A New Measure for Determining the Equivalent Symmetry of Decomposed Subsystems from Large Complex Cyber–Physical Systems

In this paper, we propose a new consistency measurement for classification rule sets that is based on the similarity of their classification abilities. The similarity of the classification abilities of the two rule sets is evaluated though the similarity of the corresponding partitions of the feature space using the different rule sets. The proposed consistency measure can be used to measure the equivalent symmetry of subsystems decomposed from a large, complex cyber–physical system (CPS). It can be used to verify whether the same knowledge is obtained by the sensing data in the different subsystems. In the experiments, five decision tree algorithms and eighteen datasets from the UCI machine learning repository are employed to extract the classification rules, and the consistency between the corresponding rule sets is investigated. The classification rule sets extracted from the use of the C4.5 algorithm on the electrical grid stability dataset have a consistency of 0.88, which implies that the different subsystems contain almost equivalent knowledge about the network stability.

Fog-Computing-Based Cyber–Physical System for Secure Food Traceability through the Twofish Algorithm

The Internet is an essential part of daily life with the expansion of businesses for maximizing profits. This technology has intensely altered the traditional shopping style in online shopping. Convenience, quick price comparison, saving traveling time, and avoiding crowds are the reasons behind the abrupt adoption of online shopping. However, in many situations, the product provided does not meet the quality, which is the primary concern of every customer. To ensure quality product provision, the whole food supply chain should be examined and managed properly. In food traceability systems, sensors are used to gather product information, which is forwarded to fog computing. However, the product information forwarded by the sensors may not be similar, as it can be modified by intruders/hackers. Moreover, consumers are interested in the product location, as well as its status, i.e., manufacturing date, expiry date, etc. Therefore, in this paper, data and account security techniques were introduced to efficiently secure product information through the Twofish algorithm and dual attestation for account verification. To improve the overall working, the proposed mechanism integrates fog computing with novel modules to efficiently monitor the product, along with increasing the efficiency of the whole working process. To validate the performance of the proposed system, a comparative simulation was performed with existing approaches in which Twofish showed notably better results in terms of encryption time, computational cost, and the identification of modification attacks.