@conference {34,
	title = {NETWRAP: An NDN Based Real-Time Wireless Recharging Framework for Wireless Sensor},
	booktitle = { IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS {\textquoteright}14)},
	year = {2014},
	abstract = {<p>Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from NDN (Named Data Networking) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.</p>
},
	keywords = {mobile energy replenishing, mobile vehicles, named data networking, perpetual operation, recharge coordination, wireless recharging, Wireless sensor networks},
	url = {http://www.ece.sunysb.edu/~fanye/papers/tmc14-netwrap.pdf},
	author = {Cong Wang and Ji Li and Fan Ye and Yuanyuan Yang}
}
@conference {35,
	title = {Recharging Schedules for Wireless Sensor Networks with Vehicle Movement Costs and Capacity Constraints},
	booktitle = {IEEE International Conference on Sensing, Communication, and Networking (SECON {\textquoteright}14)},
	year = {2014},
	abstract = {<p>Several recent works have studied the schedule for mobile vehicles to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked the important factors of the vehicles{\textquoteright} moving energy consumption and limited recharging capacity. These oversights may lead to problematic schedules or even stranded vehicles. In this paper, we study the recharging schedule that maximizes the recharging profit - the amount of replenished energy less the cost of vehicle movements - under these important constraints. We first derive the minimum number of vehicles needed for energy neutral condition and discover a set of desired network properties. Then we formulate the recharge schedule optimization into a Profitable Traveling Salesmen Problem with capacity and battery deadline constraints, which we prove to be NP-hard. We propose two algorithms to solve the problem. The first one is a greedy algorithm that maximizes the recharge profit at each step; the second one first adaptively partitions the network based on recharge requests, then forms Capacitated Minimum Spanning Tree in each partition followed by route improvements. Finally, we evaluate and compare the performance of proposed algorithms and validate the correctness of theoretical results through extensive simulations. Given a sufficient number of vehicles, the adaptive algorithm can keep the number of nonfunctional nodes at zero. Compared to the greedy algorithm, it reduces the percentage of transient energy depletion by 30-50\% with 10-20\% energy saving on vehicles.</p>
},
	keywords = {adaptive network partitioning, data collection, perpetual operations, vehicle scheduling, Wireless rechargeable sensor networks},
	url = {http://www.ece.sunysb.edu/~fanye/papers/secon14.pdf},
	author = {Cong Wang and Ji Li and Fan Ye and Yuanyuan Yang}
}
