Context Recognition of Humans and Objects
by Distributed Zero-Energy IoT Devices
Teruo Higashino, Akira Uchiyama, Shunsuke Saruwatari
Hirozumi Yamaguchi and Takashi Watanabe
Graduate School of Information Science and Technology
Osaka University, Suita-shi, Osaka 565-0871, Japan{higashino, uchiyama, saru, h-yamagu, watanabe}@ist.osaka-u.ac.jp
Abstract—Understanding humans and its environmentis a key enabler of smart, intelligent applications andservices for a future smart society. To deploy such services
in our ambient environment, it is expected to fully utilize
battery-less and maintenance-free IoT devices andtechnologies for more ambient, distributed computing. In
recent years, Wi-Fi-based communications are becoming
more energy-efficient, and channel state information (CSI)
has the potential to sense more detailed information about
the things in the real world. Besides, ambient backscatterhas appeared as a promising technology for zero-energy
sensing and communications. Leveraging those state-of-theart technologies, energy harvested IoT devices for context
recognition of humans and objects will be in reality. A
significant challenge is how to make use of inferior, lesspowerful zero-energy IoT devices to achieve processing ofinterest, i.e., accurate recognition of humans and objects,
while a single device does not work. Therefore, we consider
orchestrating distributed tiny IoT devices for both sensing
and communications. Particularly, distributed machinelearning in the local environment will achieve highly
promising sensing in our ambient environment. In thispaper, we survey the state-of-the-art technologies for zeroenergy sensing and communications in the context ofhumans and objects sensing and recognition. Then, we
address the challenges to be tackled in terms of such
distributed, intelligent sensing using zero-energy devices.
Finally, we introduce the concept of utilizing distributed
IoT devices, followed by the statement about our ongoingwork toward future zero-energy sensing and processing.
Keywords—zero-energy
communication; context
information

IoT
devices;backscatterrecognition; channel state

I. INTRODUCTION
Recently, a variety of methodologies and technologies toaddress the issues in smart and connected communities (S&CC)
have been actively studied [1] [2]. Besides, in the context of theInternet of Things, it is said that there exist 1.5 trillion objects
in the real world and that only 0.6% of the objects are connectedto the Internet [3]. With a more enhanced capacity of theInternet connection of those IoT devices in future,
unpredictable amount of sensing data will be generated, andmore intelligent, smart data processing (such as AI) will be
expected to mine significant knowledge and values from the big
data. Especially, understanding humans and its environment isa key enabler of smart, intelligent applications and services fora future smart society.
To deploy such services in our ambient environment, it isexpected to fully utilize battery-less and maintenance-free IoT
devices and technologies for more ambient, distributed
computing. In general, IoT devices spend energy for sensing,computation and communication. Sensing can be executed in
the order of µW up to tens of µW. However, conventional
wireless communication consumes several tens or several
hundreds of mW for amplifying a radio signal. Even BLE
consumes the order of mW. To this end, ultra-low power
communication technologies are essential. In recent years, a
new wireless communication technology called ambient
backscatter communication [4][5][6] is being developed,
which can reduce the power consumption to about 1/10,000(about 10 µW) by utilizing radio waves in the wild such as TV
and Wi-Fi. In the ambient backscatter communication, bychanging the resistance values of the antenna, the device cancontrol the radio waves that propagate in the environments.
Using this feature, we can transmit 0/1 bit signals with zeroenergy. Originally, the backscatter communication is atechnology for RFID. RFID communicates by transmitting acontinuous carrier wave from a reader and each RFID tag sends
back its ID by backscattering received carrier waves. Since thecarrier waves are generated externally, the RFID tag only needs
to change the impedance by controlling ON/OFF of RF switch,and the controller consumes almost no power. While traditional
backscatter uses continuous carrier waves, ambient backscatter
uses radio signals emitted by surrounding equipment/devices.
As shown in Fig. 1, recently we can combine general Wi-Fi
communication with Wi-Fi-based ambient backscatter
communication (for details, see Section IV.A).
If we can develop a mechanism to detect motion andenvironment of humans and objects (e.g. walking, stay, sitdown, object movement, temperature change, and shapechange) by radio waves fluctuation using ambient backscatter
technologies, it will contribute to zero-energy sensing of
humans and objects. In recent years, Wi-Fi-based ambient
backscatter is able to transmit and receive data in several tenschildren, (v) grasping wind and ground fluctuation of sloping
lands in disasters. We are working toward context recognition
that will be mandatory in our connected societies. It is alsosignificant to deploy design and development support. The goalof this paper is to promote such research activities that
contribute to ambient, zero-maintenance human and object
sensing. We will discuss the issues mentioned above presenting
our ongoing research on zero-energy communication anddistributed sensing.
II. RELATED WORK

