An improved SIRS model considering communication radius and distributed density of nodes is proposed. The proposed model captures both the spatial and temporal dynamics of worms spread process. Using differential dynamical theories, we investigate dynamics of worm propagation to time in wireless sensor networks (WSNs). Reproductive number which determines global dynamics of worm propagation in WSNs is obtained. Equilibriums and their stabilities are also found. If reproductive number is less than one, the infected fraction of the sensor nodes disappears and if the reproduction number is greater than one, the infected fraction asymptotically stabilizes at the endemic equilibrium. Based on the reproduction number, we discuss the threshold of worm propagation about communication radius and distributed density of nodes in WSNs. Finally, numerical simulations verify the correctness of theoretical analysis.
We model a wireless sensor network composed of nodes. The nodes are uniformly distributed in area (nodes average density is ) and the wireless communication range of every node is . The topological structure of a WSN is shown in Figure 1.
Feng Zhao Wireless Sensor Networks.pdf
From 03/2010 to 08/2010, I interned at Microsoft Research Asia working on platform services for mobile phones. From 10/2006 to 02/2007, I was a visiting student at UC Berkeley working on debris flow monitoring system by using wireless sensor network.
where P(i) is the comprehensive probability that the i-th pixel pi is perceived by the wireless sensor network, k and α denote the gain coefficients, and d(pi,sj) is the distance between the pixel pi and the sensor node sj.
Step 5. If the new position to be moved is outside the monitoring area Λ, the moving process will not be carried out, and the wireless sensor node will not move, so go to step 6. Otherwise, move to a new position and go to step 6.
The parameters of the VF algorithm are as follows: k1=1,α1=2,Fth=1,MaxStep=5 m. The parameters of this algorithm are as follows: sensing probability parameters, β=2,λ=0.2; gain coefficients, k=2,α=2; VF threshold, Fth=1; minimum sensing probability allowed by a wireless sensor network, Pμ=1; maximum moving distance allowed by a sensor, MaxStep=5 m; sensing radius, R=16 m. The parameters related to the optimization of the particle swarm are set as follows: acceleration factors of the particle swarm, c1=c2=c3=1; maximum number of iterations, MaxNumber=300; velocity interval of particles [vmin,vmax]=0.2[0,50]=[0,10].
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