• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4864-4882
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• 2016

In this work, we consider the optimization problem of minimizing energy consumption for real-time multicast over wireless multi-hop networks. Previously, a distributed primal-dual subgradient algorithm was used for finding a solution to the optimization problem. However, the traditional subgradient algorithms have drawbacks in terms of i) sensitivity to iteration parameters; ii) need for saving previous iteration results for computing the optimization results at the current iteration. To overcome these drawbacks, using a joint network coding and scheduling optimization framework, we propose a novel distributed primal-dual Random Deflected Subgradient (RDS) algorithm for solving the optimization problem. Furthermore, we derive the corresponding recursive formulas for the proposed RDS algorithm, which are useful for practical applications. In comparison with the traditional subgradient algorithms, the illustrated performance results show that the proposed RDS algorithm can achieve an improved optimal solution. Moreover, the proposed algorithm is stable and robust against the choice of parameter values used in the algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3120-3137
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• 2021

Small-sized IoT wireless sensing devices can be deployed with small aircraft such as drones, and the deployment of mobile IoT devices can be relocated to suit data collection with efficient relocation algorithms. However, the terrain may not be able to predict its shape. Mobile IoT devices suitable for these terrains are hopping devices that can move with jumps. So far, most hopping sensor relocation studies have made the unrealistic assumption that all hopping devices know the overall state of the entire network and each device's current state. Recent work has proposed the most realistic distributed network environment-based relocation algorithms that do not require sharing all information simultaneously. However, since the shortest path-based algorithm performs communication and movement requests with terminals, it is not suitable for an area where the distribution of obstacles is uneven. The proposed scheme applies a simple Monte Carlo method based on relay nodes selection random variables that reflect the obstacle distribution's characteristics to choose the best relay node as reinforcement learning, not specific relay nodes. Using the relay node selection random variable could significantly reduce the generation of additional messages that occur to select the shortest path. This paper's additional contribution is that the world's first distributed environment-based relocation protocol is proposed reflecting real-world physical devices' characteristics through the OMNeT++ simulator. We also reconstruct the three days-long disaster environment, and performance evaluation has been performed by applying the proposed protocol to the simulated real-world environment.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.545-553
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• 2017

This paper investigates a synchronization algorithm for a distributed network which does not have a centralized infrastructure. In order to operate a distributed network, synchronization across distributed terminals should be acquired in advance, and hence, a plenty of distributed synchronization algorithms have been studied extensively in the past. However, most of the previous studies focus on the synchronization only in fault-free networks. Thus, if there are some malfunctioning terminals in the network, the synchronization can not be guaranteed with conventional distributed synchronization methods. In this paper, we propose a reliability-based adaptive consensus algorithm which can effectively acquire the synchronization across distributed terminals and confirm performance of the proposed algorithm by conducting numerical simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6A
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• pp.803-819
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• 1999

We introduces a wireless multimedia CDMA system configuring multiple transmission links between a user and radio ports. We propose a centralized reservation access control scheme with transmission scheduling and dynamic allocation (CRMA/TSDA) to support the diverse multimedia traffic in the introduced CDMA system. We propose two types of transmission allocation algorithms: slot and link allocation algorithms with local information and global information. The transmission allocation algorithm proposed in this paper allocates a set of ports configuring multiple radio links and transmission slot/power to each of scheduled transmission requests. We perform simulations for the proposed system and algorithms. Through the simulation, we show that the performance of the algorithm with local information stands comparison with that of the quasi-optimum algorithm with global information. Also, the two algorithms in the system has shown to have better performance than the conventional CDMA system with a distributed random transmission method.

• IE interfaces
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• v.22 no.3
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• pp.278-286
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• 2009

The anti-collision algorithms to identify a number of tags in real-time in RFID systems are divided into the anti-collision algorithms based on the Framed slotted ALOHA that randomly select multiple slots to identify the tags, and the anti-collision algorithms based on the Tree-based algorithm that repeat the questions and answer process to identify the tags. In the hybrid algorithm which is combined the advantages of these algorithms, tags are distributed over the frames by selecting one frame among them and then identified by using the Query tree frame by frame. In this hybrid algorithm, however, the time of identifying all tags may increase if many tags are concentrated in a few frames. In this study, to improve the performance of the hybrid algorithm, we suggest an improved algorithm that the tags select a specific group of frames based on the earlier bits of the tag ID so that the tags are distribute equally over the frames. By using the simulation and mathematical analysis, we show that the suggested algorithm outperforms traditional hybrid algorithm from the viewpoint of the number of queries per frame and the time of identifying all tags.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.709-726
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• 2018

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3456-3468
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• 1999

Most of previous parallel join algorithms assume a database partition system(DPS), where each database partition is owned by a single processing node. While the DPS is novel in the sense that it can interconnect a large number of nodes and support a geographically distributed environment, it may suffer from poor facility for load balancing and system availability compared to the database sharing system(DSS). In this paper, we propose a dynamic load balancing strategy by exploiting the characteristics of the DSS, and then extend the conventional hash join algorithms to the DSS by using the dynamic load balancing strategy. With simulation studies under a wide variety of system configurations and database workloads, we analyze the effects of the dynamic load balancing strategy and differences in the performances of hash join algorithms in the DSS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.782-791
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• 2013

This paper proposes the location estimation techniques of distributed targets with the multi-sensor data perceived through IR sensors of the military robots. In order to match up targets with measured azimuths, we apply the maximum likelihood (ML), depth-first, and breadth-first tree search algorithms, in which we use the measured azimuths and the number of pixels on IR screen for pruning branches and selecting candidates. After matching up targets with azimuths, we estimate the coordinate of each target by obtaining the intersection point of the azimuths with the least square error (LSE) algorithm. The experimental results show the probability of missing target, mean of the number of calculating nodes, and mean error of the estimated coordinates of the proposed algorithms.