Large Sensor Research Articles

Distributed Intra and Inter Cluster Chaining Framework for Energy-Efficient, Delay Bounded and Scalable Data Gathering Applications in Large Scale Sensor Network

This paper proposes a scalable, energy-efficient and scalable, energy efficient, delay bounded intra and inter cluster routing framework viz. GIICCF (Generalized Intra and Inter cluster chaining framework) for efficient data gathering in large scale wireless sensor networks. This approach extricates the benefits of both pure chain-based as well as pure cluster-based data gathering schemes in large scale Wireless Sensor Network (WSNs) without undermining with the drawbacks. GIICCF defines a localized energy-efficient chaining scheme among the member nodes within the cluster with bounded data delivery delay to the respective cluster-heads (CH) as well as enables the CH to deliver data to the Base station (BS) following an energy-efficient multi-hop fashion. Detailed experimental analysis and simulation results reveal GIICCF increases the performance of any pure cluster-based and chain-based protocols by a huge margin.

Reliability and Energy Efficiency of DEAR Protocol with Cooperative Caching in IEEE802.15.4 based large ubiquitous Wireless Sensor Networks

Reliability and Energy Consumption issues in large ubiquitous Wireless Sensor Networks are a cause of concern especially because there is an inherent conflict between the two: an increase in reliability usually leads to an increase in energy consumption. Conversely, energy conservation has been a priority research concern in wireless sensor nodes. Data aggregation from various nodes and its transmission to the sink node through multiple hops which is important for network reliability increases the overall energy consumption in the network. Several schemes were proposed in the past to address the reliability needs and also to minimize the energy consumption in the network. In this context, this paper proposes a novel strategy for IEEE802.15.4/ZigBee based networks by incorporating a Distributed Energy Aware Routing (DEAR) protocol with a localized Cooperative Caching algorithm that addresses the query generated by a requester node or sink node with datum already existing in the locally available cache memory or in the memory of its one-hop neighbors or by the source node. The DEAR protocol considers battery level as a key factor to include nodes in its routing path. The proposed model is evaluated on the basis of three scenarios which were considered to illustrate the impact of energy consumption on the reliability of WSNs.

Large measurement range and high sensitivity temperature sensor with FBG cascaded Mach-Zehnder interferometer

A high sensitivity and wide measurement range temperature sensor with Mach-Zehnder interferometer (MZI) and fiber Bragg grating (FBG) cascaded is proposed and experimentally implemented. The proposed MZI is fabricated by inserting a section of single mode fiber between two sections of single mode fibers with 62 μm core-offset, where the length of the inserted fiber is 300 μm. Polydimethylsiloxane (PDMS), as a kind of thermo-sensitive material, is covered on core-offset region. Meanwhile, FBG is adopted to enlarge the measurement range. Temperature experiment results turn out to be fruitful for improving the performance of sensitivity and measurement range, and the sensitivity of proposed sensor is up to 10.389 nm/°C from 10 °C to 59.4 °C with good linearity. Moreover, the exhaustively comparison with other optical fiber interferometers reveals that the proposed sensor is simple making, miniature size, good reproducibility, high sensitivity and large measurement range, which has tempting commercial prospect.

An intensity-modulated and large bandwidth magnetic field sensor based on a tapered fiber Bragg grating

An intensity-modulated and large bandwidth magnetic field fiber sensor with tapered fiber Bragg grating (TFBG) had been proposed and demonstrated by experiments. Fiber Bragg grating (FBG) was created into a tapered fiber to form TFBG, which not only had powerful evanescent field but also possessed large bandwidth for the fiber refractive index decreased along the tapered region. The TFBG inserted into a 0.3 mm diameter glass tube which was filled with the magnetic-fluid (MF), and then this structure was packaged to form an integration magnetic-field fiber sensor. The refractive index of the MF will changed under varied magnetic-field, so the reflection spectral of the sensor should have a corresponding changed with the magnetic-field changed due to the tunable refractive index of MF. We designed the experiments and tested the sensor. The experimental results show that a nonlinear curve is between light intensity variation and changed MF intensity variation, the uniformity fitting degrees are 0.999 and 0.996 among the magnetic field increased and declined and the maximum sensitivity is −0.1933 dB/Oe and −0.1533 dB/Oe, respectively. Moreover, we find magnetic hysteresis has no obvious affection on the magnetic vectors (0 and 180 deg). This proposed magnetic-field sensor has the benefits of no direction disturbance, large bandwidth, high sensitivity, and integration.

