ATHOME: Automatic Tunable Wireless Charging for Smart Home
Abstract:
We present ATHOME, an Automatic Tunable wireless charging framework for smart low-power devices in current and future HOME. ATHOME automatically tunes the charging power of multiple stationary wireless chargers to provide enough energy that can continuously power up smart devices with varying working power pro€les, while minimizing the total charging power to reduce energy cost. To reach this goal, ATHOME €rst solves a hard open problem of calculating the minimum required charging power for powering up a device with limited energy storage size and varying power pro€le. Based on the minimum charging power obtained for each device, ATHOME then provides an optimal solution with polynomial-time complexity that automatically tunes the charging power of all chargers in a real-time fashion, which enables all devices to work continuously while minimizing total charging power. We implement ATHOME on a WISP platform with 8 rechargeable nodes. Experiments demonstrate that ATHOME provides sucient and tight charging power that enables devices to work continuously.
EXISTING SYSTEM:
Rechargeable sensor networks research has received much a.ention recently due to its wide application domains [11, 15, 17, 19–23, 25, 30, 32]. Based on the types of chargers deployed in the network, rechargeable sensor networks can be divided into two categories: mobile charger-based networks and stationary charger-based networks. Several mobile charger scheduling algorithms were studied to sustain the lifetime of wireless sensor networks [12, 20, 30]. Xie et al. [30] adopted a mobile charger traveling policy within the network for charging sensor nodes continuously. While traveling within the network, the charger makes a number of stops and charge sensor nodes near those stops. By satisfying certain constraints, none of the sensor nodes in the network will ever run out of energy. In [20], the authors make an assumption that the charging time for each sensor node is much longer than the mobile charger’s traveling time, and thus the spatial aspects of the problem is ignored by the scheduling algorithms. In [12], He et al. divide the sensor nodes into di.erent groups, and apply TSP algorithms to recharge nodes within each group. Fu et al. [11] solved this problem by planning the optimal movement strategy of the RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. .eir study provides reasonable performance with the assumption that sensor nodes do not operate during charging period. .erefore, they are not suitable for practical usage of sensor nodes because nodes can be charged while operating.
PROPOSED SYSTEM:
We present ATHOME, an Automatic Tunable wireless charging framework for smart embedded devices in current and future smart HOME. ATHOME automatically tunes the charging power of multiple stationary wireless chargers to provide enough energy that continuously powers up smart embedded devices with varying working power pro€les, while minimizing the total charging power to reduce energy cost. To reach this goal, ATHOME €rst resolves a key open problem due to energy over.ow: how to derive the optimal charging power required to power up a device with energy storage of limited capacity and a varying working power pro€le. A novel analytic geometry method is presented that precisely characterizes the over.owed energy and the energy actually received by a device given a speci€c charging power. A set of novel techniques are proposed to validate whether a given charging power is sucient to enable a device to work continuously, and then a step further, whether it is the minimum charging power a all sucient ones (Sec. 3). .en based on each device’s minimum required charging power and the locations of all chargers and devices, ATHOME provides an optimal solution with polynomial-time complexity that tunes all chargers’ charging power, such that all devices can be powered up continuously while the total charging power provided by chargers is minimized. ATHOME can automatically tune the chargers’ charging power with dynamic device join/leave events in a real-time fashion
CONCLUSION
In this paper, we present ATHOME, which automatically tunes the charging power of multiple stationary wireless chargers to provide enough energy that continuously power up smart devices with varying working power pro€les, while minimizing the total charging power. We implement ATHOME on a WISP platform with 8 rechargeable nodes. Evaluation results based on both experiments and simulations demonstrate that ATHOME provides sucient, tight,and adaptive charging power enables nodes to work continuously.
REFERENCES
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