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Demo Abstract: Software-defined Wireless Charging ofInternet of Things using Distributed Beamforming
Ufuk MuncukNortheastern [email protected]
Subhramoy MohantiNortheastern [email protected]
Kubra AlemdarNortheastern [email protected]
M. Yousof NaderiNortheastern University
Kaushik R. ChowdhuryNortheastern University
ABSTRACTSmart homes will compose of multiple sensors that will sense,compute and transmit information to a central cloud, all ofwhich are energy consuming tasks. We propose to demon-strate a software-defined solution for wirelessly charging thesesensors using RF energy, thereby extending their lifetimes.In our demo, the actions of more than one energy transmit-ter (ET) are synchronized in phase and frequency in realtime using periodic feedback from the target sensor, butwithout any common clock reference. The controller se-lects the optimal subset of ETs to satisfy the energy re-quest from a given sensor, which cooperatively beamformRF energy towards that sensor. Our software-defined frame-work, implemented in Python, allows the central controllerto automatically discover the installed sensors, obtain energyneeds, and schedule charging tasks in an asynchronous andnon-blocking manner that allows the network to scale. Thedemonstration includes advancements in design and fabri-cation of RF energy harvesting circuits that interface withthe TI EZ430 sensors, implementation of a software-definedcontrol and data plane, as well as a real-time distributedbeamforming algorithm on USRP radios that results in abattery-free network of sensors.
1. INTRODUCTIONBattery-operated sensors and Internet of Things (IoT) are
enhancing the quality of human experience and safety- re-lated measures when deployed in homes, transportation andmanufacturing floors. The operational state of such sensorsis heavily dependent on the continuous supply of energy.Periodic battery replacement becomes a costly maintenancetask in these scenarios, apart from introducing undesirabledowntimes. We address this problem by using contactlessand on-demand RF-charging [2], and further improve thescalability of powering a network of sensors through an au-tomated software-defined control plane. This demo presentsan implementation showing the various steps of the networkoperation, including device discovery, selection of energy
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DOI: http://dx.doi.org/10.1145/2994551.2996545
Figure 1: Software-defined Wireless Charging ofIoTs Architecture
transmitters (ETs), and coordinated digital beamformingso that the energy is transferred to the sensors efficientlywithout increasing the interference to neighboring devicessignificantly.
An overview of the demonstration network is shown inFigure 1. It is composed of multiple ETs, each of which isa universal software radio peripheral (USRP) N210 device.A number of TI EZ430 sensors integrated with our custom-designed passive energy harvesting board are deployed inthe same space. In addition, there is a controller node thatruns the ET/sensor registration and task scheduling algo-rithms. All network components connect to a WiFi networkthrough an access point. The sensor provides direct 1-bitfeedback broadcasts, which is used by the ETs to continu-ously tune the phase and frequency of their continuous waveRF emissions. Furthermore, the controller can selectivelyactivate ETs (or power them down) for energy transfer orassign a wholly different task like reporting back receivedsignal strength values from the local environment. A novelfeature of our control plane design is that it is built uponPython’s Twisted framework that allows scaling the networkto very large number of sensors through the asynchronousand non-blocking message flow between the various devices.This design choice helps the control plane to achieve unin-terrupted query resolution and makes the operation robustin failure scenarios.
2. SYSTEM DESIGN ANDIMPLEMENTATION
2.1 Software-defined Control PlaneThe Python-based control plane uses the Twisted frame-
work, which uses a single event loop to cycle through tasks
Figure 2: Setup for software-defined wireless charging of IoTs
Figure 3: Received energy com-parison according to number ofETs and usage of Beamformingscheme
Figure 4: Charging time com-parison according to number ofETs and usage of Beamformingscheme
in small time units instead of branching and threading. Thecontroller accepts registration beacons emitted by sensorsand ETs, as they enter in the network over WiFi. Each bea-con contains not only the unique device ID, but also a de-scription of the functions supported by the discovered devicein a pre-determined JavaScript Object Notation (JSON) for-mat. This is especially useful for the ETs, and future proofsthe system design, i.e., as newer and enhanced capabilitiesget incorporated into ETs, we only need to extend the JSONdefinitions. We modified the UHD driver for the USRP de-vices to automatically transmit these beacons, if the deviceis not already registered. Twisted runs two reactor loops atboth ends of the ET/sensor-controller connection, allowingeach device to accept higher level directives as and whenthey arrive, while also allowing the controller to receive en-ergy requests from sensors, and also forward user- configura-tion requests to any of the network devices. The controllerthen executes remote procedural calls, wherein it invokesspecific functions in the ETs (such as tuning the center fre-quency and starting the beamforming algorithm) based onthe incoming energy requests.
