Stefano Galzarano

A framework for collaborative computing and multi-sensor data fusion in body sensor networks

Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multi-sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals’ meeting.

From Modeling to Implementation of Virtual Sensors in Body Sensor Networks

Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security, and highly interactive games. It is therefore important to define design methodologies and programming frameworks which enable rapid prototyping of WBSN applications. Several effective application development frameworks have been already proposed for WBSNs designed for TinyOS-based sensor platforms, e.g. CodeBlue, SPINE, and Titan. In this paper we present an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring. The agent-oriented programming abstractions provided by MAPS allow effective and rapid prototyping of the sensor-side software. In particular, the architecture of the developed system is a typical star-based WBSN composed of a coordinator node and two sensor nodes located respectively on the waist and the thigh of the monitored assisted living. The coordinator relies on a JADE-based enhancement of the SPINE coordinator and allows configuring sensors, receiving their data, and recognizing pre-defined human activities. On the other hand, each sensor node runs a MAPS-based agent that performs sensing of the 3-axial accelerometer sensor, computation of significant features on the acquired data, feature aggregation and transmission to the coordinator. The experimentation phase of the prototype, which allows evaluating the obtainable monitoring performances and activity recognition accuracy, is described. Moreover, a comparison of the monitoring system based on MAPS, AFME and SPINE in terms of programming effectiveness and system performances is discussed.

Embedded self-healing layer for detecting and recovering sensor faults in body sensor networks

Wireless Body Sensor Networks (WBSNs) have proved to be a suitable technology for supporting the monitoring of physical and physiological activities of the human body. However, avoiding erroneous behavior of WBSN-based systems is an issue of fundamental importance, especially for critical health-care applications. In this regard, proper self-healing techniques should be able to fulfill requirements such as fault tolerance and reliability by detecting, and possibly recovering, faults and errors at runtime. In this paper, we focus on data faults, by first studying the impact of corrupted data, affecting sensed data by different kind of data-fault models, on the accuracy of a human activity recognition system. Then, we describe how the SPINE-* framework is able to enhance the WBSN system by adding instrumental autonomic elements providing the necessary self-healing operations. We find that the use of autonomic elements makes the system much more efficient and reliable thanks to its improved tolerance to data faults, as demonstrated by experimental results.

Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).

QL-MAC: A Q-Learning Based MAC for Wireless Sensor Networks

WSNs are becoming an increasingly attractive technology thanks to the significant benefits they can offer to a wide range of application domains. Extending the system lifetime while preserving good network performance is one of the main challenges in WSNs. In this paper, a novel MAC protocol (QL-MAC) based on Q-Learning is proposed. Thanks to a distributed learning approach, the radio sleep-wakeup schedule is able to adapt to the network traffic load. The simulation results show that QL-MAC provides significant improvements in terms of network lifetime and packet delivery ratio with respect to standard MAC protocols. Moreover, the proposed protocol has a moderate computational complexity so to be suitable for practical deployments in currently available WSNs.

Collaborative Body Sensor Networks

In this paper we propose reference architectures and SPINE-based middleware for Collaborative Body Sensor Networks (CBSNs) that can enable new smart wearable systems in the context of physical pervasive computing environments. CBSNs are wireless BSNs that are able to cooperate to support a common goal. Cooperation is therefore based on interaction among the CBSNs and distributed computation across the interacting CBSNs. In particular, interaction can be activated when CBSNs are in proximity and based on service-specific protocols that allow for service management between the involved CBSNs. Specifically, the paper presents C-SPINE, an enhancement of the SPINE middleware for CBSN applications. Finally, a collaborative emotion detection system, integrating heart rate sensing with handshake detection, is developed through C-SPINE and experimentally analyzed.

Java-based Mobile Agent Platforms for Wireless Sensor Networks

This paper proposes an overview and comparison of mobile agent platforms for the development of wireless sensor network applications. In particular, the architecture, programming model and basic performance of two Java-based agent platforms, Mobile Agent Platform for Sun SPOT (MAPS) and Agent Factory Micro Edition (AFME), are discussed and evaluated. Finally, a simple yet effective case study concerning a mobile agent-based monitoring system for remote sensing and aggregation is proposed. The proposed case study is developed both in MAPS and AFME so allowing to analyze the differences of their programming models.

A learning-based MAC for energy efficient wireless sensor networks

Designing energy-efficient communication protocols is one of the main challenges in wireless sensor networks. This work presents an adaptive radio scheduling schema employing a reinforcement learning algorithm for reducing the energy consumption while preserving the other network performances. By means of a decentralized on-line approach, each nodes determines the most beneficial radio schedule by dynamically adapting to its own traffic load and to the neighbors’ communication activities. We compare our approach with other learning-based MAC protocols as well as conventional MAC approaches and show that, under different simulating scenarios and traffic conditions, our protocol achieves better trade-offs in terms of energy consumption, latency and throughput.

Integrating jade and MAPS for the development of agent-based WSN applications

Recent years have seen rapid advancements in wireless sensor networks (WSNs) and software agents resulting, among others, in maturation of their technology platforms. Furthermore, benefits of combining these research areas have been analyzed. The MAPS agent platform allows fusion of agents and WSNs. However, due to the hardware limitation, MAPS misses important functionalities needed, for instance, in advanced decision support. Such functions are available, among others, in the JADE agent platform, geared towards more powerful computing devices. Therefore, integration of MAPS and JADE had to be considered. The aim of this paper is to discuss technical issues involved in achieving this goal.