Dida Midekso

Towards end-user development of REST client applications on smartphones

HTML5 can be used to develop client applications by composing REST web services within the context of Web 2.0. However, the possibility of implementing cross-platform smartphone applications with REST services needs to be studied. Accordingly, we developed a REST-based cross-platform application with PhoneGap. The application was deployed on the Android, Windows Phone, and iOS platforms; subsequently we evaluated its usability. We observed that REST-based cross-platform smartphone applications can be implemented with HTML5 and PhoneGap, which can be scaled-up into a REST service composition tool. Moreover, the applications usability remains unaffected on the native platforms and adaptation required only minimal effort. We explore the implications of implementing cross-platform smartphone applications with REST services.A REST-based cross-platform application was developd with PhoneGap.The application was deployed on the Android, Windows Phone, and iOS platforms;.Usability of the application remains unaffected on the native platformsAdaptation required only minimal effort, mainly for SDK configuration

Evaluating Usability of Cross-Platform Smartphone Applications

The computing power of smartphones is increasing as time goes. However, the proliferation of multiple different types of operating platforms affected interoperable smartphone applications development. Thus, the cross-platform development tools are coined.

A Neural Network Model for Road Traffic Flow Estimation

Real-time road traffic state information can be used for traffic flow monitoring, incident detection and other related traffic management activities. Road traffic state estimation can be done using either data driven or model based or hybrid approaches. The data driven approach is preferable for real-time flow prediction but to get traffic data for performance evaluation, hybrid approach is recommended. In this paper, a neural network model is employed to estimate real-time traffic flow on urban road network. To model the traffic flow, the microscopic model Simulation of Urban Mobility (SUMO) is used. The evaluation of the model using both simulation data and real-world data indicated that the developed estimation model could help to generate reliable traffic state information on urban roads.

Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation

This paper presents real time road traffic state estimation framework together with its evaluation. To evaluate the framework, a three-layer Artificial Neural Network model is proposed and used to estimate complete link traffic state. The inputs to the ANN model include probe vehicles position, time stamps and speeds. To model the arterial road network the microscopic simulation SUMO is used to generate aggregated speed and FCD export files which are used in the training and evaluation of the ANN model. Besides, real A-GPS data gathered using A-GPS mobile phone on a moving vehicle on the sample roads is used to evaluate the ANN model. The performance of the ANN model is evaluated using the performance indicators RMSE and MPAE and on average the MPAE is less than 1.2%. The trained ANN model is also used to estimate the sample road link speeds and compared with ground truth speed (aggregate edge states) on a 10-minute interval for 1hr. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated and used to indicate real-time urban road traffic condition.

Road traffic state estimation framework based on hybrid assisted global positioning system and uplink time difference of arrival data collection methods

With the rapid increase of vehicles on the road, road traffic flow information is indispensible to our daily life. Different Intelligent Transport System applications like advanced Traffic Transport System are dependent on proper road traffic state information. Among the different activities in road traffic flow estimation, road traffic data collection plays the great role. The current state-of-the-practice road traffic data collection tools used to gather information about traffic flow are fixed sensor technologies which are limited in road coverage and affected by maintenance and deployment costs. Using the existing cellular network infrastructure to gather road traffic data offers large coverage capability and it is faster to set up, easier to install and needs less maintenance. Based on the analysis of relevant studies on road traffic state estimation, this paper proposes a universal framework based on experimentally evaluated hybrid Assisted Global Positioning System (A-GPS) and Uplink Time Difference Of Arrival (U-TDOA) real-time road traffic data collection system. The framework integrates several models with appropriate technologies to realize traffic data collection, processing, analysis, state estimation and optimization and presentation of traffic flow information to road users. In Data analysis component a new approach of taking probe sample, i.e. dynamic “Pinpoint-Temporal” sampling frequency method is proposed.

