Develop using the Android SDK and Android Studio

Android Things lets you experiment with building devices on a trusted platform, without previous knowledge of embedded system design:

Access hardware such as displays and cameras natively through the Android framework
Connect your apps with Google services
Integrate additional peripherals through the Peripheral I/O APIs (GPIO, I2C, SPI, UART, PWM)
Use the Android Things Console to push over-the-air feature and security updates

Hardware

Android Things enables you to build apps on top of popular hardware platforms like the Raspberry Pi 3. The Board Support Package (BSP) is managed by Google, so no kernel or firmware development is required. Software images are built and delivered to devices through the Android Things Console. This gives you a trusted platform to develop on with standard updates and fixes from Google.

Get started quickly with one of our developer kits.

SDK

Android Things extends the core Android framework with additional APIs provided by the Things Support Library, which lets you integrate with new types of hardware not found on mobile devices.

Developing apps for embedded devices is different from mobile in a few important ways such as:

  • More flexible access to hardware peripherals and drivers than mobile devices
  • System apps are not present to optimize startup and storage requirements
  • Apps are launched automatically on startup to immerse your users in the app experience.
  • Devices expose only one app to users, instead of multiple like with mobile devices.

See the Features and API page for more information on this library.

See the Platform differences page for more information on the similarities and differences between Android Things and the Android framework.

Console

When you’re ready to begin publishing your code to devices, the Android Things Consoleprovides tools to install and update the system image on supported hardware devices.This allows you to push seamless updates to users in the field as well as test deployments on your own hardware. Using the console, you can:

  • Download and install the latest Android Things system image
  • Build software images that bundle your applications with the operating system
  • Push images over-the-air (OTA) to devices as updates

See the Android Things Console documentation for more information on all of its features.

Platform differences

Android Things is based on the Android platform and is optimized for embedded devices. Along with new features and capabilities, Android Things includes a variety of system and API differences from Android. This document highlights some of the key differences that you should understand and account for in your apps.

Installed apps

The collection of installed apps is fixed by the developer/device maker. These are changed through OTA updates, not managed by the end-user.

Android Things is streamlined for single app use. One app is automatically launched at system startup.

Unsupported APIs

Android Things is optimized for embedded devices which may not contain the same feature set as an Android phone or tablet. For example, graphical user interfaces are optional as not all devices include a display.

The following table outlines the set of Android features not currently supported by Android Things devices, and the affected framework APIs:

Feature API
System UI
(status bar, navigation buttons, quick settings)
NotificationManager
KeyguardManager
WallpaperManager
VoiceInteractionService SpeechRecognizer
android.hardware.fingerprint FingerprintManager
android.hardware.nfc NfcManager
android.hardware.telephony SmsManager
TelephonyManager
android.hardware.usb.accessory UsbAccessory
android.hardware.wifi.aware WifiAwareManager
android.software.app_widgets AppWidgetManager
android.software.autofill AutofillManager
android.software.backup BackupManager
android.software.companion_device_setup CompanionDeviceManager
android.software.picture_in_picture Activity Picture-in-picture
android.software.print PrintManager
android.software.sip SipManager

Note: Use hasSystemFeature() to determine whether a given device feature is supported.

Common intents

Android Things doesn’t include the standard suite of system apps and content providers. Avoid using common intents as well as the following content provider APIs in your apps:

CalendarContract
ContactsContract
DocumentsContract
DownloadManager
MediaStore
Settings
Telephony
UserDictionary
VoicemailContract

Runtime permissions

Declare permissions that you need in your app’s manifest file.

The granting of app permissions is done differently for Android Things than for typical Android apps since many IoT applications do not require a user interface or input device. Permissions are granted using Android Studio or the Android Things Console.

When running an app from Android Studio, all permissions (including dangerous permissions) are granted at install time. This applies to new app installs and updated <uses-permission> elements in existing apps. You can use the adb tool to grant or revoke permissions for testing.

When you are ready to distribute your apps using the Android Things Console, you grant the dangerous permissions (instead of the end user) for all apps as part of the build creation process. You can override this during development, but not on actual products; end users cannot modify these permissions.

Native code

Android Things is compatible with the Android NDK for including C/C++ code into your app. However, since Android Things devices are typically memory constrained, the platform requires apps to keep native libraries inside the APK at runtime using the extractNativeLibs manifest attribute.

<manifest …>
<application
android:extractNativeLibs=”false” …>

</application>
</manifest>

 

Distributed Worker-Job Matching Architecture for Crowdsourcing

Towards a Distributed Worker-Job Matching
Architecture for Crowdsourcing

Towards a Distributed Worker-Job Matching

Abstract— While the crowdsourcing paradigm facilitates the use of human-enacted resources from large groups of individuals, matching workers with jobs is limited by the need for these potential workers to proactively subscribe to various networks.
This subscription phase is part of an “open call model” that reduces the ability for crowdsourcing platforms to scale or retain crowd-oriented workers. Leveraging collaborative filtering techniques, in this paper, we propose an alternative model that seeks to address the issue through a recommendation technique and system that exploits a push-pull model.

Crowdsourcing [1], through the advent of the Internet and Web 2.0 technologies, has provided a new paradigm for employment, to harness mass human computation and has given new avenues for businesses and researchers to quickly distribute work across a global cross-section of potential workers [1][2]. As defined by Howe [3], the paradigm entails an open call model via the Internet to anonymous individuals to solicit services for work, usually at a much cheaper cost than traditional outsourcing [4]. Labor markets [5] such as Amazon Mechanical Turk, Microworkers and UpWork (formerly ODesk) exhaustively use this model. The model however has a significant deficiency [6]; that is the challenge of attracting and maintaining a crowd [7][8]. Via the open call model, tasks requiring human intelligence or HITs are posted for workers to accept relevant task offerings. Those being exposed to the offering are typically members or subscribers of a labor market’s community pool or workers [6]. However, there exist massive crowds of potential workers outside of the subscribed labor market pools and currently the open call model is not capable of leveraging this untapped pool of workers [6][9].
In this paper, augmented by collaborative filtering, we propose a service-oriented architecture based on an open pushpull worker-job matching model capable of harnessing the wisdom and labor potential of diverse communities external to current labor markets. The architecture incorporates transactional web services that implement services pertinent to crowdsourcing including recruitment, job allocation and compensation. We continue by reviewing the open call model including current recruitment strategies and techniques, and worker-job recommender strategies within and external to the paradigm of crowdsourcing. We follow by presenting our proposed collaborative filtering augmented architecture for open push-pull worker-job matching.

Providing Privacy-Aware Incentives in Mobile Sensing Systems

Providing Privacy-Aware Incentives in Mobile
Sensing Systems

Providing Privacy-Aware Incentives in Mobile

Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously.
In this paper, we propose two credit-based privacy-aware incentive schemes for mobile sensing systems, where the focus is on privacy protection instead of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and ensure that malicious users cannot abuse the system to earn unlimited credits.
Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse attacks. The second scheme considers scenarios where no online TTP is available. It applies blind signature, partially blind signature, and a novel extended Merkle tree technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.
Index Terms – Privacy; Incentive; Mobile Sensing

