In this work, we propose SEGA, a secured edge gateway microservices architecture for Industrial IoT-based monitoring of machines in industries. SEGA allows the secured collection, transmission, and temporary storage of data within the edge network. A KNN-based analytics module hosted on the edge gateway processes time-sensitive machine monitoring data on the gateway itself and identifies machines’ operational status.

In this paper, we propose a dynamic consensus-based blockchain system -A-Blocks -for efficiently managing the data produced by the sensors in an Industrial Internet of Things (IIoT) environment. Typically, industries deal with a heterogeneous set of data from a diverse range of sensors. Conventional blockchain adoptions are a popular choice in such scenarios for data security while satisfying both transparency and immutability. However, stringent consensus algorithms are inadequate for managing heterogeneous data, especially due to its implicit constraints. For instance, while PoW provides inevitable security and is highly distributive, it is not scalable and requires more energy. In contrast, PoS is energy-efficient but has reduced scalability and PBFT is suitable for faster processing. A-Blocks exploits the features of the available consensus algorithms and dynamically selects the best one in real-time. It operates in two phases: 1) Categorizing the data into groups based on their traits and then 2) Selecting the appropriate consensus algorithm.

In this work, we propose Shadows, a virtual blockchain (VC) for achieving parallel consensus and efficient management of data in industries by utilizing BC. Typically, industrial processes involve heterogeneous activities which require real-time consensus, managed execution, isolation, data sharing, accelerated computation, and efficient utilization of various computational resources such as CPU, RAM, and storage. Achieving these in real-time using a single conventional blockchain (BC) leads to the exertion of computational power. To achieve resource-efficient real-time consensus, we virtualize the nodes of the BC network and create different BC for various activities. Further, to virtualize BC and provide better access to data, we propose smart contracts liable for providing a unified view of a single BC, dynamically creating BCs, allocating resources to these, and making communication between the same.

In this work, we propose a method for communicating between multiple heterogeneous blockchains (BCs) (BCs with different consensus mechanisms) and sharing data between them in a secure manner. To achieve this, we propose a smart contract that is capable of providing access rights to the BC nodes in the system and enabling communication between them. Any node from a BC that attempts to communicate with other BCs first participates in a network called the routing network. Then the node from sender BC sends a simple request to the smart contract deployed in the routing BC. The smart contract checks the authorization access for the node, and based on that, the routing BC collects the required data from the BC and encodes it in a specified format. Further, it sends the data to the nearest node of target BC based on routing information that is stored in the form of a smart contract in the routing BC. The data packet then gets decoded according to the protocol stored in the target BC and gets added to the BC as a new block.

In this paper, we propose Store and Generate (S\&G) blocks, a method for addressing the challenges mentioned earlier. The crux of this work is the ability of an ARIMA model to generate sequences from a given time-series data. We strategically select portions (or traces) of the industrial data for storing on the blocks of a BC along with the metadata for training the model (using the same trace). The same is repeated for the next batch of sensor data. We use metadata for generating back the sensor data and concatenating it with the traces before presenting it on the screen on request from the end-users. We determine the minimal size of the traces by considering the training time, block size, and error (after generation). In summary, S\&G does not require the need to store the sensor data over distributed resource-constrained BC deployments in its entirety and only stores the traces and the necessary metadata, which reduces the overall storage size.