BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks

<div><svg height="16.757pt" id="M213" style="vertical-align:-6.3829pt" version="1.1" viewbox="-0.0498162 -10.3741 35.596 16.757" width="35.596pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z" id="g113-84"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="472" vert-adv-y="472"></glyph.data></g><g transform="matrix(.0095,0,0,-0.0095,6.395,-5.984)"><path d="M554 433L549 437C539 437 513 441 503 443C481 447 456 451 436 451C354 451 268 415 216 368C150 308 99 205 99 106C99 24 136 -12 165 -12C196 -12 243 14 268 33C318 70 374 118 415 181H419L395 39C374 -84 332 -149 295 -175C269 -193 245 -197 212 -197C137 -197 93 -156 76 -102C71 -87 61 -87 51 -96C36 -110 24 -130 24 -147C24 -183 76 -257 171 -257C226 -257 285 -227 323 -200C387 -155 455 -76 480 94C499 225 538 391 554 433ZM460 386C457 361 438 287 426 254C408 218 373 176 332 133C294 93 246 61 217 61C198 61 186 80 186 122C186 167 206 250 231 294C262 349 288 375 317 387C334 394 358 400 381 400C413 400 439 393 460 386Z" id="g50-104"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="578" vert-adv-y="578"></glyph.data></g><g transform="matrix(.0095,0,0,-0.0095,6.327,3.944)"><path d="M481 430L460 436C440 442 393 451 366 451C348 451 325 448 313 446C267 437 195 409 147 367C79 308 24 211 24 105C24 37 56 -12 93 -12C121 -12 156 1 199 32C246 66 313 122 349 168H351L286 -147C274 -205 261 -215 210 -224L199 -226L193 -255L438 -243L442 -214L417 -212C364 -208 359 -200 370 -140L433 204C455 324 469 389 481 430ZM391 385C382 336 366 260 358 238C321 178 209 59 143 59C129 59 115 83 115 131C115 207 152 317 220 373C243 392 282 403 314 403C348 403 374 395 391 385Z" id="g50-114"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="499" vert-adv-y="499"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,12.395,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z" id="g113-41"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,17.083,0)"><path d="M238 681C243 705 239 712 230 712C217 712 156 682 75 674L70 648H105C148 648 153 641 144 598L39 110C18 11 35 -12 55 -12C90 -12 166 36 221 103L205 125C174 93 130 65 118 65C112 65 108 68 114 96L238 681Z" id="g113-109"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="269" vert-adv-y="269"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,20.729,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="228" vert-adv-y="228"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,26.089,0)"><path d="M324 430H196L233 583L223 592L145 529L120 430H54L29 396L31 388H111L56 126C33 15 54 -12 77 -12C137 -12 214 57 250 95L233 119C208 92 155 59 138 59C126 59 120 70 131 125L186 390L298 394L324 430Z" id="g113-117"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="341" vert-adv-y="341"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,30.709,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z" id="g113-42"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g></svg> with the different trust degrees of two nodes.</div>

Journal of Sensors

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Figure 3

Figure 3: BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks 