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

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Internet of Things in Agricultural Innovation and Security

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

On the development of mobile agent systems for wireless sensor networks : issues and solutions

Due to the growing exploitation of wireless sensor networks (WSNs) for enhancing all major conventional application domains and enabling brand new application domains, the development of applications based on WSNs has recently gained a significant focus. Thus, design methods, middleware and frameworks have been defined and made available to support high-level programming of WSN applications. However, even though many proposals do exist, more research efforts should still be devoted to the definition of WSN-oriented methodologies and tools fully supporting the development lifecycle of WSN applications. In this chapter, we promote the use of the mobile agent paradigm for the development of WSN applications and, specifically, describe issues and solutions for the development of mobile agent systems on resource-constrained wireless sensor platforms. In particular we discuss about the design of MAPS (Mobile Agent Platform for Sun SPOTs) and TinyMAPS, our Java-based mobile agent systems for WSNs, which enable agent-oriented development of WSN applications. In particular, while MAPS can run on the capable SunSPOT sensor devices, TinyMAPS is a version of MAPS tailored for more constrained Java-based sensor platforms such as Sentilla JCreate. An analysis of MAPS and TinyMAPS is provided showing analogies and differences among the two platforms. Finally a comparison of MAPS with AFME, another Java-based mobile agent system running on SunSPOT and based on a different architecture and programming model, is presented

An autonomic plane for wireless body sensor networks

Wireless Body Sensor Networks (WBSN) have proved to be a potential technology for developing applications that can monitor and control physical and biochemical parameters on the human body. Developing such applications is rather cumbersome, since these have to meet a combination of conflicting requirements. Achieving accuracy, efficiency, correctness, fault-tolerance, adaptability and reliability on WBSN is tricky because these features have to be provided beyond the design/implementation phase, notably at execution time. In this paper we explore the viability and convenience of autonomic computing in the context of WBSNs. In particular, we propose to extend a conventional WBSN framework with an autonomic plane, a way for separating out the provision of self-* properties from the WBSN application logic. This separation of concerns leads to an ease of deployment and run-time management of new applications. We study this approach in the context of SPINE2 framework, showing how this can be readily enhanced with an autonomic layer. We find that this enhancement brings not only considerable functional improvements but also measurable performance benefits

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

An autonomic plane for wireless body sensor networks

Wireless Body Sensor Networks (WBSN) have proved to be a potential technology for developing applications that can monitor and control physical and biochemical parameters on the human body. Developing such applications is rather cumbersome, since these have to meet a combination of conflicting requirements. Achieving accuracy, efficiency, correctness, fault-tolerance, adaptability and reliability on WBSN is tricky because these features have to be provided beyond the design/implementation phase, notably at execution time. In this paper we explore the viability and convenience of autonomic computing in the context of WBSNs. In particular, we propose to extend a conventional WBSN framework with an autonomic plane, a way for separating out the provision of self-* properties from the WBSN application logic. This separation of concerns leads to an ease of deployment and run-time management of new applications. We study this approach in the context of SPINE2 framework, showing how this can be readily enhanced with an autonomic layer. We find that this enhancement brings not only considerable functional improvements but also measurable performance benefits

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

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

A task-based architecture for autonomic body sensor networks

This paper proposes an architecture conceived for supporting rapid design and implementation of Wireless Body Sensor Network (WBSN) applications having autonomic characteristics. WBSNs represent the most suitable systems for monitoring and controlling physical and biochemical parameters on the human body, and thus supporting high-impact applications in a variety of human-centered domains The effectiveness of WBSN applications is a critical issue since their correctness and efficiency have to be assured not only at design/implementation time but notably at execution time. The autonomic computing paradigm can perfectly meet the critical requirements of WBSN applications such as fault tolerance, adaptability, and reliability, due to its fundamental self-* properties. In particular, our solution is based on the SPINE2 framework and its important distinctive feature is an architecture for guaranteeing the separation of concerns between the user-defined application logic and the autonomic-related operations. Our proposal is finally exemplified through a case study concerning the development of an autonomic application for human activity monitoring