Nestable arched triboelectric nanogenerator for large deflection biomechanical sensing and energy harvesting

Flexible and wearable electronics has presented an opportunity to evolve our society into an intelligent world. Especially for biomechanical sensing and gesture recognition, wearable devices are quite indispensable. Here, we present a nestable arched triboelectric nanogenerator (NA-TENG), which can be wore on fingers, acting as a large deflection biomechanical sensor and energy harvester. With arched structure, the sensor can detect large bending angle of fingers with good stability. Meanwhile, the NA-TENG devices can be assembled like matryoshka, which greatly increases the extensibility for optimizing output performance of the NA-TENG, and provides a convenient method to regulate characteristics of TENG devices in wearable applications.

Performance optimization of capacitive motion sensing (CMS) system for intra-fraction motion detection during stereotactic radiosurgery

The purpose of this investigation is to improve intra-fractional motion detection during cranial stereotactic radiosurgery with a novel capacitive motion sensing (CMS) system. Previous work showed that a capacitive detection system, based on a MPR121 capacitance-to-digital converter, provided a number of advantages over existing patient imaging systems used in the clinic, by uniquely offering ionizing-radiation-free and continuous monitoring without modification to the immobilization mask or treatment room. However, in order to provide submillimeter detection accuracy, the MPR121-based CMS system required relatively large sensors in close proximity to the patient. Therefore, the aim of this investigation was to improve sensitivity of the system, allowing reduction in sensor size and preserving its stable operation in the linear accelerator environment. For this, we developed, characterized and compared motion detection capabilities of four CMS systems based on different capacitance-to-digital converters: MPR121, CPT212B, FDC1004 and FDC2214. Among all candidates, the FDC2214-based system was found to uniquely combine accurate 3D motion detection in real time, with stable performance under ionizing radiation. It exhibited an order of magnitude improvement in sensitivity in comparison with the proof-of-study system, allowing a spatial precision as low as 0.3 mm, and its overall performance was found to satisfy the AAPM practice guidelines of positioning tolerance within 1 mm. Furthermore, the high sensitivity of the system allows both reduction of the sensor area and location more distant from the patient surface, which are key improvements with regard to development of a clinical device.

A Large Measurement Range Bending Sensor Based on Microfiber Probe

A large measurement range bending sensor based on a microfiber probe is proposed and experimentally demonstrated. The microfiber probe is simply fabricated by snapping a multimode biconical microfiber in the nonadiabatic tapering process, forming a reflection-type sensing unit. Such probe-type sensor is operated as an in-line Michelson interferometer with compact size. Bending deformation applied on the sensor will affect the refractive index profile of microfiber probe, resulting in the wavelength shift of resonance dip. The experimental results indicate that the microfiber probe-based bending sensor has a large curvature range of 0-11.82m−1. Meanwhile, the maximum curvature sensitivity of 700pm/m−1 has been achieved in the range of 8.73-11.82m−1. The microfiber probe-based bending sensor has the excellent advantages of compact size, large curvature range and good flexibility, which is suitable to measure bending deformation in semi-enclosed spaces and provides a great application prospect in wearable sensing devices for human motion monitoring.

Thermally Carbonized Porous Silicon for Robust Label-Free DNA Optical Sensing.

In this work, thermal carbonization is shown to provide the necessary surface passivation to enable highly robust DNA detection on a porous silicon (PSi) platform, overcoming previous corrosion challenges with detection of negatively charged biomolecules. The stability of thermally carbonized PSi (TCPSi), oxidized PSi (OPSi), and undecylenic acid-modified PSi (UAPSi) is compared in phosphate-buffered saline and during DNA sensing experiments. Reflectance measurements reveal an improvement in stability and DNA sensor response for TCPSi compared to OPSi and UAPSi. TCPSi exhibits a large positive sensor response with >90% DNA hybridization efficiency. In comparison, UAPSi shows a smaller positive DNA sensor response, likely lessened by a small corrosion effect, while OPSi exhibits a large negative sensor response, indicating significant induced PSi corrosion that confounds the ability of OPSi to yield meaningful readouts of DNA hybridization events. This work expands the application of TCPSi for its more widespread usage in sensing applications where competing substrate corrosion may influence device stability.

