An entropy evaluation algorithm to improve transmission efficiency of compressed data in pervasive healthcare mobile sensor networks

Data transmission is the most critical operation for mobile sensors networks in term of energy waste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of service also by means of energy saving policies. Communication and data transmission are among the most critical operation for such devises in term of energy waste. In this paper we present a novel approach to increase battery life-span by means of shorter transmission due to data compression. On the other hand, since this latter operation has a non-neglectable energy cost, we developed a compression efficiency estimator based on the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for the evaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication. In facts the developed technique, due to its independence from the string or file length, is extremely robust both for small and big data files, as well as to evaluate whether or not to compress data before transmission. Since the proposed solution provides a quantitative analysis of the source's entropy and the related statistics, it has been implemented as a preprocessing step before transmission. A dynamic threshold defines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatly reduce the transmission length. On the other hand a data compression algorithm should be used only when the energy gain of the reduced transmission time is presumably greater than the energy used to run the compression software. In this paper we developed an automatic evaluation system in order to optimize the data transmission in mobile sensor networks, by compressing data only when this action is presumed to be energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well as standard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensive with respect to a compression algorithm. Finally the computational complexity of the proposed approach is virtually neglectable with respect to the compression and transmission routines themselves

Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing

We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol\u27s performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other

Cpu Usage Pattern Discovery Using Suffix Tree For Computational Resource Advisory System

Dalam alam pengkomputeraan grid, sumber pengkomputeraan yang boleh diguna sentiasa berubah dari masa ke masa. In grid computing environment, resource availability often changes from time to time

CPU Usage Pattern Discovery Using Suffix Tree For Computational Resource Advisory System [QA76.76.P426 I11 2006 f rb].

Dalam alam pengkomputeraan grid, sumber pengkomputeraan yang boleh diguna sentiasa berubah dari masa ke masa. Penjadual memerlukan aktiviti ramalan supaya ia dapat berfungsi dengan cekap. In grid computing environment, resource availability often changes from time to time. Thus, schedulers require resource prediction help to make effective scheduling decision

Deviation prediction and correction on low-cost atmospheric pressure sensors using a machine-learning algorithm

The datasets used in this work are available in the Zenodo repository, with digital identifier (DOI) as 10.5281/zenodo.3560299.Atmospheric pressure sensors are important devices for several applications, including environment monitoring and indoor positioning tracking systems. This paper proposes a method to enhance the quality of data obtained from low-cost atmospheric pressure sensors using a machine learning algorithm to predict the error behaviour. By using the extremely Randomized Trees algorithm, a model was trained with a reference sensor data for temperature and humidity and with all low-cost sensor datasets that were co-located into an artificial climatic chamber that simulated different climatic situations. Fifteen low-cost environmental sensor units, composed by five different models, were considered. They measure - together - temperature, relative humidity and atmospheric pressure. In the evaluation, three categories of output metrics were considered: raw; trained by the independent sensor data; and trained by the low-cost sensor data. The model trained by the reference sensor was able to reduce the Mean Absolute Error (MAE) between atmospheric pressure sensor pairs by up to 67%, while the same ensemble trained with all low-cost data was able to reduce the MAE by up to 98%. These results suggest that low-cost environmental sensors can be a good asset if their data are properly processed.- (undefined

Efficient prediction model management in mobile systems

With the advent of affordable mobile devices such as smartphones and tablets, the vision of Pervasive Computing has made a big step closer to becoming reality. In order to become truly ubiquitous and seamlessly integrate into everyday life, the design of context-aware applications is essential. Using contextual information obtained for example from the device's sensors such as motion sensors and gps receiver, context-aware applications can adapt their behavior depending on the environment the user is in. In some scenarios, context aware applications can also benefit from knowledge about future contexts. This necessitates the use of a context prediction model. We examine a social network scenario where in addition, the context in question is originally being acquired on another user's device. In this scenario, the prediction model could for example be used to predict the next location or activity of a friend. Prior to that, the prediction model needs to be distributed to and stored on the mobile device running the application. Both high transfer cost and limited space make it imperative to produce small prediction models which still predict the context considerably well. In this thesis, we examined methods to compress Markov-based prediction models of higher order in a lossless and lossy fashion and evaluated these methods on real world and generated data. Our evaluation showed clearly that the compression mechanisms introduced can be successfully applied to significantly reduce the size of the prediction models with only a minor impact on prediction performance

Ontologies for context-aware applications

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

A prior case study of natural language processing on different domain

In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model