Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

High Efficient Reconfigurable and Self Testable Architecture for Sensor Node

Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network lifetime.The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results.Due to different network resource constraints and malicious attacks,security assurance in wireless sensor networks has been a difficult task.The implementation of these features requires larger space due to distributed module.This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission.The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment.The proposed system supports the real-time application in the range of 33 nanoseconds.The obtained results have been compared with the existing Microcontroller-based system.The power consumption of the proposed system consumes only 3.9 mW,and it is only 24%percentage of AT mega-based node architecture.

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently,the application of Wireless Sensor Networks(WSNs)has been increasing rapidly.It requires privacy preserving data aggregation protocols to secure the data from compromises.Preserving privacy of the sensor data is a challenging task.This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data.The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes.Instead of sending the complete data to the cluster head,the sensor nodes only send the coefficients of the non-linear function.This will reduce the communication overhead of the network.The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data.The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead,enhance data aggregation accuracy,and preserve data privacy.

Fuzzy Based BEENISH Protocol for Wireless Sensor Network

The main parameter to be considered in the wireless sensor network is the amount of energy that is available in each sensor node. The lifetime of the sensor node (SN) depends on it. As the SNs are deployed in remote locations, if the entire energy is consumed, it would be very difficult to replace or recharge the energy source immediately. Hence the energy consumed by each node is very important. If individual SNs send information directly to the base station (BS), then the availability of energy in such SN decreases very fast. This will lead to reduction in the life time of the SN. Instead, the SNs can send the data to the cluster head (CH), then the CH consolidates the received data. The CH sends it to the BS periodically. In this way, utilizing CH for sending the information to the BS increases the lifetime of the SN. The cluster head selection is very crucial in such networks. This paper proposes a novel fuzzy based BEENSIH protocol for CH selection.

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

TelG Mote: A Green Wireless Sensor Node Platform for Smart Home and Ambient Assisted Living

The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.

Wireless Sensor Network Components for Air Pollution Monitoring in the Urban Environment:Criteria and Analysis for Their Selection

Wireless sensor networks (WSNs) are fast evolving technology for collecting data in real time. Every wireless sensor network (WSN) is consisted of technical and software components which have to refer to the selected application. The paper focuses on the selection of WSN components. The WSN will be situated in the center of Olomouc City (OWSN). It will focus on measurements of harmful air pollutants and selected basic meteorological elements. The criteria for selection of WSN components including the most important parameters will be chosen and the final evaluation of the option utility will be made on the basis of multicriteria decision making process.

Ubiquitous Sensor Network for Chemical Sensors

Wireless sensor networks have been identified as one of the most important technologies for the 21 st century.Recent advances in micro sensor fabrication technology and wireless communication technology enable the practical deployment of large-scale,low-power,inexpensive sensor networks.Such an approach offers an advantage over traditional sensing methods in many ways:large-scale,dense deployment not only extends spatial coverage and achieves higher resolution,but also increases the system's fault-tolerance and robustness.Moreover,the ad-hoc nature of wireless sensor networks makes them even more attractive for military and other risk-associated applications,such as environmental observation and habitat monitoring.

Selective Cluster-Based Temperature Monitoring System for Homogeneous Wireless Sensor Networks

Over the past few decades, there has been a revolution in ICT, and this has led to the evolution of wireless sensor networks （WSN）, in particular, wireless body area networks. Such networks comprise a specialized collection of sensor nodes （SNs） that may be deployed randomly in a body area network to collect data from the human body. In a health monitoring system, it may be es-sential to maintain constant environmental conditions within a specific area in the hospital. In this paper, we propose a tempera-ture-monitoring system and describe a case study of a health-monitoring system for patents critically ill with the same disease and in the same environment. We propose Enhanced LEACH Selective Cluster （E-LEACH-SC） routing protocol for monitoring the tem-perature of an area in a hospital. We modified existing Selective Cluster LEACH protocol by using a fixed-distance-based thresh-old to divide the coverage region in two subregions. Direct data transmission and selective cluster-based data transmission ap-proaches were used to provide short-range and long-distance coverage for the collection of data from the body of ill patients. Ex-tensive simulations were run by varying the ratio of node densities of the two subregions in the health-monitoring system. Last Node Alive （LNA）, which is a measure of network lifespan, was the parameter for evaluating the performance of the proposed scheme. The simulation results show that the proposed scheme significantly increases network lifespan compared with traditional LEACH and LEACH-SC protocols, which by themselves improve the overall performance of the health-monitoring system.

A Coprocessor Architecture for 80/112-bit Security Related Applications

We have proposed a flexible coprocessor key-authentication architecture for 80/112-bit security-related applications over GF(2m)field by employing Elliptic-curve Diffie Hellman(ECDH)protocol.Towards flexibility,a serial input/output interface is used to load/produce secret,public,and shared keys sequentially.Moreover,to reduce the hardware resources and to achieve a reasonable time for cryptographic computations,we have proposed a finite field digit-serial multiplier architecture using combined shift and accumulate techniques.Furthermore,two finite-statemachine controllers are used to perform efficient control functionalities.The proposed coprocessor architecture over GF(2^(163))and GF(2^(233))is programmed using Verilog and then implemented on Xilinx Virtex-7 FPGA(field-programmable-gate-array)device.For GF(2^(163))and GF(2^(233)),the proposed flexible coprocessor use 1351 and 1789 slices,the achieved clock frequency is 250 and 235MHz,time for one public key computation is 40.50 and 79.20μs and time for one shared key generation is 81.00 and 158.40μs.Similarly,the consumed power over GF(2^(163))and GF(2^(233))is 0.91 and 1.37mW,respectively.The proposed coprocessor architecture outperforms state-of-the-art ECDH designs in terms of hardware resources.

