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检索条件"机构=Department of Computer Networks and Distributed Systems"

共 84 条记录，以下是1-10 订阅

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WifiGO: Assessment of PUV Viability for Opportunistic Coverage Mapping of WiFi networks

WifiGO: Assessment of PUV Viability for Opportunistic Covera...

引用

2024 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2024

作者： Pecson, Jeff Nereo G. Tan, Wilson M. Festin, Cedric Angelo M. Dulva, Philip Angelo Y. Networks and Distributed Systems Group Department of Computer Science University of the Philippines - Diliman Metro Manila Philippines

ISBN: (纸本)9798331521219

Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-effectiveness. Due to the downsides of both these methods, opportunistic wardriving is proposed which utilizes public utility vehicles (PUVs). This study investigates the feasibility of PUVs as potential wardriving vehicles for opportunistic coverage mapping. This method is cost-efficient and removes the problem of data recency because of its continuous daily trips. The findings suggest that opportunistic wardriving with PUVs is viable for wardriving with its capability to detect a high volume of access points (APs). Multiple runs within the University of the Philippines Diliman campus showed the PUVs’ effectiveness in detecting a significant number of APs, with higher success rates at closer distances. Comparatively, warwalking detected fewer APs, but there was a significant overlap between the methods. The study also highlighted significant differences in detected AP types, with wardriving finding more mobile hotspots and miscellaneous devices than warwalking. Overall, the results underline the effectiveness of PUVs in providing extensive WiFi coverage data and further enhancements to the detection system could optimize the approach. ©2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization 29

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

引用

29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Ortiz, Sebastian Luis S. Enriquez, Justin Gabriel R. Tan, Wilson M. Festin, Cedric Angelo M. University of the Philippines - Diliman Networks and Distributed Systems Laboratory Department of Computer Science Metro Manila Philippines

ISBN: (纸本)9798331517816

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application's passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware 3

Comparative Study of Static Analysis and Machine Learning Ap...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ahmed, Alaa M. Saeed, Mogeeb A. Hamood, Amr A. Alazab, Abduljabar A. Ahmed, Kamel A. Computer Networks & Distributed Systems Al-saeed It Taiz University Faculty of Engineering Taiz Yemen Taiz University Al-saeed Faculty of Engineering & It Computer Networks & Distributed Systems Department Taiz Yemen

ISBN: (纸本)9798350305333

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware. © 2023 IEEE.

关键词： Android malware

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

引用

5th International Conference on Pattern Analysis and Intelligent systems, PAIS 2023

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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Utilization of Database Spatial Queries for Region of Interest Extraction in Stored Images

Utilization of Database Spatial Queries for Region of Intere...

引用

2022 IEEE Region 10 International Conference, TENCON 2022

作者： Valdez, Julian Troy C. Tan, Wilson M. University of the Philippines Diliman Department of Computer Science Networks and Distributed Systems Group Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Image processing, specifically extracting regions of interest, being a costly operation would benefit from a method of storing its output region and input image data in a database for future querying so as to prevent re-execution of the processing operation. This study investigates the performance of using built-in spatial queries found in a couple of modern Database systems to store and retrieve specific regions of image data. After testing two widely used Database software: MongoDB and Postgresql, results have shown that MongoDB performs better in terms of average and tail latency as well as efficiency in disk space consumption. © 2022 IEEE.

关键词： Query processing

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WifiGO: Assessment of PUV Viability for Opportunistic Coverage Mapping of WiFi networks

WifiGO: Assessment of PUV Viability for Opportunistic Covera...

引用

IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Jeff Nereo G. Pecson Wilson M. Tan Cedric Angelo M. Festin Philip Angelo Y. Dulva Department of Computer Science Networks and Distributed Systems Group University of the Philippines - Diliman Metro Manila Philippines

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

关键词： Accuracy Limiting Refining Interference WiMAX Hardware Object recognition Printers Transient analysis Wireless fidelity

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

引用

Asia-Pacific Conference on Communications

作者： Sebastian Luis S. Ortiz Justin Gabriel R. Enriquez Wilson M. Tan Cedric Angelo M. Festin Department of Computer Science Networks and Distributed Systems Laboratory University of the Philippines - Diliman Metro Manila Philippines

ISBN: (数字)9798331517816

ISBN: (纸本)9798331517823

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application’s passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection.

