Giancarlo Fortino

Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications

Wireless body sensor networks (BSNs) possess enormous potential for changing peoples daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed.

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Abstract Body Sensor Networks (BSNs) have emerged as a revolutionary technology in many application domains in health-care, fitness, smart cities, and many other compelling Internet of Things (IoT) applications. Most commercially available systems assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments; fusion of the data from multiple, potentially heterogeneous, sensor sources is therefore becoming a fundamental yet non-trivial task that directly impacts application performance. Nevertheless, only recently researchers have started developing technical solutions for effective fusion of BSN data. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on multi-sensor fusion in the area of BSN. This survey discusses clear motivations and advantages of multi-sensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision). The survey also covers data fusion in the domains of emotion recognition and general-health and introduce relevant directions and challenges of future research on multi-sensor fusion in the BSN domain.

BodyCloud: A SaaS approach for community Body Sensor Networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behavior surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.

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.

A Java-Based Agent Platform for Programming Wireless Sensor Networks†

Wireless sensor networks (WSNs) are emerging as powerful platforms for distributed embedded computing supporting a variety of high-impact applications. However, programming WSN applications is a complex task that requires suitable paradigms and technologies capable of supporting the specific characteristics of such networks which uniquely integrate distributed sensing, computation and communication. Mobile agents are a distributed computing paradigm based on code mobility that has already demonstrated high effectiveness and efficiency in IP-based highly dynamic distributed environments. Due to their intrinsic characteristics, mobile agents may provide more benefits in the context of WSNs than in conventional distributed environments. In this paper we present the design, implementation and experimentation of MAPS (Mobile Agent Platform for Sun SPOT), an innovative Java-based framework for wireless sensor networks based on Sun SPOT technology which enables agent-oriented programming of WSN applications. The MAPS architecture is based on components that interact through events. Each component offers a minimal set of services to mobile agents that are modeled as multi-plane state machines driven by ECA rules. In particular, the offered services include message transmission, agent creation, agent cloning, agent migration, timer handling and easy access to the sensor node resources (sensors, actuators, input switches, flash memory and battery). Agent programming with MAPS is presented through both a simple example related to mobile agent-based monitoring of a sensor node and a more complex case study for real-time human activity monitoring based on wireless body sensor networks. Moreover, a performance evaluation of MAPS carried out by computing micro-benchmarks, related to agent communication, creation and migration, is illustrated.

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.

SPINE: a domain-specific framework for rapid prototyping of WBSN applications

Wireless body sensor networks (WBSNs) enable a broad range of applications for continuous and real‐time health monitoring and medical assistance. Programming WBSN applications is a complex task especially due to the limitation of resources of typical hardware platforms and to the lack of suitable software abstractions. In this paper, SPINE (signal processing in‐node environment), a domain‐specific framework for rapid prototyping of WBSN applications, which is lightweight and flexible enough to be easily customized to fit particular application‐specific needs, is presented. The architecture of SPINE has two main components: one implemented on the node coordinating the WBSN and one on the nodes with sensors. The former is based on a Java application, which allows to configure and manage the network and implements the classification functions that are too heavy to be implemented on the sensor nodes. The latter supports sensing, computing and data transmission operations through a set of libraries, protocols and utility functions that are currently implemented for TinyOS platforms. SPINE allows evaluating different architectural choices and deciding how to distribute signal processing and classification functions over the nodes of the network. Finally, this paper describes an activity monitoring application and presents the benefits of using the SPINE framework. Copyright

BodyCloud: Integration of Cloud Computing and body sensor networks

Spatially distributed sensor nodes can be used to monitor systems and humans conditions in a wide range of application domains. A network of body sensors in a community of people generates large amounts of contextual data that requires a scalable approach for storage and processing. Cloud computing can provide a powerful, scalable storage and processing infrastructure to perform both online and offline analysis and mining of body sensor data streams. This paper presents BodyCloud, a system architecture based on Cloud Computing for the management and monitoring of body sensor data streams. It incorporates key concepts such as scalability and flexibility of resources, sensor heterogeneity, and the dynamic deployment and management of user and community applications.

Agent-oriented smart objects development

The Internet of Things (IoT) term is recently emerging to envision a global infrastructure of networked physical objects. As different definitions of IoT do currently exist, we specifically refer to IoT as a loosely coupled, decentralized system of smart objects (SOs), which are autonomous physical/digital objects augmented with sensing/actuating, processing, and networking capabilities. SOs are able to sense, log, and interpret information generated within themselves and around the neighboring external world where they are situated, act on their own, cooperate with each other, and exchange information with humans. The development of a IoT based on SOs raises many issues involving hw/sw system architecture and application development methodology. A few approaches (e.g. FedNet, UbiComp, Smart Products) have been to date proposed to support the vision of an SO-based IoT infrastructure. In this paper we first discuss the suitability of the agent paradigm and technology to effectively support the development of such an IoT infrastructure and then propose a multi-layered agent-based architecture for the development of proactive, cooperating and context-aware smart objects. Our architecture takes into account a wide variety of smart objects, from reactive to proactive, from small to very large, from stand-alone to social. The implementation phase can be based on multiple agent languages and platforms (JADE, JADEX, LEAP, MAPS) atop heterogeneous computing systems (computers, smartphones, and sensor nodes).

A flexible building management framework based on wireless sensor and actuator networks

Future buildings will be constantly monitored and managed through intelligent systems that allow having information about the building health, keeping a good comfort level for the building inhabitants and optimizing the energy spent. Despite many WSN programming frameworks have been to date developed and, in some cases, applied to support monitoring of buildings, none of them possesses all the specific features needed to develop WSN-based building applications. In this article a multi-platform domain specific framework based on Wireless Sensor and Actuator Networks (WSANs) for enabling efficient and effective management of buildings is presented. The proposed Building Management Framework (BMF) provides powerful abstractions that capture the morphology of buildings to allow for the rapid development and flexible management of pervasive building monitoring applications. The functionalities of the framework are shown in an emblematic case study concerning the SmartEnergyLab that is an effective operating scenario related to the monitoring of the usage of workstations in laboratories and offices. Finally, a performance evaluation of a WSAN running the BMF in terms of network usage and system lifetime is shown.