Bence Pásztor

Evolution and sustainability of a wildlife monitoring sensor network

As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments. In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment. We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Opportunistic Mobile Sensor Data Collection with SCAR

Sensors are now embedded in all sorts of devices (such as phones and PDAs) and attached to many moving things such as robots, vehicles and animals. The collection of data from these mobile sensors presents challenges related to the variability of the topology of the sensor network and the need to limit communication (for energy or bandwidth saving). Fortunately, the data collected, despite considerable, is often delay tolerant and its delivery to the sinks is, in most cases, not time critical. We have devised SCAR, a context aware opportunistic routing protocol which allows efficient routing of sensor data to sinks, through selection of best paths by prediction over movement patterns and current battery level of nodes. In this paper we present the implementation of the protocol in Contiki and validate the approach through the use of the COOJA simulator with mobility traces provided by the ZebraNet Project. We compare the performance with respect to random choice based dissemination.

WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring

The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers (Meles meles) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Selective reprogramming of mobile sensor networks through social community detection

We target application domains where the behavior of animals or humans is monitored using wireless sensor network (WSN) devices. The code on these devices is updated frequently, as scientists acquire in-field data and refine their hypotheses. Wireless reprogramming is therefore fundamental to avoid the (expensive) re-collection of the devices. Moreover, the code carried by the monitored individuals often depends on their characteristics, e.g., the behavior or preferred habitat. We propose a selective reprogramming approach that simplifies and automates the process of delivering a code update to a target subset of nodes. Target selection is expressed through constraints injected in the WSN, triggering automatic dissemination of code updates whenever verified. Update dissemination relies on a novel protocol exploiting the social behavior of the monitored individuals. We evaluate our approach through simulation, using real-world animal and human traces. The results shows that our protocol is able to capture the social network structure in a way comparable to existing offline algorithms with global knowledge while allowing runtime adaptation to community structure changes, and that existing dissemination approaches based on gossip generate up to three times more network overhead than our socially-aware dissemination.

Wildlife and environmental monitoring using RFID and WSN technology

Wireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take considerable effort to replace, it is of major importance to use energy carefully. We present two efficient ways of extending the lifetime of such systems: 1. an adaptive duty cycling protocol and 2. an adaptive data management protocol. Further, we present some details of our deployed sensor network in Wytham Woods, Oxfordshire.

Data collection in delay tolerant mobile sensor networks using SCAR

Cecilia Mascolo Dept. of Computer Science, University College London Gower Street, London WC1E 6BT, United Kingdom c.mascolo@cs.ucl.ac.uk Mirco Musolesi Dept. of Computer Science, University College London Gower Street, London WC1E 6BT, United Kingdom m.musolesi@cs.ucl.ac.uk Bence Pasztor Dept. of Computer Science, University College London Gower Street, London WC1E 6BT, United Kingdom b.pasztor@cs.ucl.ac.uk

OSone: A distributed operating system for energy efficient Sensor Network

In this paper, we take a new approach of thinking about programming Wireless Sensor Networks (WSNs) and introduce OSone, a distributed operating system (OS) for sensor transparency. Our philosophy is to make the network look like an ordinary computer, where each sensor of the network can be thought of one or multiple applications. Such a system allows software developers to abstract away from networking protocols and low-level operating system issues, and develop complex, cooperating systems. In our system, the base station acts as the “kernel” of the OS, while applications run on the sensor nodes. To evaluate the system, we use smart home as our application scenario, where the house adapts to the people living in it - turns on and off the heating and light, warns if the doors are left open, and so on. For such an application, a sensor network is the ideal solution. We show that our system can scale up to a hundred nodes without affecting the responsiveness, it can move 96% of the energy consumption to the central kernel node; and can be about 30% more efficient than a traditional approach.

Selective code dissemination in mobile wireless sensor networks

We present a programming model and distributed protocol to reprogram application-defined subsets of nodes in a wireless sensor network (WSN). the protocol operation is rooted in the use of social information inferred from the behavior of the individuals nodes are attached to.