Fig. 1. Ambient Backscatter Communication[64]of meters with several Mbps. Some recent RFID technologies
enable several meters of transmission. Besides, sensing devices
using only harvested energy have been investigated anddeveloped, and they can be used for human and object sensing.
We have also been developing human sensing technologies ina variety of contexts and services [7][8][9][10][11][12][13].
The context recognition technologies for humans andobjects using energy-efficient or zero-energy sensors have been
actively investigated and presented at top-tier ubiquitous andpervasive conferences such as UbiComp and PerCom, andpublished in journals such as Pervasive and Mobile Computing
and Proc. of the ACM on Interactive, Mobile, Wearable andUbiquitous Technologies (IMWUT). On the other hand, most of
the existing sensing technologies using backscatter
communication or similar zero-energy technologies are still inprogress. Particularly, they are focusing on the detection of thepresence of humans, etc., which is not comparable with thoseusing powerful sensors. To complement this poor
computational capability, we believe distributed processing
where tiny IoT devices are united is promising. With nicely
designed protocols and algorithms, we can execute even neuralnetworks for machine learning. We have already proposed thedesign concept of such distributed machine learning in [7],where tiny IoT devices are operated in a distributed fashion to
run Convolutional Neural Networks (CNN). We will later
introduce this concept.
In this paper, we survey the technologies for context
recognition of humans and objects using the state-of-the-art
ambient backscatter communication technologies, channel state
information (CSI) and other wireless sensing technologies. We
also address issues to be discussed for distributed processing by
coordination of IoT devices, each of which is energy-harvested.
We consider the following plausible examples of context
recognition of humans and objects that should be realized in thenear future by using zero-energy IoT devices with ambient
backscatter communication or some other technologies. They
are, for example, (i) elderly monitoring at nursing home or theirhomes, (ii) activity recognition of athletes, (iii) tracking human
trajectories and detecting intrusion of wild animals, (iv)construction of sociograms to visualize the friendship among

Here, we survey the latest technology for ambient
backscatter communication, channel state information (CSI),
and human/object context recognition using IoT devices.
A. Ambient backscatter communication
The study of ambient backscatter communication [4][5][6]has two directions. The first direction is to borrow existing radio
signals for energy harvesting communication purposes. For
example, there are methods using radio signals from TV
broadcasting and mobile communication [6][14], Wi-Fi signals[15][16][17], LoRa signals [18], Bluetooth signals [19] and FM
broadcasting signals [20]. Ref. [14][16] address speeding up of
Wi-Fi backscatter. Especially, BackFi [16] has succeeded toachieve high throughput backscatter communication using WiFi signals by making an access point having the in-band fullduplex capability [21][22].
The second direction is to generate ambient backscatter
packets that can be received by existing wireless communication
devices such as Wi-Fi and LoRa. By replacing radio signals usedin the above first direction with those emitting from a plug-in
device that transmits continuous waves, it is possible to generate
packets receivable by the existing radio communication devices.
Ref. [23][24] have successfully generated Wi-Fi packets and
LoRa packets in backscatter communication, respectively. Also,inter-technology backscatter is succeeded (e.g., the generationof Wi-Fi packets using radio signals from Bluetooth [18], andthe generation of ZigBee packets using radio signals from WiFi [17]). Ref. [25][26] have realized new backscatter-based
communication methods such as backscatter video [25] andvoice [26], NetScatter [27] supports a massive number of
ambient backscatter nodes, and Ref. [28] has proposed in-bandfull-duplex communication between ambient backscatter nodes.
B. Wireless Sensing
In recent years, wireless sensing technology for sensing
various objects by using radio waves without using sensors has
attracted attention. Wi-Fi Channel State Information (CSI) is an
emerging approach for wireless sensing, which is widely
applicable to various types of sensing. Several works haveproposed congestion estimation and people counting by feeding
CSI-based features into estimation models built by machinelearning and/or deep learning. For example, in [29], the feature
quantity called Percentage of nonzero Elements (PEM) is
defined, the magnitude of the fluctuation in the propagation pathof radio waves is quantified, and the number of people in theroom is estimated based on the Gray model. Also, FreeCount[30] realizes the number of people in the room by using Transfer

