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The nodes deployed in the MHWN randomly can be divided into jammed ones, boundary nodes, and unaffected ones according to different degree of jamming produced by the jammer:
Input: the state and position of MHWN nodes Output: the estimated jammer's position (1) Initialize number of iterations (T), number of particles (N), acceleration, velocity and position of the particle, gravity coefficient and time constant a.; (2) for t =1: T do (3) Random select N particles in the jammed area; (4) Calculate the fitness function for each particle; (5) Save the best value at this iteration and update the global best value; (6) Calculate the mass and normalized mass of the particles; (7) Calculate the resultant force in each dimension for all the particles; (8) Update the gravity coefficient and acceleration, velocity, position of the particle.
In order to reduce the sensitivity of the existing algorithms to the MHWN nodes deployment and parameters of the jammer for the jammer localization, we have presented a novel localization strategy based on Gravitational Search Algorithm (GSA), which is an evolutionary algorithm based on Newton's law of universal gravitation and mass interactions.
Parameter Meaning Value M Simulation times 200 Q Number ofMHWN nodes 400 L Radius of MHWN 400 m [P.sub.j] Transmitting power ofjammer 10 mW [P.sub.T] Transmitting power of node 10 mW [P.sub.N] Power ofnoise -60 dBm n Fading exponent 2 [G.sub.r] Gain of the transmitting antenna 1 [G.sub.t] Gain of the receiving antenna 1 N Number of particles 50 [G.sub.0] Initial value of gravity coefficient 100 [alpha] Time constant 20 T Number of iterations 50 Table 2: Average error under different settings.
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