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To make it possible for the algorithm to work a grid of beacon nodes must be previously created so that all nodes have at least three beacon nodes in their communication range. This infrastructure can be considered as a disadvantage of the system, but Cricket is very accurate and able to work in indoor scenarios. 11.4.7 Comments About the Localization Algorithm The localization algorithm is the main component of a localization system. This component de nes how the available information that is provided by the beacon nodes, by the distances estimations, and by the position computations will be manipulated in order to allow the localization information to expand from the beacon nodes to the nodes of the sensor network. As previously mentioned, the localization systems, especially the ones with multihop, had been extensively studied with the advent of the WSN. This way, a number of other localization algorithms have recently been proposed that focus on different aspects like errors, number of beacons, number of settled nodes, or GPS usage,. 39 Extended In VS .NET Using Barcode drawer for .Related: Generate QR Code .NET , Create QR Code .NET Image, Print QR Code .NET Size

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A number of cooperative and competitive PSO implementations that make use of ultiple swarms have been developed Some of these are described below Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a di erent task, or exhibits a di erent behavior The behavior of a group or task performed by a group usually changes over time in response to the group s interaction with the environment It can also happen that individuals may migrate between groups The breeding between sub-swarms developed by L vberg et al [534, 536] (refer to Section 1653) is one form of cooperative PSO, where cooperation is implicit in the exchange of genetic material between parents of di erent sub-swarms Al-Kazemi and Mohan [16, 17, 18] explicitly divide the main swarm into two subswarms, of equal size Particles are randomly assigned to one of the sub-swarms Each sub-swarm can be in one of two phases: Attraction phase, where the particles of the corresponding sub-swarm are allowed to move towards the global best position Repulsion phase, where the particles of the corresponding sub-swarm move away from the global best position.

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The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle s position is only updated if the new position results in improved performance Let the tuple (w, c1 , c2 ) represent the values of the inertia weight, w, and acceleration coe cients c1 and c2 Particles that nd themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1 , c2 ) = (1, 1, 1) Particles in phase 2 have (w, c1 , c2 ) = (1, 1, 1), forcing them to move away from the global best position Sub-swarms witch phases either when the number of iterations in the current phase exceeds a user speci ed threshold, or when particles in any phase show no improvement in tness during a userspeci ed number of consecutive iterations In addition to the velocity update as given in equation (1676), velocity vectors are periodically initialized to new random vectors Care should be taken with this process, not to reinitialize velocities when a good solution is found, since it may pull particles out of this good optimum To make sure that this does not happen, particle velocities can be reinitialized based on a reinitialization probability This probability starts with a large value that decreases over time This approach ensures large diversity in the initial steps of the algorithm, emphasizing exploration, while exploitation is favored in the later steps of the search Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms Particle positions are not updated using the standard position update equation Instead, a hill-climbing process is followed to ensure that the tness of a particle is monotonically decreasing (increasing) in the case of a minimization (maximization) problem The position vector is updated by randomly selecting consecutive components from the velocity vector and adding these velocity components to the corresponding position components If no improvement is obtained for any subset of consecutive components, the position vector does not change If an improvement is obtained, the corresponding position vector is accepted The value of changes for each particle, since it is randomly selected, with U (1, max ), with max initially small, increasing to a maximum of nx (the dimension of particles) The attractive and repulsive PSO (ARPSO) developed by Riget and Vesterstr m [729, 730, 877] follows a similar process where the entire swarm alternates between an attraction and repulsion phase The di erence between the MPPSO and ARPSO lies in the velocity equation, in that there are no explicit sub-swarms in ARPSO, and ARPSO uses information from the environment to switch between phases While.

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Drawing QR In NET Framework Using Barcode generation for the word performance was used to make reference to This identi es is the mean error of the position estimates and lso shows if this mean error is repeated in similar but different scenarios (consistence of the mean) This mean error limits the usage of the localization system to the applications where this level of inaccuracies is acceptable r Communication Cost This identi es the algorithm complexity in terms of packets exchanged It also identi es the cost of the localization system to the sensor network r Number of Settled Nodes This de nes the percentage of network nodes that were able to compute their positions at the end of the localization algorithm The ideal is that all nodes should be able to compute their position, but in many cases it is not possible r Number of Beacon Nodes.

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