Conventional Sensor Networks Research Articles

A computer vision–aided methodology for bridge flexibility identification from ambient vibrations

AbstractThis paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.

An Efficient Processing Scheme for Concurrent Applications in the IoT Edge

Due to the large volume of IoT data, conventional sensor network based and the cloud base IoT systems cannot handle latency-sensitive and resource-consuming IoT applications. Sensor networks do not have enough computation resources and also suffer from a limited network lifetime. On the other hand, the cloud based IoT system is far away from the users and the physical world, and cannot satisfy the real-time requirement of IoT applications. We adopt the IoT edge network to address these challenges and process IoT applications in modern IoT systems. The IoT edge network is an emerging computing architecture in the IoT. Compared to the sensor nodes in conventional sensor networks, the edge servers have more computation resources. Compared to the remote cloud, the edge servers are closer to the users and the physical world. However, processing IoT applications in the edge network still remains challenging. First, how to process concurrent IoT applications has not been fully investigated. Second, the inner relationship between the network resource and the application latency has not been deeply analyzed. Third, the function conflict problem in edge servers has not been taken seriously. To solve the above challenges, we propose the Energy and Latency Efficient Processing Plan for Concurrent IoT Applications Problem which aims to construct an application processing plan by jointly considering the concurrency, the energy-latency relationship, and the function conflict problems. We prove that such a problem is NP-Hard, and algorithms are proposed accordingly. Furthermore, we also estimate the performance of the proposed algorithms by numerical results.

IoT-based edge computing (IoTEC) for improved environmental monitoring

This research considered several applications of a coupled Internet of Things sensor network with Edge Computing (IoTEC) for improved environmental monitoring. Two pilot applications, covering environmental monitoring of vapor intrusion and system performance of wastewater-based algae cultivation, were designed to compare data latency, energy consumption, and economic cost between the IoTEC approach and the conventional sensor monitoring method. The results show that the IoTEC monitoring approach, compared with conventional IoT sensor networks, could significantly reduce data latency by 13 %, and the amount of data transmission decreased by an average of 50 %. In addition, the IoTEC method can increase the duration of power supply by 130 %. Collectively, these improvements could lead to a compelling cost reduction of 55–82 % per year for monitoring vapor intrusion at five houses, with more houses leading to more significant savings. Additionally, our results demonstrate the feasibility of deploying machine learning tools at edge servers for more advanced data processing and analysis.

Dual-line data collection scheme for efficient mobile sink operation in solar-powered wireless sensor networks

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate.

Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event

Rapid appraisal of damages related to hazard events is of importance to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following a disaster. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages, and then develops a text classification model for rapid damage assessment. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks. The 2019 Ridgecrest, California earthquake sequence is investigated as a case study.

Reproduction strategy of radiation data with compensation of data loss using a deep learning technique

In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

A Novel Fabry-Pérot Optical Sensor for Guided Wave Signal Acquisition.

In this paper, a novel hybrid damage detection system is proposed, which utilizes piezoelectric actuators for guided wave excitation and a new fibre optic (FO) sensor based on Fabry-Perot (FP) and Fiber Bragg Grating (FBG). By replacing the FBG sensors with FBG-based FP sensors in the hybrid damage detection system, a higher strain resolution is achieved, which results in higher damage sensitivity and higher reliability in diagnosis. To develop the novel sensor, optimum parameters such as reflectivity, a wavelength spectrum, and a sensor length were chosen carefully through an analytical model of the sensor, which has been validated with experiments. The sensitivity of the new FBG-based FP sensors was compared to FBG sensors to emphasize the superiority of the new sensors in measuring micro-strains. Lastly, the new FBG-based FP sensor was utilized for recording guided waves in a hybrid setup and compared to the conventional FBG hybrid sensor network to demonstrate their improved performance for a structural health monitoring (SHM) application.

