Efficient Aggregation Processing in the Presence of Duplicately Detected Objects in WSNs

Jun-Ki Min, Raymond T. Ng, Kyuseok Shim

Abstract: Wireless sensor networks (WSNs) have received increasing attention in the past decades. Owing to an enhancement of MEMS technology, various types of sensors such as motion detectors, infrared radiation detectors, ultrasonic sensors (sonar), and magnetometers can detect the objects within a certain range. Under such an environment, an object without an identifier can be detected by several sensor nodes. However, existing studies for query processing in WSNs simply assume that the sensing regions of sensors are disjoint. Thus, for query aggregation processing, effective deduplication is vital. In this paper, we propose an approximate but effective aggregate query processing algorithm, called DE-Duplication on the Least Common Ancestor<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04990005pt" id="M1" height="10.1524pt" version="1.1" viewBox="-0.0498162 -10.1025 6.17869 10.1524" width="6.17869pt"><g transform="matrix(.0091,0,0,-0.0091,0,-5.741)"><path id="g50-43" d="M486 158C486 177 478 202 466 220C413 228 386 236 336 262C386 288 413 297 466 304C478 323 486 347 485 366C470 376 444 381 422 380C389 338 368 319 321 288C323 345 329 372 349 422C339 442 322 461 305 470C289 461 271 442 262 422C281 372 287 345 290 288C243 319 222 338 189 380C167 381 142 376 125 366C125 347 133 322 145 304C198 296 225 288 275 262C225 236 198 227 145 220C133 201 125 177 126 158C141 148 167 143 189 144C222 186 243 205 290 236C288 179 282 152 262 102C272 82 289 63 306 54C322 63 340 82 350 102C330 152 324 179 321 236C368 205 390 186 422 144C444 143 470 148 486 158Z"/></g></svg> (abbreviated as DELCA<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04990005pt" id="M2" height="10.1524pt" version="1.1" viewBox="-0.0498162 -10.1025 6.17869 10.1524" width="6.17869pt"><g transform="matrix(.0091,0,0,-0.0091,0,-5.741)"><use xlink:href="#g50-43"/></g></svg>).</span> In contrast to most existing studies, since we assume that each object does not have a unique identifier, we perform deduplication based on similarity. To recognize the duplicately detected events earlier, we utilize the locality-sensitive hashing (LSH) technique. In addition, since the similarity measures are not generally transitive, we adapt three duplicate semantics. In our experiments, by using a transmission cost model, we demonstrate that our proposed technique is energy-efficient. We also show the accuracy of our proposed technique.