Byung Jun Lee

An Energy Efficient Routing Protocol in Wireless Sensor Networks

Wireless sensor network consisting of a large number of small sensors is effective for gathering various data in a variety of environments. Since each sensor operates on battery, energy efficient data transfer is indispensable to maximize the lifetime of the network. In this paper we propose a new data transfer protocol employed in large-scale wireless sensor network. Here only one designated cluster-head sends the data received from other cluster-heads to the base station during one round of communication. Also, we adopt a method that replaces weak cluster-head using a proxy node. Comparison with the existing schemes such as LEACH and PEACH through computer simulation reveals that the proposed scheme significantly improves the network lifetime for practical operational conditions.

Reduction of Association Rules for Big Data Sets in Socially-Aware Computing

Reduction of the number of association rules in data mining is a very important issue in the field of socially-aware computing in which big data need to be manipulated. The existing schemes based on the frequency of occurrences are not effective for relatively large size dataset. In this paper we propose the tabular-algorithm that assigns a weight to each rule for the removal of unimportant rules and employs the Quine-Mccluskey method for rule reduction. Computer simulation reveals that the proposed scheme significantly improves support, credibility, rule reduction rate, and processing time compared to the representative existing schemes such as Apriori and FP-growth algorithm.

Situation Awareness Based on Dempster-Shafer Theory and Semantic Similarity

In pervasive computing environment the low-level context data provided by the sensors are usually meaningless, and thus higher-level context needs to be extracted. Situation is the semantic interpretation of low-level context, permitting a higher-level specification of human behavior in the scene and the corresponding system service. Context modeling and reasoning are the two key parts in the situation awareness. In this paper we present a multiple level architecture for context modeling, and a reasoning approach based on the Dempster-Shafer Theory (DST) and semantic similarity. The Dempster-Shafer theory is employed to analyze low-level context and eliminate the conflict among different sensors. Semantic similarity is used to reason out the higher-level context information based on the ontology. Computer simulation reveals that the proposed approach allows more efficient and accurate reasoning of higher-level context information compared to the existing approach.

Enhanced Naive Bayes Classifier for real-time sentiment analysis with SparkR

Correct and fast sentiment analysis of continuously generated data such as Twitter message is very important for providing real-time customized service to the users. While Naive Bayes Classifier(NBC) is the most popular classifier employed for sentiment analysis, the existing studies on it have been based on single server environment. Consequently, they are not adequate for handling real-time stream data. In this paper, thus, we propose a scheme adopting the Laplace Smoothing technique with Binarized NBC for enhancing the accuracy, and employing SparkR for speed-up via distributed and parallel processing. Computer simulation with Sentiment140 reveals that the proposed approach consistently allows higher accuracy than the existing schemes. It also identifies that the SparkR environment allows faster training than R.

Round Trip Time Based Adaptive Congestion Control with CoAP for Sensor Network

Constrained Application Protocol (CoAP) was developed to support the communication between resource constrained nodes via low-power links. As an Internet protocol, CoAP needs congestion control primarily to stabilize the networking operation. In this paper we propose a new round trip time based adaptive congestion control scheme, which improves CoAP by utilizing the retransmission count information in estimating the retransmission timeout. An experiment is conducted based on Californium CoAP framework and real devices. It shows that the proposed scheme significantly improves CoAP in terms of throughput and rate of successful transaction.

Considering block popularity in disk cache replacement for enhancing hit ratio with solid state drive

The Solid State Drive (SSD) is now becoming a main stream in storage systems. It is widely deployed as cache for hard disk drive (HDD) to speed up the execution of data intensive applications. In this paper we propose a novel block replacement algorithm for flash-based disk cache, named Block Replacement based on Popularity(BRP). Using the frequency and recency of block access, it calculates the block popularity to select the block which will be evicted from SSD. This avoids cache pollution and keeps popular blocks in SSD cache, leading to high hit ratio. Meanwhile, the proposed scheme reduces block replacements, and thus incurs less write operations to SSD. As a result, BRP enhances the performance of storage and the lifetime of SSD. Computer simulation demonstrates that the proposed scheme consistently outperforms five existing cache replacement algorithms with two different kinds of traces.

An SSD-Based Accelerator Using Partitioned Bloom Filter for Directory Parsing

With the advancement in IT including mass storage device and super-fast communication, cloud computing is now in high demand. At the same time, the explosive increase in the volume of diverse and extensive data has brought forth the age of big data organized as countless directories within a file system. The exponential increase in data volumes causes slow retrieval of metadata of the requested files. This paper proposes a novel directory parsing technique using an SSD-based Bloom filter. The proposed scheme employs partitioned Bloom filter for accurate and fast directory parsing. Computer simulation reveals that the proposed scheme significantly reduces the rate of false positive and shortens the time spent on directory parsing in comparison to the existing Bloom filter-based scheme.

Energy-Efficient Gossiping Protocol of WSN with Realtime Streaming Data

Nowadays, wireless sensor networks (WSNs) are widely used, and various routing schemes have been developed for them. Its lifetime is limited as each node of WSN is battery-powered, and unnecessary data transmissions of the nodes shorten the lifetime of entire network. Gossiping protocol has been recognized as one of the most effective routing schemes employed for WSN. In this paper we propose a new scheme called energy efficient gossiping (E-Gossip) protocol to maximize the lifetime of WSN by properly adjusting the gossip probability and controlling the operation of each sensor node based on the remaining energy. Redundant data transmission is also reduced. The proposed scheme is simulated with NS-2, and the simulation results show that it substantially reduces and balances the energy consumption compared to AODV and the existing gossiping protocol. As a result, the proposed scheme is able to effectively prolong the lifetime of WSN.

Reduction of IPTV channel zapping time by utilizing the key input latency

With IPTV, channel zapping time is one of the significant problems. It occurs when the user wants to change the channel but needs to wait until the target channel is available. Various schemes reducing the time have been suggested, which download the candidate channels of the future use in advance. In this paper we propose a new method which further reduces the channel zapping time by pre-downloading the candidate channels during the time latency between two successive push operations of the buttons of remote controller made by the user. Computer simulation verifies its effectiveness in various operational conditions. More importantly, it can be employed together with other scheme of reducing channel zapping time without any additional overhead.

Enhanced query processing using weighted predicate tree in edge computing environment

Edge computing is a novel computing paradigm in which the edge nodes of Internet of Things (IoT) are heavily involved in data processing. Resource Description Framework (RDF) plays an important role in Semantic Web due to the explosive growth of data of IoT. RDF is a graph-based data model employed for representing the Uniform Resource Identifiers (URIs), and SPARQL is the standard query language used for processing the query of RDF data. Growth of data throws a big challenge to the data storing and processing. In this paper a new data storing and query processing approach is proposed using weighted predicate tree. The predicate tree is used for effective storing of data and extracting the weights indicating the relation of the data. Computer simulation with SP2 Bench data set and SPARQL query reveals that the proposed approach allows substantially higher performance than three existing representative query processing schemes.