The ever-increasing popularity of mobile devices such as smart phones and tablets and the rich set of embedded sensors that usually come with them (e.g., GPS, accelerometer and microphone) have created a huge opportunity of sensing. Mobile sensing tries to harness this opportunity by collecting sensing data through mobile devices and utilizing the data to obtain rich information about people and their surroundings. It has many applications in healthcare [1], [2], traffic monitoring [3], and environmental monitoring
[4]. However, the large-scale deployment of mobile sensing applications is hindered by two obstacles. First, there is a lack of incentives for mobile device users to participate in mobile sensing. To participate, a user has to trigger her sensors to measure data (e.g., to obtain GPS locations), which may consume much power of her smart phone.
Also, the user needs to upload data to a server which may consume much of her 3G data quota (e.g., when the data is photos). Moreover, the user may have to move to a specific location to sense the required data. Considering these efforts and resources required from the user, an incentive scheme is strongly desired for mobile sensing applications to proliferate. Second, private information may be derived from a user’s contributed data. Such privacy concern also prevents users from participating. For instance, to monitor the propagation of a new flu, a server will collect information on who have been infected by this flu. However, a patient may not want to provide such information since it is very sensitive. To effectively motivate users to participate, both obstacles should be overcome. Several privacy-protection schemes [5]–[14] have been proposed to provide anonymity for users, and many incentive schemes [15]–[28] have been designed to promote participation by paying credits to users. However, they address privacy and incentive separately. It is nontrivial to simultaneously address incentive and privacy. One may consider simply combining a privacy protection scheme and a credit-based incentive scheme to provide both privacy and incentive, but such combination is not easy since those schemes have been designed under different system models and assumptions. More importantly, a simple combination cannot address the new challenges that only arise when both incentive and privacy are considered and were not addressed by the privacy protection scheme or the incentive scheme. In particular, existing privacy preserving schemes provide anonymity for users. Anonymity may allow a greedy user to anonymously submit unlimited data reports for the same sensing task (which is not always desirable) and earn unlimited credits without being detected.
This will increase the cost of data collection. Moreover, under the protection of anonymity, a malicious user who has compromised other users’ mobile devices can steal those users’ security credentials such as cryptographic keys and anonymously use the stolen credentials to cheat and earn as many credits as possible without being detected. Thus, the key new challenge with designing credit-based privacyaware incentive schemes for mobile sensing is how to prevent various abuse attacks while preserving privacy. This challenge calls for new designs that integratively address
incentive and privacy.
Our previous work [29] designs a privacy-aware incentive scheme for a special scenario of mobile sensing where each sensing task requires only one data report from each user (such a task is referred to as a single-report task). An example of single-report task is “Report the noise level around you now,” which only requires each user to submit a single data report of his measured noise level. In the real world, however, there are many sensing tasks that require multiple reports submitted at different times from each user (such task is referred to as the multiple-report task)1. An example of multiple-report task is “Report the noise level around you every 10 minutes in the following week.” Many other examples can be found in various mobile sensing systems [3], [4]. Unfortunately, that work cannot be directly extended to support multiple-report tasks, since its cryptographic construction only allows each user to earn credits from one report. Although it is possible to create one task for each report and then apply that
scheme, this will induce high overhead in computation and communication, and greatly increase the complexity of task management. For example, to collect the same amount of data that the aforementioned multiple-report task can do, one single-report task should be created every 10 minutes, and one set of cryptographic credentials should be computed, distributed, and processed for each task. In this paper, we propose two privacy-aware incentive schemes for mobile sensing that can support multiple-report tasks. We adopt a credit-based approach which allows each user to earn credits by contributing its data without leaking which data it has contributed. At the same time, the approach ensures that malicious users cannot abuse the system to earn unlimited amount of credits. In particular, the first scheme is designed for scenarios where an online trusted third party (TTP) is available. It relies on the TTP to protect privacy and prevent abuse attacks, and has very low computation cost at each user. The second scheme does not require any online TTP. It applies blind signature, partially
blind signature, and an extended Merkle tree to protect privacy and prevent abuse attacks. The remainder of this paper is organized as follows. Section 2 presents system models. Section 3 presents an overview of our solution. Section 4 and Section 5 present our two incentive schemes. Section 6 presents cost evaluations. Section 7 presents discussions. The last two sections review related work and conclude the paper. 

mHealth Systems for Monitoring Patients with Chronic Diseases

Model for Personalization of mHealth Systems for Monitoring Patients with Chronic Diseases

