The Internet revolution and Moore’s Law drove the rapid expansion of connected consumer electronics. As massive data is generated by Internet of Things (IoT) devices, edge computing has been developed and applied in consumer electronics to provide agile and real-time services. In this paper, we provide an overview of artificial intelligence-empowered edge computing in consumer electronics. We start with the background of consumer electronics and artificial intelligence and outline key characteristics and challenges, which are used to evaluate the state of research in smart homes, smart buildings, smart healthcare, and intelligent diagnosis. The evaluation shows that research in AI-empowered edge computing for consumer electronics has mainly focused on latency, robustness, and reliability over the past 5 years. However, explainability and load balancing are less mentioned and have great potential for future research. Although AI-empowered edge computing for consumer electronics is a thriving and widely used research area, there are still many unsolved problems and challenges that researchers can address.

Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the . Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

Environmental protection is an important issue in recent decades, and renewable energy is an ideal solution for eco-friendly power generation. Solar-power generation is a popular renewable energy with low cost and small environmental footprint, which leads to exponential growth and high industrial investment. A mature solar business model has been established, but some uncertainties hinder the development, especially when focusing on the lack of solar-radiation. To address these issues, in this article we propose a hedging system to hedge the low-radiation risk for solar-investors through the designed IoT-based data, edge-based models for predicting solar-radiation as well as hedging options. Our experimental results show that the edge-based predictive models can obtain an R-squared value of 0.841 and a correlation coefficient of 0.917. For binary options designed in the hedging system, the broker can obtain stable payoffs with the highest Sharpe ratio of 3.354, and the investors can obtain large payoffs during low-radiation. Our simulation results show the effectiveness of the proposed hedging system for investors (buyer-side), simultaneously, present the motivation of the broker (seller-side) to join the designed hedging system utilized in solar-power generation.

Greenhouse gas emissions are critical issues for mankind, especially from the viewpoint of electricity consumption. The smart grid is an emerging issue in terms of efficiency, sustainability, reliability, and security. This article proposes a concept for an AI-based electricity management system that includes prediction, anomaly detection, grid management, and market equalization modules. The prediction module predicts electricity supply and demand, and the anomaly detection module detects potential attacks and failures for security considerations. Based on prediction and detection, the grid management module determines sustainable subsidies and reliable operating reserve. Finally, the market equilibrium model balances electricity supply and demand.

With the rapid development of quantitative trading, stock selection is an ongoing task that requires consideration of the characteristics of stocks and investment strategies. Fuzzy set theory is excellent for modeling and describing abstract characteristics to establish the link between stocks and strategies. In this paper, we present our idea for a stock selection system called TripleS. It is based on the suitability index (SI) derived from fuzzy-set theory. The TripleS relies on the position size to extract the stock characteristics SI that describe not only the characteristics of each stock, but also the extent to which they are suitable for certain strategies. Then, the SI undergoes a transformation process in the hands of the developed fuzzy-set modules for stock selection and investment. There are some other proposed methods for stock selection respectively called TripleS-T1, TripleS-T2, TripleS-T2U, TripleS-EF and TripleS-EFT. Experimental results show that the proposed TripleS has minor improvement in precision but significant improvement in investment performance. The TripleS-EFT has outstanding profitability, the highest annual return in all strategies and the highest Sharpe ratio in two of the four strategies, and achieves an annual return of 10.01% and a Sharpe ratio of 1.032, which significantly outperform the benchmark system. The TripleS-EFT also significantly outperforms the state-of-the-art fuzzy-based system in measuring annual return and even has double the annual return in several strategies.