrement smart sensing system for concrete bridge girders damage location identification

Health monitoring of infrastructures is one of the essential modern services for civil engineering structures. A design of a Structural Health Monitoring (SHM) system that allows a continuous assessment of the structure’s integrity is required to increase safety, and to optimize operational and maintenance costs. Nowadays, non-destructive techniques are common to predict the damage as it monitors the structure during its service lifetime. The advances of using Fiber Bragg Grating (FBG) array sensors for continuous monitoring has been proven to be far superior and more accurate than strain gauges and accelerometers due to the great reduction in noise signals. The Random Decrement (RD) technique is one of the output measurement diagnostic techniques used for identifying the dynamic properties of bridge girders. The technique to be applied on multi-channels for enhancing the efficiency of data aggregation and integrity identification using fiber optic multi-channel sensor networks system known as Multi-Channel Random Decrement (MCRD) for concrete bridge monitoring. The significance of RD and MCRD approaches in SHM is that it determines the bridge’s dynamic properties without the prior knowledge of the magnitude of excitation forces; so the identification system of the bridge is based on random bridge actual traffic loading response through the installed FBG sensing system to record the dynamic response due to the applied traffic loading.The concept has been used before for steel and metal structure. However, the concrete material is a nonlinear elasto-plastic damaged material with unique qualities of cracking, tension softening, shear degradation and tension stiffening effects. The emphasis for the steel beam monitoring is focused on the change in the natural frequency; however, the emphasis for the concrete beam monitoring is focused on the change in the Random Decrement Damping (RDD) between the intact and damaged case. The change in RDD is more evident for concrete beams due the rapid change in stiffness due to cracking and the propagation of cracking due to loading. The focus in this work is to assess the applicability of using MCRD with FBG arrays for monitoring reinforced concrete (RC) girders based on experimental testing. For validating the feasibility of the proposed approach, a laboratory investigation has been performed on a main and secondary girder to identify the suitability of using FBG sensors on RC girders to detect the damage and locate it. The results of the investigation showed that MCRD using FBG sensors has the potential to detect the damage location that could be implemented effectively in bridge monitoring strategies.

다중 채널 무선 센서 네트워크에서 전송 거리 기반 채널 할당

In this paper, we propose a transmission scheme to reduce energy consumption on wireless multi-channel sensor networks. This proposed scheme differentiates the number of usable channels based on a priority. Sensor nodes consume the most energy to transmit data. Also, as transmission distance is far, they consume the more energy. Therefore retransmission due to transmission failure of sensor nodes that are long transmission distance is required more energy consumption. In this paper, we provide a stable transmission environment by allocating a high priority for data that is sent far away. The received data with a high priority is more allocated the number of usable channels. In the experiment results, the receiving failure probability and the restransmission energy consumption of proposed scheme is superior to ones of comparison scheme.

A Design of a Congestion Control Scheme Based on Multichannel in Wireless Sensor Networks

We propose a congestion control scheme on wireless multichannel sensor networks. The proposed congestion control scheme consists of a congestion detection strategy and two cases of congestion control strategies. The congestion detection strategy modeled in the Morkov chain provides two thresholds that can detect two congestion conditions, or light and heavy congestion conditions. The two congestion control strategies consist of a weak congestion control strategy that can control light congestion and a strong congestion control strategy that can control heavy congestion. In addition, the proposed congestion control scheme assigns a priority to a received packet according to their transmission distance, and it differentiates the number of useable channels when congestion occurs. We can reduce energy consumption due to retransmission because packets received from long distance can use more channels in the proposed scheme.

Radio and Medium Access Contention Aware Routing for Lifetime Maximization in Multichannel Sensor Networks

In this paper, we develop cross-layer techniques suitable for wireless sensor networks (WSNs) that are capable of multichannel access. More specifically, we propose energy and cross-layer aware routing schemes for multichannel access WSNs that account for radio, MAC contention, and network constraints. By doing so, we guarantee to meet data rate requirements of end-to-end flows while maximizing the network lifetime. When MAC contention constraints associated with the shared wireless medium are not included in routing formulations, routing solutions may not be feasible, in that the shared medium may not be able to support the required data rates of these flows. In this paper, we first derive three sets of sufficient conditions that ensure feasibility of data rates in multichannel access WSNs. Then, utilizing these sets, we devise three different MAC-aware routing optimization schemes, each aiming to maximize the network lifetime. Finally, we perform extensive simulation studies to evaluate and compare the performance of the proposed routing approaches under various network conditions.