Distributed computation for a hypercube network of sensor-driven processors with communication delays including setup time

In this paper, the problem of optimal distribution of measurement data to be processed in minimal time on a hypercube network of sensor driven processors is considered. An analytical model is developed for solving the problem efficiently. Unlike the previous models, this model considers: 1) explicitly the setup time which constrains exploiting all the available processors; 2) simultaneous use of links to expedite the communication; 3) partial solution combining time to encompass wider class of related problems. By deriving a lower bound on the amount of data to be received by a processor for efficient distribution, a new technique called fractal hypercube is introduced here to get the optimal solution with fewer processors, An optimal iterative method for hypercubes and a near-optimal recursive method with a refinement are presented for the same with the analysis. The effect of varying the originating processor and the choice of fractal hypercube are discussed with an effective technique called processor isomorphism. This study reveals that always the fractal hypercubes outperform the other two methods, the optimal iterative method for hypercubes and the near-optimal method.

[1]  Nils Sandell,et al.  Detection with Distributed Sensors , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Belur V. Dasarathy.  Decision fusion strategies in multisensor environments , 1991, IEEE Trans. Syst. Man Cybern..

[3]  Amy R. Reibman,et al.  Optimal Detection and Performance of Distributed Sensor Systems , 1987 .

[4]  Thomas G. Robertazzi,et al.  Distributed computation with communication delay (distributed intelligent sensor networks) , 1988 .

[5]  S. Lyengar,et al.  Distributed sensor networks-introduction to the special section , 1991 .

[6]  Thomas G. Robertazzi,et al.  Ultimate performance limits for networks of load sharing processors , 1992 .

[7]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Richard M. Fujimoto,et al.  Multicomputer Networks: Message-Based Parallel Processing , 1987 .

[9]  E. Tse,et al.  Distributed hypothesis formation in sensor fusion systems , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[10]  Ramanarayanan Viswanathan,et al.  Optimal Decision Fusion in Multiple Sensor Systems , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Thomas G. Robertazzi,et al.  Distributed computation for a tree network with communication delays , 1990 .

[12]  John R. Rice,et al.  Numerical methods, software, and analysis , 1983 .

[13]  Belur V. Dasarathy.  Paradigms for information processing in multisensor environments , 1990, Defense, Security, and Sensing.

[14]  Belur V. Dasarathy,et al.  Decision fusion , 1994 .

[15]  Thomas G. Robertazzi,et al.  Bus-oriented load sharing for a network of sensor driven processors , 1991, IEEE Trans. Syst. Man Cybern..

[16]  L.W. Nolte,et al.  Design and Performance Comparison of Distributed Detection Networks , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Narsingh Deo,et al.  Graph Theory with Applications to Engineering and Computer Science , 1975, Networks.

[18]  Cathleen Stasz,et al.  Network Structures for Distributed Situation Assessment , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Debasish Ghose,et al.  Distributed computation in linear networks: closed-form solutions , 1994 .

[20]  Hyoung Joong Kim,et al.  Optimal load distribution for tree network processors , 1996 .