Distributed Sensor Networks Based Shallow Subsurface Imaging and Infrastructure Monitoring

Distributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surface-wave imaging adopts background ambient sounds from a far-field energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image small-scale and shallow objects. In this article, we propose to use local seismic spatial autocorrelation coefficients, obtained by the combinations of independent dense sensor network measurements and pre-processed readings of its neighbor(s), to perform real-time collaborative imaging of the shallow subsurface objects. First, we derive the high-frequency spectral coefficient based shallow subsurface imaging method. Then, we apply the proposed approach to image a shallowly buried pipeline and obtain promising results. Furthermore, based on a time-lapse manner, the water leakage from the buried pipeline can also be detected using distributed computations between sensors. Comparisons and analysis of field deployments are made to validate the effectiveness and performance of the proposed method.

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count.

Simulation and Visualization of Acoustic Underwater Sensor Networks using Aqua-Sim and Aqua-3D : An Evaluation

Contrasting the Radio Frequency (RF) or Electro Magnetic (EM) wave based terrestrial network communication technologies, acoustic communication on Underwater Sensor Networks (UWSNs) add a unique set of challenges due their long propagation delays, low data rates, and high transmit power. Due to characteristic of basic acoustic nature and hence require an entire redesign in all the layer of network protocol stack. Most importantly, the current established network technology that were developed for conventional terrestrial wireless sensor networks (TWSNs) are not suitable for directly applying them in underwater scenarios. Due to this reason, the research in network technology of UWSNs has been underway to overcome the lack of available network technology in UWSNs. In this work, we are going to study some of the available simulators for simulating a UWSNs environment and enlighten the reasons for selecting Aqua-Sim and Aqua-3D for our underwater network simulations. In this research, the performance of the basic routing protocols in Aqua- Sim such as Underwater Flooding (UWFlooding), Vector- Based Forwarding (VBF), and Hop-by-Hop Vector-Based Forwarding Protocol (HH-VBF) is evaluated with the metrics packet delivery ratio (PDR), cumulative delay, throughput and energy consumption under dense network environment. The result of our study showed that VBF and HH-VBF are performed better in terms of PDR, cumulative delay and energy consumption, while UWFlooding is performed better in terms of cumulative delay.

Resource allocation for relay-aided underwater acoustic sensor networks with energy harvesting

Underwater acoustic sensor networks play a significant role in ocean observation, pollution monitoring and underwater remote control. Different from conventional sensor networks, transmission efficiency and lifetime for nodes in underwater acoustic sensor networks is vital since it is difficult to change batteries of buoy relays and underwater sensors. In this paper, two resource allocation algorithms for energy harvesting time and transmit power allocation in underwater acoustic sensor networks with single and multiple wireless energy harvesting buoy nodes are proposed to maximize the sum rate and energy efficiency. Both of the circuit power consumption and transmission power consumption are considered as the power consumption of the system. We first propose an optimal algorithm to allocate the time of energy harvesting slot for sum rate maximization. From the derivation, we show that the sum rate of the network grows as the transmission power increases. However, when the transmission power is extremely large, the energy efficiency of the network starts to decrease. Thus a joint algorithm with energy harvesting time and transmit power allocation is further provided for energy efficiency maximization. Simulation results validate the sum rate improvement under our proposed sum rate maximization algorithm and energy efficiency improvement under the proposed energy efficiency maximization algorithm.

Mobile crowd sensing – Taxonomy, applications, challenges, and solutions

Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional sensor networks. Then, a taxonomy of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions.

Vehicle-Based Relay Assistance for Opportunistic Crowdsensing Over Narrowband IoT (NB-IoT)

The Internet of Things (IoT) undergoes a fundamental transformation by augmenting its conventional sensor network deployments with more advanced and mobile devices, such as connected and self-driving cars. This fusion of embedded and automotive domains promises to deliver unprecedented mutual benefits, where vehicles will receive timely updates from their proximate sensors while assisting them in delivering their sensory data to the remote network infrastructure. In this paper, we put forward the vision of opportunistic crowdsensing applications, in which the ubiquitous deployments of low-cost and battery-constrained IoT sensors take advantage of more capable and energy-abundant vehicle-mounted mobile relays. In particular, we consider the use of the emerging narrowband IoT radio technology recently ratified by 3GPP and offering efficient means for underlying wireless connectivity. Our rigorous mathematical analysis supported with comprehensive system-level evaluations reveals the effects of vehicle-based relays on the important metrics of interest, such as connection reliability, transmission latency, and communication energy efficiency. These systematic findings advocate for an extensive utilization of vehicular relays as part of the next-generation IoT ecosystem.

Multi-Metric Route Cost Based Routing in Wireless Multimedia AD-HOC Networks

To improve the quality of life, bigger cities in the world addressing their problems by adopting latest technology. IOT connect physical world to the internet and such concept is adopted by bigger cities having large population growth rate. Wireless multimedia sensor network helps to monitor various parts of a city without use of cables. This network becomes the primary component of every smart city due to its surveillance and monitoring capability In contrast with conventional sensor networks, multimedia sensor nodes are equipped with cameras which acquire, process, compress and transmit information about an event happing at some remote area in the form of an image or video. However, like conventional WSNs this network is having multiple constrained and new metric needs to be considered to improve network life time and Quality of service parameters. Multiple metric based route cost function is defined and routing mechanism based upon this cost function is proposed. Since each multimedia sensor node is battery operated and connected through low power wireless links. This work helps to find an optimal path having minimum energy consumption, maximum link reliability, minimum number of hops and minimum routing table magnitude. A cost function considers constraints of wireless multimedia sensor networks. It satisfies desirable network requirements and improves network life time through efficient routing. The proposed algorithm is tested, simulated and compared with previous routing algorithms.

A Fast Distributed Variational Bayesian Filtering for Multisensor LTV System With Non-Gaussian Noise.

For multisensor linear time-varying system with non-Gaussian measurement noise, how to design distributed robust estimator to increase the accuracy and robustness to outliers at a relatively low computation and communication cost is a fundamental task. This paper proposes a fast distributed variational Bayesian (VB) filtering algorithm to recursively estimate the state and noise distribution over three conventional sensor networks: 1) incremental-based; 2) diffusion-based; and 3) consensus-based. To be specific, the non-Gaussian measurement noise of each sensor is modeled as Student- t distribution, and the system state and the parameters of the distribution are estimated via VB approach in each iteration step. An interaction scheme is then added to obtain the global optimal parameter by fusing the local optimal parameters over incremental, diffusion, and consensus communication topology. An efficient sensor selection criterion under these topologies based on the Cramér-Rao lower bound is proposed to reduce the communication and computation burden. Compared with the existing centralized VB filtering algorithms, the proposed algorithm in this paper can extensively increase the robustness to node or link failure at a lower computation cost with acceptable estimation performance and communication load. The theoretic results and simulation results are given to show the efficiency of our proposed algorithm.

$k$ -Coverage Estimation Problem in Heterogeneous Camera Sensor Networks With Boundary Deployment

Coverage estimation is one of the fundamental issues of camera sensor networks (CSNs), and is also an important metric used to evaluate the monitoring quality of field of interest (FoI). In comparison with conventional omnidirectional sensor networks, coverage estimation in CSNs is more challenging due to the sensing region of camera sensor that is a sector-disk region. Moreover, the heterogeneous deployment of CSNs makes the coverage estimation problem even more complex. Currently, most of the literatures assume that a great number of camera sensors are directly deployed in the FoI. This paper assumes that all heterogeneous camera sensors are stochastically deployed outside the FoI. For such heterogeneous CSNs, we derived a $k$ -coverage probabilistic expression to estimate the minimum number of camera sensors required for a desired level of $k$ -coverage. To evaluate the performance of the proposed $k$ -coverage estimation expression, several simulation experiments are conducted to validate the theoretical results.

