node-bin-packing-first-fit-biased.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "import math\n",
    "import random\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(421.6819802995513, 10, 125)\n",
      "Ptx = 15\n",
      "sesitivity = -132.75\n",
      "max distance = 9066.33925485\n"
     ]
    }
   ],
   "source": [
    "## parameters\n",
    "\n",
    "packetLength = 20 # in bytes\n",
    "avgSendTime = 90000\n",
    "\n",
    "# define the maximum simulation area\n",
    "maxX = 1000 # in m\n",
    "maxY = 1000 # in m\n",
    "\n",
    "# lets use a constant ratio here\n",
    "nrBS = 10\n",
    "nrNodes = 10000\n",
    "\n",
    "# log shadow model parameters\n",
    "Ptx = 15 # in dBm\n",
    "gamma = 2.2394\n",
    "# d0 = 40.0 # in m\n",
    "d0 = 40.0 # from drive tests\n",
    "var = 0 # variance ignored for now. \n",
    "# Lpld0 = 127.41\n",
    "Lpld0 = 95.0038 # from drive test\n",
    "GL = 0\n",
    "nSF = 6 # spreading factors available = 6\n",
    "\n",
    "def dBmtomW(pdBm):\n",
    "    pmW = 10**(pdBm/10.0)\n",
    "    return pmW\n",
    "\n",
    "def dBmtofW(pdBm):\n",
    "    pfW = 10**((pdBm+90.0)/10.0)\n",
    "    return pfW\n",
    "\n",
    "def getRXPower(pTX, distance):\n",
    "    # get ideal RX power estimate assuming log-distance model\n",
    "    pRX = pTX - Lpld0 - 10.0*gamma*np.log10(distance/d0)\n",
    "    return pRX\n",
    "    \n",
    "def getTXPower(pRX, distance):\n",
    "    # get ideal TX power estimate assuming log-distance model\n",
    "    pTX = pRX + Lpld0 + 10.0*gamma*np.log10(distance/d0)\n",
    "    return pTX\n",
    "\n",
    "def getDistanceFromPL(pTX, pRX):\n",
    "    d = d0*10**((pTX-pRX-Lpld0)/(10.0*gamma))\n",
    "    return d\n",
    "\n",
    "## order: spreading factor, 125 kHz, 250 kHz, 500 kHz\n",
    "sensi = np.array([[7,-126.5,-124.25,-120.75],\n",
    "                 [8,-127.25,-126.75,-124.0],\n",
    "                 [9,-131.25,-128.25,-127.5],\n",
    "                 [10,-132.75,-130.25,-128.75],\n",
    "                 [11,-134.5,-132.75,-128.75],\n",
    "                 [12,-133.25,-132.25,-132.25]])\n",
    "\n",
    "def airtime(sf,cr,pl,bw):\n",
    "    H = 0        # implicit header disabled (H=0) or not (H=1)\n",
    "    DE = 0       # low data rate optimization enabled (=1) or not (=0)\n",
    "    Npream = 8   # number of preamble symbol (12.25  from Utz paper)\n",
    "\n",
    "    if bw == 125 and sf in [11, 12]:\n",
    "        # low data rate optimization mandated for BW125 with SF11 and SF12\n",
    "        DE = 1\n",
    "    if sf == 6:\n",
    "        # can only have implicit header with SF6\n",
    "        H = 1\n",
    "\n",
    "    Tsym = (2.0**sf)/bw\n",
    "    Tpream = (Npream + 4.25)*Tsym\n",
    "    payloadSymbNB = 8 + max(math.ceil((8.0*pl-4.0*sf+28+16-20*H)/(4.0*(sf-2*DE)))*(cr+4),0)\n",
    "    Tpayload = payloadSymbNB * Tsym\n",
    "    return Tpream + Tpayload\n",
    "\n",
    "def getUSMaxTransmitDistance(RXSensi, pTX, plen):\n",
    "    \"\"\"\n",
    "    Based on the transmit time and bandwidth used, get the parameters which give the best range for allowed power.\n",
    "    This is dependent on the packet length as the max transmission size is also limited.\n",
    "    \n",
    "    TODO: Similar functions should be defined for other regions.\n",
    "    \"\"\"\n",
    "    # Allowed max power for each bandwidth in the US\n",
    "    \n",
