The Internet of Things (IoT), as an important technical domain in the future, has received wide attention globally from different disciplines. The research output in this domain, therefore, grew rapidly in the past 10 years. This study deeply investigates the knowledge base and knowledge development trajectory of the domain since the IoT was proffered in 1999. By adopting basic statistical and keyword co-occurrence methods, the most influential countries, institutions, journals and scholars in the domain, as well as the division of core, hot keywords and subdomains, are investigated. By using the main path analysis method, a survey is conducted on the development trajectory of the domain on the basis of knowledge inheritance; the four core branches-industry, big data, cloud computing and IoT security; and the three application studies of industry, healthcare and smart city.

There is a large amount of data available about salary and job positions in every state of the United States on the Bureau of Labor Statistics website. But there has been little analysis and visualization done to understand what impacts these industries may have on the cost of living. This research focus on how to analyze impacts on cost of living due to industry changes for decision support of smart cities. This paper suggests using leveraged Power BI framework to filter and visualize the data in a simple and digestible format which can assist to formulate conclusions on the effects that these industries may have on the cost of living. After taking two snapshots in time of the quantity of people employed in each industry and comparing it to the change in cost of living over these two snapshots in time via Power BI, the framework obtains linear regressions to determine the relationships. The framework can calculate how strong the relationship is between the cost of living in an area compared to the average salary as well as cost of living compared to the prevalence of an industry. The results indicate that the Power BI framework can effectively and innovatively provide analysis on the cost of living and industry prevalence. Through the reformulated data for an individual city, people can analyze smart city functions, such as forecast on job trends, on the metropolitan level.

Heat metering and charging can help raise residents’ awareness of energy conservation and save heat consumption in buildings. The heat metering and charging in China, however, was promoted insignificantly. To explore the behavior mechanism of heat-supply enterprises implementing heat metering and charging under the background of complex network in China’s heating region, and put forward the promotion strategies of heat metering and charging, this paper uses the heat-supply enterprises as the sample, improves the utility function of heat-supply enterprises by considering their difference aversion preference and altruistic preference at the same time based on the Difference Averse Surplus Maximizer Model. Then, we establish a complex network evolutionary game model among different heat-supply enterprises, and an empirical analysis is conducted to examine the impact of different factor on heat-supply enterprises in China’s heating region to implement heat metering and charging. The conclusions are as follows: (1) With the current heating cost and income, even if the government subsidizes all environmental benefit generated by heat metering and charging to heat-supply enterprises, the heat metering and charging could not be promoted in different scales of networks. (2) Increasing metering heat price, government fine, environmental protection tax and carbon emission trading price have positive effects on the promotion of heat metering and charging in different scales of networks. (3) The value of metering heat price, government fine, environmental protection tax and carbon emission trading price have no significant impacts on the evolutionary speeds of different scales of networks.