Success Rate Queue-Based Relocation Algorithm of Sensory Network to Overcome Non-Uniformly Distributed Obstacles

With the recent development of big data technology that collects and analyzes various data,the technology that continuously collects and analyzes the observed data is also drawing attention.Moreover,its importance is growing in data collection in areas where people cannot access.In general,it is not easy to properly deploy IoT wireless devices for data collection in these areas,and it is also inappropriate to use general wheel-based mobile devices for relocation.Recently,researches have been actively carried out on hopping moving models in place of wheel-based movement for the inaccessible regions.The majority of studies,however,so far have unrealistic assumptions that all IoT devices know the overall state of the network and the current state of each device.Moreover,various physical terrain environments,such as coarse gravel and sand,can change from time to time,and it is impossible for all devices to recognize these changes in real-time.In this paper,with the migration success rate of IoT hopping devices being relocated,the method of estimating the varying environment is proposed.This method can actively reflect the changing environment in real-time and is a realistic distributed environment-based relocation protocol on behalf of non-realistic,theory-based relocation protocols.Also,one of the significant contributions of this paper is to evaluate its performance using the OMNeT++simulation tool for the first time in the world to reflect actual physical environmental conditions.Compared to previous studies,the proposed protocol was able to actively reflect the state of the surrounding environment,which resulted in improved migration success rates and higher energy efficiency.

A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone.Compared to the previous studies done on this topic,less attention has been given to hybrid methods.This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home.First,it employs various algorithms with different characteristics to detect anomalies from sensory data.Then,it aggregates their results using a Bayesian network.In this Bayesian network,abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods.Experimental evaluation of a real dataset indicates the effectiveness of the proposed method by reducing false positives and increasing true positives.

Comparison of two data fusion methods for localization of wheeled mobile robot in farm conditions

Localization of a mobile robot with any structure,work space and task is one of the most fundamental issues in the field of robotics and the prerequisite for moving any mobile robot that has always been a challenge for researchers.In this paper,the Dempster-Shafer(D.S.)and Kalman filter(K.F.)methods are used as the two main tools for the integration and processing of sensor data in robot localization to achieve the best estimate of positioning according to the unsteady environmental conditions in agricultural applications.Also,by providing a new method,the initial weighing on each of these GPS sensors and wheel encoders is done based on the reliability of each one.Also,using the two MAD and MSE criteria,the localization error was compared in both K.F.and D.S.methods.In normal Gaussian noise,the K.F.with a mean error of 2.59%performed better than the D.S.method with a 3.12%error.However,in terms of non-Gaussian noise exposure,the K.F.information was associated with amoderate error of 1.4,while the D.S.behavior in the face of these conditions was not significantly changed.The experimental tests confirmed the statement.