A Novel Hybrid Optimization Scheme on Connectivity Restoration Processes for Large Scale Industrial Wireless Sensor and Actuator Networks

In the wireless sensor and actuator networks (WSANs) of industrial field monitoring, maintaining network connectivity with coverage perception plays a decisive role in many industrial process scenarios. The mobile actuator node is responsible for collecting data from the sensing nodes and performing diverse specific collaborative operation tasks. However, the failure of the nodes usually causes coverage vulnerability and partition of the network. Urgent and time-sensitive applications expect a minimum coverage loss to complete an instant connectivity restoration. This paper presents a hybrid coverage perception-based connectivity restoration algorithm, which is designed to restore network connectivity with minimal coverage area loss. The algorithm uses a backup node, which is selected nearby the critical node, to ensure a timely restoration when the critical node encounters failure. In the process of backup node migration, the optimal destination will be reselected to maintain the best network coverage after network connectivity recovery. The effectiveness of the proposed algorithm was verified by some simulation experiments.

On the topology design of large wireless sensor networks for distributed consensus with low power consumption

Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

A hybrid state-of-charge estimation method based on credible increment for electric vehicle applications with large sensor and model errors

Abstract State-of-charge (SOC) estimation is a key issue in electric vehicles, which is affected by model and sensor errors. In this paper, a novel SOC estimation method based on the SOC increment with high credibility is proposed to deal with the large errors of the model and sensor. Firstly, the characteristics of SOC error increment about the ampere-hour counting (AHC) and extended Kalman filter (EKF) methods are examined and the more credible SOC increment is determined. Secondly, a hybrid SOC estimation method, which utilizes the no correlation between the AHC method and the errors of the voltage sensor and the equivalent circuit model, in combination with the weak correlation between the EKF method and the errors of the current sensor and the water tank model, is developed over the whole SOC range. Finally, the proposed hybrid method is evaluated by two types of batteries under three dynamical stress tests, and the results demonstrate that the proposed method can greatly suppress the large sensor and model errors and achieve high accuracy and robustness.

Noise characteristics with CMOS sensor array scaling

An important consideration when scaling semiconductor sensor devices is the effect it may have on noise performance. Overall signal to noise ratio can be improved both by increasing sensor size, or alternatively by averaging the signal from two or more smaller sensors. In the design of sensor systems it is not immediately clear which is the best strategy to pursue. In this paper, we present a detailed theoretical and experimental study based on three different sensor arrays that show that an array of small independent sensors is always less noisy than a large sensor of the same size.

Low channel count montages using sensor tying for VEP-based BCI

Objective. Brain computer interfaces (BCIs) are slowly making their appearance on the consumer market, accompanied by a higher popularity among the general public. This new group of users requires easy-to-use headsets with robustness to non-precise placement. In this paper, an optimized fixed montage EEG headset for VEP BCIs is proposed. Approach. The proposed layout covers only the most relevant area with large sensors to account for slight misplacement. To obtain large sensors, without having them physically available, we tie multiple sensors together and simulate the effect by averaging the signal of multiple sensors. Main results. In simulations based on recorded 256-channel EEG data, it is shown that a circular center-surround configuration with sensor tying, leading to only eight channels covering a large part of the occipital lobe, can provide high performance and good robustness to misplacement. Automatically optimized layouts were unable to achieve better performance, demonstrating the utility of this manual design. Finally, the performance and benefits of sensor tying in the manual design are then validated in a physical experiment. Significance. The resulting proposed layout fulfills most requirements of an easy to use consumer EEG headset.

Organic Photodetectors and their Application in Large Area and Flexible Image Sensors: The Role of Dark Current

AbstractOrganic photodetectors (OPDs) have gained increasing interest as they offer cost‐effective fabrication methods using low temperature processes, making them particularly attractive for large area image detectors on lightweight flexible plastic substrates. Moreover, their photophysical and optoelectronic properties can be tuned both at a material and device level. Visible‐light OPDs are proposed for use in indirect‐conversion X‐ray detectors, fingerprint scanners, and intelligent surfaces for gesture recognition. Near‐infrared OPDs find applications in biomedical imaging and optical communications. For most applications, minimizing the OPD dark current density (Jd) is crucial to improve important figures of merits such as the signal‐to‐noise ratio, the linear dynamic range, and the specific detectivity (D*). Here, a quantitative analysis of the intrinsic dark current processes shows that charge injection from the electrodes is the dominant contribution to Jd in OPDs. Jd reduction is typically addressed by fine‐tuning the active layer energetics and stratification or by using charge blocking layers. Yet, most experimental Jd values are higher than the calculated intrinsic limit. Possible reasons for this deviation are discussed, including extrinsic defects in the photoactive layer and the presence of trap states. This provides the reader with guidelines to improve the OPD performances in view of imaging applications.

