2,362 research outputs found
A review of the role of sensors in mobile context-aware recommendation systems
Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios
Gaining deep knowledge of Android malware families through dimensionality reduction techniques
This research proposes the analysis and subsequent characterisation of Android malware families by means of low
dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from
the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction
techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood
Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual
analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android
malware families. Interesting conclusions are obtained from the real-life data set under analysis
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
Economics of Electric Vehicle Charging: A Game Theoretic Approach
In this paper, the problem of grid-to-vehicle energy exchange between a smart
grid and plug-in electric vehicle groups (PEVGs) is studied using a
noncooperative Stackelberg game. In this game, on the one hand, the smart grid
that acts as a leader, needs to decide on its price so as to optimize its
revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs,
which act as followers, need to decide on their charging strategies so as to
optimize a tradeoff between the benefit from battery charging and the
associated cost. Using variational inequalities, it is shown that the proposed
game possesses a socially optimal Stackelberg equilibrium in which the grid
optimizes its price while the PEVGs choose their equilibrium strategies. A
distributed algorithm that enables the PEVGs and the smart grid to reach this
equilibrium is proposed and assessed by extensive simulations. Further, the
model is extended to a time-varying case that can incorporate and handle slowly
varying environments
- …