关键词： Cellular networks Crowdsensing Geology Government Data visualization Sensors Mobile applications Stakeholders Reliability Wireless fidelity

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Enhancing MANETs Security Against Black Hole Attacks through Time-Based Node Analysis in AOMDV Protocol

Enhancing MANETs Security Against Black Hole Attacks through...

引用

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah A. Naji Mogeeb A. Saeed Ammar H. Almagashi Alaa M. Ahmed Hashem S. Al-Atefi Mohammed N. Khaled Olayan M. Rashed Computer Network & Distributed Systems Department Al-Saeed Faculty Of Engineering &IT Taiz University Computer Networks & Distributed Systems Al-Saeed Faculty Of Engineering &IT Taiz University Computer Center & Information Technology Taiz University

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Mobile ad-hoc networks (MANETs) rely solely on direct node communication and function without a fixed infrastructure. Due to their decentralized design, they are susceptible to numerous security risks, with black hole attacks posing significant threats to routing integrity and confidentiality. This paper proposes a technique to enhance the Ad hoc On-Demand Multi-path Distance Vector (AOMDV) routing protocol's security against black hole attacks. The method involves a time-based reply message analysis to identify and mitigate malicious nodes. This algorithm improves the protocol's ability to detect and prevent black hole attacks by closely analyzing response time differences between malicious and normal nodes. The results of the simulation show that the suggested solution greatly enhances network performance by lowering the Routing Overhead Ratio from 1388.04% to 0.73% and the Packet Loss Rate from 74% to 11% while keeping a high Packet Delivery Ratio—up to 93% in different scenarios. These enhancements sustain strong data transmission in dynamic MANET environments, supporting network integrity and reliability.

关键词： Protocols Packet loss Routing Ad hoc networks Vectors Security Time factors

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware

Comparative Study of Static Analysis and Machine Learning Ap...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Alaa M. Ahmed Mogeeb A. Saeed Amr A. Hamood Abduljabar A. Alazab Kamel A. Ahmed Faculty of Engineering Computer Networks & Distributed Systems Al-saeed IT Taiz University Taiz Yemen Computer Networks & Distributed Systems department Al-saeed Faculty of Engineering & IT Taiz University Taiz Yemen

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Predicting fall in elderly people using machine learning

Predicting fall in elderly people using machine learning

引用

2021 International Congress of Advanced Technology and Engineering, ICOTEN 2021

作者： Saeed, Mogeeb A. Hashem Almourish, Mohammed Alqady, Yasmeen Ali Alsharabi, Hanadi Alkhorasani, Haleema Alsorori, Samah Saeed, Ahmed Y. A. Taiz University Faculty of Engineering and It Department of Computer Networks and Distributed Systems Taiz Yemen

ISBN: (纸本)9781665412247

Fall is a serious health problem, it may threaten the life of many people in general and the life of the elderly in particular. That is why we tried very hard to develop a system to notify the mechanisms of their fall from the lane by monitoring their movement by means of wearable sensors in certain places on their bodies. This paper presents five supervised machine learning algorithms (SVM, Neural Network, Decision Tree, Random Forest, and Naïve Bayes) to predict fifteen falls in the elderly. We compared the five models in terms of performance measures (accuracy, precision and recall), and the Random Forest model achieved the best result with an accuracy of 95.91%. In the future, we plan to improve the results by pre-processing the data for better features and then classifying and predicting them using advanced algorithms in artificial intelligence techniques to build a system that will be able to predict with high speed and accuracy. © 2021 IEEE.

关键词： Wearable technology

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