Self-Corrective Dynamic Networks via Decentralized Reverse Computations

The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation of aggregation functions, e.g. summation, are linked to the actual nodes connected in the network, for instance, counting the sum using input values from only connected nodes. Reverse computations are required to maintain a high aggregation accuracy when nodes leave or fail. This paper introduces an autonomic agent-based model for highly dynamic self-corrective networks using decentralized reverse computations. The model is generic and equips the nodes with the capability to disseminate connectivity status updates in the network. Highly resilient agents to the dynamic network migrate to remote nodes and orchestrate reverse computations for each node leave or failure. In contrast to related work, no other computational resources or redundancy are introduced. The self-corrective model is experimentally evaluated using real-world data from a smart grid pilot project under highly dynamic network adjustments that correspond to catastrophic events with up to 50% of the nodes leaving the network. The model is highly agile and modular and is applied to the large-scale decentralized aggregation network of DIAS, the Dynamic Intelligent Aggregation Service, without major structural changes in its design and operations. Results confirm the outstanding improvement in the aggregation accuracy when self-corrective actions are employed with a minimal increase in communication overhead

Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends

Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored