TY - GEN
T1 - Demo
T2 - 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015
AU - Hussain, Muhammed Mas Ud
AU - Wongse-Ammat, Panitan
AU - Trajcevski, Goce
N1 - Funding Information:
Research supported in part by NSF grants CNS-0910952 and III 1213038, and ONR grant N00014-14-1-0215.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - This work presents a distributed implementation for processing Maximizing Range Sum (MaxRS) query in Wireless Sensor Networks (WSN). MaxRS query is useful in many spatially-distributed event monitoring and target tracking applications. Given the location and current readings of the nodes, and a rectangle R, MaxRS finds a location of R that maximizes the sum of the readings of all the nodes covered by R. Our system performs MaxRS query in a userspecified time-interval λ and using the result obtained, attempts to maintain a certain degree of energy conservation in the WSN, based on a user-defined threshold δ. Since centralized processing of the raw readings and subsequently determining the MaxRS may incur significant communication overheads, we developed a distributed algorithm to compute MaxRS. We implemented our system in a heterogeneous WSN consisting of TelosB and SunSPOT motes, and illustrate the end-user tools: GUI for specifying required parameters, and real-time visualization of MaxRS solutions and estimated network energy consumption.
AB - This work presents a distributed implementation for processing Maximizing Range Sum (MaxRS) query in Wireless Sensor Networks (WSN). MaxRS query is useful in many spatially-distributed event monitoring and target tracking applications. Given the location and current readings of the nodes, and a rectangle R, MaxRS finds a location of R that maximizes the sum of the readings of all the nodes covered by R. Our system performs MaxRS query in a userspecified time-interval λ and using the result obtained, attempts to maintain a certain degree of energy conservation in the WSN, based on a user-defined threshold δ. Since centralized processing of the raw readings and subsequently determining the MaxRS may incur significant communication overheads, we developed a distributed algorithm to compute MaxRS. We implemented our system in a heterogeneous WSN consisting of TelosB and SunSPOT motes, and illustrate the end-user tools: GUI for specifying required parameters, and real-time visualization of MaxRS solutions and estimated network energy consumption.
UR - http://www.scopus.com/inward/record.url?scp=84962834140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962834140&partnerID=8YFLogxK
U2 - 10.1145/2809695.2817863
DO - 10.1145/2809695.2817863
M3 - Conference contribution
AN - SCOPUS:84962834140
T3 - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
SP - 479
EP - 480
BT - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 1 November 2015 through 4 November 2015
ER -