The document outlines various C# projects related to IEEE 2015, covering topics such as optimal scheduling in cognitive cellular networks, malware propagation in networks, and predictive analytics using social media data. Each project provides a brief abstract highlighting its main focus and findings, including advancements in cloud computing authentication, quantum key distribution, and medical data transmission in wireless ad hoc networks. These projects demonstrate innovative approaches to existing problems in technology and data management.

1. IEEE 2015 C# Projects
Web : www.kasanpro.com Email : sales@kasanpro.com
List Link : http://kasanpro.com/projects-list/ieee-2015-c-projects
Title :Optimal Scheduling for Multi-radio Multi-channel Multi-hop Cognitive Cellular Networks
Language : C#
Project Link :
http://kasanpro.com/p/c-sharp/optimal-scheduling-multi-radio-multi-channel-multi-hop-cognitive-cellular-networks
Abstract : Due to the emerging various data services, current cellular networks have been experiencing a surge of
data traffic and already overloaded, thus not able to meet the ever exploding traffic demand. In this study, we first
introduce a Multi-radio Multi-channel Multi-hop Cognitive Cellular Network (M3C2N) architecture to enhance network
throughput. Under the proposed architecture, we then investigate the minimum length scheduling problem by
exploring joint frequency allocation, link scheduling, and routing. In particular, we first formulate a maximal
independent set based joint scheduling and routing optimization problem called Original Optimization Problem (OOP).
It is a Mixed Integer Non-Linear Programming (MINLP) and generally NP-hard problem. Then, employing a column
generation based approach, we develop an ?-bounded approximation algorithm which can obtain an ?-bounded
approximate result of OOP. Noticeably, in fact we do not need to find all the maximal independent sets in the
proposed algorithm, which are usually assumed to be given in previous works although finding all of them is
NP-complete. We also revisit the minimum length scheduling problem by considering uncertain channel availability.
Simulation results show that we can efficiently find the ?-bounded approximate results and the optimal result as well,
i.e., when ? = 0% in the algorithm.
Title :Malware Propagation in Large-Scale Networks
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/malware-propagation-large-scale-networks
Abstract : Malware is pervasive in networks, and poses a critical threat to network security. However, we have very
limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagate
in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model
for malware propagation from network to network. Based on the proposed model, our analysis indicates that the
distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and
power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed
through two real-world global scale malware data sets, and the results confirm our theoretical findings.
Title :Predicting iPhone Sales from iPhone Tweets
Language : ASP.NET with C#
Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/predicting-iphone-sales-iphone-tweets
Abstract : Recent research in the field of computational social science have shown how data resulting from the
widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as
movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this
research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating
are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is
persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used
to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions,
activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a
linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average
error close to the established prediction models from investment banks. This strong correlation between iPhone
tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the
findings and conclude with implications for predictive analytics with big social data.
Title :Differential Phase-Shift Quantum Key Distribution Systems

2. Language : C#
Project Link : http://kasanpro.com/p/c-sharp/differential-phase-shift-quantum-key-distribution-system
Abstract : Differential phase-shift (DPS) quantum key distribution (QKD) is a unique QKD protocol that is different
from traditional ones, featuring simplicity and practicality. This paper overviews DPS-QKD systems.
Title :Human Mobility Enhances Global Positioning Accuracy for Mobile Phone Localization
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/mobile-phone-localization-withs-global-positioning-accuracy
Abstract : Global Positioning System (GPS) has enabled a number of geographical applications over many years.
Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1m to
20m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS
and human mobility captured by mobile phones. Our key observation is that smartphone-enabled dead reckoning
supports accurate but local coordinates of users' trajectories, while GPS provides global but inconsistent coordinates.
Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global
positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the
ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both
crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30%
improvement on average error with respect to GPS. GloCal uses merely mobile phones and requires no infrastructure
or additional reference information. As an effective and light-weight augmentation to global positioning, GloCal holds
promise in real-world feasibility.
IEEE 2015 C# Projects
Title :Distributed Smart-home Decision-making in a Hierarchical Interactive Smart Grid Architecture
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/distributed-smart-home-decision-making-smart-grid-architecture
Abstract : In this paper, we develop a comprehensive real-time interactive framework for the Utility and customers in
a smart grid while ensuring grid-stability and Quality-of-Service (QoS). First, we propose a hierarchical architecture for
the Utility-customer interaction consisting of sub-components of customer load prediction, renewable generation
integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the
problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A
scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: (1)
centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid
and (2) distributed auctioning among all customers based on the optimal load profile obtained by solving the first
problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as
a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the
actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic
gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes
in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading
opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey
auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by
introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique
incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer's (microgrid
controller's) profit. The paper also explicitly discusses how this two- step solution approach can be scaled to be
suitable for more complicated smart grid architectures beyond the assumed abstract model.
Title :Discovery of Ranking Fraud for Mobile Apps
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/ranking-fraud-discovery-mobile-apps
Abstract : Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of
bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use
shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the
importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in
this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection
system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active

3. periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local
anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e.,
ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and
review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation
method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world
App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of
the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud
activities.
Title :Shared Authority Based Privacy-preserving Authentication Protocol in Cloud Computing
Language : C#
Project Link :
http://kasanpro.com/p/c-sharp/shared-authority-based-privacy-preserving-authentication-protocol-cloud-computing
Abstract : Cloud computing is emerging as a prevalent data interactive paradigm to realize users' data remotely
stored in an online cloud server. Cloud services provide great conveniences for the users to enjoy the on-demand
cloud applications without considering the local infrastructure limitations. During the data accessing, different users
may be in a collaborative relationship, and thus data sharing becomes significant to achieve productive benefits. The
existing security solutions mainly focus on the authentication to realize that a user's privative data cannot be
unauthorized accessed, but neglect a subtle privacy issue during a user challenging the cloud server to request other
users for data sharing. The challenged access request itself may reveal the user's privacy no matter whether or not it
can obtain the data access permissions. In this paper, we propose a shared authority based privacy-preserving
authentication protocol (SAPA) to address above privacy issue for cloud storage. In the SAPA, 1) shared access
authority is achieved by anonymous access request matching mechanism with security and privacy considerations
(e.g., authentication, data anonymity, user privacy, and forward security); 2) attribute based access control is adopted
to realize that the user can only access its own data fields; 3) proxy re-encryption is applied by the cloud server to
provide data sharing among the multiple users. Meanwhile, universal composability (UC) model is established to
prove that the SAPA theoretically has the design correctness. It indicates that the proposed protocol realizing
privacy-preserving data access authority sharing, is attractive for multi-user collaborative cloud applications.
http://kasanpro.com/ieee/final-year-project-center-thanjavur-reviews
Title :Safeguarding Quantum Key Distribution Through Detection Randomization
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/quantum-key-distribution-safeguarding-through-detection-randomization
Abstract : We propose and experimentally demonstrate a scheme to render the detection apparatus of a quantum
key distribution system immune to the main classes of hacking attacks in which the eavesdropper explores the
back-door opened by the single-photon detectors. The countermeasure is based on the creation of modes that are not
deterministically accessible to the eavesdropper. We experimentally show that the use of beamsplitters and extra
single-photon detectors at the receiver station passively creates randomized spatial modes that erase any knowledge
the eavesdropper might have gained when using bright-light faked states. Additionally, we experimentally show a
detectorscrambling approach where the random selection of the detector used for each measurement--equivalent to
an active spatial mode randomization--hashes out the side-channel open by the detection efficiency mismatch-based
attacks. The proposed combined countermeasure represents a practical and readily implementable solution against
the main classes of quantum hacking attacks aimed on the single-photon detector so far, without intervening on the
inner working of the devices.
Title :Postprocessing of the Oblivious Key in Quantum Private Query
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/postprocessing-oblivious-key-quantum-private-query
Abstract : Private query is a kind of cryptographic protocols to protect both users' privacies in their communication.
For instance, Alice wants to buy one item from Bob's database. The aim of private query is to ensure that Alice can
get only one item from Bob, and simultaneously, Bob cannot know which one was taken by Alice. In pursuing high
security and efficiency, some quantum private query protocols were proposed. As a practical model, Quantum-
Oblivious-Key-Transfer (QOKT)-based private query, which utilizes a QOKT protocol to distribute oblivious key
between Alice and Bob and then applies the key to achieve the aim of private query, has drawn much attention. Here,
we focus on postprocessing of the oblivious key, and the following two contributions are achieved. 1) We analyze
three recently proposed dilution methods and find two of them have serious security loophole. That is, Alice can

