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In order to achieve the equal usage of limited resources in the wireless sensor networks (WSNs), we must aggregate the sensor data before passing it to the base station. In WSNs, the aggregator nodes perform a data aggregation process. Careful selection of the aggregator nodes in the data aggregation process results in reducing large amounts of communication traffic in the WSNs. However, network conditions change frequently due to sharing of resources, computation load, and congestion on network nodes and links, which makes the selection of the aggregator nodes difficult. In this paper, we study an aggregator node selection method in the WSNs. We formulate the selection process as a top-k query problem, where we efficiently solve the problem by using a modified Sort-Filter-Skyline (SFS) algorithm. The main idea of our approach is to immediately perform a skyline query on the sensor nodes in the WSNs, which enables to extract a set of sensor nodes that are potential candidates to become an aggregator node. The experiments show that our method is several times faster compared to the existing approaches.

The rapid economic development in South Korea has resulted in increase of crimes. Timely detection and reduction of crimes are primary focus of police officers. Internet of Things (IoT) and increasingly cheap and wearable sensors can be used to facilitate this task. Generally, the application of IoT technologies to the fields of smart cities, smart logistics and healthcare can be seen more often. In this paper, we present the design of IoT based smart crime detection system. The proposed system is able to detect crimes in real-time by analyzing the human emotions.

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.