Data trustworthiness in IoT

Suhail, Sabah; Hong, Choong Seon; Lodhi, M. Ali; Zafar, Faheem; Khan, Abid; Bashir, Faisal

doi:10.1109/icoin.2018.8343151

Cited by 19 publications

(19 citation statements)

References 14 publications

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“…In this subsection, we address the challenges and constraints of a Provenance-Aware IoT system keeping in view the provenance as overhead for IoT devices that are highly constrained in terms of physical size, available memory, CPU power, and battery life. We have also identified requirements and challenges for the integration of secure provenance with IoT in [6] and [16] thoroughly.…”

Section: Challenges and Constraints Of A Provenance-aware Iot Systemmentioning

confidence: 99%

“…Another way is to embed the node ID of each participating node in the packet traversal called as Provenance as ID (PID) [16]. However, it will increase the provenance size exponentially at each hop count.…”

Section: Pppt: Provenance Encodingmentioning

confidence: 99%

“…For instance, in the IoT, things are globally accessible, operating in an unattended environment, resource constrained in nature, and connected through the lossy links. Keeping in view the constraints associated with IoT and the challenges associated with provenance collection mechanism [16], it is more feasible to take into account the recommended underlying protocol for IoT devices such as RPL. Such consideration allows the exploitation of existing infrastructure of the pro-tocol, and hence, put less burden on the resource-consuming nodes during provenance collection and validation.…”

mentioning

confidence: 99%

“…• We provide a comparison in terms of provenance generation time and average power consumption between RPL-only and the proposed provenanceenabled RPL PPPT scheme. • We also compare our approach with bloom filter (BF) based techniques [10], [11] and Provenance as ID (PID) [16] in terms of provenance size.…”

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confidence: 99%

“…Under such circumstances, provenance data can help to identify the root cause of disruption in the system. Other supporting examples elucidating the need for provenance are discussed in [16].…”

mentioning

confidence: 99%

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Provenance-enabled packet path tracing in the RPL-based internet of things

et al. 2020

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The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make them vulnerable to attacks including data forging, false data injection, and packet drop that affects applications with critical decisionmaking processes. For data trustworthiness, reliance on provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenges, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to network disruptions) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, a system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of the packet sequence number is enclosed as a routing entry in the routing table of each participating node. Lossless in nature, both approaches conserve the provenance size satisfying processing and storage requirements of IoT devices. Finally, we evaluate the efficacy of the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.

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Section: Challenges and Constraints Of A Provenance-aware Iot Systemmentioning

confidence: 99%

Section: Pppt: Provenance Encodingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Provenance-enabled packet path tracing in the RPL-based internet of things

et al. 2020

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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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Trustworthiness in Sensor Networks A Reputation-Based Method for Weather Stations

Figueiredo

Rodríguez

2020

2020 International Conference on Omni-Layer Intelligent Systems (COINS)

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Data trustworthiness in IoT

Cited by 19 publications

References 14 publications

Provenance-enabled packet path tracing in the RPL-based internet of things

Provenance-enabled packet path tracing in the RPL-based internet of things

A Survey on Trustworthiness for the Internet of Things

Trustworthiness in Sensor Networks A Reputation-Based Method for Weather Stations

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