Visualization of real-time displacement time history superimposed with dynamic experiments using wireless smart sensors and augmented reality

Wireless smart sensors(WSS)process field data and inform inspectors about the infrastructure health and safety.In bridge engineering,inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements.Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time.Displacement data from WSS in the field undergoes additional processing and is seen at a different location.If inspectors were able to see structural displacements in real-time at the locations of interest,they could conduct additional observations,creating a new,information-based,decision-making reality in the field.This paper develops a new,human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality(AR).It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments.The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser.Using this new AR interface tool,inspectors can observe and compare displacement data,share it across space and time,visualize displacements in time history,and understand structural deflection more accurately through a displacement time history visualization.

Laser-Induced Graphene:En Route to Smart Sensing

The discovery of laser-induced graphene(LIG) from polymers in 2014 has aroused much attention in recent years.A broad range of applications,including batteries,catalysis,sterilization,and separation,have been explored.The advantages of LIG technology over conventional graphene synthesis methods are conspicuous,which include designable patterning,environmental friendliness,tunable compositions,and controllable morphologies.In addition,LIG possesses high porosity,great flexibility,and mechanical robustness,and excellent electric and thermal conductivity.The patternable and printable manufacturing process and the advantageous properties of LIG illuminate a new pathway for developing miniaturized graphene devices.Its use in sensing applications has grown swiftly from a single detection component to an integrated smart detection system.In this minireview,we start with the introduction of synthetic efforts related to the fabrication of LIG sensors.Then,we highlight the achievement of LIG sensors for the detection of a diversity of stimuli with a focus on the design principle and working mechanism.Future development of the techniques toward in situ and smart detection of multiple stimuli in widespread applications will be discussed.

Distributed Information Flow Verification for Secure Service Composition in Smart Sensor Network

Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.

Selection of a Minimal Complexity of a Transformation Functions of Pressure Sensors with a Maintained Given Measurement Accuracy

For series manufacture of pressure sensors, stage of technological tests is performed, related to a definition of the manufacturing accuracy of the sensors. Technological test plan of pressure sensors involves testing the sensors on certain fixed temperature and pressure points available in the table. According to a test results, we determine transformation function mathematical model coefficients of sensors and accordance by the claimed accuracy class, of the manufactured sensors. The cost of pressure sensors mostly depends on the cost of this step and determined by the complexity of the used transformation function model. The analysis of a contemporary works associated with the choice of transformation functions for smart pressure sensors. A new proposed indicator of model complexity of a sensor transformation function. In details shown features of the complexity indicator use and given an example. In the article was set and resolved the task to reduce the cost of the tests for commercially available sensors, by reducing the number of temperature points, without compromising the accuracy of the sensor measurement ability.

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets.By utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital transmission.Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent decades.WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video information.Many video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data reliability.Because of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)attacks.Deep Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture.The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network.It reduces the attacker detection delay and improves the detection accuracy.In order to protect the network against DoS assaults,an improved machine learning technique must be offered.An efficient Deep Neural Network approach is provided for detecting DoS in WMSN.The required parameters are selected using an adaptive particle swarm optimization technique.The ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

Recent developments in self-powered smart chemical sensors for wearable electronics

The next generation of electronics technology is purely going to be based on wearable sensing systems. Wearable electronic sensors that can operate in a continuous and sustainable manner without the need of an external power sources, are essential for portable and mobile electronic applications. In this review article, the recent progress and advantages of wearable self-powered smart chemical sensors systems for wearable electronics are presented. An overview of various modes of energy conversion and storage technologies for self-powered devices is provided. Self-powered chemical sensors (SPCS) systems with integrated energy units are then discussed, separated as solar cell-based SPCS, triboelectric nano-generators based SPCS, piezoelectric nano-generators based SPCS, energy storage device based SPCS, and thermal energy-based SPCS. Finally, the outlook on future prospects of wearable chemical sensors in self-powered sensing systems is addressed.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

Recent Advances on Early-Stage Fire-Warning Systems: Mechanism, Performance, and Perspective

Early-stage fire-warning systems(EFWSs)have attracted significant attention owing to their superiority in detecting fire situations occurring in the pre-combustion process.Substantial progress on EFWSs has been achieved recently,and they have presented a considerable possibility for more evacuation time to control constant unintentional fire hazards in our daily life.This review mainly makes a comprehensive summary of the current EFWSs,including the working mechanisms and their performance.According to the different working mechanisms,fire alarms can be classified into graphene oxide-based fire alarms,semiconductor-based fire alarms,thermoelectric-based fire alarms,and fire alarms on other working mechanisms.Finally,the challenge and prospect for EFWSs are briefly provided by comparing the art of state of fire alarms.This work can propose a more comprehensive understanding of EFWSs and a guideline for the cutting-edge development direction of EFWSs for readers.

