On LOS Contribution to Ultra-Dense Network

Ultra-dense networks (UDNs) are widely considered as an effective solution to greatly improve coverage by shortening the communication distance between user equipments (UEs) and base stations (BSs). The reality of UDN is that line-of-sight (LOS) communication becomes more likely to occur but this desirable result also complicates the performance analysis of random UDNs and puts an obstacle on the design and optimization of UDNs. The aim of this paper is to derive analytical results that take into account the phenomenon of having mixed LOS and non line-of-sight (NLOS) links in UDNs. In particular, the use of an arbitrary shaped thinning process to model the LOS wireless links allowed us to investigate a wide set of scenarios for what concerns the desired and interfering power levels. Our contribution is an accurate approximation in closed form for the success content delivery probability (SCDP) that decouples the contribution from LOS and NLOS links. Simulation results corroborate the accuracy of the proposed approximation

A Game Theoretic Optimization Framework for Home Demand Management Incorporating Local Energy Resources

Facilitated by advanced ICT infrastructure and optimization techniques, smart grid has the potential to bring significant benefits to the energy consumption management. This paper presents a game theoretic consumption scheduling framework based on the use of mixed integer programming to schedule consumption plan for residential consumers. In particular, the optimization framework incorporates integration of locally generated renewable energy in order to minimise dependency on conventional energy and the consumption cost. The game theoretic model is designed to coordinatively manage the scheduling of appliances of consumers. The Nash equilibrium of the game exists and the scheduling optimization converges to an equilibrium where all consumers can benefit from participating in. Simulation results are presented to demonstrate the proposed approach and the benefits of home demand management

Ultra Dense Edge Caching Networks With Arbitrary User Spatial Density

Cache-enabled small cells can be an effective solution to deliver contents to mobile users with much lower power and latency. While the trend for getting smaller and denser cells is clear, interference will soon become unmanageable and an obstacle when the number of content requests is massive. Moreover, content request is seldom a spatially homogeneous process due to physical impediments (e.g., buidings) and social activities, which makes resource allocation for content delivery more challenging. In this paper, we consider an ultra-dense network (UDN) in which content requests are served by cache-enabled access nodes which can either be active for delivering contents to users, or inactive to reduce interference and network energy consumption. Our aim is to devise an approach that can locally adapt the caching node density and content caching probabilities to accommodate any arbitrary user density and content request for maximizing the network’s successful content delivery probability (SCDP). With a non-homogeneous spatial distribution for user equipments (UEs), we find that user-load, a parameter at the access node, plays a major role in the overall optimization. Simulation results illustrate that the proposed method can obtain superior performance against the considered benchmarks, with up to 150-160% increase, and our optimized solutions effectively adapt to the spatial-dependent user density

Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. Firstly, methods of constructing sparse stealth attacks are developed for two typical scenarios: random attacks in which arbitrary measurements can be compromised and targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method but without the combinatorial complexity. Thirdly, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems

A statistical strategy to identify recombinant viral ribonucleoprotein of avian, human, and swine influenza A viruses with elevated polymerase activity

Objectives: Reassortment of influenza A viruses can give rise to viral ribonucleoproteins (vRNPs) with elevated polymerase activity and the previous three pandemic influenza viruses contained reassorted vRNPs of different origins. These suggest that reassorted vRNP may be one of the factors leading to a pandemic virus. In this study, we reconstituted chimeric vRNPs with three different viral strains isolated from avian, human and swine hosts. We applied a statistical strategy to identify the effect that the origin of a single vRNP protein subunit or the interactions between these subunits on polymerase activity. Design: Eighty one chimeric vRNPs were reconstituted in 293T cells at different temperatures. Polymerase activity was determined by luciferase reporter assay and the results were analysed by multiway anova and other statistical methods. Results: It was found that PB2, PB1, NP, PB2-PB1 interaction, PB2-PA interaction and PB1-NP interaction had significant effect on polymerase activity at 37°C and several single subunits and interactions were identified to lead to elevation of polymerase activity. Furthermore, we studied 27 out of these 81 different chimieric vRNPs in different combinations via fractional factorial design approach. Our results suggested that the approach can identify the major single subunit or interaction factors that affect the polymerase activity without the need to experimentally reproduce all possible vRNP combinations. Conclusions: Statistical approach and fractional factorial design are useful to identify the major single subunit or interaction factors that can modulate viral polymerase activity. © 2013 John Wiley & Sons Ltd.published_or_final_versio

Hard and easy instances of L-Tromino tilings

In this work we study tilings of regions in the square lattice with L-shaped trominoes. Deciding the existence of a tiling with L-trominoes for an arbitrary region in general is NP-complete, nonetheless, we identify restrictions to the problem where it either remains NP-complete or has a polynomial time algorithm. First, we characterize the possibility of when an Aztec rectangle has an L-tromino tiling, and hence also an Aztec diamond; if an Aztec rectangle has an unknown number of defects or holes, however, the problem of deciding a tiling is NP-complete. Then, we study tilings of arbitrary regions where only 180∘ rotations of L-trominoes are available. For this particular case we show that deciding the existence of a tiling remains NP-complete; yet, if a region does not contain so-called “forbidden polyominoes” as subregions, then there exists a polynomial time algorithm for deciding a tiling

Security challenges of small cell as a service in virtualized mobile edge computing environments

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users

Adequacy of therapy for people with both COPD and heart failure in the UK : historical cohort study

Acknowledgments We thank Derek Skinner for his contributions to the data acquisition and handling and Carole Nicholls and Priyanka Raju Konduru for statistical support. Writing and editorial support was provided by Elizabeth V. Hillyer, DVM, supported by Novartis Pharma AG, Basel, Switzerland. Funding This work was supported by Novartis. Employees of the sponsor (listed as authors) participated in the study design, interpretation of the results, writing of the report, and the decision to submit the paper for publication.Peer reviewedPublisher PD

Non-polynomial Worst-Case Analysis of Recursive Programs

We study the problem of developing efficient approaches for proving worst-case bounds of non-deterministic recursive programs. Ranking functions are sound and complete for proving termination and worst-case bounds of nonrecursive programs. First, we apply ranking functions to recursion, resulting in measure functions. We show that measure functions provide a sound and complete approach to prove worst-case bounds of non-deterministic recursive programs. Our second contribution is the synthesis of measure functions in nonpolynomial forms. We show that non-polynomial measure functions with logarithm and exponentiation can be synthesized through abstraction of logarithmic or exponentiation terms, Farkas' Lemma, and Handelman's Theorem using linear programming. While previous methods obtain worst-case polynomial bounds, our approach can synthesize bounds of the form $\mathcal{O}(n\log n)$ as well as $\mathcal{O}(n^r)$ where $r$ is not an integer. We present experimental results to demonstrate that our approach can obtain efficiently worst-case bounds of classical recursive algorithms such as (i) Merge-Sort, the divide-and-conquer algorithm for the Closest-Pair problem, where we obtain $\mathcal{O}(n \log n)$ worst-case bound, and (ii) Karatsuba's algorithm for polynomial multiplication and Strassen's algorithm for matrix multiplication, where we obtain $\mathcal{O}(n^r)$ bound such that $r$ is not an integer and close to the best-known bounds for the respective algorithms.Comment: 54 Pages, Full Version to CAV 201