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Ms Project Topics

1) Green Cloud Computing:

In 1999, Previous researchers stated that “it is estimated that the Internet equipment consumed roughly 8% of the total electricity in the United States, with a prediction to grow to 50% within a decade.” He also stated that “in the United States it takes 1 kg of coal to produce enough energy to send 5 MB of data over Internet.” Current rate of energy use in ICT sector is dramatically increasing. The downside is that the energy waste is even more dramatic. Energy hungry computers, handheld devices and server systems work with very low efficiency in terms of energy. To overcome this problem, “green computing” is being proposed (i.e. energy efficient computer and network systems).

Based on our energy optimization knowledge gained from wireless sensor networks, where battery is the most important resource and cannot be replaced easily, we will propose new methodologies to increase the energy efficiency of cloud systems; methodologies such as sleep schedules, multilayer designs, optimal placements, topology and routing optimizations. In order to work on this problem, you should have knowledge on: optimization methods, operating systems, distributed systems, tools such as MATLAB, OPNET, (maybe CPLEX/Gurobi), cluster systems. (If you are missing some of this knowhow, don't worry, we will help you learn them soon). (Coadvised with Rabun Kosar and Yunus Donmez)

2) Human Activity Classification with Wireless Sensor Networks using Active Learning:

In the last decade, Wireless Sensor Networks (WSNs) appeared as one of the emerging technologies that combine automated sensing, embedded computing and wireless networking into tiny embedded devices. This evolution not only enabled the use of miniaturized wireless sensors ranging from simple temperature sensors to more complex sensors that can monitor health status, but also enabled the use of WSNs in various applications ranging from military applications to healthcare. Monitoring human activities with multi-modal sensors can be used in different application areas. Medical applications, home monitoring and assisted living are some of the most prominent application domains. The aim of this project is to develop a WSN system that can enable the monitoring of daily activities of its users without disturbing their daily routine. The differentiator characteristics of the project are: • processing of multi-modal sensors data and inference using machine learning, • leaning the user behaviors through methods such as active learning and supporting the user in the case of drifts from their daily routines, • enabling a lifelong-learning and user specific system

3) Heterogeneous and Small Cell Networks (HetSNets):

Parallel to recent advancements in mobile user equipments and proliferation of bandwidth-hungry multimedia applications, user data traffic requirements are increasing dramatically each passing day. Eventually, conventional cellular networks become insufficient to accommodate the offered traffic load. To increase the capacity of the network, the idea of “bringing the network closer to the end-users” adopted by the telecommunications community. As a result; low-power access points, such as picocell and femtocell, deployed indoors (homes, offices, buses, etc.) in addition to macrocells and microcells mostly operate outdoors. Thus, a new kind of network which is comprised of several different cell types emerges: Heterogeneous and Small Cell Networks (HetSNets). However, this heterogeneity raises many challenging issues that still need to be addressed such as frequent handovers, topology control, self organization, interference management and security. Main contribution of this thesis is expected to be proposals of effective techniques to solve one or more of those challenges introduced by HetSNets. For this topic; very good knowledge of MATLAB, OPNET and at least one programming language required. Familiarity with a linear and a nonlinear optimization tool along with a modeling language (preferably AMPL) will be a plus.

4)Packet Level Performance Evaluation of Optimally Designed Wireless Sensor Networks (WSNs)

There are two separate research communities in the WSN domain. Some groups formulate and solve optimal network design problems through formal techniques and commercial solvers. The other group use discrete event simulators (e.g., OPNET) for evaluating the packet level performance of the WSNs. This thesis is offered for bridging the gap between these communities. We will take the optimal solutions and evaluate their packet level performance by carefully designing the necessary experiments. If needed, we will recommend changes in the optimal WSN design problem bu modifying the objective funcion or the constraints.