Development of smart wearable sensors for life healthcare

Wearable biosensors are gaining tremendous interest in the clinical and biological medical fields for their potential in providing patients with real-time diagnostic tools and time-sensitive information,non-invasive measurements of biochemical markers distributed in body fluids throughout the body.These sensors replace part of the equipment that can only be installed in hospitals and become a new choice for assessing human health.Herein,critical perspectives are put forward regarding wearable sensors in the future digital health monitoring industry.Moreover,the materials and processing technologies involved have also been discussed in recent years,along with their monitoring schemes and system-level integration technologies.Finally,the probability of wearable sensors being used in early disease detection is considerable.Thus,despite potential challenges,it is still quite promising for wearable sensors to come into production.

Lightweight Privacy-Aware Yet Accountable Secure Scheme for SM-SGCC Communications in Smart Grid

Smart grid is envisioned as a critical application of cyber-physical systems and of the internet of things. In the smart grid, smart meters equipped with wireless sensors can upload meter readings （data） to smart grid control and schedule centers via the advanced metering infrastructure to improve power delivery efficiency. However, data gathered in short intervals, such as 15 minutes, will expose customers＇ detailed daily activities （for example, when they get up and when they use oven） using nonintrusive appliance load monitoring. Thus, data must be hidden to protect customers＇ privacy. However, data accountability is still required for emergency responses or to trace back suspected intrusions, even though the data is anonymous. In addition to desired security requirements, this imposes two extra tasks： Sensors in smart meters usually have resource constraints; thus, the desired security protocols have to remain lightweight in terms of computation and storage cost. Furthermore, scalability and flexibility are required since there exist vast meters. This paper presents a lightweight Privacy-aware yet Accountable Secure Scheme called PASS which guarantees privacy-aware accountability yet tackles the above challenges in the smart grid. A formal secu- rity analysis justifies that PASS can attain the security goals, while a performance analysis verifies that PASS requires few computations, and is scalable and flexible.

Big data and machine learning: A roadmap towards smart plants

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved.This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets(processes,units,etc.)involved.Established technologies central to the digital integrating components are smart sensing,mobile communication,Internet of Things,modelling and simulation,advanced data processing,storage and analysis,advanced process control,artificial intelligence and machine learning,cloud computing,and virtual and augmented reality.An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry.Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques.The purpose of this paper is twofold:a)to present the state-of-the-art of the aforementioned technologies,and b)to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short-and long-term production management.

Analytical modeling of self-powered electromechanical piezoelectric bimorph beams with multidirectional excitation

Unused mechanical energies can be found in numerous ambient vibration sources in industry including rotating equipment,vehicles,aircraft,piping systems,fluid flow,and even external movement of the human body.A portion of the vibrational energy can be recovered using piezoelectric transduction and stored for subsequent smart system utilization for applications including powering wireless sensor devices for health condition monitoring of rotating machines and defence communication technology.The vibration environment in the considered application areas is varied and often changes over time and can have components in three perpendicular directions,simultaneously or singularly.This paper presents the development of analytical methods for modeling of self-powered cantilevered piezoelectric bimorph beams with tip mass under simultaneous longitudinal and transverse input base motions utilizing the weak and strong forms of Hamiltonian’s principle and space-and time-dependent eigenfunction series which were further formulated using orthonormalization.The reduced constitutive electromechanical equations of the cantilevered piezoelectric bimorph were subsequently analyzed using Laplace transforms and frequency analysis to give multi-mode frequency response functions(FRFs).The validation between theoretical and experimental results at the single mode of eigenfunction solutions reduced from multi-mode FRFs is also given.