Model for Personalization of mHealth Systems

Abstract— Today chronic diseases are a major health problem worldwide. Treatment for these diseases requires proper monitoring and frequent doctor visits. In recent years several ICT tools have been designed to provide remote patients monitoring, but it is still complicated to offer a useful tool for all kind of patients, since each one has different characteristics and needs. This paper presents a new model to enable the personalization of mobile health systems. The model is particularly oriented to be used for monitoring patients with chronic diseases, such as obesity and diabetes. The proposed model comprises the use of mobile applications, Bluetooth sensors and NFC tags to enable personalization. The resulting model can be adapted to different diseases and provides health professionals with a tool that allows monitoring patients with different needs and characteristics.
Keywords— mobile software, mHealth, Bluetooth, NFC, sensors.

Job Recruitment and Job Seeking Processes

Job Recruitment and Job Seeking Processes: How Technology Can Help

Job Recruitment

Job seeking and recruiting processes have drastically changed during the past decade. Today’s companies are exploiting online technology (job portals, corporate websites, and so on) to make job advertisements reach an ever-growing audience. However, this advantage can create a higher post-processing burden for recruiters, who must sort through the huge amount of résumés and curricula vitae received, often  expressed in different languages and formats. Similarly, job seekers spend considerable time filtering job offers and restructuring their résumés to effectively communicate their strong points and address the job requirements.

Consequently, job recruiters and seekers often use various special-purpose tools, such as job aggregators (including www.jobrapido.com and www.indeed.com)1 and social networks (including www.linkedin.com, www.glassdoor.com, and www.jackalopejobs.com).2 To further optimize selection processes with respect to processing time and accuracy, job portals such as Monster (www. monster.com) and Jobnet (www.jobnetchannel.com) have started to develop advanced search engines to automatically sort résumés based on job offer requirements. These approaches could exploit, among others, supervised and unsupervised learning, software agents, and genetic algorithms. 3–8 Nonetheless, creating such tools is a complex task that requires identifying which variables influence the user’s final choice

eMedicine, eShops and eRestaurants

Privacy-Preserving Utility Verification Of The Data Published By Non-Interactive Differentially Private Mechanisms

emedicine Prescription – Recommendation Mobile App

eShop Mobile Based Stock Management System

erestaurant Mobile App – Real-Time Process Management System By Sharing Food Order Information

Ediagnostic Lab Online Reporting Mobile App

Hybrid Cloud Approach For Secure Authorized Reduplication

Discovery Of Ranking Fraud For Mobile Apps

Online System Complaints & Bug Tracking System For MNC Companies

Expert Discovery And Interactions In Mixed Service-Oriented Systems

Efficient And Anonymous Mobile User Authentication Protocol Using Self-Certified Public Key Cryptography For Multi-Server Architectures

eShop Mobile Based Stock Management System

In the modern world, technology has flourished in a very tremendous way. Where ever we go we come across digital gadgets and everything has been atomized whether it is an institution or business sector or any commercial sector for that matter, anything and everything has become technicality oriented in this cyberspace world.

The project “SMART BUSINESS” is a small approach to automate the ledges of the retailers, distributors and stockiest and help them to overcome stress when comes to investment analysis and management of stocks, orders and maintaining products such as baby care, biscuits, body care, hair care etc…, eShop Mobile Based Stock Management System

This project deals about the marketing and requirement strategy of the clients. These marketing strategies differ from place to place, time to time and from product to product. This is an application which is been developed and customized based on the categories of clients. The categories of clients are i) Retailer   ii) Distributor    iii) Stockiest.

Using this application the retailer can maintain his/her profile. He/She can find all the distributors available for the product for which has registered. He/She can order the products from the nearest and available distributors based on the demands of the customers.