    "    PTx125 = min(30,pTX) # in dBm\n",
    "    PTx500 = min(26,pTX) # in dBm\n",
    "    \n",
    "    # The US spec doesn't have a 250 kHz option (and there is no good reason to use it)\n",
    "    Lpl125 = PTx125 - RXSensi[:,1]\n",
    "    Lpl500 = PTx500 - RXSensi[:,3]\n",
    "    \n",
    "    LplMatrix = np.concatenate((Lpl125.reshape((6,1)), Lpl500.reshape((6,1))), axis=1)\n",
    "    distMatrix = np.dot(d0, np.exp(np.divide(LplMatrix - Lpld0, 10*gamma)))\n",
    "    \n",
    "    packetAirtimeValid = np.zeros((6,2))\n",
    "    for i in xrange(6):\n",
    "        packetAirtimeValid[i,0] = (airtime(i+7, 1, plen, 125) <= 400)\n",
    "        packetAirtimeValid[i,1] = (airtime(i+7, 1, plen, 500) <= 400)\n",
    "        \n",
    "    Index = np.argmax(np.multiply(distMatrix, packetAirtimeValid))\n",
    "    \n",
    "    sfInd, bwInd = np.unravel_index(Index, (6,2))\n",
    "    \n",
    "    if packetAirtimeValid[sfInd, bwInd] == 0:\n",
    "        raise ValueError(\"packet length too large\")\n",
    "    \n",
    "    maxSF = sfInd + 7\n",
    "    if bwInd == 0:\n",
    "        maxBW = 125\n",
    "    else:\n",
    "        maxBW = 500\n",
    "    \n",
    "    return distMatrix[sfInd,bwInd], maxSF, maxBW\n",
    "\n",
    "## Place base-stations randomly\n",
    "BSLoc = np.zeros((nrBS, 5))\n",
    "nodeLoc = np.zeros((nrNodes, 9))\n",
    "\n",
    "# bestDist, bestSF, bestBW = getUSMaxTransmitDistance(sensi, packetLength)\n",
    "bestSF = 10 # for max range\n",
    "bestBW = 125 # for max range\n",
    "bestDist = getDistanceFromPL(Ptx, sensi[bestSF-7, 1+(bestBW/250)])\n",
    "\n",
    "print getUSMaxTransmitDistance(sensi, Ptx, packetLength)\n",
    "\n",
    "print \"Ptx = {}\".format(Ptx)\n",
    "print \"sesitivity = {}\".format(sensi[bestSF-7, 1+(bestBW/250)])\n",
    "print \"max distance = {}\".format(bestDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Location generation =====\n",
      "Max range = 9066.33925485 at SF = 10, BW = 125\n",
      "generated locations for 10 base-stations\n",
      "generated locations for 10000 nodes\n"
     ]
    }
   ],
   "source": [
    "def placeRandomly(number, locArray, xRange, yRange):\n",
    "    for n in xrange(number):\n",
    "        x = random.uniform(xRange[0], xRange[1])\n",
    "        y = random.uniform(yRange[0], yRange[1])\n",
    "        locArray[n,:] = [n, x, y, -1, 0]\n",
    "        \n",
    "def placeRandomlyInRange(number, locArray, xRange, yRange, refLoc):\n",
    "    for n in xrange(number):\n",
    "#         rounds = 0\n",
    "        while True:\n",
    "            # This could technically turn into an infinite loop but that shouldn't ever practically happen.\n",
    "            # add check here later\n",
    "            x = np.random.normal((xRange[0]+xRange[1])*0.3, (xRange[1]-xRange[0])/10)\n",
    "            y = np.random.normal((yRange[0]+yRange[1])*0.3, (yRange[1]-yRange[0])/10)\n",
    "            if np.any(np.sum(np.square(refLoc[:,1:3] - np.array([x,y]).reshape(1,2)), axis=1) <= bestDist**2):\n",
    "                locArray[n,:] = [n, x, y, -1, 125, -1, 1, -1, avgSendTime]\n",
    "                break\n",
    "        \n",
    "print \"===== Location generation =====\"\n",
    "\n",
    "# seed the random generator\n",
    "random.seed(1)\n",
    "\n",
    "print \"Max range = {} at SF = {}, BW = {}\".format(bestDist, bestSF, bestBW)\n",
    "\n",
    "if nrBS == 1:\n",
    "    BSLoc[0] = [0, maxX*0.5, maxY*0.5, 0, 0]\n",
