Towards sustainable smart cities: A secure and scalable trading system for residential homes using blockchain and artificial intelligence

This paper proposes a secure blockchain based energy trading system for residential homes. In the system, a new proof-of-computational closeness (PoCC) consensus protocol is proposed for the selection of miners and the creation of blocks. Moreover, an analytical energy pricing policy is designed to solve the problem of existing energy pricing policies in a distributed trading environment. A dynamic multi-pseudonym mechanism is developed for the prosumers to preserve their transactional privacy during energy trading. Since it requires extra storage and computing resources for the blockchain miners to simultaneously execute both mining process and application intensive tasks, therefore, an improved sparse neural network (ISNN) is proposed for computation offloading to the cloud servers. In ISNN, a Jaya optimization algorithm is used to accelerate the error convergence rate while reducing the number of connections between different layers of neurons. Besides, ISNN optimizes the overall computational cost of the system. Furthermore, the security of the prosumers is ensured using blockchain technology while security analysis shows that the system is robust against the Sybil attack. The proposed blockchain based peer-to-peer secure energy trading system is extremely important for sustainable cities and society. Simulations are conducted to evaluate the effectiveness of the proposed system. The proposed pricing policy is compared with time-of-use pricing, critical peak pricing and real-time pricing policies. From the results, it is proved that the prosumers achieve a higher degree of satisfaction and utility when using the proposed pricing policy. Moreover, the probability of a successful Sybil attack is zero as the number of attackers’ identities and computational capacities increases. Under different sizes of data to be uploaded, the proposed ISNN scheme has the least average computational cost and data transmission time as compared to deep reinforcement learning combined with genetic algorithm (DRGO) and sparse evolutionary training and multi-layer perceptron (SET-MLP) schemes in the literature. Moreover, the proposed system is tested for scalability by increasing the number of prosumers. Extensive simulations are performed and the results depict the satisfactory performance of the proposed system.