Saving Mobile Battery Over Cloud Using Image Processing

Transcription

1 Saving Mobile Battery Over Cloud Using Image Processing Khandekar Dipendra J. Student PDEA S College of Engineering,Manjari (BK) Pune Maharasthra Phadatare Dnyanesh J. Student PDEA S College of Engineering,Manjari (BK) Pune Maharasthra Akolkar Amol S. Student PDEA S College of Engineering,Manjari (BK) Pune Maharasthra ABSTRACT Mobile devices with multimedia capabilities are becoming more common each day. The most important resource for the consumer in a mobile device is the battery lifetime. Incisively, the battery capacity is also the most crucial hardware resource in a mobile device. We present the saving mobile battery over cloud using image processing for extending the battery lifetime of a mobile device. Energy conservation is achieved by mobile device computation to the cloud using image processing, enabling the mobile device to save energy in the idle mode. The local execution energy consumption is replaced by additional transmissions between the mobile device and the cloud. Cloud computing is defined as applications delivered as services over the Internet and the hardware and software in data centers providing those services. Cloud computing allows quick utilization of cheap, scalable services. The analysis presented in this indicates that cloud computing can potentially save energy for mobile users. However, not all applications are energy-efficient when migrated to the cloud. Mobile cloud computing services would be significantly different from cloud services for desktops since they must offer energy savings. The services should consider the energy-overhead for privacy, security, reliability, and data communication. KEYWORDS: Mobile, Image, cloud server, Mobile Battery. RELATED WORK The main objective of the project is to develop a private cloud using which multiple users can request resources such as memory and applications from the host for the time required by them and after the stipulated time the resources will be released back to the host. The application we will be using to show the working of our project is Image Processing. The Image processing software will be on the server and the clients will request it from the server and carry out their work. The client s data is then stored on the cloud rather than on the clients computer saving memory. 1

2 MOBILE IMAGE PROCESSING Mobile devices such as cell phones and PDAs are becoming increasingly popular. Most of these devices are equipped with cameras and have several gigabytes of flash storage capacity. As a result, thousands of images can be captured and stored on these devices. With such large image collections, two functionalities become important: (1) access specific sets of images from the collection, and (2) transmit the images over a wireless network to other devices and to servers for storage. For accessing a specific set of images, content-based image retrieval (CBIR) can be a better alternative when compared to manually browsing through all the images. For example, a user may want to view all images taken with a specific person, or at a specific location. Mobile image retrieval allows the user to obtain the relevant pictures by comparing images and eliminating the irrelevant matches on the mobile system. Several works propose to perform CBIR on mobile devices. Since these mobile devices are battery-powered, energy conservation is important. We show that it is energy-efficient to partition CBIR between the mobile device and server depending on the wireless bandwidth. As the bandwidth increases, offloading image retrieval saves more energy. Most of the energy consumption for offloaded applications is due to transmission. For image retrieval, transmitting the images over a wireless network consumes significant 2

3 amounts of energy. The images may be pre-processed on the mobile device before transmission in order to reduce the transmission energy. This reduction in transmission energy is achieved by reducing the file sizes. However, the amount of energy saved depends on the wireless bandwidth and the contents of the image. Pre-processing the images saves energy if the reduction in transmission energy compensates for the energy spent due to preprocessing. If the wireless bandwidth is high, the value of the former reduces. Moreover, different images may have different values of the latter based on their contents. Hence it is required to make the pre-processing adaptive based on the wireless bandwidth and the image contents. As cloud computing becomes more popular, the wireless transmission energy is the most significant bottleneck for mobile energy savings, and such techniques become increasingly significant. Several research works contribute to the development of MCC by tackling issues. However, there are still some issues which need to be addressed. SYSTEM ARCHITECTURE From the concept of MCC, the general architecture of MCC can be shown in Fig. In Fig. mobile devices are connected to the mobile networks via base stations (e.g., base transceiver station (BTS), access point, or satellite) that establish and control the connections (air links) and functional interfaces between the networks and mobile devices. Mobile users requests and information (e.g., ID and location) are transmitted to the central processors that are 3