Kernel Learning (TKL), and achieves approximately 96%
accuracy in the classification of 0 to 7 people. CSI is also used
for sensing movement of various objects. Ref. [31] uses CSI toestimate state changes of everyday objects (e.g., door
open/close). WiAG recognizes gestures by using CSI [32].
Similarly, Ref. [33] proposes sign language recognition by CSI
and CNN. Furthermore, Ref. [34] achieves an estimation of
keystrokes on laptops by learning CSI change due to thedifferent movement of fingers for different keystrokes. Recently,
CSI is also used for fall detection [35].
Backscattering RF signal is another emerging trend of
wireless sensing. Printed Wi-Fi [36] is the first concept of truly
battery-less sensors fabricated by a 3D printer by combining
plastic and conductive filament materials. Surprisingly, theprinted battery-less sensors can communicate with Wi-Fi
devices by translating physical movement (e.g., the flow ofwater) into the backscattered Wi-Fi signal. To do this, Printed
Wi-Fi leverages mechanical movements of gears, etc. andcreates the change of the antenna impedance which results in thebackscattered signals. To recognize and count repetitive motions
such as squats and steps, Motion-Fi [37] leverages repetitivechanges of RF signal from backscattering tags. To avoid
interference of backscattered signals, Motion-Fi controls thefrequency shift in the backscattered signals. Also, Word-Fi [38]recognizes handwriting letters by leveraging the backscatteringtags developed in Motion-Fi [37].
C. Context Recognition of Humans and Objects
Context recognition is one of the key technologies for a
future smart society. For example, various ubiquitous
technologies have been proposed for elderly people [39]. The
first step to support elderly people is context recognition such asactivities [40], fall detection [41], daily living patterns [42], andso on. Human Activity Recognition (HAR) has attracted a large
number of researchers [43]. Smartphones are used for mapping
our real social networks [44]. Ref. [45] leverages wireless sensor
networks for landslide monitoring and detection. Ultra-WideBand (UWB) combined with CNN is fully exploited for animal
intrusion detection which is able to classify humans and animals[46]. Ref. [47] proposes a method to infer social/organizational
relationships among occupants in a building from plug loadenergy consumption sensors. Ref. [48] analyzes the impact ofdifferent resolutions of occupancy count profiles estimated bycameras and Wi-Fi sensors on the accuracy of building
performance simulation.
III. DISTRIBUTED PROCESS ON ZERO-ENERGY IOT DEVICES
Here, we describe issues to be considered in the constructionof zero-energy IoT devices and distributed processing on such
IoT devices, and their solutions.
A. Construction of Zero-Energy IoT Device Networks
With the emerging progress of MEMS technology, it has
become relatively easy to develop energy harvesting IoT devices.
RFID tags are also used as zero-energy devices since they canharvest energy from RF emitted by RFID readers. Also, many
researchers have been working on passive sensing which relieson plug-in infrastructure devices with energy supply. However,

due to the limitation of energy harvesting, each IoT device has
limited capabilities in terms of sensing, processing, andcommunication. For example, the only capability of an IoT
device may be backscattering RF signal which does not requirea large amount of energy. To overcome such limitations, the key
is distributed processing among multiple IoT devices.
As shown in Fig. 2(a), by attaching multiple RFID tags to ahuman body, the skeleton of the person is captured by analyzing
signals backscattered from the tags. In general, sensing
capabilities of individual sensors are not so high since many of
low power sensors return limited information such as the tag’s
ID. On the other hand, by connecting a large number of theseenergy harvested sensors to the network, it is possible toconstruct a system that estimates the movement of the skeleton
with high accuracy. Furthermore, by attaching multiple RFID
tags to multiple people, the spatial resolution of sensing
increases to identify different targets. Another example is zeroenergy sensing by backscatter-based wireless sensing. Similar toprinted Wi-Fi, we may be able to translate change of temperature
into the change of antenna impedance by using a bimetallic
switch which changes its state (ON/OFF) according to theambient temperature (see Fig. 2(b)). Since the change of antenna
impedance can be directly captured by observing Wi-Fi signals
backscattered from IoT devices, zero-energy sensing isachievable. To widen the target area of sensing, multiple IoT
devices are deployed and coordinated accordingly with theassistance of infrastructure.

