Optimisation of Automation Devices Based on IOT Big Data Algorithms

With the continuous development of communication, sensor and caching technologies, IoT technology has gained rapid opportunities for growth and a huge digital revolution has taken place at all levels of society. Blockchain technology has emerged rapidly in recent years and can be seen as a distributed, time-based ledger of distributed data. It utilises technologies such as consensus protocols, modern cryptography, P2P and smart contracts, which can provide a secure, stable, transparent, auditable and low-consumption system architecture that has a traceable, stable and efficient security management capability, and can provide a new solution to identity security for the Internet of Things. This paper absorbs the existing blockchain-based access management approach and improves it, proposing a new private chain-based security management approach that solves the problems of access dynamics, low intelligence and high overhead in the traditional access management approach. This paper designs a new control management architecture, the Novel-Capability-Based Access Control (NCBAC), which draws on the microkernel and microservice ideas of operating systems. Firstly, this paper abstracts the concept of management node to solve the problem of weak computing power and low storage performance of IoT devices that cannot meet the difficulty of direct communication between IoT devices and blockchain, and at the same time can reduce the network operation overhead; secondly, it constructs a multi-level smart contract system and designs three kinds of smart contracts, AC, ACC and AMC, to build a trusted and reliable access control entity model; finally, it adopts radial basis based (RBF) neural network and combines with access policy to dynamically generate the credit degree threshold of access nodes to build an intelligent access authority management model for IoT mass sensors. The model proposed in this paper designs a token mechanism based on the fact that IoT systems have multiple requests within a short period of time in a real production environment, which, according to experimental results, improves the performance of the system to a certain extent

Decentralized Identity and Access Management Framework for Internet of Things Devices

The emerging Internet of Things (IoT) domain is about connecting people and devices and systems together via sensors and actuators, to collect meaningful information from the devices surrounding environment and take actions to enhance productivity and efficiency. The proliferation of IoT devices from around few billion devices today to over 25 billion in the next few years spanning over heterogeneous networks defines a new paradigm shift for many industrial and smart connectivity applications. The existing IoT networks faces a number of operational challenges linked to devices management and the capability of devices’ mutual authentication and authorization. While significant progress has been made in adopting existing connectivity and management frameworks, most of these frameworks are designed to work for unconstrained devices connected in centralized networks. On the other hand, IoT devices are constrained devices with tendency to work and operate in decentralized and peer-to-peer arrangement. This tendency towards peer-to-peer service exchange resulted that many of the existing frameworks fails to address the main challenges faced by the need to offer ownership of devices and the generated data to the actual users. Moreover, the diversified list of devices and offered services impose that more granular access control mechanisms are required to limit the exposure of the devices to external threats and provide finer access control policies under control of the device owner without the need for a middleman. This work addresses these challenges by utilizing the concepts of decentralization introduced in Distributed Ledger (DLT) technologies and capability of automating business flows through smart contracts. The proposed work utilizes the concepts of decentralized identifiers (DIDs) for establishing a decentralized devices identity management framework and exploits Blockchain tokenization through both fungible and non-fungible tokens (NFTs) to build a self-controlled and self-contained access control policy based on capability-based access control model (CapBAC). The defined framework provides a layered approach that builds on identity management as the foundation to enable authentication and authorization processes and establish a mechanism for accounting through the adoption of standardized DLT tokenization structure. The proposed framework is demonstrated through implementing a number of use cases that addresses issues related identity management in industries that suffer losses in billions of dollars due to counterfeiting and lack of global and immutable identity records. The framework extension to support applications for building verifiable data paths in the application layer were addressed through two simple examples. The system has been analyzed in the case of issuing authorization tokens where it is expected that DLT consensus mechanisms will introduce major performance hurdles. A proof of concept emulating establishing concurrent connections to a single device presented no timed-out requests at 200 concurrent connections and a rise in the timed-out requests ratio to 5% at 600 connections. The analysis showed also that a considerable overhead in the data link budget of 10.4% is recorded due to the use of self-contained policy token which is a trade-off between building self-contained access tokens with no middleman and link cost

Competitive MA-DRL for Transmit Power Pool Design in Semi-Grant-Free NOMA Systems

In this paper, we exploit the capability of multi-agent deep reinforcement learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free non-orthogonal multiple access (SGF-NOMA). The PP is mapped with each resource block (RB) to achieve distributed transmit power control (DPC). We first formulate the resource (sub-channel and transmit power) selection problem as stochastic Markov game, and then solve it using two competitive MA-DRL algorithms, namely double deep Q network (DDQN) and Dueling DDQN. Each GF user as an agent tries to find out the optimal transmit power level and RB to form the desired PP. With the aid of dueling processes, the learning process can be enhanced by evaluating the valuable state without considering the effect of each action at each state. Therefore, DDQN is designed for communication scenarios with a small-size action-state space, while Dueling DDQN is for a large-size case. Our results show that the proposed MA-Dueling DDQN based SGF-NOMA with DPC outperforms the SGF-NOMA system with the fixed-power-control mechanism and networks with pure GF protocols with 17.5% and 22.2% gain in terms of the system throughput, respectively. Moreover, to decrease the training time, we eliminate invalid actions (high transmit power levels) to reduce the action space. We show that our proposed algorithm is computationally scalable to massive IoT networks. Finally, to control the interference and guarantee the quality-of-service requirements of grant-based users, we find the optimal number of GF users for each sub-channel

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications