yirga yayeh

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for posit...

The main objective of this study is to develop case based recommender system for the selection of enterprise sectors and activities in Ethiopia. In order to conduct the research, the required knowledge was collected from previous sector members ’ cases, domain experts, and documents through interview and document analysis methods. After all, the knowledge is modeled by hierarchical tree model and case based reasoning system has constructed.The prototype of case based reasoning for enterprise sector and activity selection is implemented by jCOLIBRI 1 programming tool. Nearest neighbor retrieval algorithm is used to measure the similarity of new case (query) with cases in the case base. As a result, if there is a similarity between the new case and the existing case, the system assigns the solution of previous case as a solution to new case. To determine the applicability of the prototype system in the domain area, the system has been evaluated by domain experts and members through vi...

In Internet of things (IoT), indoor localization plays a vital role in everyday applications such as locating mobile users, location-based mobile advertising and requesting nearest business. Received Signal Strength (RSS) is used due to minimum cost, less operational complexity, and easy usages. In this work, we proposed a Feed-Forward Deep Neural Network (FF-DNN) Algorithm. The simulation result shows 53.123%, 78.123% and 100% of the accuracy has estimation error less than 0.5, 0.75 and 1 meters respectively. The RMSE is 0.32.

The aim of this paper is to develop a design and framework implementation of new home automation system that uses Wi-Fi technology as a network infrastructure connecting its parts. With the help of a web server and a LAN connection the system provides a scalable and a wide range coverable device. The system is highly advanced as it uses a cheaper Wi-Fi connection and a server that can store data. Basically the proposed system consists of two main components; the first part is the server (web server), which presents system core that manages, controls, and monitors users’ home. Users and system administrator can locally (LAN) or remotely (internet) manage and control system code. Second part is hardware interface module, which provides appropriate interface to sensors and actuator of home automation system. The system is secured as the user has to enter password and that is not known to any intruders.

Recently, wireless-technologies and their users are rising due to productions of sensor-networks, mobile devices, and supporting applications. Location Based Services (LBS) such as mobility prediction is a key technology for the success of IoT. However, mobility prediction in wireless network is too challenging since the network becomes very condensed and it changes dynamically. In this paper, we propose a deep neural network based mobility prediction in wireless environment to provide an adaptive and accurate positioning system to mobile users. In the system development processes, firstly, we collect RSS values from three Unmanned Aerial Vehicle Base Stations (UAV-BSs). Secondly, we preprocess the collected data to get refine features and to avoid null records or cells. Thirdly, we exhaustively train the Long-short term memory (LSTM) network to find the optimum model for mobility prediction of the smartphone users. Finally, we test the designed model to evaluate system performances...