UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities

Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration factors that impact device entry in smart cities, such as device performance, load, Bluetooth signal strength, and scan frequency. Despite existing techniques for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on entropy weights methods are improved and applied, there exist unstable decision results. Due to this, we propose a technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-based on utility function and entropy weights, named UETOPSIS, where the corresponding utility function is applied according to the influence of each attribute on the decision, ensuring the stability of the ranking of decision results. We rely on an entropy-based weights mechanism to select a suitable master controller for the design of the multi-control protocol in the smart city system, and utilize a utility function to calculate the attribute values and then combine the normalized attribute values of utility numbers, starting by analyzing the main work of the controllers. Lastly, a prototype is developed for performance evaluation purposes. Experimental evaluation and analysis show that the proposed work has better authenticity and reliability than existing works and can reduce the workload of edge computing devices when forwarding data, with stability 24.7% higher than TOPSIS, significantly improving the performance and stability of system fault tolerance and reliability in smart cities, as the second-ranked controller can efficiently take over the work when a central controller fails or damaged.