EnTracked: energy-efficient robust position tracking for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Adaptive GPS duty cycling and radio ranging for energy-efficient localization
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Evolution and sustainability of a wildlife monitoring sensor network
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Sensing through the continent: towards monitoring migratory birds using cellular sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Classification of underwater broadband bio-acoustics using spectro-temporal features
Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems
Energy-efficient localization: GPS duty cycling with radio ranging
ACM Transactions on Sensor Networks (TOSN)
Energy efficient GPS sensing with cloud offloading
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Power management for long-term sensing applications with energy harvesting
Proceedings of the 1st International Workshop on Energy Neutral Sensing Systems
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Long-term outdoor localisation with battery-powered devices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Camazotz platform as an enabler of Multimodal Activity-based Localisation~(MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term flying fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Camazotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in classifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The results show that multimodal activity-based localisation reduces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.