A system architecture for ubiquitous tracking environments

The emergence of computationally-enabled sensors and the applications that use sensor data introduces the need for a software infrastructure designed specifically to enable the rapid development and deployment of applications that draw upon data from multiple, heterogeneous sensor networks. We present the Hourglass infrastructure, which addresses this need.

2005

In the rapidly developing field of sensor networks, bridging the gap between the applications and the hardware presents a major challenge. Although middleware is one solution, it must be specialized to the qualities of sensor networks, especially energy consumption. The work presented here provides two contributions: a new operational setting for sensor networks and a middleware for easing software development in this setting. The operational setting we target removes the usual assumption of a central collection point for sensor data. Instead the sensors are sparsely distributed in an environment, not necessarily able to communicate among themselves, and a set of clients move through space accessing the data of sensors nearby, yielding a system which naturally provides context relevant information to client applications. We further assume the clients are wirelessly networked and share locally accessed data. This scenario is relevant, for example, when relief workers access the information in their zone and share this information with other workers. Our second contribution, the middleware itself, is an extension of LIME, our earlier work on middleware for mobile ad hoc networks. The model makes sensor data available through a tuple space interface, providing the illusion of shared memory between applications and sensors. This paper presents both the model and the implementation of our middleware incorporated with the Crossbow Mote sensor platform.

International Journal of Wireless and Mobile Computing, 2006

One of the main applications of sensor networks is to detect and monitor transient events of interest, or objects as they move or spread through an area. To track such objects efficiently, and accurately we need distributed mechanisms that allow the cooperation of many sensors and the exchange of real-time data. This cooperation must also be achieved in the presence of possible failures and within the constraints of the sensor nodes and the established network. In this paper we present a new distributed mechanism for tracking moving objects that addresses these desiderata. Our mechanism provides efficient setup and cooperation of the sensors within the network, while providing fault tolerant characteristics through replication. We also provide an algorithm for predicting, with high probability, the future location of an object based on the past observations of many sensors. We empirically evaluate the performance of our approach and our simulations demonstrate its efficiency and accuracy.

Proceedings - Second IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, 2004

Ubiquitous computing is rapidly emerging as building framework for deploying mobile and contextaware applications. This paper focuses on a new approach, and the resulting architecture, for implementing positioning services over ubiquitous infrastructures. Our driving idea is to estimate zones where devices are operating using a power level based positioning approach. We experiment such an approach over a Bluetooth and Wi-Fi infrastructure. The proposed architecture is independent from the devices used and from the wireless infrastructure (Bluetooth, and WiFi).

2014 IEEE International Parallel & Distributed Processing Symposium Workshops, 2014

We consider the problem of tracking mobile objects using a sensor network. We present a distributed tracking algorithm, called Mobile Object Tracking using Sensors (MOT), that scales well with the number of sensors and also with the number of mobile objects. MOT maintains a hierarchical structure of detection lists of objects that can efficiently track mobile objects and resolve object queries at any time. MOT guarantees that the cost to update (or maintain) its data structures will be at most O(min{log n, log D}) times the optimal update cost and query cost will be within O(1) of the optimal query cost in the constant-doubling graph model, where n and D, respectively, are the number of nodes and the diameter of the network. MOT achieves polylogarithmic approximations for both costs in the general graph model. MOT balances the object and bookkeeping information load at each node in the expense of only O(log n) increase in the update and query costs. The experimentation evaluation in both one by one and concurrent execution situations shows that MOT performs well in practical scenarios. To the best of our knowledge, MOT is the first algorithm for this problem in a distributed setting that is traffic-oblivious, i.e. agnostic to a priori knowledge of objects movement patterns, mobility and query rate, etc., and is load balanced. All previous solutions for this problem assumed traffic-consciousness in constructing the tracking data structure.

2008 14th IEEE International Conference on Parallel and Distributed Systems, 2008

Tracking objects is an important activity in many applications such as Augmented Reality, Visual Servoing, and Tangible Interfaces. Most of these applications are inherently dynamic and demand realtime response while tracking. Also, due to the issues of economics, such applications can benefit from tracking systems that do not depend on well-engineered setups and instead use inexpensive sensors for tracking. These requirements make distributed tracking a challenging problem that needs a thorough investigation. In this paper an approach to tracking, which uses the concepts of dynamic discovery, in a distributed environment containing autonomous vision-based trackers that use inexpensive sensors (such as Webcameras) is presented along with its empirical validation. The empirical results obtained from experiments are promising and validate the premise that Distributed Tracking Systems can be built using inexpensive vision-based sensors and concepts of dynamic resource discovery.

IEEE Transactions on Intelligent Transportation Systems, 2000

This paper presents the design and development of an on-demand low-cost positioning and tracking (P&T) system for large environments based on standard mobile phones and Global Positioning System (GPS)-enabled devices, which allows the position of a terminal to be tracked by exchanging text or multimedia messages using short message service (SMS) and/or multimedia message service (MMS). In contrast with other works in the state of the art, the presented method and system do not require an intermediate server or an intermediation center (location-based service (LBS) providers) since communication is performed, following a peer-to-peer (P2P) paradigm. The P2P communication established between two devices with means of access to the mobile network is carried out, guaranteeing the privacy, security, and anonymity in their use and avoiding thirdparty intervention. Moreover, the proposed procedure protects the user from unwanted tracking through exchange of specific information, following a predefined protocol. In addition, the confidentiality of the information exchanged is guaranteed, in part, by the underlying wireless communication protocols that encrypt the messages swapped. The presented method and system represent an improvement with respect to the state-of-the-art P&T devices since it runs on standard mobile platforms, avoiding the need for a specific hardware and guaranteeing the universalization of the service. Another major advantage with respect to existing devices is that one peer can track multiple users, and each user can be tracked by multiple peers with no third-party intervention.

2004

In the near future, we envision sensor net- works to transport high bandwidth, low latency streaming data from a variety of sources, such as cameras and microphones. Sensor networks will be called upon to perform sophisticated in-network processing such as image fusion and object tracking. It is not too difficult to imagine that computational capabilities of network nodes will scale

Applications such as transportation management and logistics, emergency response, environmental monitoring and mobile workforce management employ mobile networks as a means of enabling communication and coordination among a possibly very large set of mobile nodes. The majority of those systems may thus require real-time tracking of the nodes and interaction with all participant nodes as well as a means of adaptability in a very dynamic scenario. In this paper, we present a middleware communication service based on the OMG DDS standard that supports on-line tracking of several thousand mobile nodes and unicast, groupcast and broadcast communication modes. We then show a Fleet Tracking and Management application built using or middleware, and we present the performance results in LAN and WAN settings to evaluate our middleware in terms of scalability and robustness.

… sensor systems, 2006

CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive. CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity-to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys-to communicate with the portal. CarTel applications run on the portal, using a delaytolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delaytolerant network stack, CafNet, to communicate. CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.