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Recently, with the rapid expansion of cities, optimizing taxi driving routes for improving taxi revenue efficiency has become the core issue of taxi system. However, most current research focuses on increasing platform revenue instead of improving drivers’ revenue in a centralized dispatch taxi system just like DiDi, which results in a slower driver income growth and greater difficulties for recruiting drivers. To solve this problem, we propose a strategy of deep reinforcement learning based on driver mode. Firstly, the sequence selection process of drivers is modeled as markov decision-making process in driver mode. Then, we propose a learning scheme based on deep Q network to optimize the driver's decision-making strategy. We know that the real selection of historical taxi drivers is very helpful to the selection of current taxi drivers, so we choose the historical record of the current location as the edge data to update the edge network. Finally, we used a real data set generated by more than 1,400 taxis in Changsha. The simulation experiments show that our scheme reduced cruising time of taxis and improved the driver's income by 4-5%. The carbon emissions are obviously reduced by saving almost 6% fuel consumption, which contributes significantly to green mobility.

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents’ communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

Gathering extensive public data from the IoT devices scattered throughout a city is a significant challenge in a smart city. In this paper, a novel urban vehicle network we call the location-based urban vehicle network (LUV), is proposed to perform the non-real time data gathering task in smart cities. Different from the most works in-vehicle networks, the empirical research on the real-life 8900 private cars trace data in Changsha, China, compels us to focus on the parked vehicles and the parking places, rather than on the moving cars and the urban roads. The location-based mechanism not only provides more reliable and predictable wireless connections but also dramatically simplifies the system topology. It ensures that the vehicle network deployed on an intricate metropolitan area reaches the desired scale for gathering data.

Taxi drivers always look for strategies to locate passengers quickly and therefore increase their profit margin. In reality, the passenger seeking strategies are mostly empirical and substantially vary among taxi drivers. From the history taxi data, the top performing taxi drivers can earn 25% more than the ones with mediocre seeking strategy in the same period of time. A better strategy not only helps taxi drivers earn more with less effort, but also reduce fuel consumption and carbon emissions. It is interesting to examine the influential factors in passenger seeking strategies and find algorithms to guide taxi drivers to passenger hotspots with the right timing. With the abundant availability of history taxicab traces, the existing methods of doing taxi business have been radically changed. This paper focuses on the problem of mining efficient operation strategies from a large-scale history taxi traces collected over one year. Our approach presents generic insights into the dynamics of taxicab services with the objective of maximizing the profit margins for the concerned parties. We propose important metrics, such as trip frequency, hot spots, and taxi mileage, and provide valuable insights toward more efficient operation strategies. We analyze these metrics using techniques, such as Newton's polynomial interpolation and Gamma distribution, to understand their dynamics. Our strategies use the real taxicab traces from the city of Changsha (P.R.China), may predict the taxi rides at different times by 90.68% per day, and increase the taxi drivers income levels up to 19.38% by controlling appropriate mileage per trip and following the route across more urban hot spots.

Big data applications have become increasingly popular with the appearance of cloud computing and green computing. Therefore, internet service providers (ISPs) need to build data centers for data storage and data processing under the cloud service pattern. However, data centers often consume a significant amount of energy and lead to pollutant emissions. In recent years, the high energy consumption and environmental pollution of data centers have become a pressing issue. This paper reviews the progress of energy-saving technologies in high-performance computing, energy conservation technologies for computer rooms and renewable energy applications during the construction and operation of data centers. From multiple perspectives of energy consumption, cost reduction, and environment protection, a comprehensive set of strategies are proposed to maximize data centers’ efficiency and minimize the environmental impact. This paper also provides energy-saving trends for data centers in the future.

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem since it could improve the income of the drivers and reduce gas emissions and fuel consumption. The recent research on seeking strategies may not be optimal for the overall revenue over an extended period of time as they ignored the important impact of passengers' destinations on future passenger seeking. To address these issues, this paper investigates how to increase the revenue efficiency (revenue per unit time) of taxi drivers, and models the passenger seeking process as a Markov Decision Process (MDP). For each one-hour time slot, we learn a different set of parameters for the MDP from data and find the best move for a vacant taxi to maximize the total revenue in that time slot. A case study and several experimental evaluations on a real dataset from a major city in China show that our proposed approach improves the revenue efficiency of inexperienced drivers by up to 15% and outperforms a baseline method in all the time slots.