5) Optimal Software Defined Network (SDN) Design

It is very likely that we will see OpenFlow based SDN more and more often. There are many optimization issues which requires a solution for the efficient oepartion of SDN and related resulting overlay networks. Optimal routing on SDN is one of them. However, there are also topological problems and flow control issues. This work will consist of the formulation and solution of a related optimization problem. Moreover, a small size prototype of the resulting solution will be run on an OpenFlow based SDN for improving our hands on experience.

Bs Project Topics

1) Sensor data visualisation

This project involves development of a web-based sensor visualisation and annotation collection tool. Several house and sensor deployment layouts will be preloaded to the system together with sensor values readings and annotation about the ground truth for activity labels. The users will login, check the annotations and/or provide new labels to the configurations of sensor values. For instance, assume that the pressure sensor under the leg of the bed is “ON”. The system will depict the sensor value visually (i.e. by turning the light on for this sensor on house layout picture loaded in the system) and prompt the user about the activity of the resident. The response of the user will be recorded. Several different users will be prompted for the same house and sensor configurations and the responses will be recorded individually in order to get the agreement level for different configurations. The system will resemble amazon mechanical Turk system in that sense. The admin user will be able to get reports of the user responses visually.

2) Crowd-sourced Campus Wide Data Collection and Visualization

This project aims to collect data periodically from Android smartphone owning campus habitants. The collected data will be processed for the purposes of indoor localization and the obtained results will be visualized on the campus map. The findings will serve as helpful tool for the Bilgi İşlem Merkezi to optimize the current connectivity throughout the campus and deploy new access points. Accordingly, the data collection campaign will require a database capable of handling a large number of (possibly simultaneous) data transmissions, storing and visualizing them subject to different criteria. To that end, the 492 candidate will participate in the process of designing and implementing of the data collection software and data collection. Furthermore, the candidate will be responsible for the design and implementation of i) the data collection server and transmission setup, and ii) the data visualization interface. Required programming skills: Android, SQL(ite), Server Management and any desired Visualization Tool(s).

3) Spark-Fun

This project involves the design and development of a wearable activity recognition system. The system will be developed on Arduino platform. (http://www.arduino.cc/en/Main/ArduinoBoardLilyPad). Firstly, a very fashionable and unobtrusive smart textile will be designed and developed using Arduino Lilypad parts. Knitting skills are required at this stage. Secondly, a data collecting system that is capable of communicating with the smart textile will be developed and tested on real users. The data collection system is required to run on a touch-screen Android tablet PC or smartphone. Android (Java) knowledge is essential.

4) i-Challenge

Developing cool apps for elderly or chronically ill. Leveraging the power of mobile sensing, we will develop brand new apps with brand new Apple equipment. iphones and ipads are equipped with GPS, microphone, an accelerometer, a proximity sensor, an ambient light sensor, camera. Using these sensors we'll develop context-aware assistive applications for the elderly and/or people with cognitive disabilities. Typical applications involve location tracking using GPS, activity recognition using accelerometer, medication reminder, daily routine tracking. If you have better ideas you are encouraged to develop them. This project requires MAC and iOS (Objective-C) development skills. You are not expected to have these skills, but you are expected to develop them in a reasonably short time since the semester is only 14 weeks.

5) Andro-Challenge

Developing cool apps for elderly or chronically ill. Leveraging the power of mobile sensing, we will develop brand new apps with Android phones and Galaxy minitabs which are equipped with GPS, microphone, an accelerometer and cameras. Using these sensors we'll develop context-aware assitive applications for the elderly and/or people with cognitive disabilities. Typical applications involve location tracking using GPS, activity recognition using accelerometer, medication reminder, daily routine tracking. If you have better ideas you are encouraged to develop them. This project requires Java for Android development skills. You are expected to have these skills.


Notes for Scaring the Unwilling and Lazy:

1. CmpE 475 or equivalent is a must 476/477/523/524/58C are strongly recommended.

2. Performance evaluation and implementation projects will result in long working hours in NETLAB.

3. Willingness for hard work is a must since all of these are difficult problems.

courses/theses_offered.1410444035.txt.gz · Last modified: 2014/09/11 17:00 by bilgin
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