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With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste more efficiently has become the top concern of municipalities and other relevant departments. In this article, the above problem is transformed into the municipal waste collection vehicle routing problem (MWCVRP) to solve the problem with the minimum total waste transportation cost. Because the carrying capacity of different models is different, this article introduces a cost calculation criterion that combines the total mileage of different models of transport vehicles and the number of station services. A multi-model garbage truck path optimization model is established, and then a heuristic-based task dynamic assignment algorithm is designed to solve the problem. The Solomon dataset is used to verify the feasibility and effectiveness of the model and algorithm through experiments.

Many real-world applications require prediction that takes the most advantage of data. Classic data mining mechanisms tend to feed a prediction model pivotal data to achieve a promising result, which needs to be adjusted in different application scenarios. Recent studies have shown the potential of I Ching mechanism to improve prediction capacity. However, the I Ching prediction mechanism has several issues, including underutilized I Ching knowledge and incomplete data conversion. To address these issues, the authors designed a model to leverage I Ching knowledge and transform traditional I Ching prediction processing into data mining. The authors' investigation revealed promising results in the stock market compared to popular machine learning and deep learning algorithms such as support vector machine (SVM), extreme gradient boosting (XGBoost), and long short-term memory (LSTM).

The interplanetary file system (IPFS) has been used more and more widely because of its advantages of smooth integration with the current blockchain platform and its advantages as a distributed file system. However, the authors found that IPFS has some privacy issues; it cannot completely avoid unauthorised access to data by malicious nodes. In response to this problem, the authors propose a lightweight encryption scheme based on the characteristics of IPFS file slicing combined with AES256 and SHA256, which can be smoothly integrated into IPFS. During the upload process, this scheme encrypts some sliced file blocks according to the strategy formulated by the user. During the download process, the encrypted block is identified and decrypted according to a special encryption method. Through this scheme, the system can increase file security without affecting the performance of IPFS itself and retain the deduplication effect of IPFS to the utmost extent.

To design an efficient protocol for sharing the encrypted lock keys in the renting house system, we introduce a new notion called time- and identity-based, proxy reencryption (TIPRE) and the blockchain platform. Our CPA secure TIPRE scheme is constructed from Green et al.'s identity-based proxy reen-cryption scheme by adding the time property. In every time period, a time stamp authority generates a public key embedded with the current time stamp for each user. In our protocol for the renting house system, the TIPRE scheme is the primary building block, and the blockchain platform serves instead of a trusted third party, such as a real estate agency between landlords and tenants. The TIPRE scheme allows the landlord to change the lock key at each time period for safety. The blockchain platform allows the landlords and tenants to directly interact, and all of the interactions are recorded in the blockchain database to provide the desired security requirements, such as non-repudiation and unforgeability. Finally, we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.