Similarly, using this application a distributor can maintain his/her profile and can find all the stockiest available for the products for which he/she has registered. He/She can order the products from the nearest and available stockiest based on the demands of the retailers. They can maintain the track of retailers existing in their location so that they can expand their business.

In the same way, Stockiest can maintain their own profile and can maintain the details about their products and even their clients and orders placed by them so that it could be delivered as soon as possible. They also record the information regarding the stock availability, reorder level and expiry of products.

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

erestaurant Mobile App – Sharing Food Order Information

eRestaurant is a portal which allows admin in developing counters to advertise and sell their restaurant. This would permit rural communities to make their wares available to the rest of the world.

The objective of this project is to create a portal which would allow product information to be updated securely using a mobile device and will allow users to buy restaurant from the admin. The main concern is given to the village women’s to explore their talents and to enhance our traditional Indian culture.

In future the internet become whole and soul to the business fields, each and every trades are going to be done through it so this portal may helpful to the women as a business person in this running world. erestaurant Mobile App – Sharing Food Order Information

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

Online Mobile Applications

Android Application To Access College Activities and Management Events, Placement, Student Info, Results

Veterinary Care for animal medical soultion based Mobile Application

Nexus Mobile App For Searching Contractor And Worker In City&s

EGG Production Management System Based Mobile App

Online Matrimonial

eAyurvedic Recommended Solution For All Disease Based Mobile Application

Net classified Based Mobile App

ehealth Care Management

Local Services Info Based Mobile App

icar : Mobile App For Car Pooling Using Bootstrap Responsive Design

EGG Production Management System

Poultry Farm Management System has been developed for giving various information of the poultry farm. This application is mainly for the maintenance and management of the poultry farm. It maintains the record systematically and enables us to give information in time. EGG Production Management System

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

eAyurvedic Recommended Solution For All Disease

It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. This system provides medical suggestions for patients on any time either by go Ayurveda treatment or by consultation through online. So we hereby make a web application for patients and from that they can easily undergo treatment. The user who needs for help at their home can consult online easily. It also saves the time of the user. From this the medical suggestions get more customers from online and they earn a lot. eAyurvedic Recommended Solution For All Disease

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

Net classified Based Mobile App

My project is “NET CLASSIFIED”. Objective of this system is to providing a PC Grid. Solution focused on the workers of unorganized sector includes mason, carpenters, gardener, painter etc. PC Grid is a Web-based solution through which workers registered themselves for a specific skill. Using this system general public or organized sector user can select the workers as per their need. At the time of worker selection he/she can view the skill, references given by those who have taken their service in the past, area (worker location) and availability of a particular workers System sends SMS to a selected workers regarding work and customer details. workers confirms either through phone or this system and either fixed up meeting or work start date. Net classified Based Mobile App Organized sector user or general public can rate worker skill, charges, particularity about time, dedication, behavior, habits etc through this system. Users can put their demands regarding particular skill workers along with project location, and project details.

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

Managements Systems: University, Exam, Restaurant

Smart University Student Information Management System

Intelligent Agent Based Job Search System In Android Environment

Travel Management System Using GPS &
Geo Tagging On Android Platform

Faciliting Examination Process Via Exam Monitoring System

An Mobile App for Cloud-Based Smart Restaurant Management System in near-Field Communication

Exam And Hall Ticket Management Application System

iSearch Mobile App For Searching Lost Person

Operation Schudule For Hospital Management Based Mobile App

Online SMART BUSINESS – 1 Retailer 2) Distributor 3) Stockiest

e-Vaccination management System

Smart University-Student Information Management System

Abstract – SUSIMS is the most cutting-edge, innovative and robust solution for a university. In existing college datamanagement systems there are plenty of activities which are handled manually. All these activities are paper based which are expensive and time consuming. Various activities are handled by various departments. This leads to major problem in interlinking data and avoiding
duplicates. Hence this becomes very hard and prolonged process for students to access information from management. In the proposed system, a better solution is defined for all these activities which are paperless, cost effective and time saving.
In 21st century with the latest technology the world is moving towards cloud computing. SUSIMS is full-fledged cloud computing based information management system. It covers every minute aspects of a universities work flow and integrates all processes into smartphones with user friendly interface. Smart University-Student Information Management System It includes all major modules like Attendance module, Placement module, Alumni Association module and many more. All modules interrelated and data redundancy is eliminated.

