ITR: Scalable Location-Aware Monitoring (SLAM) Systems

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

This proposal describes SLAM, a scalable network architecture integrating millions of real-world sensors with actuators and distributed software applications. SLAM will enable a broad variety of novel monitoring and control applications including rapid disaster response, scalable crime detection and prevention, facilities maintenance, asset monitoring, and navigation. SLAM solves three problems: 1. Full exploitation of a sensor's data stream requires knowledge of contextual information, particularly location and time. 2. Fine-grained monitoring of millions of assets and facilities requires the physical deployment of sensors in the environment an intensive and cumbersome manual task. 3. Use of deployed sensors/actuators by distributed software applications requires network infrastructure. The SLAM architecture has three main components that address these issues: 1. Cricket, a ubiquitous and precise location infrastructure. No current location-sensing technology works everywhere in all places and at all times. Cricket is a novel multi-sensor location architecture to solve this problem, using a combination of RF and ultrasound indoors and at building perimeters, and GPS outdoors. Cricket incorporates self-configuration algorithms and energy-efficient protocols for scalability and longevity. 2. An activated environment and efficient activation method. SLAM requires that the subject environment be activated with sensors and actuators. Without special attention, the activation process could become unmanageable due to the complexity of the environment. Therefore SLAM provides virtual location-based tagging, typically for immobile objects. The human installer affixes virtual tags to physical regions or objects by pointing at them with a Cricket-equipped handheld device, triggering an association of a unique identifier and the tagged entity's location and other attributes in a persistent store. This eases environment activation. 3. A scalable network infrastructure connects sensor information and events to software handlers. The network consists of fixed and mobile sensor proxies, physically co-located with the objects and events they monitor, to integrate location, identity, and temporal information to form an event stream. Sensors and their proxies communicate using sensor-specific low-energy communication protocols. Applications are written as event handlers distributed across the network. SLAM provides support for dynamically distributing handlers across proxies and compute servers, routing events to handlers, and performing query processing operations. The proposed SLAM architecture introduces three innovative ideas: ubiquitous, energy-efficient location infrastructure (drawing on ideas from beacon-based location systems, computational geometry, and wireless networking); virtual region and object tagging for environment activation and asset management (drawing on ideas from geometric modeling and database management systems); and distributed proxy-based event and response processing (drawing on ideas from networking and database systems). Starting with an existing environment (a building, campus, or town), the operational model to put SLAM in place is as follows. First, the location infrastructure is activated. Location beacons are placed in the environment, and a digital representation of the environment is constructed, enabling location inference anywhere within the environment. Second, the environment is activated. Sensors and virtual and physical tags are affixed to objects of interest within the environment (and environment representation). Third, the SLAM network is activated, connecting raw sensor data streams to sensor proxies. The proxies annotate sensor data streams with location and temporal information, and forward them to appropriate handlers via the event-processing network. Handlers produce further events, as well as actions and notifications to be forwarded to actuators or humans. As a challenging test case, we plan to deploy SLAM on a large university campus with millions of interesting entities. These include many sensors in offices, machine rooms, physical plant, and laboratories to monitor power, temperature, humidity, and pressure; smoke and fire detectors; burglar alarms and physical intrusion detection systems; motion detectors; monitors of leaks, floods, chemicals, and hazardous materials; large-scale theft- and crime-prevention apparatus, and navigation aids. The goal is to monitor the university's physical assets and improve the personal safety of over ten thousand individuals moving in and around thousands of offices, labs, and common spaces in hundreds of buildings. The target SLAM system will focus initially on three capabilities at MIT with a variety of interested partners: efficient facilities monitoring and maintenance (with MIT Physical Plant); scalable asset monitoring for inventory, crime prevention and detection (with the MIT Property Office, MIT Campus Police, and MIT Libraries); and navigation assistance, including both personal way-finding and pervasive active signage (with the MIT Schedules Office and the MIT Safety Office).