Daniel Massaguer

Exploring sensor networks using mobile agents

Wireless sensor networks are often difficult to program and unable to adapt to a changing environment. Mobile agent middleware promises to address both concerns by providing higher-level programming abstractions and the ability to inject new agents into a preexisting network. The unique characteristics of wireless sensor networks like resource scarcity and emphasis on spatial locality require new algorithms for controlling agent behavior. This paper presents a procedure for one specific behavior: network exploration. Network exploration is needed by many tasks ranging from simple data collection to network health monitoring. Our proposed procedure uses a genetic algorithm to determine the number of agents and their itineraries, followed by techniques for in-network adaptation to unpredictable situations like node failure. This paper presents a genetic algorithm and its adaptation strategies. The procedure is evaluated using a wireless sensor network consisting of 25 Mica2 motes running Agilla, a mobile agent middleware for wireless sensor networks.

DrillSim: a simulation framework for emergency response drills

Responding to natural or man-made disasters in a timely and effective manner can reduce deaths and injuries, contain or prevent secondary disasters, and reduce the resulting economic losses and social disruption. Appropriate IT solutions can improve this response. However, exhaustive and realistic validation of these IT solutions is difficult; proofs are not available, simulations lack realism, and drills are expensive and cannot be reproduced. This paper presents DrillSim: a simulation environment that plays out the activities of a crisis response (e.g., evacuation). It has capabilities to integrate real-life drills into a simulated response activity using an instrumented environment with sensing and communication capabilities. IT solutions can be plugged in the simulation system to study their effectiveness in disaster management and response. This way, by using a simulation coupled with an on-going drill, IT solutions can be tested in a less expensive but realistic scenario.

Design and Implementation of a Middleware for Sentient Spaces

Surveillance is an important task for guaranteeing the security of individuals. Being able to intelligently monitor the activity in given spaces is essential to achieve such surveillance. Sentient spaces based on a large set of sensors provide the potential for such intelligent monitoring. However, heavily instrumenting a space with sensors it is not enough to build a sentient space. One needs a software architecture that allows programming all these sensors in a transparent and efficient manner. In this paper, we present SATware, a stream acquisition and transformation middleware we are developing to analyze, query, and transform multimodal sensor data streams to facilitate flexible development of sentient environments. SATware provides a powerful application development environment in which users (i.e., application builders) can focus on the specifics of the application without having to deal with the technical peculiarities of accessing a large number of diverse sensors via different protocols.

Privacy-preserving event detection in pervasive spaces

In this paper, we consider privacy challenges in event-driven pervasive spaces where multimedia streams captured by sensors embedded in the infrastructure are used to detect a variety of application-specific media events. In particular, we develop techniques to detect events without disclosing any identifying information unless necessary. We characterize the nature of inference channels that arise and model privacy preserving event detection as an optimization problem that attempts to balance disclosure with performance. We design and test efficient communication protocols that realize this tradeoff.

Synthetic humans in emergency response drills

Being able to realistically simulate humans in emergency response activities is a key challenge for advancing the field of information technology for emergency response. Building good human models enables testing information technology solutions in any emergency response simulated scenario. This paper presents an agent behavior model for mimicking peopless behavior on emergency response activities. The accompanying demo demonstrates a concrete model for evacuations integrated with an emergency drill simulation environment.

SATware: A Semantic Approach for Building Sentient Spaces

This chapter describes the architecture of a semantic-based middleware environment for building sensor-driven sentient spaces. The proposed middleware explicitly models sentient space semantics (i.e., entities, spaces, activities) and supports mechanisms to map sensor observations to the state of the sentient space. We argue how such a semantic approach provides a powerful programming environment for building sensor spaces. In addition, the approach provides natural ways to exploit semantics for variety of purposes including scheduling under resource constraints and sensor recalibration.

A semantic approach for building pervasive spaces

Large and pervasive sensing, communications, and computing infrastructures are enabling the realization of pervasive spaces. Enabling such spaces, however, encompasses a set of challenges. First, programming each application such that it connects to each sensor and it interprets the data being sensed requires a concentration of expertise that is rarely available. Second, achieving a wise and fair usage of the infrastructures is impossible with current approaches due to their lack of awareness of domain and application semantics. This paper summarizes a PhD dissertation that focuses on designing and implementing a middleware that addresses these challenges and overcomes the limitations of previous approaches by featuring a distributed streaming architecture and by being aware of the semantics of the space and applications. Namely, we focus on (i) the design and implementation of the overall system architecture and its underlying programming and execution model, (ii) a set of mechanisms to provide the right level of abstraction to applications, and (iii) a set of mechanisms that are able to protect privacy due to the inclusion of semantics in the middleware.