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Dispersion with connectivity in wireless mesh networks

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  • We study a multi-objective access point dispersion problem, where the conflicting objectives of maximizing the distance and maximizing the connectivity between the agents are considered with explicit coverage (or Quality of Service) constraints. We model the problem first as a multi-objective model, and then, we consider the constrained single objective alternatives, which we propose to solve using three approaches: The first approach is an optimal tree search algorithm, where bounds are used to prune the search tree. The second approach is a beam search heuristic, which is also used to provide lower bound for the first approach. The third approach is a straightforward integer programming approach. We present an illustrative application of our solution approaches in a real wireless mesh network deployment problem.

    Mathematics Subject Classification: Primary: 90B80; Secondary: 90B18, 90C90.

    Citation:

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  • Figure 3.  The Pareto curve with $n=5$ and with different values of $e_{min}$

    Figure 1.  An illustration of the TSWB approach

    Figure 2.  The candidate locations on the floor plan. The dashed lines in the middle of the building is a meshed glass and solid lines around the rooms (colored areas) are walls

    Figure 4.  The solution of problem 2 for $n=10$ with $c_{\min}=40$

    Figure 5.  The solutions of problem 2 for $n=5$

    Figure 6.  The solutions of problem 3 for $n=5$

    Figure 7.  The solutions of problem 3 for $n=10$

    Figure 8.  The performance of the tree search algorithm for the multi-objective problem ($n=10$, $e_{min}=0.8$). The cardinality of the set of all solutions is $\binom{70}{10} \approx 3.967\times 10^{11}$

    Figure 9.  The performance of the tree search algorithm for the problem in Figure 4. The cardinality of the set of all solutions is $\binom{70}{10} \approx 3.967\times 10^{11}$

    Figure 10.  The performance of the tree search algorithm for the problem in Figure 7(a)

    00.420.330.250.860.480.480.370.810.29
    0.4200.640.740.100.540.920.610.680.45
    0.330.6400.230.410.250.510.720.400.55
    0.250.740.2300.420.480.750.550.540.44
    0.860.100.410.4200.770.520.400.240.24
    0.480.540.250.480.7700.330.780.630.92
    0.480.920.510.750.520.3300.420.310.41
    0.370.610.720.550.400.780.4200.470.55
    0.810.680.400.540.240.630.310.4700.40
    0.290.450.550.440.240.920.410.550.400
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    0.060.400.270.310.830.730.890.04
    0.510.180.930.890.990.870.940.15
    0.990.660.980.580.400.990.790.77
    0.680.310.660.300.760.910.880.52
    0.320.260.350.630.060.480.540.89
    0.530.730.770.680.430.100.340.03
    0.340.910.420.010.100.810.130.40
    0.720.520.470.970.180.750.480.95
    0.440.680.700.060.890.820.330.73
    0.820.750.520.880.210.620.740.67
    Bounds
    0.990.660.980.580.830.990.890.77
    0.990.910.980.970.890.990.890.95
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    Table 1.  The problem parameters

    $d_0 = 1$ meter $\lambda = (3\times 10^8)/(2.4 \times 10^9)$
    $\alpha = 2$ $B= 20$ GHz
    $P_t = 4$ Watt $N_0=3.98\times10^{-17}$
    $G_t = 1$ $t^{wall}=5.4$ dB, $t^{glass}=7.7$ dB
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    Table 2.  Results of the computational study for the single objective problems

    Instance Beam Search TSWB CPLEX
    Model$n$ConstraintGapTimeTime
    25$c(\mathbf{x}) \geq 8$0.00%1129
    25$c(\mathbf{x}) \geq 9$50.00%1561
    210$c(\mathbf{x}) \geq 35$0.00%1551566
    210$c(\mathbf{x}) \geq 40$50.00%231695
    35$d(\mathbf{x}) \geq 15$0.10%128
    35$d(\mathbf{x}) \geq 20$0.00%17
    310$d(\mathbf{x}) \geq 10$0.18%22154
    310$d(\mathbf{x}) \geq 15$3.54%7912
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