• 한국인터넷방송통신학회논문지
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• 제17권3호
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• pp.29-45
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• 2017

애드 혹 센서 네트워크는 분산형 구조와 구축으로 특징지어지며 센서 네트워크는 낮은 이동성과 엄격한 에너지 요구 조건 등을 제외하고는 애드 혹 네트워크의 기본적인 특징을 모두 갖추고 있다. 기존 프로토콜은 내결함성, 분산 컴퓨팅, 견고성, 확장성 및 신뢰성과 같은 특성 간에 서로 다른 보완성을 제공한다. 지금까지 제안된 무선 프로토콜은 매우 제한되어있어 일반적으로 단일 기지국 또는 센서 데이터 수집에 중점을 두었다. 그러한 제약을 가지는 주된 이유는 네트워크 활동을 유지하기 위해 최대 수명을 유지하기 때문에 네트워크 수명은 애드 혹 네트워크에서 중요한 설계 기준이며 모든 노드가 라우터 역할을 수행하여 에너지 부족인한 일부 노드가 동작하지 않으면 다른 노드로 통신할 수 없다. 본 논문에서는 네트워크 노드의 에너지 통신을 최적화하기 위한 실험적인 애드 혹 주문형 거리 벡터 라우팅 프로토콜을 제안 한다 부하 분산은 경로 선택 단계에서 소진된 노드의 선택을 피하고 노드 간 에너지 사용의 균형을 유지하고 네트워크 수명을 극대화한다. 전송 제어 단계에서는 신호 전송 범위를 증가시키는 높은 전송 전력의 선택과 홉 수를 줄이고 네트워크 연결 비용의 부담을 줄이는 낮은 전력 수준 사이의 균형이 필요하다.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.481-504
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• 2010

Wireless smart sensor networks (WSSNs) have been proposed by a number of researchers to evaluate the current condition of civil infrastructure, offering improved understanding of dynamic response through dense instrumentation. As focus moves from laboratory testing to full-scale implementation, the need for multi-hop communication to address issues associated with the large size of civil infrastructure and their limited radio power has become apparent. Multi-hop communication protocols allow sensors to cooperate to reliably deliver data between nodes outside of direct communication range. However, application specific requirements, such as high sampling rates, vast amounts of data to be collected, precise internodal synchronization, and reliable communication, are quite challenging to achieve with generic multi-hop communication protocols. This paper proposes two complementary reliable multi-hop communication solutions for monitoring of civil infrastructure. In the first approach, termed herein General Purpose Multi-hop (GPMH), the wide variety of communication patterns involved in structural health monitoring, particularly in decentralized implementations, are acknowledged to develop a flexible and adaptable any-to-any communication protocol. In the second approach, termed herein Single-Sink Multi-hop (SSMH), an efficient many-to-one protocol utilizing all available RF channels is designed to minimize the time required to collect the large amounts of data generated by dense arrays of sensor nodes. Both protocols adopt the Ad-hoc On-demand Distance Vector (AODV) routing protocol, which provides any-to-any routing and multi-cast capability, and supports a broad range of communication patterns. The proposed implementations refine the routing metric by considering the stability of links, exclude functionality unnecessary in mostly-static WSSNs, and integrate a reliable communication layer with the AODV protocol. These customizations have resulted in robust realizations of multi-hop reliable communication that meet the demands of structural health monitoring.

• International Journal of Computer Science & Network Security
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• 제24권9호
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of Communications and Networks
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• 제16권2호
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• pp.227-237
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• 2014

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area have been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is ${\Theta}(1/\sqrt{n{\log}n})$. In this paper, we show that by exploiting directional spectrum opportunities in secondary network, the throughput of secondary network can be improved. If the beamwidth of secondary transmitter (TX)'s main lobe is ${\delta}=o(1/{\log}n)$, SUs can achieve a per-node throughput of ${\Theta}(1/\sqrt{n{\log}n})$ for directional transmission and omni reception (DTOR), which is ${\Theta}({\log}n)$ times higher than the throughput with-out directional transmission. On the contrary, if ${\delta}={\omega}(1/{\log}n)$, the throughput gain of SUs is $2{\pi}/{\delta}$ for DTOR compared with the throughput without directional antennas. Similarly, we have derived the throughput for other cases of directional transmission. The connectivity is another critical metric to evaluate the performance of random ad hoc networks. The relation between the number of SUs n and the number of PUs m is assumed to be $n=m^{\beta}$. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when ${\beta}$ > 1, and the connectivity of a single secondary path can be guaranteed when ${\beta}$ > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network, we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when ${\beta}$ > 1. Thus, to achieve the connectivity of secondary networks, the density of SUs should be (asymptotically) bigger than that of PUs.