Digital forensics challenges to big data in the cloud

As a new research area, Digital Forensics is a subject in a rapid development society. Cyber security for Big Data in the Cloud is getting attention more than ever. Computing breach requires digital forensics to seize the digital evidence to locate who done it and what has been done maliciously and possible risk/damage assessing what loss could leads to. In particular, for Big Data attack cases, Digital Forensics has been facing even more challenge than original digital breach investigations. Nowadays, Big Data due to its characteristics of three “V”s (Volume, Velocity, and Variety), they are either synchronized with Cloud (Such as smart phone) or stored on the Cloud, in order to sort out the storage capacity etc. problems, which made Digital Forensics investigation even more difficult. The Big Data-Digital Forensics issue for Cloud is difficult due to some issues. One of them is physically identify specific wanted device. Data are distributed in the cloud, customer or the digital forensics practitioner cannot have a fully access control like the traditional investigation does. The Smart City technique is making use of ICT (information communications technology) to collecting, detecting, analysing and integrating the key information data of core systems in running the cities. Meantime, the control is making intelligent responses to different requirements that include daily livelihood, PII (Personally identifiable information) security, environmental protection, public safety, industrial and commercial activities and city services. The Smart City data are Big Data, collected and gathered by the IoT (Internet of Things). This paper has summerised our review on the trends of Digital Forensics served for Big Data. The evidence acquisition challenge is discussed. A case study of a Smart City project with the IoT collected services Big data which are stored at the cloud computing environment is represented. The techniques can be generalised to other Big Data in the Cloud environment

Active RIS-Assisted mmWave Indoor Signal Enhancement Based on Transparent RIS

Due to the serious path loss of millimeter-wave (mmWave), the signal sent by the base station is seriously attenuated when it reaches the indoors. Recent studies have proposed a glass-based metasurface that can enhance mmWave indoor signals. The transparent reconfigurable intelligent surface (RIS) focuses on the mmWave signal to a specific location indoors. In this paper, a novel RIS-assisted mmWave indoor enhancement scheme is proposed, in which a transparent RIS is deployed on the glass to enhance mmWave indoor signals, and three assisted transmission scenarios, namely passive RIS (PRIS), active RIS (ARIS), and a novel hybrid RIS (HRIS) are proposed. This paper aims to maximize the signal-to-noise ratio (SNR) of the received signal for the three assisted transmission scenarios. The closed-form solution to the maximum SNR is presented in the PRIS and the ARIS-assisted transmission scenarios. Meanwhile, the closed-form solution to the maximum SNR for the HRIS-assisted transmission scenario is presented for given active unit cells. In addition, the performance of the proposed scheme is analyzed under three assisted transmission scenarios. The results indicate that under a specific RIS power budget, the ARIS-assisted transmission scenario achieves the highest data rate and energy efficiency. Also, it requires very few unit cells, thus dramatically reducing the size of the metasurface

Delay Sensitive Communications over Cognitive Radio Networks

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to dynamic resource availability because of the licensed users' activities. In this paper, we study the optimal admission control and channel allocation decisions in cognitive overlay networks in order to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performance with low complexities.Comment: 11 pages, 8 figure

Capturing the polynomial hierarchy by second-order revised Krom logic

We study the expressive power and complexity of second-order revised Krom logic (SO-KROM$^{r}$). On ordered finite structures, we show that its existential fragment $\Sigma^1_1$-KROM$^r$ equals $\Sigma^1_1$-KROM, and captures NL. On all finite structures, for $k\geq 1$, we show that $\Sigma^1_{k}$ equals $\Sigma^1_{k+1}$-KROM$^r$ if $k$ is even, and $\Pi^1_{k}$ equals $\Pi^1_{k+1}$-KROM$^r$ if $k$ is odd. The result gives an alternative logic to capture the polynomial hierarchy. We also introduce an extended version of second-order Krom logic (SO-EKROM). On ordered finite structures, we prove that SO-EKROM collapses to $\Pi^{1}_{2}$-EKROM and equals $\Pi^1_1$. Both of SO-EKROM and $\Pi^{1}_{2}$-EKROM capture co-NP on ordered finite structures

Flow Boiling Heat Transfer And Pressure Drop Characteristics Of R1234yf In A Dimpled Flat Duct

Among various heat transfer enhancement technologies, the dimpled surface, which is inspired by the resistance reduction characteristics of the specific concaves on golf balls, has the potential to improve heat transfer with a relatively low pressure-drop penalty. More and more applications of dimpled surfaces in heat exchangers have shown up in industries. However, the lack of experimental data, especially the heat transfer and pressure drop data for liquidand-vapor two-phase flow, inside the dimpled flow channels prevents the good design of the dimpled heat exchangers. In this study, a facility has been designed and built to investigate the heat transfer and pressure drop of flow-boiling R1234yf in a dimpled flat duct. The details of the facility, especially the test section, are presented. A microscope is used to measure the geometrical dimensions of the dimpled flat tube. The heat loss is tested and the heat balance is -2 -1 checked before the experiments. The experiments are performed at mass flux from 100 to 200 kg m s , heat flux of 5 kW m-2, saturation temperature of 15 oC, and vapor quality from 0.1 to 0.95. The experimental results are presented and discussed in detail

Fully Conjugated Phthalocyanine Copper Metal-Organic Frameworks for Sodium-Iodine Batteries with Long-Time-Cycling Durability

Rechargeable sodium-iodine (Na-I-2) batteries are attracting growing attention for grid-scale energy storage due to their abundant resources, low cost, environmental friendliness, high theoretical capacity (211 mAh g(-1)), and excellent electrochemical reversibility. Nevertheless, the practical application of Na-I-2 batteries is severely hindered by their poor cycle stability owing to the serious dissolution of polyiodide in the electrolyte during charge/discharge processes. Herein, the atomic modulation of metal-bis(dihydroxy) species in a fully conjugated phthalocyanine copper metal-organic framework (MOF) for suppression of polyiodide dissolution toward long-time cycling Na-I-2 batteries is demonstrated. The Fe-2[(2,3,9,10,16,17,23,24-octahydroxy phthalocyaninato)Cu] MOF composited with I-2 (Fe-2-O-8-PcCu/I-2) serves as a cathode for a Na-I-2 battery exhibiting a stable specific capacity of 150 mAh g(-1) after 3200 cycles and outperforming the state-of-the-art cathodes for Na-I-2 batteries. Operando spectroelectrochemical and electrochemical kinetics analyses together with density functional theory calculations reveal that the square planar iron-bis(dihydroxy) (Fe-O-4) species in Fe-2-O-8-PcCu are responsible for the binding of polyiodide to restrain its dissolution into electrolyte. Besides the monovalent Na-I-2 batteries in organic electrolytes, the Fe-2-O-8-PcCu/I-2 cathode also operates stably in other metal-I-2 batteries like aqueous multivalent Zn-I-2 batteries. Thus, this work offers a new strategy for designing stable cathode materials toward high-performance metal-iodine batteries

Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete

This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase