Continuous Spatial Assignment of Moving Users

• Main Authors: LEONG, Hou U, MOURATIDIS, Kyriakos, MAMOULIS, Nikos
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Assignment problems; Average Distance; Capacity constraints; Continuous queries; Euclidean; Geometric characteristics; Minkowski distance; Mobile users; Optimal assignment; Squared distances; User location; Databases and Information Systems; Digital Communications and Networking; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2450; https://ink.library.smu.edu.sg/context/sis_research/article/3449/viewcontent/Continuous_Spatial_Assignment_of_Moving_Users.pdf
• Institution: Singapore Management University

Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), there is currently no method for dynamic settings. In this paper, we consider the continuous assignment problem (CAP), where an optimal assignment must be constantly maintained between mobile users and a set of servers. The fact that the users are mobile necessitates real-time reassignment so that the quality of service remains high (i.e., their distance from their assigned servers is minimized). The large scale and the time-critical nature of targeted applications require fast CAP solutions. We propose an algorithm that utilizes the geometric characteristics of the problem and significantly accelerates the initial assignment computation and its subsequent maintenance. Our method applies to different cost functions (e.g., average squared distance) and to any Minkowski distance metric (e.g., Euclidean, L1 norm, etc).