Mobility increases the capacity of ad hoc wireless networks
IEEE/ACM Transactions on Networking (TON)
Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Controllably Mobile Infrastructure for Low Energy Embedded Networks
IEEE Transactions on Mobile Computing
Data harvesting with mobile elements in wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Bidding Protocols for Deploying Mobile Sensors
IEEE Transactions on Mobile Computing
Path Planning of Data Mules in Sensor Networks
ACM Transactions on Sensor Networks (TOSN)
MobiRoute: routing towards a mobile sink for improving lifetime in sensor networks
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
Repeatable Experiments with Mobile Nodes in a Relocatable WSN Testbed
The Computer Journal
Smart-HOP: a reliable handoff mechanism for mobile wireless sensor networks
EWSN'12 Proceedings of the 9th European conference on Wireless Sensor Networks
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In this paper, we present TrainSense, a novel infrastructure to support the development and testing of mobile sensing applications. TrainSense merges a mote and a model train into a single mobile unit, and enhances the basic model train infrastructure with several important features required for the evaluation of mobile scenarios. First, we develop a real-time controller to send control packets to model trains and motes to manage the network topology. Second, we design and implement a positioning system with centimeter precision. Third, we use the power available on the tracks to provide unlimited energy to the motes. Fourth, we provide a way for the motes to dock into a custom USB port, for reprogramming and data download. We evaluate TrainSense in two ways: (i) we establish the correctness of the implementation and measure the performance of its components, and (ii) we demonstrate its practical use with two sample wireless sensor network application scenarios: self-deployment and data muling.