Employee Monitoring System Using Android Smart Phone

The Rapid growth of android applications is creating a great impact on our lives. The aim of this research Employee monitoring system using android mobile is, to automate the employee monitoring process in company by their Employees office cell phone and also improve the organizational growth of the company. In this paper, we discuss about the design and Implementing admin application, employee application and Centralized server for monitored company employee’s using android technology. In this system we are providing dynamic database utility which retrieves data or information from centralized database. The android application in smart phone contains all information about the employee phone uses like their all Employee SMS history, Employee call Logs, Employee Locations, Data uses, Web browser history, and unauthorized data uses details. All communication between the Employee phone and the admin is done through 3G network technology. This application is user-friendly. This system improves accuracy in managing employees of the company by saving time, reducing manager efforts; avoid the unnecessary use of company phones which are provided to the Employee for their office use only. This System is also connects to the centralized server for accessing detailed history of employee phone uses. The main aspect of our paper is Managers to navigate their all company Employees through mobile phones and know the employee behavior (Good-Loyal/Average/Bad). DOI: 10.17762/ijritcc2321-8169.15022

Design and implementation of Cell Tracking system and Sync with cloud

In today?s world more than eighty percent people uses the smart phones. As the need increases the misuse of the cell phone also increases. Anyone can distrust or cheat other or suspicious of others activities. There may be loss of an important data in the big organizations due to the employees. Many criminal activities have increased in organization and teenagers are misusing the smart phones. So for the security purpose in the large organizations and to control the activities of the employees and the teenagers, software can be used which keeps the log files in a single mobile with its date and synchronize daily with restricted area in corporate with cloud

LOCALIZATION OF OPPRESSED PEOPLE BY USING GPS

For every parent, security of the child is very important, and they are unable to feel comfortable until child resumed back to home safely. Present systems are not omnipotent enough to track the incomprehensible child quickly. However the existing system gives the information pertaining to incomprehensible kid, when he/she present in only school campus and but not beyond the school campus. Since to supervise the child’s location by the parents manually is not feasible all the time. If has to do, it postulates a so much of time which ultimately consequences in the devastation of valuable functional time. Here I proposed a  GPS based system that will render current location of child/person to their concerned parents, when he faces any contends of anti social elements like kidnapping,  preterm ting the route and causing illness in anywhere in the mid-way of journey, then it insinuates to the parents. This system is a two unit system and has characteristics of high reliability, fast response time and high accuracy. In This system direct interaction between child module and parent receiver. Here transmitter is nothing but child module which is having GPS, GSM and temperature sensor and pulse sensor. The module consists RFID tag having information about child and it gives the presence of child automatically. By using this system, the guardians of children can view children’s position, which helps to ensure the safety of children in daily life

Smart Localization and Detection System for School Children

The lack of parental supervision in the past few years, contributes to the increasing number of crime against children. Many cases of missing children are reported by PDRM every year and have become a vital concern to the society. Hence, this paper presents a smart localization and detection system for school children to overcome the issues of missing children. The proposed system is implemented for tracking and notifying the location of the children using SIM908 Global Positioning System (GPS) Module with Global System for Mobile Communication (GSM) technologies and Arduino Mega 2560 microcontroller board. The module kit is placed inside the children’s school bag while they are going to school. The children positioning information is sent through GSM to the parent’s smartphone via Short Message service (SMS) that is linked to Google Map. It allows parents to know their children location on a real-time map. Thus, it can help the parents to monitor their children everywhere. The proposed system is proven to be efficient, reliable and low cost

A Safety Support System for Children\u27s Antiloss

In the recent past, crimes against children and the number of the missing children have been stayed at high. It is a tragic disaster for a family if their child is missing. Feeling safe about their children is very important for the parents. Therefore, there is an urgent requirement for safety support systems to prevent crimes against children and for anti-loss, particularly when the children are on their own, such as on the ways to and from schools. Thanks to the highly development of telecommunication and mobile technologies, preventive devices such as child ID kits, family trackers have come to light. However, they haven\u27t been impressive solutions yet as they only track current positions of the children and lack of intimations for the parents when their children are under potential dangers. In this thesis, a data mining framework is introduced, in which secure areas and secure paths of the children are learned based on their location histories. When the system predicts the children to be potentially unsafe (e.g., in a strange area or on a strange route), automatic reports will be sent to their parents. Furthermore, an indoor positioning method utilizing Bluetooth is also proposed. Based on the android platform, a prototype of the application for both children and parents is developed incorporating with the proposed techniques in this thesis