Mobile Access Coordinated Wireless Sensor Networks—Design and Analysis

This paper introduces and analyzes a novel mobile access (MA) coordinated wireless sensor network (MC-WSN) architecture. In conventional sensor networks with mobile access points (SENMA), the MA points traverse the network to collect information directly from individual sensors. While simplifying the routing process, a major limitation with SENMA is that data transmission is limited by the physical speed of the MAs and their trajectory length, resulting in low throughput and large delay. In an effort to resolve this problem, we introduce the MC-WSN architecture, for which a major feature is that: through active network deployment and topology design, the number of hops from any sensor to the MA can be limited to a prespecified number. In this paper, first, we discuss the optimal topology design for MC-WSN such that the average number of hops between the source and its nearest sink is minimized. Second, we calculate the throughput of MC-WSN and illustrate the effect of the number of hops on the throughput. Third, we establish the queuing model for the cluster heads based on the Kleinrock independence assumption and Burke's theorem, and show that the traffic at each cluster head can be modeled as an independent M/M/1 queue. Finally, based on the queue modeling, we provide an in-depth analysis on network stability, delay, and energy efficiency. Our analysis is demonstrated through numerical results. It is shown that under stable system conditions, MC-WSN achieves a much higher throughput and considerably lower delay and energy consumption over SENMA.

Demand-Addressable Sensor Network: Toward Large-Scale Active Information Acquisition

A new type of sensor network called the demand-addressable sensor network (DASN) is proposed in this paper. The DASN actively acquires the desired information by addressing user demands and delivers the information to appropriate destinations. This is in contrast to the conventional sensor networks that simply send sensed data to users. The DASN is useful for finding the desired information in a short duration of time from a large amount of sensed data generated by a large-scale sensor network. The DASN is constructed with a demand-addressable network that integrates many on-demand reconfigurable wireless sensor networks (ODRWSN) and other existing information and communications technology systems or services, such as Google Maps and Twitter. In addition to the demand-addressing mechanism, the demand-addressable network has an in-network data combining or mashup mechanism. The mashed up data are displayed on the user terminal using an ordinary Web browser without any requirement to install a dedicated application program. The functions of the ODRWSN can be dynamically customized by injecting roles specified by the user. Thus, the user can actively get the desired information by customizing the sensor network function. The main application of the DASN is wide-area disaster site monitoring, for which the DASN features outlined above are suitable. In this paper, the concept underlying the DASN, its architecture and implementation, and experimental results are presented.

Mobile Coordinated Wireless Sensor Network: An Energy Efficient Scheme for Real-Time Transmissions

This paper introduces the mobile access coordinated wireless sensor network (MC-WSN)—a novel energy efficient scheme for time-sensitive applications. In conventional sensor networks with mobile access points (SENMA), the mobile access points (MAs) traverse the network to collect information directly from individual sensors. While simplifying the routing process, a major limitation with SENMA is that data transmission is limited by the physical speed of the MAs and their trajectory length, resulting in low throughput and large delay. In an effort to resolve this problem, we introduce the MC-WSN architecture, for which a major feature is that: through active network deployment and topology design, the number of hops from any sensor to the MA can be limited to a pre-specified number. In this paper, we investigate the optimal topology design that minimizes the average number of hops from sensor to MA, and provide the throughput analysis under both single-path and multipath routing cases. Moreover, putting MC-WSN in the bigger picture of network design and development, we provide a unified framework for wireless network modeling and characterization. Under this general framework, it can be seen that MC-WSN reflects the integration of structure-ensured reliability/efficiency and ad-hoc enabled flexibility.

Completely contactless structural health monitoring of real-life structures using cameras and computer vision

Summary A newly developed, completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures. To provide contactless and practical monitoring, the current vision-based displacement measurement methods are improved by eliminating the physical target attachment. This is achieved by means of utilizing imaging key-points as virtual targets. As a result, pixel-based displacements of a monitored structural location are determined by using an improved detection and match key-points algorithm, in which false matches are identified and discarded almost completely. To transform pixel-based displacements to engineering units, a practical camera calibration method is developed because calibration standard on a physical target no longer exists. Moreover, a framework for evaluating the accuracy of vision-based displacement measurements is established for the first time, which, in return, provides users with the most crucial information of a measurement. The proposed framework along with a conventional sensor network and a data acquisition system are applied and verified on a real-life stadium during football games for structural assessment. The results obtained by the new method are successfully validated with the data acquired from sensors such as linear variable differential transformers and accelerometers. Because the proposed method does not require any type of sensor and target attachment, common field works such as sensor installation, wiring, maintaining conventional data acquisition systems are not required. This advantage enables an inexpensive and practical way for structural assessment, especially for real-life structures. Copyright © 2016 John Wiley & Sons, Ltd.