Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201

Evidence of crossover phenomena in wind speed data

In this report, a systematic analysis of hourly wind speed data obtained from three potential wind generation sites (in North Dakota) is analyzed. The power spectra of the data exhibited a power-law decay characteristic of $1/f^{\alpha}$ processes with possible long-range correlations. Conventional analysis using Hurst exponent estimators proved to be inconclusive. Subsequent analysis using detrended fluctuation analysis (DFA) revealed a crossover in the scaling exponent ($\alpha$). At short time scales, a scaling exponent of $\alpha \sim 1.4$ indicated that the data resembled Brownian noise, whereas for larger time scales the data exhibited long range correlations ($\alpha \sim 0.7$). The scaling exponents obtained were similar across the three locations. Our findings suggest the possibility of multiple scaling exponents characteristic of multifractal signals

A Multifractal Description of Wind Speed Records

In this paper, a systematic analysis of hourly wind speed data obtained from four potential wind generation sites in North Dakota is conducted. The power spectra of the data exhibited a power law decay characteristic of $1/f^{\alpha}$ processes with possible long range correlations. The temporal scaling properties of the records were studied using multifractal detrended fluctuation analysis {\em MFDFA}. It is seen that the records at all four locations exhibit similar scaling behavior which is also reflected in the multifractal spectrum determined under the assumption of a binomial multiplicative cascade model

Graphoidal Tree d - Cover

Acharya and Sampathkumar defined a graphoidal cover as a partition of edges into internally disjoint (not necessarily open) paths. If we consider only open paths in the above definition then we call it as a graphoidal path cover

The Role Of The Metaverse In Digital Marketing

The Metaverse is a postulate new release of our online world with steady online 3-D digital surroundings. The conventional advertising method transfigured into the time period ‘METAVERSE’. Metaverse adjustments the critiques of customers with product and services within the modern-day situation. The office work has a full-size type of Metaverse correlating to advertising. The strategical method, components of the Metaverse, and the scope are the principal viewpoints of Metaverse marketing. Innumerable corporations have amalgamated with Metaverse advertising and marketing technically to provoke their merchandise. The present-day article analyses the concept of the Metaverse in touching on marketing with its wide scope and strategies. The studies are predicated at the framework of strategies, ease, and the additives of Metaverse advertising. The idea of the Metaverse is mentioned on this work on the descriptive papers the concept of the Metaverse is explained within the article, when it comes to advertising and marketing. The paper also undergoes the future implications with SWOC evaluation and suggested the internal and external factors that provoke the enterprise overall performance

ECONOMIC IMPACTS OF INTERNATIONAL AGRICULTURAL RESEARCH: CASE OF US-EGYPT-IRRI COLLABORATIVE PROJECT ON THE GENERATION OF NEW RICE TECHNOLOGIES

Agricultural research managers and scientists are under increasing pressure to demonstrate the efficient and socially-effective use of funds spent on agricultural R&D. These pressures stem from heightened expectations of transparency and accountability in the use of public funds, as well as from the growing demand for evidence of impact on target social groups and environmental services. Finally, advances in agricultural biotechnology research and the ensuing dialogue about the desirability of using biotechnology tools for increasing food production in developing countries have highlighted the need to assess the impacts of international agricultural research in the US, the developing countries, and the international agricultural research centers (IARCs). The US-Egypt ATUT project, funding involves collaborative research among plant breeders, molecular geneticists, and other agricultural scientists in the US, Egypt and IRRI. ATUT rice research accelerated the utilization of three methods for improving the speed and reliability of the screening and evaluation process for identifying salt resistant varieties: shuttle breeding, anther culture and marker-assisted selection. ATUT initiated the application of Marker Assisted Selection (MAS) technology for screening Egyptian rice germplasm. Other ATUT rice technologies in the pipeline have various levels of AATUTness in their research and development. Some of the varieties to be released starting 2003 such as short duration HYVs, will have benefitted less directly from ATUT funding and scientific collaboration. Others- such as hybrid rice varieties will have been very significantly shaped by ATUT. The DREAM model under IFPRI's Global and Regional Program on Agricultural Science and Technology Policy, is used to assess the potential economic benefit of technology outputs for rice, under a range of likely adoption, market and trade scenarios. The simulation model, based on economic surplus theory, uses data and parameters from interviews with scientists, policy makers on the impact and adoption of technology. For this study, ex-ante simulations of the most likely range of outcomes with and without the innovations from ATUT investments. Analyzing the impact of technical change (a simulation over a specified number of years) has provided year-by year estimates of changes in: prices, quantities produced, consumed and traded, levels of adoption, economic benefits to consumers, economic benefits to adopters or losses (non-adopters) to producers. For US and IRRI benefits: Enhanced germplasm pool, stock of knowledge and facilities, and better informed scientists. US scientists in California and Arkansas benefit More integrated into the international rice research community. Gross benefits are estimated for governorates, by producers and consumers, by saline and normal soils, for 1997 to 2017 (end of GoE's current strategic horizon) discounted to 1997 US$. Producers in normal soils derive higher benefits than those in saline soils, some governorates reap more of the producer benefits than others; rural consumers benefit more than urban consumers. Consumer benefits are also estimated for importers of Egyptian rice such as Turkey, Sudan and aggregated Arabian countries. Cost of rice R&D and technology transfer will be measured to derive the IRR and B/C ratios.Research and Development/Tech Change/Emerging Technologies,

Minimizing the effect of sinusoidal trends in detrended fluctuation analysis

The detrended fluctuation analysis (DFA) [Peng et al., 1994] and its extensions (MF-DFA) [Kantelhardt et al., 2002] have been used extensively to determine possible long-range correlations in self-affine signals. While the DFA has been claimed to be a superior technique, recent reports have indicated its susceptibility to trends in the data. In this report, a smoothing filter is proposed to minimize the effect of sinusoidal trends and distortion in the log-log plots obtained by DFA and MF-DFA techniques

REMOVAL OF GAUSSIAN AND IMPULSE NOISE IN THE COLOUR IMAGE PROGRESSION WITH FUZZY FILTERS

This paper is concerned with algebraic features based filtering technique, named as the adaptive statistical quality based filtering technique (ASQFT), is presented for removal of Impulse and Gaussian noise in corrupted colour images. A combination of these two filters also helps in eliminating a mixture of these two noises. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian impulse noise. The experiments shows that proposed method outperforms novel modern filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peaksignal- to-noise ratio (PSNR) and the normalized color difference (NCD). The expectations filter achieves a promising performance