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(1 - 3 of 3)
- Title
- Information Security Analysis of Modern Wireless Printers
- Creator
- Mehta, Keval Samirbhai
- Date
- 2019
- Description
-
In today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned...
Show moreIn today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned scale when you give priority to one the second will get hit on somewhat level. This paper concentrates on the same thing in the case of printers. As wireless technology has been made available in the printers and they have got cheaper in recent time, the numbers of households owning the printers have increased dramatically in recent years. New printers use Wi-Fi direct or Wi-Fi AP technology to give wireless access to the user. Wi-Fi P2P also uses the same 802.11 protocol as Wi-Fi AP to help the user to print wirelessly; by directly connecting to or by directly sending commands and documents to the printers. We use a printer to print and scan important documents, which makes it a necessity, that the whole thing is secured. In this paper, I have tried to do analysis on possible security issues with wireless printers with the only wireless connection. The tests include the case where the bad guy will try to prevent the user to use the printer (DoS), from a distance and the case where the bad guy will try to sniff the packets or say important documents that the user is trying to print. I have tried to include different printers like HP, Brother, Canon to do testing, to get the overall idea of security in wireless printers. The first part includes the way of authentication available and the protocols used by the printers and the second part includes the possible ways to get bypass the security and recreate the printing materials that were printed by the user.
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- Title
- DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
- Creator
- Khairy, Sami
- Date
- 2021
- Description
-
The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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- Title
- Wireless Body Sensor Network for Tracking Human Mobility using Long Short-Term Memory Neural Network for Classification
- Creator
- Gupta, Saumya
- Date
- 2019
- Description
-
A large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks...
Show moreA large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks explore how to capture a type or types of motion, but all their sensors are placed in different locations; making their algorithms very specific to that movement. In this research, we explore the enhancement of human activity classification algorithm using long short-term memory (LSTM) neural network and wearable sensor network. There are five identical nodes used in the body sensor network to collect data. Each node incorporates an ESP8266 Microcontroller with Wi-Fi which is connected to an inertial measurement unit consisting of triple axis accelerometer and gyroscope sensor board. An analysis on the accuracy that each sensor node provides separately and in different combinations has been conducted to allow future research to focus their positioning in optimal positions. A Robot Operating System (ROS) central node is used to illustrate the in-built multi-threading capability. For demonstration, the positions chosen are waist, ankles and wrists. The raw sensor data can be observed on screen while it is being labelled live to create fitting dataset for developing an artificial neural network. Expectation is that increasing the number of sensors should raise the overall accuracy of the output but that isn’t the case observed, positioning of the sensor is pertinent to improvement. These platforms can be further extended to understand different motions and different sensor positions, also expanded to include other sensors.
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