4 connected to servers providing mobile network services. Here, mobile network operators can provide services to mobile users as AAA (for authentication, authorization, and accounting) based on the home agent (HA) and subscribers data stored in databases. After that, the subscribers requests are delivered to a cloud through the Internet. In the cloud, cloud controllers process the requests to provide mobile users with the corresponding cloud services. These services are developed with the concepts of utility computing, virtualization, and service-oriented architecture (e.g. web, application, and database servers). The details of cloud architecture could be different in different contexts. For example, four-layer architecture is explained in to compare cloud computing with grid computing. Alternatively, service oriented architecture, called Aneka, is introduced to enable developers to build.net applications with the supports of application programming interfaces (APIs) and multiple programming models. Presents an architecture for creating market-oriented clouds, and proposes an architecture for web delivered business services. In this paper, we focus on a layered architecture of cloud computing. This architecture is commonly used to demonstrate the effectiveness of the cloud computing model in terms of meeting the user s requirements. ALGORITHMS USED: 1. THRESHOLDING Thresholding is the simplest method of image segmentation. Steps / Algorithm Traverse through entire input image array. Read individual pixel color value (24-bit) and convert it into greyscale. Calculate the binary output pixel value (black or white) based on current threshold. Store the new value at same location in output image. ALGORITHM Thresholding Logic gs = (r+g+b) / 3; // grayscale if(gs < th) { pix = 0; // pure black else { pix = 0xFFFFFF; // pure white PSEUDO-CODE int input[ ][ ]; int output[ ][ ]; int r, g, b, gs, pix, x, y; int th = 128; // th value can be accepted from user or can be pre-set on users needs for(y=0;y<h;y++) { for(x=0;x<w;x++) { pix = input[y][x]; // read input pixel b = pix & 0xFF; // extract blue g = (pix >> 8) & 0xFF; // extract green r = (pix >> 16) & 0xFF; // extract red 4

5 gs = (r + g + b) / 3; // calculate grayscale component if(gs < th) { pix = 0; else { pix = 0xFFFFFF; output[y][x] = pix; // store to output image 2. EDGE DETECTION One of the most important uses of image processing is edge detection: -Really easy for humans. - Really difficult for computers. - Fundamental in computer vision. - Important in many graphics applications. STEPS IN EDGE DETECTION Edge detection algorithms typically proceed in three or four steps: - Filtering: cut down on noise. - Enhancement: amplify the difference between edges and non-edges. - Detection: use a threshold operation. - Localization (optional): estimate geometry of edges beyond pixels. EDGE ENHANCEMENT A popular gradient magnitude computation is the Sobel operator: We can then compute the magnitude of the vector (sx, sy). 5

6 CONCLUSION Mobile devices such as cell phones are becoming increasingly popular. Most of these devices are equipped with cameras and have several gigabytes of flash storage capacity. As a result, thousands of images can be captured and stored on these devices. With such large image collections, two functionalities become important: (1) access specific sets of images from the collection, and (2) transmit the images over a wireless network to other devices and to servers for storage. The analysis presented in this indicates that cloud computing can potentially save energy for mobile users. However, not all applications are energy-efficient when migrated to the cloud. Mobile cloud computing services would be significantly different from cloud services for desktops since they must offer energy savings. The services should consider the energy-overhead for privacy, security, reliability, and data communication. ACKNOWLEDGMENT It gives us immense pleasure to express our gratitude to each individual associated directly or indirectly with the successful completion of the report. We would like to express our thanks towards our project Guide Prof. Sarika Ursal for her invaluable co-operation and guidance that she gave us throughout our project report. We would also like to thank our Head of Department, Prof. R. V. Patil for inspiring us and providing us all the lab facilities with the internet, which are required for project work. REFERENCES: 1. Satyanarayanan, Mobile computing: the next decade, in Proceedings of the 1st CM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond (MCS), June M. Satyanarayanan, Fundamental challenges in mobile computing, in Proceedings of the 5th annual ACM symposium on Principles of distributed computing, pp. 1-7, May M. Ali, Green Cloud on the Horizon, in Proceedings of the 1st International Conference on Cloud Computing (CloudCom), pp , December W hite Paper, Mobile Cloud Computing Solution Brief, AEPONA, November J acson H. Christensen, Using RESTful web-services and cloud computing to create next generation mobile applications, in Proceedings of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications (OOPSLA), pp , October L. Liu, R. Moulic, and D. Shea, Cloud Service Portal for Mobile Device Management, in Proceedings of IEEE 7th International Conference on e-business Engineering (ICEBE), pp. 474, January F oster, Y. Zhao, I. Raicu, and S. Lu, Cloud Computing and Grid Computing 360-Degree 6

7 Compared, in Proceedings of Workshop on Grid Computing Environments (GCE), pp. 1, January C. Vecchiola, X. Chu, and R. Buyya, Aneka: A Software Platform for.net-based Cloud Computing, Journal on Computing Research Repository (CORR), pp , July R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Journal on Future Generation Computer Systems, vol. 25, no. 6, pp , June Y. Huang, H. Su, W. Sun, J. M. Zhang, C. J. Guo, M. J. Xu, B. Z. Jiang, S. X. Yang, and J. Zhu, Framework for building a low-cost, scalable, and secured platform for Webdelivered business services, IBM Journal of Research and Development, vol. 54, no. 6, pp , November W. Tsai, X. Sun, and J. Balasooriya, Service-Oriented Cloud Computing Architecture, in Proceedings of the 7th International Conference on Information Technology: New Generations (ITNG), pp , July