EMP: Exploiting Mobility Patterns for Collaborative Localization in Sparse Mobile Networks

Location awareness plays an indispensable role in a wide variety of application domains such as environment monitoring and vehicle tracking. In this paper we focus on the localization of mobile users in sparse mobile networks which exist in many practical scenarios where users are distributed over a vast area. The unique characteristics of sparse mobile networks present several challenges for accurate localization, such as constant movement and little information from anchors. By analyzing five large datasets of real users traces with entropy analysis from five sites, we make an important observation that there are strong patterns with user mobility. Motivated by this observation, we propose a localization approach called EMP by exploiting mobility patterns of users for localization in sparse mobile networks. EMP implements a range-free distributed algorithm, with which each user collaboratively estimates its current location by fusing two localization sources, that is, network connectivity with other nodes and mobility patterns. With trace driven simulations, we demonstrate that EMP significantly improves the localization accuracy, comparing with other existing localization approaches. 1. Introduction Location awareness plays an indispensable role in a wide variety of domains, such as environment monitoring and vehicle tracking. In this paper we focus on the localization of mobile users or devices in sparse mobile networks which exist in many practical scenarios where the users or devices are distributed over a vast area. The localization approach based on the global positioning system (GPS) suffers several limitations. First, GPS may fail in indoor environments or urban areas where urban canyons exist. Second, GPS receivers are power consuming and can easily drain power driven devices. The unique characteristics of sparse mobile networks present several challenges for accurate localization. First, users are constantly changing their locations and localization should be performed in real time. Second, in a sparse network, anchors that have location information can be few for most of the time and thus localization should usually be performed under little information from anchors. There has been extensive research on the localization problem [1–6]. Existing approaches could be classified into two categories: range-based and range-free. Examples for range-based approaches include Received Signal Strength (RSS) [7, 8], Angle of Arrival (AOA) [9], Time of Arrival (TOA) [10], and Time Difference of Arrival (TDOA) [11]. The performance of range-based