Improved Resource Allocation for TV White Space Network Based on Modified Firefly Algorithm

There is continued increased demand for dynamic spectrum access of TV White Spaces (TVWS) due to growing need for wireless broadband. Some of the use cases such as cellular (2G/3G/4G/5G) access to TVWS may have a high density of users that want to make use of TVWS. When there is a high density of secondary users (SUs) in a TVWS network, there is possibility of high interference among SUs that exceeds the desired threshold and also harmful interference to primary users (PUs). Optimization of resource allocation (power and spectrum allocation) is therefore necessary so as to protect PUs against harmful interference and to reduce the level of interference among SUs. Existing resource allocation optimization algorithms for a TVWS network ignore adjacent channel interference, interference among SUs or apply greedy algorithms which result in sub-optimal resource allocation. In this paper we propose an improved resource allocation algorithm based on continuous-binary firefly algorithm. Simulation is done using Matlab. Simulation results show that the proposed algorithm improves the SU sum throughput and SU signal to interference noise(SINR) ratio in the secondary network

Efficient Location Privacy In Mobile Applications

Location awareness is an essential part of today\u27s mobile devices. It is a well-established technology that offers significant benefits to mobile users. While location awareness has triggered the exponential growth of mobile computing, it has also introduced new privacy threats due to frequent location disclosures. Movement patterns could be used to identify individuals and also leak sensitive information about them, such as health condition, lifestyle, political/religious affiliations, etc. In this dissertation we address location privacy in the context of mobile applications. First we look into location privacy in the context of Dynamic Spectrum Access (DSA) technology. DSA is a promising framework for mitigating the spectrum shortage caused by fixed spectrum allocation policies. In particular, DSA allows license-exempt users to access the licensed spectrum bands when not in use by their respective owners. Here, we focus on the database-driven DSA model, where mobile users issue location-based queries to a white-space database in order to identify idle channels in their area. We present a number of efficient protocols that allow users to retrieve channel availability information from the white-space database while maintaining their location secret. In the second part of the dissertation we look into location privacy in the context of location-aware mobile advertising. Location-aware mobile advertising is expanding very rapidly and is forecast to grow much faster than any other industry in the digital era. Unfortunately, with the rise and expansion of online behavioral advertising, consumers have grown very skeptical of the vast amount of data that is extracted and mined from advertisers today. As a result, the consensus has shifted towards stricter privacy requirements. Clearly, there exists an innate conflict between privacy and advertisement, yet existing advertising practices rely heavily on non-disclosure agreements and policy enforcement rather than computational privacy guarantees. In the second half of this dissertation, we present a novel privacy-preserving location-aware mobile advertisement framework that is built with privacy in mind from the ground up. The framework consists of several methods which ease the tension that exists between privacy and advertising by guaranteeing, through cryptographic constructions, that (i) mobile users receive advertisements relative to their location and interests in a privacy-preserving manner, and (ii) the advertisement network can only compute aggregate statistics of ad impressions and click-through-rates. Through extensive experimentation, we show that our methods are efficient in terms of both computational and communication cost, especially at the client side