Faulty sensor detection using multi-variate sensors in internet of things (IoTs)

IoT devices are lightweight and have limited computational capabilities often exposed to harsh environments, which can cause failure on the IoT devices [1, 2]. The failure on the IoT devices is also caused due to limited battery life, hardware failure or human mistakes. Sensor faults can be categorized under one type of hardware failure, such as sensor burn, reduced sensor sensitivity and malfunctioned sensors. Any faulty on the IoT devices can cause a problem on the overall operation of the IoT system. Traditional ways in the management of IoT devices is a maintenance officer require to check each device every day [1, 3]. Any faulty devices found needs to be fixed or replaced. This traditional method is not practical and very challenging especially in the management of a large scale deployment of IoT consist of hundreds or thousands devices. Because of this, we proposed a faulty sensor detection and identification mechanism using multivariate sensors. Two methods of decision making are introduced in detecting faulty sensors, which are logical and correlation method that implemented in smart parking system and smart agriculture system accordingly. The logical method compares state of all sensors (ultrasound, IR and hall-effect) in the smart parking system either a parking lot is occupied or available, and then determine the condition of the sensors. The drawback of this method is not able to detect faulty sensor properly for a constant fault, which the sensor reading remains the same value. The correlation method calculates the correlation between all sensors (soil moisture, soil temperature and soil water) in the smart agriculture system. This method uses a moving window technique to calculate the correlation for all sensor over time. Any incomparable and uncorrelated sensor readings means a presence of faulty sensors. Based on the experiment results, the findings shows that the proposed faulty sensor detection mechanism is working properly in detecting faulty sensor in a timely manner.

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Implementation of Faulty Sensor Detection Mechanism using Data Correlation of Multivariate Sensor Readings in Smart Agriculture

Annals of Emerging Technologies in Computing ◽

10.33166/aetic.2021.05.001 ◽

2021 ◽

Vol 5 (5) ◽

pp. 1-9

Author(s):

Ahmed Dhahir Malik ◽

Ansar Jamil ◽

Khaldoon Ammar Omar ◽

Mohd Helmy Abd Wahab

Keyword(s):

Good Condition ◽

Health Condition ◽

Data Correlation ◽

Geographical Differences ◽

Detection Mechanism ◽

Sensor Reading ◽

Water Sensor ◽

Smart Agriculture ◽

Using Data ◽

Sensor Detection

Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers, and crops regardless of their geographical differences. Faulty sensor detection is critical in IoT. When a sensor becomes faulty, missing data and/or bad data is provided to the control and management systems, which may lead to potential malfunction or even system failures. Because of this, a sensor fault detection mechanism must be implemented in an IoT system to eliminate this potential fault. This paper focuses on the implementation of a faulty sensor detection mechanism using data correlation among multivariate sensor readings, which is called Multivariate Faulty Sensor Detection Mechanism (Multi-FSDM) in a smart agriculture system. The smart agriculture system is attached with multi-variate sensors, which are moisture, temperature, and water sensor. These sensors are connected to Arduino UNO, which is equipped with an ESP8266 Wi-Fi module for internet connectivity. ThingsBoard is selected as the IoT cloud platform. The sensor readings are collected periodically and send to the cloud via the internet. Multi-FSDM calculates the correlation between each sensor reading to determine the health condition of each sensor. When all sensors are in good condition, all sensor readings are correlated with each other. However, when any sensor becomes faulty, sensor readings become uncorrelated. Once uncorrelated sensor readings occur, this means a faulty sensor is detected. Based on the findings, it is proven that Multi-FSDM can detect each sensor state on the smart agriculture system either in a good or faulty condition. When a sensor becomes faulty, Multi-FSDM detects and determines the faulty sensor successfully.

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IoT Traffic: Modeling and Measurement Experiments

IoT ◽

10.3390/iot2010008 ◽

2021 ◽

Vol 2 (1) ◽

pp. 140-162

Author(s):

Hung Nguyen-An ◽

Thomas Silverston ◽

Taku Yamazaki ◽

Takumi Miyoshi

Keyword(s):

Smart Home ◽

Large Scale ◽

Traffic Modeling ◽

The Novel ◽

Network Behavior ◽

Performance Accuracy ◽

Traffic Generator ◽

Multiple Devices ◽

Iot Devices ◽

The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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SpecTalk: Conforming IoT Implementations to Sensor Specifications

Sensors ◽

10.3390/s21165260 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5260

Author(s):

Yi-Bing Lin ◽

Sheng-Lin Chou

Keyword(s):