Artificial Neural Network Based Real-Time Urban Road Traffic State Estimation Framework

With the rapid increase of urban development and the surge in vehicle ownership, urban road transport problems like traffic accident and congestion caused huge waste of time, property damage and environmental pollution in recent years. To address these problems, use of information communication technology-based transport systems that can support maximum utilization of the existing road transport infrastructure has been proposed by different researchers. Road monitoring systems are one of these solutions which support road users to make informed decisions. However, the current road traffic monitoring systems use road side infrastructures for road traffic data collection and these technologies lack accurate and up-to-date traffic data covering the whole road network. By comparison, cellular networks are already widely deployed and can provide large road network coverage. Besides, 3G and 4G cellular networks provide mobile phone positioning facility with better performance accuracy and this opportunity can help to obtain accurate traffic flow information in cost effective manner on the entire road networks. The purpose of this chapter is to present our approach for real-time road traffic state estimation framework using the existing cellular network for road traffic data source and a neural network state estimation model. To evaluate the performance of the Artificial Neural Network model (ANN) both simulation and real world data is applied. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated using this model and the estimated data can help to indicate real-time urban road traffic condition.

Comparing Measurement and State Vector Data Fusion Algorithms for Mobile Phone Tracking Using A-GPS and U-TDOA Measurements

Multi-Sensor Data Fusion (MSDF) becomes one research area in different disciplines including science and engineering. To enhance reliability and accuracy of sensor measurements’ multisensory data fusion techniques are applied. The aim of this paper is to evaluate estimation performance of measurement fusion and state vector fusion algorithms in tracking a moving mobile phone along all journey of a vehicle. These two algorithms based on Kalman Filter are implemented in the tracking system. Performance evaluation is computed using MATLAB and the analysis show position and velocity estimation accuracy of measurement fusion algorithm is better than state vector fusion algorithm.

Agile methods in Ethiopia : an empirical study

This paper provides empirical evidence of agile method adoption in smaller companies in Ethiopia. Agile methods are emerging as best practice for software development in the global north. So, is there evidence that agile methods are being used in Ethiopia? A Grounded Theory approach was adopted using face-to-face interviews with 17 software professionals from 7 software companies, which were selected by using a snowball sampling technique. The interviews were semi-structured and open-ended and have been audio-recorded and transcribed. Participants in the study identified the importance of agile principles, values and practices. Agile practices are used to address issues with requirements and to encourage user participation. However, it was discovered that the companies in the study were conducting software projects for government clients that mandate substantial documentation with elaborate staged approval procedures, using fixed price contracts with predefined delivery schedules.

Cloud Suitability Assessment Method for Application Software.

The advantages and initial adoption success stories of the Cloud computing inspire enterprises to migrate their existing applications to the Cloud computing technology. As a result, the trend of migrating existing application software to the Cloud grows steadily. However, not all applications are ideal candidates to be ported. Moreover, very often client organizations do not have the appropriate methods to determine which of their IT services are appropriate for migration. In this respect, a method is required to assess the suitability of the existing applications before embarking on migration. This study designs a method to assess Cloud suitability of exiting application software following the design science approach. The method is a multi-step approach composed of seven activities, devised with the goal of reducing the risk of making wrong migration decisions. Further research will be used to validate and refine the proposed method.

Cloud computing as technological solutions for higher education institutions: Adoption readiness assessment model: Reseach in-progress

Cloud computing recently offers immense benefits. But higher education institutions (HEIs) are hesitating to adopt. As a result, they are not benefited more from such technology. This is because key adoption capabilities are not explored in detail in HEIs context. In addition to this, there is no comprehensive empirically validated adoption readiness assessment model which helps them to adopt Cloud solutions efficiently. Hence, to fill this gap this study identifies the critical capabilities and develops a model to assess Cloud computing adoption readiness of higher education institutions before initiating the adoption. Resource based view theory was used as theoretical lens to identify capabilities and to build the model. The conceptual model considers adoption readiness and adoption success from technological, human, organizational, financial and external environment dimensions. The model proposes that the degree to which an organization holds these capabilities is related to the degree to which they are ready to deploy and use computing resources, thereby increasing the possibility of adoption success. Further empirical research will be used to validate and refine the conceptual model.