MEC vs MCC: performance analysis of real-time applications

Hoje em dia, numerosas são as aplicações que apresentam um uso intensivo de recursos empurrando os requisitos computacionais e a demanda de energia dos dispositivos para além das suas capacidades. Atentando na arquitetura Mobile Cloud, que disponibiliza plataformas funcionais e aplicações emergentes (como Realidade Aumentada (AR), Realidade Virtual (VR), jogos online em tempo real, etc.), são evidentes estes desafios directamente relacionados com a latência, consumo de energia, e requisitos de privacidade. O Mobile Edge Computing (MEC) é uma tecnologia recente que aborda os obstáculos de desempenho enfrentados pela Mobile Cloud Computing (MCC), procurando solucioná-los O MEC aproxima as funcionalidades de computação e de armazenamento da periferia da rede. Neste trabalho descreve-se a arquitetura MEC assim como os principais tipos soluções para a sua implementação. Apresenta-se a arquitetura de referência da tecnologia cloudlet e uma comparação com o modelo de arquitetura ainda em desenvolvimento e padronização pelo ETSI. Um dos propósitos do MEC é permitir remover dos dispositivos tarefas intensivas das aplicações para melhorar a computação, a capacidade de resposta e a duração da bateria dos dispositivos móveis. O objetivo deste trabalho é estudar, comparar e avaliar o desempenho das arquiteturas MEC e MCC para o provisionamento de tarefas intensivas de aplicações com uso intenso de computação. Os cenários de teste foram configurados utilizando esse tipo de aplicações em ambas as implementações de MEC e MCC. Os resultados do teste deste estudo permitem constatar que o MEC apresenta melhor desempenho do que o MCC relativamente à latência e à qualidade de experiência do utilizador. Além disso, os resultados dos testes permitem quantificar o benefício efetivo tecnologia MEC.Numerous applications, such as Augmented Reality (AR), Virtual Reality (VR), real-time online gaming are resource-intensive applications and consequently, are pushing the computational requirements and energy demands of the mobile devices beyond their capabilities. Despite the fact that mobile cloud architecture has practical and functional platforms, these new emerging applications present several challenges regarding latency, energy consumption, context awareness, and privacy enhancement. Mobile Edge Computing (MEC) is a new resourceful and intermediary technology, that addresses the performance hurdles faced by Mobile Cloud Computing (MCC), and brings computing and storage closer to the network edge. This work introduces the MEC architecture and some of edge computing implementations. It presents the reference architecture of the cloudlet technology and provides a comparison with the architecture model that is under standardization by ETSI. MEC can offload intensive tasks from applications to enhance computation, responsiveness and battery life of the mobile devices. The objective of this work is to study and evaluate the performance of MEC and MCC architectures for provisioning offload intensive tasks from compute-intensive applications. Test scenarios were set up with use cases with this kind of applications for both MEC and MCC implementations. The test results of this study enable to support evidence that the MEC presents better performance than cloud computing regarding latency and user quality of experience. Moreover, the results of the tests enable to quantify the effective benefit of the MEC approach

Remote Phobia Treatment as a Tactile Internet Application Case Study in Edge augmented with Mobile Ad Hoc Clouds Environment

Tactile Internet is a next generation Internet that allows the transmission of haptic sensations in addition to audio and video content. It is expected to enable new latency-sensitive and critical use cases, such as remote phobia treatment, tele-surgery and autonomous driving. However, the current networking infrastructure cannot ensure the strict requirements that come with Tactile Internet, namely ultra-responsiveness and ultra-reliability. \indent Edge computing can help in solving this issue. While Cloud Computing offers powerful computing resources at distant data centers, Edge computing provides resources closer to the end user. To this end, computations can be offloaded from the cloud to the edge to obtain lower latency. In addition, as the Edge itself may prove to be insufficiently close to the end users’ devices in some cases, it can be augmented with Mobile Ad-Hoc Clouds. The Mobile Ad-hoc Clouds refer to a group of mobile devices located at the immediate vicinity of the end users, offering their available resources for computation, leading therefore to a reduced latency. Nevertheless, the design and implementation of an architecture based on edges augmented with mobile ad-hoc clouds for Tactile Internet raises several challenges. Firstly, a tactile internet-based architecture for remote phobia treatment should allow the exchange of auditory, visual and haptic information to ensure the efficiency of the therapy. Secondly, the end to end latency should be in the order of a few milliseconds to avoid “cyber-sickness”. \indent This thesis provides a case study of edge augmented with mobile ad-hoc clouds architecture for remote phobia treatment. The contributions are threefold. First, a software architecture for remote phobia treatment is designed for an edge augmented with mobile ad hoc clouds environment. Second, a proof of concept prototype for the proposed architecture is implemented and evaluated using a set of haptic devices, which include the HTC Vive VR headset, the Leap Motion hand tracking device, as well as the Gloveone haptic glove. Third, a set of experiments consisting of placing the components in the different layers (i.e. Cloud, Edge and Mobile Ad-hoc Cloud) were conducted, which allowed an evaluation of the impact on performance. A set of high-level interfaces were introduced to allow communication with the heterogeneous devices. The design of the architecture as a set of software modules allows the reusability of the architecture