Distributed Multi-hop Clustering Approach with Low Energy Consumption in WSN

The purpose of sensing the environment and geographical positions,device monitoring,and information gathering are accomplished using Wireless Sensor Network(WSN),which is a non-dependent device consisting of a distinct collection of Sensor Node(SN).Thus,a clustering based on Energy Efficient(EE),one of the most crucial processes performed in WSN with distinct environments,is utilized.In order to efficiently manage energy allocation during sensing and communication,the present research on managing energy efficiency is performed on the basis of distributed algorithm.Multiples of EE methods were incapable of supporting EE routing with MIN-EC in WSN in spite of the focus of EE methods on energy harvesting and minimum Energy Consumption(EC).The three stages of performance are proposed in this research work.At the outset,during routing and Route Searching Time(RST)with fluctuating node density and PKTs,EC is reduced by the Hybrid Energy-based Multi-User Routing(HEMUR)model proposed in this work.Energy efficiency and an ideal route for various SNs with distinct PKTs in WSN are obtained by this model.By utilizing the Approximation Algorithm(AA),the Bregman Tensor Approximation Clustering(BTAC)is applied to improve the Route Path Selection(RPS)efficiency for Data Packet Transmission(DPT)at the Sink Node(SkN).The enhanced Network Throughput Rate(NTR)and low DPT Delay are provided by BTAC.To MAX the Clustering Efficiency(CE)and minimize the EC,the Energy Effective Distributed Multi-hop Clustering(GISEDC)method based on Generalized Iterative Scaling is implemented.The Multi-User Routing(MUR)is used by the HEMUR model to enhance the EC by 20%during routing.When compared with other advanced techniques,the Average Energy Per Packet(AEPP)is enhanced by 39%with the application of proportional fairness with Boltzmann Distribution(BD).The Gaussian Fast Linear Combinations(GFLC)with AA are applied by BTAC method with an enhanced Communication Overhead(COH)for an increase in performance by 19%and minimize the DPT delay by 23%.When compared with the rest of the advanced techniques,CE is enhanced by 8%and EC by 27%with the application of GISEDC method.

Reliability analysis for WSN based on a modular k-out-of-n system

A hierarchical clustering model for a wireless sensor network (WSN) which takes into account the node fault and the balance of energy consumption is built. With the sensing and transmission as the main tasks of the WSN, its working mode is converted to a modular k-out-of-n system whose each modular is also a k-out-of-n structure. By using the theory of signature and its extending, the reliability of the WSN is computed. Finally, the reliability of WSN changing with the number of nodes, coverage requirements and node lifetimes is given by the numerical analysis. The proposed reliability analysis model is suitable for a large-scale WSN. © 2017 Beijing Institute of Aerospace Information.

Energy Efficient Direction-Based Topology Control Algorithm for WSN

A wireless sensor network consists of hundreds or thousands of small nodes which could either have a static or dynamic position. These nodes are deployed through normal or random distribution to report events of a particular area to the base station through sink nodes. Having limited onboard energy of sensor nodes, conservation of energy in wireless sensor network is necessary. For this purpose, a new algorithm is proposed titled Energy-Efficient-Direction-Based-Topology-Control-Algorithm (EEDBTC). In proposed algorithm, direction is the main concern whenever an event occurs the node will send data in the direction of base station so that less energy is consumed. The results of the same were compared with customary dense wireless sensor network, color based WSNs and it was observed that this algorithm is much better than previous topology control algorithms used.

Real Time Vehicle Status Monitoring under Moving Conditions Using a Low Power IoT System

In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks(SMPISCS)

In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks(SMPISCS)

In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.

Efficient Algorithm for Prolonging Network Lifetime of Wireless Sensor Networks

One of the fundamental design challenges in designing a Wireless Sensor Network （WSN） is to maximize the network lifetime, as each sensor node of the network is equipped with a limited power battery. To overcome this challenge, different methods were developed in the last few years using such techniques as network protocols, data fusion algorithms using low power, energy efficient routing, and locating optimal sink position. This paper focuses on finding the optimal sink position. Relay nodes are introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and, thus, their batteries are quickly depleted. A Particle Swarm Optimization （PSO） based algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. The relay nodes communicate with the sink instead of the sensor nodes. Tests show that this approach can save at least 40% of the energy and prolong the network lifetime.

BLOSSOMS： Building Lightweight Optimized Sensor Systems on aMassive Scale

As a joint effort between the Chinese Academy of Sciences and the Hong KongUniversity of Science and Technology, the BLOSSOMS sensor network project aims to identify researchissues at all levels from practical applications down to the design of sensor nodes. In thisproject, a heterogeneous sensor array including different types of application-dependent sensors aswell as monitoring sensors and intruding sensors are being developed. Application-dependentpower-aware communication protocols are also being studied for communications among sensor nodes. Anontology-based middleware is built to relieve the burden of application developers from collecting,classifying and processing messy sensing contexts. This project is also developing a set of toolsallowing researchers to model, simulate/emulate, analyze, and monitor various functions of sensornetworks.