Localization in sensor networks
Wireless sensor networks
Training a wireless sensor network
Mobile Networks and Applications
ANSWER: AutoNomouS netWorked sEnsoR system
Journal of Parallel and Distributed Computing
Dozer: ultra-low power data gathering in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Asynchronous training in SANET
Proceedings of the First ACM workshop on Sensor and actor networks
Asynchronous training in wireless sensor networks
ALGOSENSORS'07 Proceedings of the 3rd international conference on Algorithmic aspects of wireless sensor networks
Efficient training of sensor networks
ALGOSENSORS'06 Proceedings of the Second international conference on Algorithmic Aspects of Wireless Sensor Networks
Wireless sensor networks: leveraging the virtual infrastructure
IEEE Network: The Magazine of Global Internetworking
VRAC: virtual raw anchor coordinate routing in sensor networks
WONS'10 Proceedings of the 7th international conference on Wireless on-demand network systems and services
Virtual raw anchor coordinates: a new localization paradigm
ALGOSENSORS'10 Proceedings of the 6th international conference on Algorithms for sensor systems, wireless adhoc networks, and autonomous mobile entities
Collision-free routing in sink-centric sensor networks with coarse-grain coordinates
IWOCA'10 Proceedings of the 21st international conference on Combinatorial algorithms
Virtual raw anchor coordinates: A new localization paradigm
Theoretical Computer Science
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Sensor networks are expected to evolve into long-lived, autonomous networked systems whose main mission is to provide in-situ users - called actors - with real-time information in support of specific goals supportive of their mission. The network is populated with a heterogeneous set of tiny sensors. The free sensors alternate between sleep and awake periods, under program control in response to computational and communication needs. The periodic sensors alternate between sleep periods and awake periods of predefined lengths, established at the fabrication time. The architectural model of an actor-centric network used in this work comprises in addition to the tiny sensors a set of mobile actors that organize and manage the sensors in their vicinity. We take the view that the sensors deployed are anonymous and unaware of their geographic location. Importantly, the sensors are not, a priori,organized into a network. It is, indeed, the interaction between the actors and the sensor population that organizes the sensors in a disk around each actor into a short-lived, mission-specific, network that exists for the purpose of serving the actor and that will be disbanded when the interaction terminates. The task of setting up this form of actor-centric network involves a training stage where the sensors acquire dynamic coordinates relative to the actor in their vicinity. The main contribution of this work is to propose an energy-efficient training protocol for actor-centric heterogeneous sensor networks. Our protocol outperforms all know training protocols in the number of sleep/awake transitions per sensor needed by the training process. Specifically, in the presence of κ coronas, no sensor will experience more than ⌈log κ⌉ sleep/awake transitions and awake periods.