DESIGN OPTIMIZATION OF EMBEDDED SIGNAL PROCESSING SYSTEMS FOR TARGET DETECTION

Sensor networks for automated detection of targets, such as pedestrians and vehicles, are highly relevant in defense and surveillance applications. For this purpose, a variety of target detection algorithms and systems using different types of sensors have been proposed in the literature. Among them, systems based on non-image sensors are of special interest in many practical deployment scenarios because of their power efficiency and low computational loads. In this thesis, we investigate low power sensor systems for detecting people and vehicles using non-image sensors such as acoustic and seismic sensors. Our investigation is focused on design optimization across trade-offs including real-time performance, energy efficiency, and target detection accuracy, which are key design evaluation metrics for this class of systems. Design and implementation of low power, embedded target detection systems can be decomposed into two major, inter-related subproblems: (a) algorithm development, which encompasses the development or selection of detection algorithms and optimization of their parameters, and (b) system development, which involves the mapping of the algorithms derived from (a) into real-time, energy efficient implementations on the targeted embedded platforms. In this thesis, we address both of these subproblems in an integrated manner. That is, we investigate novel algorithmic techniques for improvement of accuracy without excessive computational complexity, and we develop new design methodologies, tools, and implementations for efficient realization of target detection algorithms on embedded platforms. We focus specifically on target detection systems that employ acoustic and seismic sensing modalities. These selected modalities support the low power design objectives of our work. However, we envision that our developed algorithms and implementation techniques can be extended readily to other types or combinations of relevant sensing modalities. Throughout this research, we have developed prototypes of our new algorithms and design methods on embedded platforms, and we have experimented with these prototypes to demonstrate our findings, and iteratively improve upon the achieved implementation trade-offs. The main contributions of this thesis are summarized in the following. (1). Classification algorithm for acoustic and seismic signals. We have developed a new classification algorithm for discrimination among people, vehicles, and noise. The algorithm is based on a new fusion technique for acoustic and seismic signals. Our new fusion technique was evaluated through experiments using actual measured datasets, which were collected from different sensors installed in different locations and at different times of day. Our proposed classification algorithm was shown to achieve a significant reduction in the number of false alarms compared to a baseline fusion approach. (2). Joint target localization and classification framework using sensor networks. We designed a joint framework for target localization and classification using a single generalized model for non-imaging based multi- modal sensor data. For target localization, we exploited both sensor data and estimated dynamics within a local neighborhood. We validated the capabilities of our framework by using an actual multi-modal dataset, which includes ground truth GPS information (e.g., time and position) and data from co-located seismic and acoustic sensors. Experimental results showed that our framework achieves better classification accuracy compared to state of the art fusion algorithms using temporal accumulation and achieves more accurate target localizations than a baseline target localization approach. (3). Design and optimization of target detection systems on embedded platforms using dataflow methods. We developed a foundation for our system-level design research by introducing a new rapid prototyping methodology and associated software tool. Using this tool, we presented the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles related to our algorithmic contributions. We applied a strategically-configured suite of single- and dual-modality signal processing techniques together with dataflow-based design optimization for energy-efficient, real-time implementation. Through experiments using a Raspberry Pi platform, we demonstrated the capability of our target detection system to provide efficient operational trade-offs among detection accuracy, energy efficiency, and processing speed. (4). Software synthesis from dataflow schedule graphs on multicore platforms. We developed new software synthesis methods and tools for design and implementation of embedded signal processing systems using dataflow schedule graphs (DSGs). DSGs provide formal representations of dataflow schedules, which encapsulate information about the assignment of computational tasks (signal processing modules) to processing resources and the ordering of tasks that are assigned to the same resource. Building on fundamental DSG modeling concepts from the literature, we developed the first algorithms and supporting software synthesis tools for mapping DSG representations into efficient multi-threaded implementations. Our tools replace ad-hoc multicore signal processing system development processes with a structured process that is rooted in dataflow formalisms and supported with a high degree of automation. We evaluated our new DSG methods and tools through a demonstration involving multi-threaded implementation of our proposed classification algorithm and associated fusion technique for acoustic/seismic signals