Andreas Savvides

Dynamic fine-grained localization in Ad-Hoc networks of sensors

The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

SensorSim: a simulation framework for sensor networks

The advent of wireless micro sensors promises many yet unrealized benefits. A network of such sensors or “sensor network” introduces a new set of challenges. Besides being able to communicate effectively, sensor networks have demanding sensing tasks. First, they must be aware of their environment and oftentimes are required to adapt to their surroundings. Second, they must coordinate among them to perform a greater group-sensing task. In this context, the study of sensor networks has numerous other aspects besides communication. To create a better understanding of sensor networks and to facilitate the development of new protocols and applications, detailed simulation and performance evaluation techniques need to be developed. In this paper, we introduce our ongoing efforts in the development of SensorSim, a simulation framework that introduces new models and techniques for the design and analysis of sensor networks. SensorSim inherits the core features of traditional event driven network simulators, and builds up new features that include ability to model power usage in sensor nodes, hybrid simulation that allows the interaction of real and simulated nodes, new communication protocols and real time user interaction with graphical data display. After discussing the details of SensorSim, we provide our current results, that demonstrate various capabilities of SensorSim.

The n -hop multilateration primitive for node localization problems

The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. In this paper we study node localization, a component technology that would enhance the effectiveness and capabilities of this new class of networks. The n-hop multilateration primitive presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.

On the error characteristics of multihop node localization in ad-hoc sensor networks

Ad-hoc localization in multihop setups is a vital component of numerous sensor network applications. Although considerable effort has been invested in the development of multihop localization protocols, to the best of our knowledge the sensitivity of localization to its different setup parameters (network density, ranging system measurement error and beacon density) that are usually known prior to deployment has not been systematically studied. In an effort to reveal the trends and to gain better understanding of the error behavior in various deployment patterns, in this paper we study the Cramer Rao Bound behavior in carefully controlled scenarios. This analysis has a dual purpose. First, to provide valuable design time suggestions by revealing the error trends associated with deployment and second to provide a benchmark for the performance evaluation of existing localization algorithms.

Simulating networks of wireless sensors

Previous advances in low-power embedded processors, radios, and micro-mechanical systems (MEMs) have made possible the development of networks of wirelessly interconnected sensors. With their focus on applications requiring tight coupling with the physical world, as opposed to the personal communication focus of conventional wireless networks, these wireless sensor networks pose significantly different design, implementation, and deployment challenges. We present a set of models and techniques that are embodied in a simulation tool for modeling wireless sensor networks. Our work builds up on the infrastructure provided by the widely used ns-2 simulator, and adds a suite of new features and techniques that are specific to wireless sensor networks. These features introduce the notion of a sensing channel through which sensors detect targets, and provide detailed models for evaluating energy consumption and battery lifetime.

A distributed computation platform for wireless embedded sensing

We present a low cost wireless microsensor node architecture for distributed computation and sensing in massively distributed embedded systems. Our design focuses on the development of a versatile, low power device to facilitate experimentation and initial deployment of wireless microsensor nodes in deeply embedded systems. This paper provides the details of our architecture and introduces fine-grained node localization as an example application of distributed computation and wireless embedded sensing.