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Register a new userA Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain
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Added by
Gadekallu Thippa Reddy
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.
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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.
IoT devices have been adopted widely in the last decade which enabled collection of various data from different environments. The collected data is crucial in certain applications where IoT devices generate data for critical infrastructure or systems whose failure may result in catastrophic results. Specifically, for such critical applications, data storage poses challenges since the data may be compromised during the storage and the integrity might be violated without being noticed. In such cases, integrity and data provenance are required in order to be able to detect the source of any incident and prove it in legal cases if there is a dispute with the involved parties. To address these issues, blockchain provides excellent opportunities since it can protect the integrity of the data thanks to its distributed structure. However, it comes with certain costs as storing huge amount of data in a public blockchain will come with significant transaction fees. In this paper, we propose a highly cost effective and reliable digital forensics framework by exploiting multiple inexpensive blockchain networks as a temporary storage before the data is committed to Ethereum. To reduce Ethereum costs,we utilize Merkle trees which hierarchically stores hashes of the collected event data from IoT devices. We evaluated the approach on popular blockchains such as EOS, Stellar, and Ethereum by presenting a cost and security analysis. The results indicate that we can achieve significant cost savings without compromising the integrity of the data.
Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Blockchain has received tremendous attention as a secure, distributed, and anonymous framework for the Internet of Things (IoT). As a distributed system, blockchain trades off scalability for distribution, which limits the technologys adaptation for large scale networks such as IoT. All transactions and blocks must be broadcast and verified by all participants which limits scalability and incurs computational and communication overheads. The existing solutions to scale blockchains have so far led to partial recentralization, limiting the technologys original appeal. In this paper, we introduce a distributed yet scalable Verification and Communication architecture for blockchain referred to as Vericom. Vericom concurrently achieves high scalability and distribution using hash function outputs to shift blockchains from broadcast to multicast communication. Unlike conventional blockchains where all nodes must verify new transactions/blocks, Vericom uses the hash of IoT traffic to randomly select a set of nodes to verify transactions/blocks which in turn reduces the processing overhead. Vericom incorporates two layers: i) transmission layer where a randomized multicasting method is introduced along with a backbone network to route traffic, i.e., transactions and blocks, from the source to the destination, and ii) verification layer where a set of randomly selected nodes are allocated to verify each transaction or block. The performance evaluation shows that Vericom reduces the packet and processing overhead as compared with conventional blockchains. In the worst case, packet overhead in Vericom scales linearly with the number of nodes while the processing overhead remains scale-independent.
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.
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