TY - GEN
T1 - Covering Users by a Connected Swarm Efficiently
AU - Danilchenko, Kiril
AU - Segal, Michael
AU - Nutov, Zeev
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - In this paper we study covering problems that arise in wireless networks with Unmanned Aerial Vehicles (UAVs) swarms. In the general setting we want to place a set of UAVs that should cover a given set of planar users under some constraints and we want to maintain the solution efficiently in a static and dynamic fashion. Specifically, for a set S of n non-negative weighted points in the plane, we consider a set P of m disks (or squares) of radius where the goal is to place (and maintain under dynamic updates) their location such that the sum of the weights of the points in S covered by disks from P is maximized. In the connected version, we also require that the graph imposed on P should be connected. Moreover, for the static connected version we improve the results from[1] and we obtain a constant factor approximation algorithm. In order to solve our problem under various requirements, we use a variety of techniques including dynamic grid, a reduction to Budgeted Subset Steiner Connected Dominating Set problem, tree partition, and others. We present several simple data structures that maintain an O(1)-approximate solution efficiently, under insertions and deletions of points to/from S where each update operation can be performed in logarithmic time.
AB - In this paper we study covering problems that arise in wireless networks with Unmanned Aerial Vehicles (UAVs) swarms. In the general setting we want to place a set of UAVs that should cover a given set of planar users under some constraints and we want to maintain the solution efficiently in a static and dynamic fashion. Specifically, for a set S of n non-negative weighted points in the plane, we consider a set P of m disks (or squares) of radius where the goal is to place (and maintain under dynamic updates) their location such that the sum of the weights of the points in S covered by disks from P is maximized. In the connected version, we also require that the graph imposed on P should be connected. Moreover, for the static connected version we improve the results from[1] and we obtain a constant factor approximation algorithm. In order to solve our problem under various requirements, we use a variety of techniques including dynamic grid, a reduction to Budgeted Subset Steiner Connected Dominating Set problem, tree partition, and others. We present several simple data structures that maintain an O(1)-approximate solution efficiently, under insertions and deletions of points to/from S where each update operation can be performed in logarithmic time.
KW - Approximation algorithm
KW - Budgeted covering
KW - Dynamic data structure
KW - Tree partition
UR - http://www.scopus.com/inward/record.url?scp=85096484529&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62401-9_3
DO - 10.1007/978-3-030-62401-9_3
M3 - Conference contribution
SN - 9783030624002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 32
EP - 44
BT - Algorithms for Sensor Systems - 16th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2020, Revised Selected Papers
A2 - Pinotti, Cristina M.
A2 - Navarra, Alfredo
A2 - Bagchi, Amitabha
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2020
Y2 - 9 September 2020 through 10 September 2020
ER -