Computational geometry

Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators Local caching of popular content items at the small cell base stations (SBSs) has been proposed to decrease the costly transmissions from the macrocell base stations without requiring high capacity backhaul links for connecting the SBSs with the core network However, the caching policy design is a challenging problem especially if one considers realistic parameters such as the bandwidth capacity constraints of the SBSs that can be reached in congested urban areas We consider such a scenario and formulate the joint routing and caching problem aiming to maximize the fraction of content requests served locally by the deployed SBSs This is an NP-hard problem and, hence, we cannot obtain an optimal solution Thus, we present a novel reduction to a variant of the facility location problem, which allows us to exploit the rich literature of it, to establish algorithms with approximation guarantees for our problem Although the reduction does not ensure tight enough bounds in general, extensive numerical results reveal a near-optimal performance that is even up to 38% better compared to conventional caching schemes using realistic system settings

Approximation Algorithms for Mobile Data Caching in Small Cell Networks

Vehicular ad hoc networks (VANETs) have become one of the most promising and fastest growing subsets of mobile ad hoc networks (MANETs). They are comprised of smart vehicles and roadside units (RSU) which communicate through unreliable wireless media. By their very nature, they are very susceptible to attacks which may result in life-endangering situations. Due to the potential for serious consequences, it is vital to develop security mechanisms in order to detect such attacks against VANETs. This paper aims to survey such possible attacks and the corresponding detection mechanisms that are proposed in the literature. The attacks are classified and explained along with their effects, and the solutions are presented together with their advantages and disadvantages. An evaluation and summary table which provides a holistic view of the solutions surveyed is also presented.

A survey of attacks and detection mechanisms on intelligent transportation systems

Artificial bee colony algorithm with variable search strategy for continuous optimization

The advancements in digital communication technology have made communication between humans more accessible and instant. However, personal and sensitive information may be available online through social networks and online services that lack the security measures to protect this information. Communication systems are vulnerable and can easily be penetrated by malicious users through social engineering attacks. These attacks aim at tricking individuals or enterprises into accomplishing actions that benefit attackers or providing them with sensitive data such as social security number, health records, and passwords. Social engineering is one of the biggest challenges facing network security because it exploits the natural human tendency to trust. This paper provides an in-depth survey about the social engineering attacks, their classifications, detection strategies, and prevention procedures.

/pdf/social-engineering-attacks-a-survey-53uir8j7og.pdf

Social Engineering Attacks: A Survey

In this paper, we propose and analyze a local search algorithm for the capacitated facility location problem. Our algorithm is a modification of the algorithm proposed by Zhang et al. [7] and improves the approximation ratio from 5.83 to 5. We achieve this by modifying the close, open and multi operations. The idea of taking linear combinations of inequalities used in Aggarwal et al.[1] is crucial in achieving this result. The example proposed by Zhang et al. also shows that our analysis is tight.

A 5-approximation for capacitated facility location

We present a parallel version of BIRCH with the objective of enhancing the scalability without compromising on the quality of clustering. The incoming data is distributed in a cyclic manner (or block cyclic manner if the data is bursty) to balance the load among processors. The algorithm is implemented on a message passing share-nothing model. Experiments show that for very large data sets the algorithm scales nearly linearly with the increasing number of processors. Experiments also show that clusters obtained by PBIRCH are comparable to those obtained using BIRCH.

PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data

We consider the facility location problem where each facility can serve at most U clients. We analyze a local search algorithm for this problem which uses only the operations of add, delete and swap and prove that any locally optimum solution is no more than 3 times the global optimum. This improves on a result of Chudak and Williamson who proved an approximation ratio of ${3+2\sqrt{2}}$ for the same algorithm. We also provide an example which shows that any local search algorithm which uses only these three operations cannot achieve an approximation guarantee better than 3.

A 3-approximation algorithm for the facility location problem with uniform capacities

MIB: Using mutual information for biclustering gene expression data

We consider the facility location problem where each facility can serve at most U clients. We analyze a local search algorithm for this problem which uses only the operations of add, delete and swap and prove that any locally optimum solution is no more than 3 times the global optimum. This improves on a result of Chudak and Williamson who proved an approximation ratio of $3+2\sqrt{2}$ for the same algorithm. We also provide an example which shows that our analysis is tight.

Neelima Gupta

Papers

A 5-approximation for capacitated facility location

PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data

A 3-approximation algorithm for the facility location problem with uniform capacities

MIB: Using mutual information for biclustering gene expression data

A 3-approximation for facility location with uniform capacities