4. illegally obtain much additional information about Bob's database by multiple queries. For example, Alice can obtain
the whole database, which contains 104 items, by only 53.4 queries averagely. 2) We present an effective
error-correction method for the oblivious key, which can address the realistic scenario with channel noises and make
QOKT-based private query more practical.
IEEE 2015 C# Projects
Title :Inference Patterns from Big Data using Aggregation, Filtering and Tagging- A Survey
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/big-data-omferemce-patterns-aggregation-filtering-tagging
Abstract : This paper reviews various approaches to infer the patterns from Big Data using aggregation, filtering and
tagging. Earlier research shows that data aggregation concerns about gathered data and how efficiently it can be
utilized. It is understandable that at the time of data gathering one does not care much about whether the gathered
data will be useful or not. Hence, filtering and tagging of the data are the crucial steps in collecting the relevant data to
fulfill the need. Therefore the main goal of this paper is to present a detailed and comprehensive survey on different
approaches. To make the concept clearer, we have provided a brief introduction of Big Data, how it works, working of
two data aggregation tools (namely, flume and sqoop), data processing tools (hive and mahout) and various
algorithms that can be useful to understand the topic. At last we have included comparisons between aggregation
tools, processing tools as well as various algorithms through its pre-process, matching time, results and reviews.
Title :Rank-Based Similarity Search: Reducing the Dimensional Dependence
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/rank-based-similarity-search
Abstract : This paper introduces a data structure for k-NN search, the Rank Cover Tree (RCT), whose pruning tests
rely solely on the comparison of similarity values; other properties of the underlying space, such as the triangle
inequality, are not employed. Objects are selected according to their ranks with respect to the query object, allowing
much tighter control on the overall execution costs. A formal theoretical analysis shows that with very high probability,
the RCT returns a correct query result in time that depends very competitively on a measure of the intrinsic
dimensionality of the data set. The experimental results for the RCT show that non-metric pruning strategies for
similarity search can be practical even when the representational dimension of the data is extremely high. They also
show that the RCT is capable of meeting or exceeding the level of performance of state-of-the-art methods that make
use of metric pruning or other selection tests involving numerical constraints on distance values.
Title :Efficient and Cost-Effective Hybrid Congestion Control for HPC Interconnection Networks
Language : C#
Project Link :
http://kasanpro.com/p/c-sharp/efficient-cost-effective-hybrid-congestion-control-hpc-interconnection-networks
Abstract : Interconnection networks are key components in high-performance computing (HPC) systems, their
performance having a strong influence on the overall system one. However, at high load, congestion and its negative
effects (e.g., Head-of-line blocking) threaten the performance of the network, and so the one of the entire system.
Congestion control (CC) is crucial to ensure an efficient utilization of the interconnection network during congestion
situations. As one major trend is to reduce the effective wiring in interconnection networks to reduce cost and power
consumption, the network will operate very close to its capacity. Thus, congestion control becomes essential. Existing
CC techniques can be divided into two general approaches. One is to throttle traffic injection at the sources that
contribute to congestion, and the other is to isolate the congested traffic in specially designated resources. However,
both approaches have different, but non-overlapping weaknesses: injection throttling techniques have a slow reaction
against congestion, while isolating traffic in special resources may lead the system to run out of those resources. In
this paper we propose EcoCC, a new Efficient and Cost-Effective CC technique, that combines injection throttling and
congested-flow isolation to minimize their respective drawbacks and maximize overall system performance. This new
strategy is suitable for current commercial switch architectures, where it could be implemented without requiring
significant complexity. Experimental results, using simulations under synthetic and real trace-based traffic patterns,
show that this technique improves by up to 55 percent over some of the most successful congestion control
techniques.
Title :Medical Data Compression and Transmission in Wireless Ad Hoc Networks
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/medical-data-compression-transmission

5. Abstract : A wireless ad hoc network (WANET) is a type of wireless network aimed to be deployed in a disaster area
in order to collect data of patients and improve medical facilities. The WANETs are composed of several small nodes
scattered in the disaster area. The nodes are capable of sending (wirelessly) the collected medical data to the base
stations. The limited battery power of nodes and the transmission of huge medical data require an energy efficient
approach to preserve the quality of service of WANETs. To address this issue, we propose an optimizationbased
medical data compression technique, which is robust to transmission errors. We propose a fuzzy-logic-based route
selection technique to deliver the compressed data that maximizes the lifetime of WANETs. The technique is fully
distributed and does not use any geographical/location information. We demonstrate the utility of the proposed work
with simulation results. The results show that the proposed work effectively maintains connectivity of WANETs and
prolongs network lifetime.
Title :Towards Effective Bug Triage with Software Data Reduction Techniques
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/effective-bug-triage-software-data-reduction-techniques
Abstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of
fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in
manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the
problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combine
instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word
dimension. To determine the order of applying instance selection and feature selection, we extract attributes from
historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the
performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and
Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of
bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and
high-quality bug data in software development and maintenance.
IEEE 2015 C# Projects
Title :Joint Power Splitting and Antenna Selection in Energy Harvesting Relay Channels
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/joint-power-splitting-antenna-selection-energy-harvesting-relay-channels
Abstract : The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple
antennas is considered in this letter, where a "harvest-and-forward" strategy is proposed. In particular, the relay
harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna
selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to
the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable
rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a
two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting,
and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the
two-stage procedure, and demonstrate that the proposed "harvest-and-forward" strategy outperforms the
conventional amplify-and-forward (AF) relaying and the direct transmission.
http://kasanpro.com/ieee/final-year-project-center-thanjavur-reviews
Title :Detectors for Cooperative Mesh Networks With Decode-and-Forward Relays
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/cooperative-mesh-network-detectors-with-decode-forward-relays
Abstract : We consider mesh networks composed of groups of relaying nodes which operate in decode-and-forward
mode. Each node from a group relays information to all the nodes in the next group. We study these networks in two
setups, one where the nodes have complete state information about the channels through which they receive the
signals, and anotherwhen they only have the statistics of the channels. We derive recursive expressions for the
probabilities of errors of the nodes and present several implementations of detectors used in these networks. We
compare the mesh networks with multihop networks formed by a set of parallel sections of multiple relaying nodes.
We demonstrate with numerous simulations that there are significant improvements in performance of mesh over

6. multihop networks in various scenarios.