Named data networking (NDN) is a content-centric network for the future of the internet. An NDN a packet is delivered based on a content name instead of a destination IP address. The name lookup for packet forwarding is challenging because the content name is variable, and its space is unbounded. This paper proposes a novel name lookup scheme that employs a hashing technique combined with Patricia tries. In this scheme, hash tables are dynamically maintained according to the hierarchical structure of the name, so the name can effectively accommodate variable and unbounded content names. Unlike chaining, a Patricia trie compares a key string only once at a leaf node, so it provides a fast name lookup. The proposed lookup scheme is implemented and evaluated by using an Intel Core i7-2600 CPU and 4GB of memory. Experimental results show that our scheme gives good scalability and a high throughput of name lookup with a controlled memory consumption.

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.

This paper proposes an elaborate route prefix caching scheme for fast packet forwarding in named data networking (NDN) which is a next-generation Internet structure. The name lookup is a crucial function of the NDN router, which delivers a packet based on its name rather than IP address. It carries out a complex process to find the longest matching prefix for the content name. Even the size of a name prefix is variable and unbounded; thus, the name lookup is to be more complicated and time-consuming. The name lookup can be sped up by using route prefix caching, but it may cause a problem when non-leaf prefixes are cached. The proposed prefix caching scheme can cache non-leaf prefixes, as well as leaf prefixes, without incurring any problem. For this purpose, a Bloom filter is kept for each prefix. The Bloom filter, which is widely used for checking membership, is utilized to indicate the branch information of a non-leaf prefix. The experimental result shows that the proposed caching scheme achieves a much higher hit ratio than other caching schemes. Furthermore, how much the parameters of the Bloom filter affect the cache miss count is quantitatively evaluated. The best performance can be achieved with merely 8-bit Bloom filters and two hash functions.

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change.

Named data networking (NDN) is a content-centric network architecture that requires efficient name lookup to forward packets based on hierarchical content names. Whereas IP lookup operates on fixed-length addresses, NDN name lookup must identify the longest matching prefix (LMP) from a variable-length name space, making it computationally challenging. Hash table-based approaches provide O (1) search complexity but often require multiple accesses due to the unknown LMP length. To mitigate excessive off-chip memory accesses, Bloom filter-assisted pre-checking is commonly employed. However, conventional Bloom filter-based approaches can only perform membership tests and do not provide information about the next search range, which limits their ability to effectively reduce Bloom filter accesses. This paper proposes a distance-encoded Bloom filter to improve name lookup efficiency. It encodes two distance values into the Bloom filter, enabling a more refined search range compared to binary search-based methods. By utilizing these encoded distances, the proposed scheme not only reduces the number of Bloom filter queries but also ensures that only prefix nodes need to be stored in the hash table. This helps reduce hash collisions and minimize off-chip memory accesses. Experimental evaluation using large-scale FIB datasets shows that the proposed approach reduces both Bloom filter accesses and hash table lookups, which contributes to improving overall lookup performance.