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29 pages, 454 KiB

Open AccessArticle

End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks

by Vicente Casares-Giner, Tatiana Inés Navas, Dolly Smith Flórez and Tito Raúl Vargas Hernández

Information 2019, 10(4), 135; https://doi.org/10.3390/info10040135 - 10 Apr 2019

Cited by 6 | Viewed by 4950

Abstract

In this work it is considered a circular Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At the lower level, a cluster configuration is adopted in which the sensed information is [...] Read more.

In this work it is considered a circular Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At the lower level, a cluster configuration is adopted in which the sensed information is transferred from sensor nodes to a cluster head (CH) using a random access protocol (RAP). At CH level, CHs transfer information, hop-by-hop, ring-by-ring, towards to the sink located at the center of the sensed area using TDMA as MAC protocol. A Markovian model to evaluate the end-to-end (E2E) transfer delay is formulated. In addition to other results such as the well know energy hole problem, the model reveals that for a given radial distance between the CH and the sink, the transfer delay depends on the angular orientation between them. For instance, when two rings of CHs are deployed in the WSN area, the E2E delay of data packets generated at ring 2 and at the “west” side of the sink, is 20% higher than the corresponding E2E delay of data packets generated at ring 2 and at the “east” side of the sink. This asymmetry can be alleviated by rotating from time to time the allocation of temporary slots to CHs in the TDMA communication. Also, the energy consumption is evaluated and the numerical results show that for a WSN with a small coverage area, say a radio of 100 m, the energy saving is more significant when a small number of rings are deployed, perhaps none (a single cluster in which the sink acts as a CH). Conversely, topologies with a large number of rings, say 4 or 5, offer a better energy performance when the service WSN covers a large area, say radial distances greater than 400 m. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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17 pages, 1078 KiB

Open AccessArticle

Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability

by Mathias Longo, Matías Hirsch, Cristian Mateos and Alejandro Zunino

Information 2019, 10(3), 86; https://doi.org/10.3390/info10030086 - 26 Feb 2019

Cited by 14 | Viewed by 4000

Abstract

With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, [...] Read more.

With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing/storage features and pervasiveness, but also due to their capability to render services for several hours, even days, without being plugged to the electricity grid. Nonetheless, misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device’s computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner’s activity and relevant device state variables. The model includes a feature extraction approach to obtain representative features/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a related work. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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21 pages, 618 KiB

Open AccessArticle

Energy Efficiency and Renewable Energy Management with Multi-State Power-Down Systems

by James Andro-Vasko, Wolfgang Bein and Hiro Ito

Information 2019, 10(2), 44; https://doi.org/10.3390/info10020044 - 29 Jan 2019

Cited by 2 | Viewed by 3702

Abstract

A power-down system has an on-state, an off-state, and a finite or infinite number of intermediate states. In the off-state, the system uses no energy and in the on-state energy it is used fully. Intermediate states consume only some fraction of energy but [...] Read more.

A power-down system has an on-state, an off-state, and a finite or infinite number of intermediate states. In the off-state, the system uses no energy and in the on-state energy it is used fully. Intermediate states consume only some fraction of energy but switching back to the on-state comes at a cost. Previous work has mainly focused on asymptotic results for systems with a large number of states. In contrast, the authors study problems with a few states as well as systems with one continuous state. Such systems play a role in energy-efficiency for information technology but are especially important in the management of renewable energy. The authors analyze power-down problems in the framework of online competitive analysis as to obtain performance guarantees in the absence of reliable forecasting. In a discrete case, the authors give detailed results for the case of three and five states, which corresponds to a system with on-off states and three additional intermediate states “power save”, “suspend”, and “hibernate”. The authors use a novel balancing technique to obtain optimally competitive solutions. With this, the authors show that the overall best competitive ratio for three-state systems is

\frac{9}{5}

and the authors obtain optimal ratios for various five state systems. For the continuous case, the authors develop various strategies, namely linear, optimal-following, progressive and exponential. The authors show that the best competitive strategies are those that follow the offline schedule in an accelerated manner. Strategy “progressive” consistently produces competitive ratios significantly better than 2. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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34 pages, 17968 KiB

Open AccessArticle

An Empirical Study of Exhaustive Matching for Improving Motion Field Estimation

by Vanel Lazcano

Information 2018, 9(12), 320; https://doi.org/10.3390/info9120320 - 12 Dec 2018

Cited by 5 | Viewed by 8634

Abstract

Optical flow is defined as the motion field of pixels between two consecutive images. Traditionally, in order to estimate pixel motion field (or optical flow), an energy model is proposed. This energy model is composed of (i) a data term and (ii) a [...] Read more.