Large Scale ◽

Application Programming Interface ◽

Signal Frequency ◽

Visual Test ◽

Gray Area ◽

Control Signals ◽

Information And Communication ◽

Monitoring Camera ◽

Self Test ◽

Iot Devices

Due to the fast evolution of Sensor and Internet of Things (IoT) technologies, several large-scale smart city applications have been commercially developed in recent years. In these developments, the contracts are often disputed in the acceptance due to the fact that the contract specification is not clear, resulting in a great deal of discussion of the gray area. Such disputes often occur in the acceptance processes of smart buildings, mainly because most intelligent building systems are expensive and the operations of the sub-systems are very complex. This paper proposes SpecTalk, a platform that automatically generates the code to conform IoT applications to the Taiwan Association of Information and Communication Standards (TAICS) specifications. SpecTalk generates a program to accommodate the application programming interface of the IoT devices under test (DUTs). Then, the devices can be tested by SpecTalk following the TAICS data formats. We describe three types of tests: self-test, mutual-test, and visual test. A self-test involves the sensors and the actuators of the same DUT. A mutual-test involves the sensors and the actuators of different DUTs. A visual-test uses a monitoring camera to investigate the actuators of multiple DUTs. We conducted these types of tests in commercially deployed applications of smart campus constructions. Our experiments in the tests proved that SpecTalk is feasible and can effectively conform IoT implementations to TACIS specifications. We also propose a simple analytic model to select the frequency of the control signals for the input patterns in a SpecTalk test. Our study indicates that it is appropriate to select the control signal frequency, such that the inter-arrival time between two control signals is larger than 10 times the activation delay of the DUT.

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Web-based framework for smart parking system

International Journal of Information Technology ◽

10.1007/s41870-021-00725-8 ◽

2021 ◽

Author(s):

Awad Alharbi ◽

George Halikias ◽

Mohammad Yamin ◽

Adnan Ahmed Abi Sen

Keyword(s):

Web Based ◽

Parking System ◽

Smart Parking

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Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

Scientific Reports ◽

10.1038/s41598-020-79063-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qing Cheng ◽

Zeyi Liu ◽

Guangquan Cheng ◽

Jincai Huang

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Reproduction Number ◽

Correlation Method ◽

Control Measures ◽

Time Varying ◽

Short Period ◽

Significant Difference ◽

Prevention And Control Measures ◽

And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

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Deterministic and Empirical Approach for Millimeter-Wave Complex Outdoor Smart Parking Solution Deployments

Sensors ◽

10.3390/s21124112 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4112

Author(s):

Fidel Alejandro Rodríguez-Corbo ◽

Leyre Azpilicueta ◽

Mikel Celaya-Echarri ◽

Peio Lopez-Iturri ◽

Ana V. Alejos ◽

...

Keyword(s):

Large Scale ◽

Radio Channel ◽

Path Loss ◽

Ground Level ◽

Directional Antennas ◽

Scale Parameters ◽

Parking Lot ◽

Smart Parking ◽

Wireless Communications Systems

The characterization of different vegetation/vehicle densities and their corresponding effects on large-scale channel parameters such as path loss can provide important information during the deployment of wireless communications systems under outdoor conditions. In this work, a deterministic analysis based on ray-launching (RL) simulation and empirical measurements for vehicle-to-infrastructure (V2I) communications for outdoor parking environments and smart parking solutions is presented. The study was carried out at a frequency of 28 GHz using directional antennas, with the transmitter raised above ground level under realistic use case conditions. Different radio channel impairments were weighed in, considering the progressive effect of first, the density of an incremental obstructed barrier of trees, and the effect of different parked vehicle densities within the parking lot. On the basis of these scenarios, large-scale parameters and temporal dispersion characteristics were obtained, and the effect of vegetation/vehicle density changes was assessed. The characterization of propagation impairments that different vegetation/vehicle densities can impose onto the wireless radio channel in the millimeter frequency range was performed. Finally, the results obtained in this research can aid communication deployment in outdoor parking conditions.

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A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

Engineering Proceedings ◽

10.3390/ecsa-7-08261 ◽

2020 ◽

Vol 2 (1) ◽

pp. 92

Author(s):

Rahim Rahmani ◽

Ramin Firouzi ◽

Sachiko Lim ◽

Mahbub Alam

Keyword(s):

Large Scale ◽

Cluster Head ◽

Edge Computing ◽

Computing Technology ◽

Proximal Algorithm ◽

Data Intensive ◽

Distributed Ledger ◽

Smart Contract ◽

Distributed Ledger Technology ◽

Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

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