A Dependability Evaluation for OBD-II Edge Devices: An Internet of Intelligent Vehicles Perspective

Signoretti, Gabriel; Silva, Marianne Lucena da; Araujo, J. da C.; Silva, Ivanovitch; Silva, Diego Augusto Santos; Ferrari, Paolo; Sisinni, Emiliano

doi:10.1109/ladc48089.2019.8995679

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…We also found a proposal for an optimization approach for some dependability characteristics [86] and a theoretical approach based on Markov models to deal with vulnerabilities in a healthcare system [87]. [88], [89], [90], evaluate protocols [91], and communication interfaces [92]. Two papers compare different machine learning techniques for malicious data detection [93], [94], and another one compares machine learning techniques for data (image) reconstruction [95].…”

Section: ) Mathematical Approachmentioning

confidence: 99%

“…It also includes connection replication using different communication technology on the same IoT device [82], [83]. For example, in case of a connection failure in the LPWAN interface, the device may use a Wi-Fi or 4G connection to reestablish communication with other nodes [92].…”

Section: ) Redundancymentioning

confidence: 99%

“…The IoT may use different communication technologies with diverse packet formats and specificities. Some systems deploy IoT nodes with two or more communication protocols [92].…”

Section: A Connection Redundancymentioning

confidence: 99%

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A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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Section: ) Mathematical Approachmentioning

confidence: 99%

Section: ) Redundancymentioning

confidence: 99%

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A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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“…These sensors monitor the physical properties associated with their deployment environments—twenty-four hours a day, seven days a week—making them a critical facilitator to increase the volume of the data generated, due to the collection performed in a wide variety of scenarios [ 9 , 10 ]. Note that these data streams can form a large volume of data very rapidly, and as a consequence, they generate the need for a vast amount of disk storage space [ 11 , 12 ]. For example, accelerometers embedded in vehicles are constantly collecting vibration patterns for various types of roads.…”

Section: Introductionmentioning

confidence: 99%

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Signoretti

Silva

Andrade

et al. 2021

Sensors

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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

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“…No setor automotivo, em especial, essa transformaçãoé ainda mais notória. Os veículos modernos apresentam um elevado grau de instrumentação, centenas de sensores são embarcados em um típico veículo além de várias unidades de processamento (On Board Units -OBU) (Signoretti et al, 2019). Além disso, protocolos de comunicação habilitam a troca de informação entre veículos e os fabricantes, criando um novo cenário conhecido pela Internet dos Veículos Inteligentes (Internet of Intelligent Vehicles -IoIV em tradução para o idioma Inglês) (Zhou et al, 2020).…”

Section: Introductionunclassified

Uma plataforma para telemetria veicular e criação de indicadores de desempenho no contexto da Internet dos Veículos Inteligentes

Queiroz

Silva

Andrade

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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A Internet das Coisas (IoT, do inglês Internet of Things) vem provocando diversas transformações em vários setores industriais, como é o caso do automotivo. A quantidade de sensores e poder computacional que os veículos modernos apresentam cresce significativamente, municiando uma oportunidade de instrumentação, monitoramento e criação de algoritmos de diagnóstico cada vez mais eficientes. Os desafios emergem em diversas frentes, desde a coleta dos dados veiculares, processamento embarcado, comunicação com plataformas computacionalmente distribuídas e a análise propriamente dita dos dados. Assim, nesse trabalho é proposto uma plataforma para o monitoramento em tempo real da telemetria veicular assim como a criação de um indicador de desempenho relacionado com o perfil dos condutores. A validação da proposta foi conduzida com experimentos reais em trechos localizados em municípios da Paraíba, Rio Grande do Norte e com seis condutores e diversos veículos. Os resultados demonstraram a viabilidade da proposta assim como a identificação dos diferentes perfis de condução.

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A Dependability Evaluation for OBD-II Edge Devices: An Internet of Intelligent Vehicles Perspective

Cited by 8 publications

References 34 publications

A Survey on Trustworthiness for the Internet of Things

A Survey on Trustworthiness for the Internet of Things

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Uma plataforma para telemetria veicular e criação de indicadores de desempenho no contexto da Internet dos Veículos Inteligentes

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