My publications

Publications

Pozza R, Gluhak A, Nati M (2012) SmartEye: An energy-efficient obse r ver platform for internet of things testbeds, Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM pp. 59-66
The recently growing need to experiment with Internet of Things (IoT) technologies in more realistic environments requires the experimenter to have remote and precise observation of heterogeneous IoT devices under test. This paper introduces SmartEye, an energy efficient observer platform for IoT testbeds. SmartEye embeds many features that are currently available on different observer systems into a common platform, while placing energy efficiency for autonomous operation at the core of its design. It will provide the foundation for a new class of IoT testbeds to be deployed in more realistic testbed environments such as cities, urban areas or more remote deployment environments without compromising on the rich observational and management features provided by today's observer boards. Copyright © 2012 ACM.
Pozza R, Georgoulas S, Moessner K, Nati M, Gluhak A, Krco S (2016) An Arrival and Departure Time Predictor for Scheduling Communication in Opportunistic IoT,Sensors 16 (11) 1852 MDPI
In this article, an Arrival and Departure Time Predictor (ADTP) for scheduling communication in opportunistic Internet of Things (IoT) is presented. The proposed algorithm learns about temporal patterns of encounters between IoT devices and predicts future arrival and departure times, therefore future contact durations. By relying on such predictions, a neighbour discovery scheduler is proposed, capable of jointly optimizing discovery latency and power consumption in order to maximize communication time when contacts are expected with high probability and, at the same time, saving power when contacts are expected with low probability. A comprehensive performance evaluation with different sets of synthetic and real world traces shows that ADTP performs favourably with respect to previous state of the art. This prediction framework opens opportunities for transmission planners and schedulers optimizing not only neighbour discovery, but the entire communication process.
Pozza R, Nati M, Georgoulas S, Moessner K, Gluhak A (2015) Neighbor Discovery for Opportunistic Networking in Internet of Things Scenarios: A Survey,IEEE ACCESS 3 pp. 1101-1131 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Pozza R, Nati M, Georgoulas S, Gluhak A, Moessner K, Krco S (2014) CARD: Context-Aware Resource Discovery for mobile Internet of Things scenarios, 2014 IEEE 15TH INTERNATIONAL SYMPOSIUM ON A WORLD OF WIRELESS, MOBILE AND MULTIMEDIA NETWORKS (WOWMOM) IEEE
So far, the Internet of Things (IoT) has been concerned with the objective of connecting every-thing, or any object to the Internet world. By collaborating towards the creation of new services, the IoT has introduced the opportunity to add smartness to our cities, homes, buildings and healthcare systems, as well as businesses and products. In many scenarios, objects or IoT devices are not always statically deployed, but they may be free to move around being carried by people or vehicles, while still interacting with static IoT infrastructure. The Opportunistic Networking paradigm states that, exploiting opportunistic interactions between static and mobile IoT devices, provides for increased network capacity, additional connectivity, reduced deployment costs, improved reliability and overall network lifetime improvements. IoT scenarios do illustrate the increased need to identify and exploit opportunistic interactions between IoT devices in order to recognize when an opportunity for communication is possible. For example, statically deployed devices (i.e. road side sensors) may need to find mobile devices (this may be sensors or actuators) (i.e. connected cars) for exploiting them for collecting and relaying data towards destinations without relying on a static infrastructure. This means that discovery in IoT scenarios needs to determine the availability of other devices in scenarios in which devices' presence is uncertain or may change over time. This directly leads to a contradicting objective where resource wastage in device discovery is to be kept at a minimum. This thesis presents two contributions that provide solutions to overcome the clash between these contradicting objectives. Firstly, a Context Aware Resource Discovery mechanism is introduced, capable of providing optimized discovery and adapting available resources based on learned mobility patterns. Secondly, an Arrival and Departure Time Prediction and Discovery framework is defined and investigated; this framework aims to predict future arrival and departure times and helps to plan the use of devices' resources in advance based on the foreseen resource demand patterns.
Jiang J, Pozza R, Gunnarsdottir K, Gilbert GN, Moessner K (2017) Recognising Activities at Home: Digital and Human Sensors,Proceedings of ICFNDS ?17, Cambridge, United Kingdom, July 19-20, 2017 ACM, the Association for Computing Machinery
What activities take place at home? When do they occur, for how
long do they last and who is involved? Asking such questions is
important in social research on households, e.g., to study energyrelated
practices, assisted living arrangements and various aspects
of family and home life. Common ways of seeking the answers
rest on self-reporting which is provoked by researchers (interviews,
questionnaires, surveys) or non-provoked (time use diaries). Longitudinal
observations are also common, but all of these methods
are expensive and time-consuming for both the participants and
the researchers. The advances of digital sensors may provide an
alternative. For example, temperature, humidity and light sensors
report on the physical environment where activities occur, while
energy monitors report information on the electrical devices that
are used to assist the activities. Using sensor-generated data for
the purposes of activity recognition is potentially a very powerful
means to study activities at home. However, how can we quantify
the agreement between what we detect in sensor-generated
data and what we know from self-reported data, especially nonprovoked
data? To give a partial answer, we conduct a trial in a
household in which we collect data from a suite of sensors, as well
as from a time use diary completed by one of the two occupants.
For activity recognition using sensor-generated data, we investigate
the application of mean shift clustering and change points
detection for constructing features that are used to train a Hidden
Markov Model. Furthermore, we propose a method for agreement
evaluation between the activities detected in the sensor data and
that reported by the participants based on the Levenshtein distance.
Finally, we analyse the use of different features for recognising
different types of activities.
Jiang Jie, Pozza Riccardo, Gunnarsdottir Kristrun, Gilbert Geoffrey, Moessner Klaus (2017) Using Sensors to Study Home Activities,Journal of Sensor and Actuator Networks 6 (4) MDPI
Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries.
So far, the Internet of Things (IoT) has been concerned with the objective of connecting every-thing, or any object to the Internet world. By collaborating towards the creation of new services, the IoT has introduced the opportunity to add smartness to our cities, homes, buildings and healthcare systems, as well as businesses and products. In many scenarios, objects or IoT devices are not always statically deployed, but they may be free to move around being carried by people or vehicles, while still interacting with static IoT infrastructure. The Opportunistic Networking paradigm states that, exploiting opportunistic interactions between static and mobile IoT devices, provides for increased network capacity, additional connectivity, reduced deployment costs, improved reliability and overall network lifetime improvements.

IoT scenarios do illustrate the increased need to identify and exploit opportunistic interactions between IoT devices in order to recognize when an opportunity for communication is possible. For example, statically deployed devices (i.e. road side sensors) may need to find mobile devices (this may be sensors or actuators) (i.e. connected cars) for exploiting them for collecting and relaying data towards destinations without relying on a static infrastructure. This means that discovery in IoT scenarios needs to determine the availability of other devices in scenarios in which devices' presence is uncertain or may change over time. This directly leads to a contradicting objective where resource wastage in device discovery is to be kept at a minimum.

This thesis presents two contributions that provide solutions to overcome the clash between these contradicting objectives. Firstly, a Context Aware Resource Discovery mechanism is introduced, capable of providing optimized discovery and adapting available resources based on learned mobility patterns. Secondly, an Arrival and Departure Time Prediction and Discovery framework is defined and investigated; this framework aims to predict future arrival and departure times and helps to plan the use of devices' resources in advance based on the foreseen resource demand patterns.