mTrigger

Location-based triggers are the fundamental capability for supporting location-based advertisements, location-based entertainment applications, personal reminders, as well as presence-based information sharing applications. In this chapter, we describe the design and the implementation of mTrigger, an event-based framework for scalable processing of location-based mobile triggers (location triggers for short). A location trigger is a standing spatial trigger specified with the spatial region over which the trigger is set, the actions to be taken when the trigger conditions are met, and the list of recipients to whom the notification will be sent upon the firing of the location trigger. The mTrigger framework consists of three alternative architectures for supporting location triggers: (1) the client-server architecture, which allows mobile clients to register and install location triggers of interest on the mTrigger server system; the server being responsible for processing location triggers, performing associated actions and sending out notifications upon firing of triggers; (2) the client-centric architecture, which enables mobile users to manage and process location triggers on their own mobile clients; and (3) the decentralized peer-to-peer architecture, which allows mobile users to collaborate with one another in terms of location trigger processing. The server-centric architecture is particularly suitable for supporting public and shared location triggers, enabling effective sharing of location trigger processing among multiple users. The client-centric architecture is more suitable for users possessing mobile clients with high computational capacity and more sensitive to the location privacy of their location triggers. The decentralized peer-to-peer architecture provides on-demand and opportunistic collaboration in terms of location trigger evaluation. Clearly, the performance optimizations for server-centric architecture should focus on efficient and scalable processing of location triggers by reducing the bandwidth consumption and the amount of redundant computation at the server; whereas, the performance optimizations for client-centric architecture and decentralized architecture should also take into account energy efficiency of mobile clients in addition to computational efficiency. In addition, processing of location triggers with moving target of interest requires the knowledge of position information of the moving target and may not be suitable for the client-centric architecture. This chapter will describe the design principles and the performance optimization techniques of the mTrigger framework, including a suite of energy-efficient spatial trigger grouping techniques for optimizing both wake-up times and check times of location trigger evaluations.