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

Faciliting Examination Process Via Exam Monitoring System

This paper presents an Android application aimed to ease the task of Chief Invigilators and fellow invigilators during an examination conduct in a university environment. The Chief Invigilator delegates tasks to fellow invigilators at the examination venue before the start of the examination including collecting attendance slips and monitoring for candidates’ misconducts. After the examination ends, the invigilators need to ensure all answer scripts are submitted by the attendees of the examination. The Anrid application provides functionalities to facilitate task delegation among invigilators and improve efficiency of post examination tasks such as ensuring correct number of answer scripts are collected based on the right number of candidates. Faciliting Examination Process Via Exam Monitoring System

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

e Restaurant – Online Shopping For Food

e Restaurant is a portal which allows admin in developing counters to advertise and sell their restaurant. This would permit rural communities to make their wares available to the rest of the world.

The objective of this project is to create a portal which would allow product information to be updated securely using a mobile device and will allow users to buy restaurant from the admin. The main concern is given to the village women’s to explore their talents and to enhance our traditional Indian culture. e Restaurant – Online Shopping For Food

In future the internet become whole and soul to the business fields, each and every trades are going to be done through it so this portal may helpful to the women as a business person in this running world.

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

College Campus and Career Guidance Mobile App

Android Based College Campus App

Android base order recommender system

Design of an Enhanced Logistics Service Provider Selection Model for e-Commerce Application

DETSApp: An App for Disaster Event Tweets Summarization using Images Posted on Twitter

An Android based Mobile Application for Career Guidance

Requirements for a Flexible and Enabling Mobile Crowdsensing mHealth Applications

Android Based College Campus App

Technology has changed our daily life routine as well as living style. So, student of school or colleges or university require application that supports smart phone to get all type of information related to examination, lecture notes, placement regarding question, notification, events, transportation etc. instead of calling system because almost all mobile users has smart phone now days. Each and every educational institute provides limited services to their users including students, parents, guardian and public. If provided services are more than ease of using is very difficult. Android Based College Campus App That is why students interest towards using college or school or university is decreasing day by day. We designed an application to fulfil the requirement of students or parents or employee based on present scenario of
market and latest technology

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586

Job Search System In Android Environment

Abstract: The Job selection process in today’s global economy can be a daunting task for prospective employees no matter their experience level. It involves a detailed search of newspapers, job websites, human agents, etc, to identify an employment opportunity that is perceived compatible to abilities, anticipated remuneration and social needs. Search sites such as Seek, Academic keys.com, CareerBuilder.com, Job-hunt.org, Monster.com, etc allow prospective employees to register online and search and apply for employment.
However most do very little to profile employers against employees or even attempt to confirm the validity of the data submitted by prospective employees. Also no information exist on feedback of the employer too on various criteria submitted by employees. Taking all these into consideration we here have proposed an intelligent agent (instead of the human agent) to perform the same search operations by interacting with the employer and job search coordinator agents. Job Search System In Android Environment The proposed solution would involve the creation of an applicant, job search and employer agents that would use fuzzy preference rules to make a proper decision in getting a list of jobs based on the user’s search criteria and also feed the rating of the employer based on feedback submitted by the past & current employees which is unique and first of its kind.
All results applicable are organized based on a dynamic calculation of expected utility from highest to lowest and displayed as the job search listing.

Software Requirements: –

Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Tools:
Android Emulator
xampp-win32-5.5.19-0-VC11
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
jdk-8u66-windows-i586