Wire Arc Additive Manufacturing (WAAM) involves strongly coupled, nonlinear and time-varying thermal–geometric dynamics, making autonomous control of layer geometry and surface quality a persistent challenge. Existing data-driven and supervised learning approaches primarily address forward prediction and not well suited for real-time, multi-objective decision-making under evolving process conditions. This paper presents a model-based Markov Decision Process (MDP) framework for closed-loop, real-time optimization of WAAM process parameters, explicitly targeting the control of layer-geometry accuracy and reduction of surface roughness and interlayer heat-input variation. The proposed method formulates current and travel speed selection as sequential decision-making actions within an MDP, supported by Generalized Additive Models (GAMs) to predict layer height, width, and roughness for unseen parameter combinations. A novel multi-criteria reward function is designed to jointly penalize geometric deviation, surface undulations along both layer height and width, and abrupt interlayer heat-input changes, enabling an adaptive trade-off that static or single-objective models cannot capture. MDP is employed to compute optimal policies within the inter-pass cooling time, making the approach suitable for real-time deployment without GPU acceleration. Experimental validation on thin-walled SS308L structures demonstrates that the proposed MDP-based control strategy significantly outperforms open-loop deposition. Compared to conventional parameter selection, layer height and width accuracy improved by 2.12% and 10%, respectively, while average surface roughness was reduced by 10.74% along the width and 25.2% along the height. Statistical analysis confirms tighter confidence intervals for both geometry and roughness, indicating improved process stability. The results establish the proposed framework as a computationally efficient, autonomous decision-making controller for WAAM, advancing automation capabilities for high-precision metal additive manufacturing.

In this work, we propose “SocioLink”, a user-centric blockchain for the management of social media data. The current data management landscape faces various issues, including centralized control, data breaches, and limited data portability, which collectively erode user privacy and control over their personal information. User-centric solutions are potential responses to these issues; however, they often fall short of delivering resolutions to various challenges such as security, transparency, immutability and others. To resolve these issues, we propose SocioLink which seeks to address these issues with a blockchain-based system. By isolating user data from applications, SocioLink not only tackles the problems of centralized control, data breaches, and limited data portability; however, by enhancing immutability, transparency, and data accessibility, So-cioLink promises to elevate data sharing efficiency and scalability, ultimately transforming the landscape of global communications. Experimental results show a 66.6% reduction in mining time and a 53.8% better utilization of CPU.

In this work, we propose a reconfigurable blockchain (BC)—“PerBlocks”—for handling data from heterogeneous activities and achieving scalability as well as throughput in the industrial environment. Typically, industries deal with the pervasive deployment of various sensors, resulting in heterogeneous data with varying services. The adoption of conventional BC satisfies transparency, immutability, and security. However, static block size and stringent consensus algorithms are unable to serve the needs of heterogeneous activities. In order to resolve this, we propose reconfigurable service-oriented BC, which dynamically selects the consensus algorithm and block size according to the requirement of heterogeneous activities. Through experimental results, we demonstrate that the proposed method can utilize the resources efficiently and provide user-oriented services as well as scalability. In particular, the proposed method uses 28.2% CPU and 82% memory while reducing response time by 6%, compared to a conventional BC and improving the quality of services by 60%.

In this research work, we propose – UtilityChain –, a method for efficient resource allocation of miners involved in both mining and service-related activities. The aim of this approach is to enhance the miner's utility (overall efficiency) in response to the increasing need for real-time data processing generated by IoT devices. The security of the data is a crucial matter of concern, and the integration of blockchain technology for secure data storage and edge computing for real-time processing is deemed a suitable approach. However, the utilization of edge computing devices adds an extra cost burden. Additionally, fluctuations in resource prices and mining rewards frequently result in miner departures and underutilized resources. To tackle these challenges, UtilityChain employs the resources of miners for mining and providing services to the end-users, without the use of edge computing devices. This is achieved through the utilization of advanced deep reinforcement learning technique to dynamically allocate miner resources for both mining and service tasks. The experimental results demonstrate the efficacy of UtilityChain, with a resource allocation accuracy of 99.2% and a miner allocation accuracy of 98.8%. Additionally, UtilityChain exhibits a resource utilization rate of 8.2% for CPU and 78.2% for memory.

In this work, we proposeShadows, 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. Through lab-scale experiments, we demonstrate thatShadowsis capable of utilizing the resources efficiently and achieving real-time consensus. In particular,Shadowsuses 18% CPU and 92% memory while reducing consensus time by 56%, compared to a single conventional BC.Shadowsalso accesses the data efficiently by utilizing smart contracts and dynamically balances the load by migrating the virtual nodes. Further,Shadowsreduces the number of migrations to make the balance system by 67%.

Storing data from Industrial-Internet-of-Things (IIoT) sensors in blockchain (BC) for monitoring the applications leads to management issues like bloating. The crux of this work is generating traces (the part of industrial data) using an ARIMA model and storing only the metadata over the network, resulting in reduced delay and managed data. We determine the size of the traces for storing on the store and generate (S&G) blocks (blocks that store traces along with their metadata) by considering principal parameters, such as training time, block size, and error. In general, S&G consists of three phases: 1) categorizing the data into groups based on their sampling rates, 2) storing the trace of data and metadata into the blocks, and 3) retrieving the entire data. We demonstrate the feasibility of S&G with errors and regret in the range of 0.07–0.10 and 0.20–0.25, respectively, using the appropriate ARIMA model.

In this work, we propose – "CartelChain" – for the secure communication of blockchains (BCs) and achieving opti-mal throughput. With the advancement of BC technology, many industries are adopting it to maintain a secure, immutable, and decentralized system. Industries such as IoT, supply chain, and finance apply BC technology to maintain a decentralized database and automate different activities using smart contracts. However, storing data of different scenarios from the Industrial Internet of Things (IIoT) in separate BCs (multi-chains) leads to isolated data islands. This results in difficulty for these multi-chains to interact with one another efficiently and credibly. For the seamless operation of industries, it is of significant importance to achieve interoperability among different BCs. Toward achieving this, we propose a solution that utilizes smart contracts for enabling data exchange among various BCs. Further, for secure and reliable communication, we use encryption and an access control mechanism that makes the same more credible and reduces the latency compared with multi-chains sharing the data sequentially. Through experimental results, we demonstrate that the proposed method can utilize the resources more efficiently and reduce CPU as well energy usage by 8% and 6%, respectively. Apart from this, the throughput of the proposed method is 900 tps at 200 requests.

In this article, 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. Extensive experimental results using open industrial data sets demonstrate the effectiveness of A-Blocks with 8% CPU and 78% memory consumptions on resource-constrained devices. Furthermore, compared to the existing methods, although A-Blocks increases energy consumption by 11%, it also reduces mining time by 7%.