Saving Battery of Mobile Station & Response Time by Server with Compression
Rohit Kulkarni1, Raghvendra Singh2, Piyush Mathur3
1Rohit Kulkarni, Department of Computer Engineering, University of Mumbai, G. M. Vedak Institute of Technology, Tala, Dist. – Raigad, Maharashtra, India.
2Raghvendra Singh, Department of Computer Engineering, University of Mumbai, G. M. Vedak Institute of Technology, Tala, Dist. – Raigad, Maharashtra, India.
3Piyush Mathur, Department of Computer Engineering, Rajasthan Technical University, Sobhasaria Engineering College, Sikar, Rajasthan, India.
Manuscript received on December 01, 2014. | Revised Manuscript Received on December 09, 2014. | Manuscript published on December 20, 2014. | PP: 35-41 | Volume-3 Issue-1, December 2014. | Retrieval Number: A0563123114/2014©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are some mobile applications which receive the information from application servers by user generated queries. Processing the request on the mobile devices drain the mobile battery. On the other hand, processing user-queries at application servers causes increased response time because of the communication latency during transmission of the large size query. In this thesis work, to minimize battery drain as well as response time query processing on one mid network node (Relay Node) had done. Leasing processing power form mid network node may decrease battery usage on the mobile devices and response times, so that is totally depend on service provider how much it has to lease? The trade of processed data with response time, memory required & energy required is studied. The dynamic programming approach for the optimality to distribute the amount of query processing load on relay node is also used. Here I extended our work with the compression & encryption. LZ4-HC compression technique is used to minimize the size of data so that its processing is automatically decreased thereby it’s obvious that there is further more save of battery. At mobile station compression is done. We do feature extraction at relay node as a part of query processing. Encryption is also applied to the extracted features for security purpose at relay node. On the other hand, at application server feature decryption has done with training & classification which are application level functions.
Keywords: AES, Artificial Neural Network(ANN), Feature, Extraction, LZ4-HC.