Secure Data Aggregation Mechanism for Water Distribution System using Blockchain

Development of intelligent systems in particular Water Distribution Systems (WDS) increases the demand of implementing a secure scheme that can preserve user’s identification and data consumption through maintaining confidentiality, authentication and integrity. Decentralization topology has investigated a lot recently in the literature with the development of bitcoins and Ethereum networks in different IoT disciplines such as power systems and healthcare systems. In this paper, feasibility and uses cases studies on the integration WDS with Blockchain Technology are discussed. Moreover, the customer’s data and identity anonymity techniques that can be integrated with the network are discussed. Furthermore, a data aggregation mechanism of the smart meters in Water Distribution System (WDS) based on distributed ledger and Blockchain technologies is proposed. Further, the customer’s identity using bloom filter is simulated and optimal parameters of the bloom filter are suggest

SECURITY RESEARCH FOR BLOCKCHAIN IN SMART GRID

Smart grid is a power supply system that uses digital communication technology to detect and react to local changes for power demand. Modern and future power supply system requires a distributed system for effective communication and management. Blockchain, a distributed technology, has been applied in many fields, e.g., cryptocurrency exchange, secure sharing of medical data, and personal identity security. Much research has been done on the application of blockchain to smart grid. While blockchain has many advantages, such as security and no interference from third parties, it also has inherent disadvantages, such as untrusted network environment, lacking data source privacy, and low network throughput.In this research, three systems are designed to tackle some of these problems in blockchain technology. In the first study, Information-Centric Blockchain Model, we focus on data privacy. In this model, the transactions created by nodes in the network are categorized into separate groups, such as billing transactions, power generation transactions, etc. In this model, all transactions are first encrypted by the corresponding pairs of asymmetric keys, which guarantees that only the intended receivers can see the data so that data confidentiality is preserved. Secondly, all transactions are sent on behalf of their groups, which hides the data sources to preserve the privacy. Our preliminary implementation verified the feasibility of the model, and our analysis demonstrates its effectiveness in securing data source privacy, increasing network throughput, and reducing storage usage. In the second study, we focus on increasing the network’s trustworthiness in an untrusted network environment. A reputation system is designed to evaluate all node’s behaviors. The reputation of a node is evaluated on its computing power, online time, defense ability, function, and service quality. The performance of a node will affect its reputation scores, and a node’s reputation scores will be used to assess its qualification, privileges, and job assignments. Our design is a relatively thorough, self-operated, and closed-loop system. Continuing evaluation of all node’s abilities and behaviors guarantees that only nodes with good scores are qualified to handle certain tasks. Thus, the reputation system helps enhance network security by preventing both internal and external attacks. Preliminary implementation and security analysis showed that the reputation model is feasible and enhances blockchain system’s security. In the third research, a countermeasure was designed for double spending. Double spending is one of the two most concerned security attacks in blockchain. In this study, one of the most reputable nodes was selected as detection node, which keeps checking for conflict transactions in two consecutive blocks. Upon a problematic transaction was discovered, two punishment transactions were created to punish the current attack behavior and to prevent it to happen in future. The experiment shows our design can detect the double spending effectively while using much less detection time and resources

Blockchain for secured IoT and D2D applications over 5G cellular networks : a thesis by publications presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Author's Declaration: "In accordance with Sensors, SpringerOpen, and IEEE’s copyright policy, this thesis contains the accepted and published version of each manuscript as the final version. Consequently, the content is identical to the published versions."The Internet of things (IoT) is in continuous development with ever-growing popularity. It brings significant benefits through enabling humans and the physical world to interact using various technologies from small sensors to cloud computing. IoT devices and networks are appealing targets of various cyber attacks and can be hampered by malicious intervening attackers if the IoT is not appropriately protected. However, IoT security and privacy remain a major challenge due to characteristics of the IoT, such as heterogeneity, scalability, nature of the data, and operation in open environments. Moreover, many existing cloud-based solutions for IoT security rely on central remote servers over vulnerable Internet connections. The decentralized and distributed nature of blockchain technology has attracted significant attention as a suitable solution to tackle the security and privacy concerns of the IoT and device-to-device (D2D) communication. This thesis explores the possible adoption of blockchain technology to address the security and privacy challenges of the IoT under the 5G cellular system. This thesis makes four novel contributions. First, a Multi-layer Blockchain Security (MBS) model is proposed to protect IoT networks while simplifying the implementation of blockchain technology. The concept of clustering is utilized to facilitate multi-layer architecture deployment and increase scalability. The K-unknown clusters are formed within the IoT network by applying a hybrid Evolutionary Computation Algorithm using Simulated Annealing (SA) and Genetic Algorithms (GA) to structure the overlay nodes. The open-source Hyperledger Fabric (HLF) Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The quantitative arguments demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported methods. The proposed lightweight blockchain model is also better suited to balance network latency and throughput compared to a traditional global blockchain. Next, a model is proposed to integrate IoT systems and blockchain by implementing the permissioned blockchain Hyperledger Fabric. The security of the edge computing devices is provided by employing a local authentication process. A lightweight mutual authentication and authorization solution is proposed to ensure the security of tiny IoT devices within the ecosystem. In addition, the proposed model provides traceability for the data generated by the IoT devices. The performance of the proposed model is validated with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results indicate that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios. Despite the increasing development of blockchain platforms, there is still no comprehensive method for adopting blockchain technology on IoT systems due to the blockchain's limited capability to process substantial transaction requests from a massive number of IoT devices. The Fabric comprises various components such as smart contracts, peers, endorsers, validators, committers, and Orderers. A comprehensive empirical model is proposed that measures HLF's performance and identifies potential performance bottlenecks to better meet blockchain-based IoT applications' requirements. The implementation of HLF on distributed large-scale IoT systems is proposed. The performance of the HLF is evaluated in terms of throughput, latency, network sizes, scalability, and the number of peers serviceable by the platform. The experimental results demonstrate that the proposed framework can provide a detailed and real-time performance evaluation of blockchain systems for large-scale IoT applications. The diversity and the sheer increase in the number of connected IoT devices have brought significant concerns about storing and protecting the large IoT data volume. Dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. A layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system is proposed to mitigate these challenges by using the HLF platform for distributed ledger solutions. The need for a centralized server and third-party auditor is eliminated by leveraging HLF peers who perform transaction verification and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata is stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of throughput, latency, communication, and computation costs. The results indicate the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the big data ecosystem using the HLF blockchain