PERFORMANCE ANALYSIS AND EVALUATION OF RANDOM WALK ALGORITHMS ON WIRELESS NETWORKS

2012 ◽  
Vol 23 (04) ◽  
pp. 779-802 ◽  
Author(s):  
KEQIN LI
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Network Model ◽  
Simple Random Walk ◽  
Cover Time ◽  
Planar Point ◽  
Random Walk Algorithm ◽  
Node Distribution ◽  
Random Node ◽  
Walk Algorithm

We propose a model of dynamically evolving random networks and give an analytical result of the cover time of the simple random walk algorithm on a dynamic random symmetric planar point graph. Our dynamic network model considers random node distribution and random node mobility. We analyze the cover time of the parallel random walk algorithm on a complete network and show by numerical data that k parallel random walks reduce the cover time by almost a factor of k. We present simulation results for four random walk algorithms on random asymmetric planar point graphs. These algorithms include the simple random walk algorithm, the intelligent random walk algorithm, the parallel random walk algorithm, and the parallel intelligent random walk algorithm. Our random network model considers random node distribution and random battery transmission power. Performance measures include normalized cover time, probability distribution of the length of random walks, and load distribution.

RANDOM WALK LEARNING MACHINE

2000 ◽  
Vol 14 (08) ◽  
pp. 869-876
Author(s):  
MIRCEA ANDRECUT
Keyword(s):  
Random Walk ◽  
Learning Algorithm ◽  
Simple Random Walk ◽  
Associative Memories ◽  
Storage Density ◽  
Random Walk Algorithm ◽  
Learning Machine ◽  
Special Case ◽  
Walk Algorithm

A simple random walk learning algorithm for associative memories is described. The Hebbian memory matrix optimized by the random walk algorithm leads to a perfect learning in associative memories. Also, in the special case of a binary memory matrix, the random walk learning algorithm leads to an increase of the critical storage density from α c = 0.102 to α c = 0.25.

scholarly journals Emergence of an optimal search strategy from a simple random walk

2013 ◽  
Vol 10 (86) ◽  
pp. 20130486 ◽  
Author(s):  
Tomoko Sakiyama ◽  
Yukio-Pegio Gunji
Keyword(s):  
Random Walk ◽  
Power Law ◽  
Search Strategy ◽  
Simple Random Walk ◽  
Foraging Strategies ◽  
Optimal Search ◽  
Time Step ◽  
Random Walk Algorithm ◽  
Walk Algorithm ◽  
Diffusion Properties

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.

On coupling of random walks and renewal processes

1996 ◽  
Vol 33 (1) ◽  
pp. 122-126
Author(s):  
Torgny Lindvall ◽  
L. C. G. Rogers
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Simple Random Walk ◽  
Renewal Processes ◽  
Renewal Theorem ◽  
One Dimensional ◽  
Continuous State Space ◽  
Continuous State ◽  
Efficient Coupling ◽  
Blackwell's Renewal Theorem

The use of Mineka coupling is extended to a case with a continuous state space: an efficient coupling of random walks S and S' in can be made such that S' — S is virtually a one-dimensional simple random walk. This insight settles a zero-two law of ergodicity. One more proof of Blackwell's renewal theorem is also presented.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

2014 ◽  
Vol 38 (8) ◽  
pp. 753-763 ◽  
Author(s):  
D.P. Onoma ◽  
S. Ruan ◽  
S. Thureau ◽  
L. Nkhali ◽  
R. Modzelewski ◽  
...  
Keyword(s):  
Random Walk ◽  
Fdg Pet ◽  
Random Walk Algorithm ◽  
Locally Adaptive ◽  
Walk Algorithm

On the number of times where a simple random walk reaches its maximum

1992 ◽  
Vol 29 (02) ◽  
pp. 305-312 ◽  
Author(s):  
W. Katzenbeisser ◽  
W. Panny
Keyword(s):  
Asymptotic Behavior ◽  
Random Walk ◽  
Random Walks ◽  
Simple Random Walk ◽  
Probability And Statistics

Let Qn denote the number of times where a simple random walk reaches its maximum, where the random walk starts at the origin and returns to the origin after 2n steps. Such random walks play an important role in probability and statistics. In this paper the distribution and the moments of Qn , are considered and their asymptotic behavior is studied.

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