4th International ICST Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

Research Article

Power-Managed Block Level File Decryption in Wireless Network Computing

  • @INPROCEEDINGS{10.1109/WIOPT.2006.1666474,
        author={Savvas  Gitzenis and Nicholas  Bambos},
        title={Power-Managed Block Level File Decryption in Wireless Network Computing},
        proceedings={4th International ICST Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks},
        publisher={IEEE},
        proceedings_a={WIOPT},
        year={2006},
        month={8},
        keywords={},
        doi={10.1109/WIOPT.2006.1666474}
    }
    
  • Savvas Gitzenis
    Nicholas Bambos
    Year: 2006
    Power-Managed Block Level File Decryption in Wireless Network Computing
    WIOPT
    IEEE
    DOI: 10.1109/WIOPT.2006.1666474
Savvas Gitzenis1,2,*, Nicholas Bambos3,4,*
  • 1: Sun Microsystems Laboratories, 16 Network Circle, UMPK16-158
  • 2: Menlo Park, CA 94025
  • 3: Stanford University, 238 Packard Building, 350 Serra Mall
  • 4: Stanford, CA, 94305
*Contact email: Savvas.Gitzenis@sun.com, bambos@stanford.edu

Abstract

Migrating (uploading) encrypted file blocks from mobile wireless devices to network servers for decryption provides substantial performance gains, including (i) reduced battery drain at the device, and (ii) fast execution at server’s powerful processors. This, however, introduces the risk of communicating through an unreliable wireless channel of varying (soft) connectivity, which can induce substantial performance degradation. To address the dilemma of decrypting blocks locally at the device, or remotely at the network server, we develop a parsimonious stochastic model that leverages the Dynamic Programming methodology and captures the dominant performance trade-offs. Based on this model, we obtain efficient algorithms for making upload decisions, as well as power management at the wireless device. Of particular interest is the case of RSA decoding at wireless sensors where the developed algorithms are demonstrated to achieve substantial performance gains over conventional approaches.