Extending the battery lifetime of wearable sensors with embedded machine learning

© 2018 IEEE. Smart health home systems and assisted living architectures rely on severely energy-constrained sensing devices, such as wearable sensors, for the generation of data and their reliable wireless communication to a central location. However, the need for recharging the battery regularly constitutes a maintenance burden that hinders the long-term cost-effectiveness of these systems, especially for health-oriented applications that target people in need, such as the elderly or the chronically ill. These sensing systems generate raw data that is processed into knowledge by reasoning and machine learning algorithms. This paper investigates the benefits of embedded machine learning, i.e. executing this knowledge extraction on the wearable sensor, instead of communicating abundant raw data over the low power network. Focusing on a simple classification task and using an accelerometer-based wearable sensor, we demonstrate that embedded machine learning has the potential to reduce the radio and processor duty cycle by several orders of magnitude; and, thus, substantially extend the battery lifetime of resource-constrained wearable sensors

Adaptive Security in ODMAC for Multihop Energy Harvesting Wireless Sensor Networks

Energy Harvesting Wireless Sensor Networks (EH-WSNs) represent an interesting new paradigm where individual nodes forming a network are powered by energy sources scavenged from the surrounding environment. This technique provides numerous advantages, but also new design challenges. Securing the communications under energy constraints represents one of these key challenges. The amount of energy available is theoretically infinite in the long run but highly variable over short periods of time, and managing it is a crucial aspect. In this paper we present an adaptive approach for security in multihop EH-WSNs which allows different nodes to dynamically choose the most appropriate energy-affecting parameters such as encryption algorithm and key size, providing in this way energy savings. In order to provide evidence of the approach's feasibility in a real-world network, we have designed and implemented it as extension of on-demand medium access control (ODMAC), a receiver-initiated (RI) MAC protocol specifically designed and developed to address the foundational energy-related needs of Energy Harvesting Wireless Sensor Networks