An economically viable solution to geofencing for mass-market applications

Abstract

Geofencing services deliver location-relevant information to mobile subscribers, who enter a geographic "fence" or boundary around the information's demarcation area. With Geographic Positioning System (GPS) capabilities on many phones, such services facilitate a variety of mass-market applications ranging from mobile proximity marketing to proximity-based dating. Applications delivering such services operate on top of a geofencing engine that manages location tracking of subscribers and returns triggers when fence-crossing events occur. We discuss the critical aspects of geofencing and the challenges faced when deploying such services to the mass market. We present an economically viable geofencing solution that scales to large populations and supports a high number of fences per subscriber. Through distributed processing supported by an appropriate client-server protocol, this solution optimizes the air interface usage and mobile battery power. The technical viability of our solution is supported by actual traffic data from mobile proximity marketing and a social networking service. © 2011 Alcatel-Lucent.The importance of location to mobile communications was anticipated long before the application store was conceived. In 1996, the U.S. Federal Communications Commission (FCC) crafted rules for the Emergency 911 service that require mobile network operators (MNOs) in the United States to provide geographic location information for emergency calls initiated from mobile handsets [2]. Standard bodies including the 3rd Generation Partnership Project (3GPP), 3GPP2, and the Open Mobile Alliance (OMA) then created supporting standardization for location technologies [5]. Presently, a variety of location technologies are supported. One method provides a rough IntroductionIn the last few years, the mobile industry has seen tremendous growth. Today's mobile subscribers can select from a wide range of methods to communicate through their handset. Besides traditional voice and a wide range of messaging services (short, instant, and multimedia messaging), they can engage in mobile e-mail and Web-browsing sessions. With the advent of smartphones, data plans, and application stores, a diversification has occurred in the mobile application space, which merges traditional means of communication with elements of mobility and location to create new methods of information distribution, interaction, and entertainment. location estimate based on the cell area where the handset resides. Another approach applies cell tower trilateration using round-trip delay measurements between the handset and the closest set of cell towers, resulting in higher spatial accuracy. Since the majority of handsets are equipped with Geographic Positioning System (GPS) chips, even greater accuracy is available through trilateration with satellites [4]. While GPS on many phones relies on additional information from the cellular network, referred to as assisted GPS (A-GPS), the most recent generation of smartphones supports autonomous GPS that can operate wit...

InnerCircle: A parallelizable decentralized privacy-preserving location proximity protocol

“…The former is a common case in the scenario of geofencing [13]. The latter two means that the usability of the protocol will be boosted by future hardware development.…”

Infrastructure-Assisted Geofencing: Proactive Location-Based Services with Thin Mobile Clients and Smart Servers

“…In most cases, a Geofencing system can be classified as either being a mobile-based or centralized solution [9]. In a centralized system, a mobile device is mainly being tracked by the surrounding infrastructure, e.g.…”

“…In addition, several companies provide their own mobile-based Geofencing solutions as part of their 3rd Party SDKs [10], [14], [15], [5], [16], [17], [18]. Another very promising Geofencing approach is introduced in [9]. It is based upon a hybrid solution as a combination of a central component within the infrastructure and a geofence matching engine at the mobile device.…”

“…The high energy consumption is mainly caused by the positioning modules and is tackled by selecting an appropriate positioning technique based on the needed accuracy [19], [20], the current environment [21] or by adapting the location update frequency according to the speed towards a geofence [22]. In a centralized solution, the position fixes can be determined through network-centric positioning methods as suggested in [9] or being provided directly by the mobile devices itself as proposed within our approach. In both cases, the servers within the infrastructure are responsible to continuously apply spatial matching functions to compare a huge amount of position fixes with a huge amount of geofences, in a scalable manner [23].…”

“…If a mobile device is located very close to the limits of a geofence the sampling rate gets fixed by the periodic update interval mainly in order for the LMU to precisely detect a geofence entry or leave event at least according to a defined time granularity for monitoring time constraints in geofence scenarios. The periodic update time interval does also fix the time a mobile client has to reside at least within respectively outside of a geofence in order for the LMU to detect the enter respectively leave event, named as the hovertime [9]. Hence, there is a tradeoff to be made between the periodic update interval respectively sampling rate with its implications on the energy consumption and the hovertime, which should be defined in a proper service level agreement.…”