Distributed recognition of human actions using wearable motion sensor networks
Journal of Ambient Intelligence and Smart Environments
Combining cloud computing and wireless sensor networks
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Starfish: policy driven self-management in wireless sensor networks
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Opportunistic strategies for lightweight signal processing for body sensor networks
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
WH '10 Wireless Health 2010
Proceedings of the 14th Communications and Networking Symposium
Distributed recognition of human actions using wearable motion sensor networks
Journal of Ambient Intelligence and Smart Environments
Editorial: Integration of Cloud computing and body sensor networks
Future Generation Computer Systems
BodyCloud: A SaaS approach for community Body Sensor Networks
Future Generation Computer Systems
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We present an open-source platform for wireless body sensor networks called DexterNet. The system supports real-time, persistent human monitoring in both indoor and outdoor environments. The platform utilizes a three-layer architecture to control heterogeneous body sensors. The first layer called the body sensor layer (BSL) deals with design of heterogeneous body sensors and their instrumentation on the body. At the second layer called the personal network layer (PNL), the body sensors on a single subject communicate with a mobile base station, which supports Linux OS and the IEEE 802.15.4 protocol. The BSL and PNL functions are abstracted and implemented as an open-source software library, called Signal Processing In Node Environment (SPINE). A DexterNet network is scalable, and can be reconfigured on-the-fly via SPINE. At the third layer called the global network layer (GNL), multiple PNLs communicate with a remote Internet server to permanently log the sensor data and support higher-level applications. We demonstrate the versatility of the DexterNet platform via several real-world applications.