Energy Efficient Clustering and Congestion Control in WSNs with Mobile Sinks

Large scale Wireless Sensor Networks (WSNs) often utilize multiple mobile sink nodes to improve the network lifetime and scalability. However, most of the studies conducted in this context, consider unlimited buffer capacity for the sink nodes. But, this model cannot truly describe the behavior of WSNs and causes congestion in the sink nodes. To solve this problem, in this paper, we use limited buffer capacity for each mobile sink node in WSNs and present a two-level Fuzzy Logic Controller (FLC)-based dynamic clustering scheme and congestion prevention. In this scheme, sink nodes try to predict current load based on their loads in previous rounds by using ARIMA method and based on it, the first FLC selects the nearest uncongested sink node from multiple available mobile sink nodes. Then, the second FLC applies the output of the first FLC to select appropriate nodes as cluster heads to mitigate the energy consumption in the network. Extensive simulation results indicate the effectiveness of the proposed fuzzy logic-based solution in reducing congestion in the mobile sink nodes and improving load balancing in them which these result in the network lifetime improvement and decreasing the number of retransmissions.

LBCSM: Load Balanced Constant Scheduling Model for Node Recharging in Wireless Rechargeable Sensor Networks using Multiple Mobile Charging Devices

In large-scale Wireless Rechargeable Sensor Networks (WRSNs), limited battery capacity of nodes may reduce the network longevity. For enhancing the network lifetime, the nodes in the network can recharged periodically based on their operational executions. The rechargeable sensor nodes in the network are replenished using external sources. Using single charging device can be feasible only for small scale WSNs, whereas in managing large scale wireless sensor networks, multiple charging devices are to be modelled for efficiently recharging the sensor nodes, since single devices are having energy constraints to recharge more number of nodes. On focussing those issues, this paper contributes on developing a new model called Load Balanced Constant Scheduling (LBCS) for the replenishment of the sensor nodes. Moreover, multiple Mobile Charging Devices (MCDs) are used here for recharging the sensor nodes effectively, without facing resource limitations. In this model, constant and time based charge scheduling approach and charging route for MCD has been frame optimally. The scheduling mode focuses on a concrete classification procedure for avoiding needless visits of nodes having adequate energy. Providing further improvement in schedule based node replenishment, algorithm for Charging Route Definition (CRD) is also developed in this work. For evidencing the efficiency of the proposed model, the work is simulated and evaluated. The simulation results are compared with some existing models based on the network lifetime, time taken for recharge and efficiency.

A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

Investigation of deferred charge effects in Large Synoptic Survey Telescope ITL sensors

The traditional characterization of charge transfer inefficiency (CTI) in charge-coupled devices (CCDs) can suffer from a number of deficiencies: CTI is often only calculated for a limited number of signal levels, CTI is calculated from a limited number of pixels, and the sources of CTI are usually assumed to occur at every pixel-to-pixel transfer. A number of serial CTI effects have been identified during preliminary testing of CCDs developed by Imaging Technology Laboratory (ITL) for use in the Large Synoptic Survey Telescope (LSST) camera focal plane that motivate additional study beyond the traditional CTI characterization. This study describes a more detailed examination of the serial deferred charge effects in order to fully characterize the deferred charge measured in the serial overscan pixels of these sensors. The results indicate that in addition to proportional CTI loss that occurs at each pixel transfer, ITL CCDs have additional contributions to the deferred charge measured in serial overscan pixels, likely caused by fixed CTI loss due to charge trapping, and an electronic offset drift at high signal.

Speed prediction in large and dynamic traffic sensor networks

Smart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from ∼1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks.

Fiber-Based Large Dynamic Range Vibration Sensing With Dual-Wavelength Phase Unwrapping

A large dynamic range vibration sensor is demonstrated with a fiber-based Mach-Zehnder interferometer, in combination with the 3 × 3 fiber coupler based phase demodulation and dual-wavelength linear regression phase unwrapping. Due to the relief of the π-phase constraint in the digital phase demodulation, the measurement range of the vibration sensor is significantly enlarged, with a phase noise level the same as that of the single-wavelength technique. A sampling rate much higher than the Nyquist rate, which is used to limit the phase change within a sampling period, is no longer necessary, and the trade-off between the dynamic range and the measurement bandwidth is eliminated. The proposed vibration sensing system is able to measure signal frequencies ranging from 10 Hz to 7.0 kHz, with almost flat frequency response, good linearity, and a dynamic range independent of frequency.