Optical flow is defined as the motion field of pixels between two consecutive images. Traditionally, in order to estimate pixel motion field (or optical flow), an energy model is proposed. This energy model is composed of (i) a data term and (ii) a regularization term. The data term is an optical flow error estimation and the regularization term imposes spatial smoothness. Traditional variational models use a linearization in the data term. This linearized version of data term fails when the displacement of the object is larger than its own size. Recently, the precision of the optical flow method has been increased due to the use of additional information, obtained from correspondences computed between two images obtained by different methods such as SIFT, deep-matching, and exhaustive search. This work presents an empirical study in order to evaluate different strategies for locating exhaustive correspondences improving flow estimation. We considered a different location for matching random locations, uniform locations, and locations on maximum gradient magnitude. Additionally, we tested the combination of large and medium gradients with uniform locations. We evaluated our methodology in the MPI-Sintel database, which represents the state-of-the-art evaluation databases. Our results in MPI-Sintel show that our proposal outperforms classical methods such as Horn-Schunk, TV-L1, and LDOF, and our method performs similar to MDP-Flow. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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18 pages, 1034 KiB

Open AccessArticle

A Diabetes Management Information System with Glucose Prediction

by Cláudio Augusto Silveira Lélis and Renan Motta Goulart

Information 2018, 9(12), 319; https://doi.org/10.3390/info9120319 - 12 Dec 2018

Cited by 4 | Viewed by 3558

Abstract

Diabetes has become a serious health concern. The use and popularization of blood glucose measurement devices have led to a tremendous increase on health for diabetics. Tracking and maintaining traceability between glucose measurements, insulin doses and carbohydrate intake can provide useful information to [...] Read more.

Diabetes has become a serious health concern. The use and popularization of blood glucose measurement devices have led to a tremendous increase on health for diabetics. Tracking and maintaining traceability between glucose measurements, insulin doses and carbohydrate intake can provide useful information to physicians, health professionals, and patients. This paper presents an information system, called GLUMIS (GLUcose Management Information System), aimed to support diabetes management activities. It is made of two modules, one for glucose prediction and one for data visualization and a reasoner to aid users in their treatment. Through integration with glucose measurement devices, it is possible to collect historical data on the treatment. In addition, the integration with a tool called the REALI System allows GLUMIS to also process data on insulin doses and eating habits. Quantitative and qualitative data were collected through an experimental case study involving 10 participants. It was able to demonstrate that the GLUMIS system is feasible. It was able to discover rules for predicting future values of blood glucose by processing the past history of measurements. Then, it presented reports that can help diabetics choose the amount of insulin they should take and the amount of carbohydrate they should consume during the day. Rules found by using one patient’s measurements were analyzed by a specialist that found three of them to be useful for improving the patient’s treatment. One such rule was “if glucose before breakfast

\in [47, 89],

then glucose at afternoon break in

[160, 306]

”. The results obtained through the experimental study and other verifications associated with the algorithm created had a double objective. It was possible to show that participants, through a questionnaire, viewed the visualizations as easy, or very easy, to understand. The secondary objective showed that the innovative algorithm applied in the GLUMIS system allows the decision maker to have much more precision and less loss of information than in algorithms that require the data to be discretized. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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15 pages, 3002 KiB

Open AccessArticle

Prototyping a Traffic Light Recognition Device with Expert Knowledge

by Thiago Almeida, Hendrik Macedo, Leonardo Matos and Nathanael Vasconcelos

Information 2018, 9(11), 278; https://doi.org/10.3390/info9110278 - 13 Nov 2018

Cited by 2 | Viewed by 4125

Abstract

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. [...] Read more.

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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17 pages, 667 KiB

Open AccessArticle

Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

by Hamzeh Eyal Salman, Mustafa Hammad, Abdelhak-Djamel Seriai and Ahed Al-Sbou

Information 2018, 9(9), 222; https://doi.org/10.3390/info9090222 - 3 Sep 2018

Cited by 22 | Viewed by 5589

Abstract

Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). [...] Read more.

Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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Review

Jump to: Research

22 pages, 507 KiB

Open AccessReview

The Impact of Code Smells on Software Bugs: A Systematic Literature Review

by Aloisio S. Cairo, Glauco de F. Carneiro and Miguel P. Monteiro

Information 2018, 9(11), 273; https://doi.org/10.3390/info9110273 - 6 Nov 2018

Cited by 27 | Viewed by 8761

Abstract

Context: Code smells are associated to poor design and programming style, which often degrades code quality and hampers code comprehensibility and maintainability. Goal: identify published studies that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We [...] Read more.

Context: Code smells are associated to poor design and programming style, which often degrades code quality and hampers code comprehensibility and maintainability. Goal: identify published studies that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the stated goal. Results: The SLR selected studies from July 2007 to September 2017, which analyzed the source code of open source software projects and several code smells. Based on evidence of 16 studies covered in this SLR, we conclude that 24 code smells are more influential in the occurrence of bugs relative to the remaining smells analyzed. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques, and procedures that should be applied to analyze the influence of the smells. Conclusions: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade. Full article

(This article belongs to the Special Issue Information Technology: New Generations (ITNG 2018))

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