Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Towards a Theory of Context Spaces
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Exploiting mobility for energy efficient data collection in wireless sensor networks
Mobile Networks and Applications
Controllably Mobile Infrastructure for Low Energy Embedded Networks
IEEE Transactions on Mobile Computing
A middleware for context-aware agents in ubiquitous computing environments
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
Multiple controlled mobile elements (data mules) for data collection in sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
On-the-Fly Situation Composition within Smart Spaces
NEW2AN '09 and ruSMART '09 Proceedings of the 9th International Conference on Smart Spaces and Next Generation Wired/Wireless Networking and Second Conference on Smart Spaces
| Hi-index | 0.00 |
With advent of pervasive computing and considerable acceptance of sensor networks, smart sensing techniques and data collection have been topics of interest. This paper presents a smart sensing and data collection technique from sensor networks using context aware high powered mobile objects within the environment. The paper proposes CAM-Ra context aware robot that can move within smart environments sensing new sensor sources and collecting sensory originated data efficiently. Based on these sensed data sources, we propose an extension to context spaces model that builds a virtual model of the intelligent environment. This intelligent environment model built using extended context spaces can be used by number of context aware applications to efficiently query and retrieve data from the sensor network using CAM-Rbased data collection approach. We also present a prototype implementation of CAM-Rbuilt using off-the-shelf hardware and a context based cost function used to compute data collection decisions. We validate our system by implementing the virtual modelling of the intelligent environment based on simulated input obtained from CAM-Rand sensors. We also evaluate CAM-Rby simulating and comparing the energy spent by the sensor nodes during data collection process using our proposed approach and traditional fixed sink based approach.