Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation

<p>The proportion of sum of the top <svg height="12.0653pt" id="M48" style="vertical-align:-3.4294pt" version="1.1" viewbox="-0.0498162 -8.6359 5.61923 12.0653" width="5.61923pt" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M394 607C394 634 378 655 350 655C315 655 294 620 294 594C294 564 314 546 339 546C372 546 394 574 394 607ZM360 349C373 414 374 448 351 448C321 448 249 404 183 338L197 313C223 336 265 367 276 367C285 367 283 355 275 313C241 129 220 23 189 -102C175 -158 153 -197 124 -197C106 -197 85 -187 70 -177C64 -173 51 -177 44 -183C34 -193 23 -209 23 -228C23 -244 46 -261 67 -261C86 -261 127 -245 181 -197C237 -147 279 -52 303 66L360 349Z"></path></g></svg> singular values.</p>

Wireless Communications and Mobile Computing

fig1

Figure 1

Figure 1: Real-Time Data Recovery in Wireless Sensor Networks Using Spatiotemporal Correlation Based on Sparse Representation 