    "else:\n",
    "    placeRandomly(nrBS, BSLoc, [maxX*0.1, maxX*0.9], [maxY*0.1, maxY*0.9])\n",
    "print \"generated locations for {} base-stations\".format(nrBS)\n",
    "\n",
    "placeRandomlyInRange(nrNodes, nodeLoc, [0, maxX], [0, maxY], BSLoc)\n",
    "print \"generated locations for {} nodes\".format(nrNodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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o0N4AoGkadu/e7bzllluOvvzyyxm7du2quOKKK447HI7Atb788suUK664Yg8A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\n+apcvp2KK9tx8wQmfJLxvDjhBL+vDU9nJe27u+UeJce9lKSmfG9DCT6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Ucc6BAfk/7MC9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\n1o2ViuRegFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118bce050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotLocations():\n",
    "    loc_plot = plt.figure(figsize = (5,5))\n",
    "    loc_ax = plt.gca()\n",
    "    \n",
    "    for b in BSLoc[:,1:3]:\n",
    "        loc_ax.add_artist(plt.Circle((b[0], b[1]), bestDist, fill=False, hatch='//', color = 'red'))\n",
    "    \n",
    "    nodePoints, = plt.plot(nodeLoc[:,1], nodeLoc[:,2], \"b.\", mfc='none', label=\"end device\")\n",
    "    bsPoints, = plt.plot(BSLoc[:,1], BSLoc[:,2], \"r^\", label=\"base station\")\n",
    "    \n",
    "    plt.xticks([0, 5000, 10000, 15000, 20000], [0,5,10,15,20])\n",
    "    plt.yticks([0, 5000, 10000, 15000, 20000], [0,5,10,15,20])\n",
    "    \n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.axis('equal')\n",
    "    plt.xlim((0, maxX))\n",
    "    plt.ylim((0, maxY))\n",
    "    \n",
    "    plt.xlabel('distance (km)')\n",
    "    plt.ylabel('distance (km)')\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "plotLocations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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amlqF57qGhYVh4sSJ4tfFu4GlpTIseuwvv/wis+x9+/ahR48ectUjMDBQoh4M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cXgcDh4+PAhbXlgYCCaNWuGuXPnSq4XFRXB3NwcJ0+eRL9+/ZA2fz4wfz5OCIV42bYtMvh83ImP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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118637450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strategy_smallest_last = 10\n",
      "[[   0.          207.49139529  777.94698955    1.            0.        ]\n",
      " [   1.          711.01969518  304.05522059    0.            0.        ]\n",
      " [   2.          496.34806967  459.59285183    7.            0.        ]\n",
      " [   3.          621.27437818  730.97868091    6.            0.        ]\n",
      " [   4.          175.08766942  122.67798122    5.            0.        ]\n",
      " [   5.          768.61208314  446.21365432    4.            0.        ]\n",
      " [   6.          709.82406597  101.68484268    3.            0.        ]\n",
      " [   7.          456.30975524  677.23202587    2.            0.        ]\n",
      " [   8.          283.00977702  856.21655644    1.            0.        ]\n",
      " [   9.          821.14196609  124.47198643    0.            0.        ]]\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "for i,bs in enumerate(BSLoc):\n",
    "    bsid = int(bs[0])\n",
    "    dist2 = (BSLoc[:,1] - bs[1])**2 + (BSLoc[:,2] - bs[2])**2\n",
    "    index = np.nonzero(dist2 <= (2*bestDist)**2)\n",
    "    G.add_node(bsid)\n",
    "    for node in BSLoc[index,0][0]:\n",
    "        if int(node) != bsid:\n",
    "            G.add_edge(bsid, int(node))\n",
    "#     print \"BS: {}, ind: {}\".format(bsid, ind)\n",
    "    \n",
    "nx.draw(G)\n",
    "plt.show()\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_largest_first)\n",
    "# print \"strategy_largest_first = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_random_sequential)\n",
    "# print \"strategy_random_sequential = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_smallest_last)\n",
    "print \"strategy_smallest_last = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "for i,color in coloring.viewitems():\n",
    "    BSLoc[i,3] = color % 8\n",
    "    \n",
    "print BSLoc\n",
    "    \n",
    "# print BSLoc[:,3]\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_independent_set)\n",
    "# print \"strategy_independent_set = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_connected_sequential_bfs)\n",
    "# print \"strategy_connected_sequential_bfs = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_connected_sequential_dfs)\n",
    "# print \"strategy_connected_sequential_dfs = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "# coloring = nx.greedy_color(G, strategy = nx.coloring.strategy_saturation_largest_first)\n",
    "# print \"strategy_saturation_largest_first = {}\".format(coloring[max(coloring, key=coloring.get)] + 1)\n",
    "\n",
    "\n",
    "# plt.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bucketLimit = (nrNodes/nrBS)/2\n",
    "\n",
    "airtimeFactors = []\n",
    "\n",
    "for sf in xrange(7,11):\n",
    "    airtimeFactors.append(airtime(sf, 1, 20, 125)/airtime(7, 1, 20, 125))\n",
    "\n",
    "bsDict = {}\n",
    "for bs in xrange(BSLoc.shape[0]):\n",
    "    for i in xrange(7,13):\n",
    "        bsDict[bs, i] = 0\n",
    "\n",
    "for i in xrange(nodeLoc.shape[0]):\n",
    "    dist = np.sqrt((nodeLoc[i,1] - BSLoc[:,1])**2 + (nodeLoc[i,2] - BSLoc[:,2])**2)\n",
    "    index = np.nonzero(dist < bestDist)\n",
    "    bsAssocId = random.randint(0, BSLoc.shape[0]-1)\n",
    "    nodeLoc[i,3] = BSLoc[bsAssocId,3]\n",
    "    nodeLoc[i,4] = 125\n",
    "    nodeLoc[i,5] = 10\n",
    "    nodeLoc[i,6] = 1\n",
    "    nodeLoc[i,7] = 0\n",
    "    nodeLoc[i,8] = 900000      \n",
    "                \n",
    "#     print len(sortedBSList), sortedBSList\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.  0.  6.  7.  5.  1.  7.  5.  5.  7.  7.  7.  5.  7.]\n"
     ]
    }
   ],
   "source": [
    "print nodeLoc[1:15,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for i in xrange(nodeLoc.shape[0]):\n",
    "#     dist = np.sqrt((nodeLoc[i,1] - BSLoc[:,1])**2 + (nodeLoc[i,2] - BSLoc[:,2])**2)\n",
    "#     minDistInd = np.argmin(dist)\n",
    "#     minDist = dist[minDistInd]\n",
    "#     p = np.ceil(max(min(getTXPower(sensi[bestSF-7, 1 + bestBW/250], minDist), Ptx),0))\n",
    "#     nodeLoc[i,3] = BSLoc[minDistInd,3]\n",
    "#     nodeLoc[i,4] = bestBW\n",
    "#     nodeLoc[i,5] = bestSF\n",
    "#     nodeLoc[i,6] = 1\n",
    "#     nodeLoc[i,7] = p\n",
    "#     nodeLoc[i,8] = 900000\n",
    "    \n",
    "np.save(\"sensys_tests/first_fit_biased_B{}_N{}_BS\".format(nrBS, nrNodes),BSLoc)\n",
    "np.save(\"sensys_tests/first_fit_biased_B{}_N{}_Nodes\".format(nrBS, nrNodes),nodeLoc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:lorasim-env]",
   "language": "python",
   "name": "conda-env-lorasim-env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}