2.2 Beamforming AlgorithmThe demo begins with a sensor sending a request for en-
ergy (see Figure 2) to the controller, which assigns twoUSRP N210 radios to perform the ET role. The ETs orga-nize themselves into a virtual antenna array and focus theirtrans- mission energy in the direction of the sensor, such thatthe emitted waveforms add up constructively at the targetsensor. The energy harvesting circuit converts the incidentRF energy into DC voltage stored in the capacitor. The sen-sor estimates the received signal strength (RSS) of the netincoming signal and broadcasts a single bit to all the ETsto indicate whether this value is higher or lower than thatmeasured in the previous time slot. If the RSS is higher,the ETs update this information and perturb their phasesetting using the last setting as the baseline. If the RSS islower, the ETs revert their phase selection to that of theprevious time slot, before beginning the subsequent roundof phase perturbation. This randomized ascent procedureis repeated until the ETs converge to phase coherence [1].This significantly improves the energy efficiency: an N-nodebeamforming virtual array transmitting at fixed power canattain RSS in the order of N2 . From our experiments, wefound that with 2 ETs, the RSS at the sensor, at a distanceof 1.3 meters from the ETs was 0.019W. The RSS changedto 0.04W with 3 ETs. For demo purposes, we set a thirdUSRP radio near the sensor as an energy receiver to confirmthe RSS readings at the sensor.
2.3 Energy Harvesting CircuitThe RF energy harvesting circuit contains four compo-
nents: antenna, impedance matching network sub-circuit, 4-stage diode-based Dickson voltage rectifier, and 3300µF ca-pacitor for energy storage, as seen in Figure 2. We use 6 dBidirectional PCB-printed antenna whose response lies withinthe energy reception frequency. The impedance matchingnetwork sub-circuit maximize the energy transfer and min-imizes power reflection between the antenna and voltagerectifier. For efficient DC conversion, we design a 4-stagediode-based Dickson voltage rectifier by choosing the schot-tky diode that operates with quick activation time and lowerforwarding voltage drop [3]. The 3300µF capacitor storesenergy from the voltage rectifier, and provides energy tothe TI EZ430 sensor. As shown in Figure 4, the conver-sion efficiency depends upon the accurate phase matchingof the ETs and the circuit design. Once the voltage acrossthe capacitor reaches 3.6V, the sensors disconnect from thecharging phase and resume their normal operation.
3. RESULTS AND CONCLUSIONSOur experiments with two ETs and one TI EZ430 sensor
show that all ETs synchronize their phase during beamform-ing over time (see Figure 3). Upon synchronization, we ob-serve that a the sensor node begins to gain back the energylost during network communication (see Figure 4) owing tothe constructive addition of the incoming RF signals. Thischarging approach extends the lifetime of sensor devices byreplenishing the charge in the battery and can ideally leadto eliminating batteries completely in IoT deployments.
4. ACKNOWLEDGMENTSThis work is partially supported by the funds available
through the US National Science Foundation award CNS1452628.
5. REFERENCES[1] R. Mudumbai, J. Hespanha, U. Madhow, and
G. Barriac. Distributed transmit beamforming usingfeedback control. IEEE Transactions on InformationTheory, 1256(1):411–426, January 2010.
[2] M. Naderi, P. Nintanavongsa, and K. R. Chowdhury.RF-MAC: A medium access control protocol forre-chargeable sensor networks powered by wirelessenergy harvesting. IEEE Transactions on WirelessCommunication, 13(7), July 2014.
[3] P. Nintanavongsa, U. Muncuk, D. R. Lewis, and K. R.Chowdhury. Design optimization and implementationfor rf energy harvesting circuits. IEEE Journal onEmerging and Selected Topics in Circuits and Systems,2:24–33, March 2012.
