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Register a new userTrusted Wireless Monitoring based on Blockchain over NB-IoT Connectivity
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Added by
Lam Duc Nguyen
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The data collected from Internet of Things (IoT) devices on various emissions or pollution, can have a significant economic value for the stakeholders. This makes it prone to abuse or tampering and brings forward the need to integrate IoT with a Distributed Ledger Technology (DLT) to collect, store, and protect the IoT data. However, DLT brings an additional overhead to the frugal IoT connectivity and symmetrizes the IoT traffic, thus changing the usual assumption that IoT is uplink-oriented. We have implemented a platform that integrates DLTs with a monitoring system based on narrowband IoT (NB-IoT). We evaluate the performance and discuss the tradeoffs in two use cases: data authorization and real-time monitoring.
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Distributed ledger systems (i.e., blockchains) have received a lot of attention recently. They promise to enable mutually untrusted participants to execute transactions, while providing the immutability of the transaction history and censorship resistance. Although decentralized ledgers may become a disruptive innovation, as of today, they suffer from scalability, privacy, or governance issues. Therefore, they are inapplicable for many important use cases, where interestingly, centralized ledger systems quietly gain adoption and find new use cases. Unfortunately, centralized ledgers have also several drawbacks, like a lack of efficient verifiability or a higher risk of censorship and equivocation. In this paper, we present Aquareum, a novel framework for centralized ledgers removing their main limitations. By combining a trusted execution environment with a public blockchain platform, Aquareum provides publicly verifiable, non-equivocating, censorship-evident, private, and high-performance ledgers. Aquareum ledgers are integrated with a Turing-complete virtual machine, allowing arbitrary transaction processing logics, including tokens or client-specified smart contracts. Aquareum is fully implemented and deployment-ready, even with currently existing technologies.
Blockchain technology has drawn attention fromvarious communities. The underlying consensus mechanism inBlockchain enables a myriad of applications for the integrityassurance of stored data. In this paper, we utilize Blockchaintechnology to verify the authenticity of a video captured by astreaming IoT device for forensic investigation purposes. Theproposed approach computes the hash of video frames beforethey leave the IoT device and are transferred to a remote basestation. To guarantee the transmission, we ensure that this hashis sent through a TCP-based connection. The hash is then storedon multiple nodes on a permissioned blockchain platform. Incase the video is modified, the discrepancy will be detected byinvestigating the previously stored hash on the blockchain andcomparing it with the hash of the existing frame in question.In this work, we present the prototype as proof-of-concept withexperiment results. The system has been tested on a RaspberryPi with different quality of videos to evaluate performance. Theresults show that the concept can be implemented with moderatevideo resolutions.
Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as users attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.
Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can be used to secure these devices in a plug-and-protect manner. However, anomaly detection models must be trained for a long time in order to capture all benign behaviors. Furthermore, the anomaly detection model is vulnerable to adversarial attacks since, during the training phase, all observations are assumed to be benign. In this paper, we propose (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to ensemble an anomaly detection model in a distributed environment. Blockchain framework incrementally updates a trusted anomaly detection model via self-attestation and consensus among the IoT devices. We evaluate our method on a distributed IoT simulation platform, which consists of 48 Raspberry Pis. The simulation demonstrates how the approach can enhance the security of each device and the security of the network as a whole.
The application of machine learning (ML) algorithms are massively scaling-up due to rapid digitization and emergence of new tecnologies like Internet of Things (IoT). In todays digital era, we can find ML algorithms being applied in the areas of healthcare, IoT, engineering, finance and so on. However, all these algorithms need to be trained in order to predict/solve a particular problem. There is high possibility of tampering the training datasets and produce biased results. Hence, in this article, we have proposed blockchain based solution to secure the datasets generated from IoT devices for E-Health applications. The proposed blockchain based solution uses using private cloud to tackle the aforementioned issue. For evaluation, we have developed a system that can be used by dataset owners to secure their data.
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