Fig. 2. Examples of Zero-energy IoT Device Networks

Besides, research and development of learning mechanisms
using energy harvested sensor groups are conducted. In order todevelop such zero-energy IoT device networks and/or energy
harvest sensing systems, it is essential to construct networks
based on a communication scheme with ultra-low power
consumption such as backscatter communication.
B. Distributed Processing on Zero-Energy IoT Device
Networks
In context recognition systems using zero-energy IoT
devices, since the recognition capability of each device is not sohigh, there are cases where a large number of IoT devices must
be arranged in a target area. In such a case, we need to consider(i) what kind of queries are to be sent to multiple IoT devices inthe target area, (ii) what timing those queries should be sent, and(iii) whether all the necessary data can be forwarded efficiently
at a certain period (k times per second, for example),
simultaneously. It is also important to consider a communication
mechanism not to make packets from the multiple IoT devices
collide each other. It is also assumed that it may be necessary toconstruct a mechanism for transmitting and receiving data
concurrently using multiple radio channels. Meanwhile, it istroublesome for a system designer to individually designatethese transmission/reception timing, channel assignment,
recovery procedure when the transmission/reception errors
occur in a given zero-energy IoT device network.

indirectly using radio waves, it is difficult to acquire detailed
physical information of the target. However, it is possible toacquire spatial information easily because of using radio waves.
There is no need to install sensors on humans and objects. By
combining fine detail information of ambient backscatter andsuper multidimensional information brought by coarse grain
spatial information of wireless sensing by deep learning, it
becomes possible to handle fine grain spatial information.

Therefore, if (i) the 3D map and obstacle information of atarget IoT device network, (ii) the required informationcollection cycle, and (iii) the recovery method at the time oferrors are designated, it is desirable that we can devise amechanism to estimate the appropriate information collection
mechanism, automatically generate the necessary informationcollection algorithm, and develop a design development support
environment by using the radio wave propagation evaluation
tool and network simulator together.
In recent years, research on machine learning such as deeplearning has been actively conducted, and attempts to machinelearning on a wireless sensor network have also begun [7][49][51][53][54][55]. Although it is conceivable to transfer all thesensing data to the cloud for machine learning, continuoustransmission of sensing data from all the IoT devices to the cloudincreases communication costs. The power consumption of theIoT devices can be reduced by holding their sensing data asmuch as possible within their own IoT devices andimplementing a learning mechanism within the local IoT device
network. Generally, zero-energy IoT devices are ineffective andeven if they can utilize power supply mechanisms such asenvironmental power generation, there is a high possibility thatmachine learning mechanisms consisting of zero-energy IoT
devices will not function well when communication loadincreases. Multi-layer neural networks have recently attracted a
lot of attention, and Convolutional Neural Networks (CNNs)
have been utilized in many domains. Since wireless sensor
networks (WSNs) continuously create 2D or 3D geographicaldata, we can train CNNs to process such data from WSNs. For
this purpose, it is desirable that we can design a distributed
version of CNNs restricting wireless communication amounts
among wireless sensor nodes and a neuron assignment algorithm
which can naturally handle operations like convolution, poolingand backward propagation in a fully distributed manner,
averaging communication and processing tasks over wirelesssensor nodes.
Since we often need multiple IoT devices for context
recognition of humans and objects, CNNs on WSNs canintegrate ambient backscatter based direct sensing using various
sensors with ultra-low power IoT devices and wireless sensing
based indirect sensing using RSSI and CSI with existing
wireless networks as shown in Fig. 3. Ambient backscatter andwireless sensing are complementary. Ambient backscatter canoverwhelmingly reduce the energy consumption ofcommunication. It also can directly and precisely acquire target
physical information using maintenance-free sensor nodes, buttime and effort for installation on humans and objects remain.
On the other hand, since wireless sensing performs sensing

Fig. 3. Context Recognition on Zero-Energy IoT Device Networks

C. Context Recognition Technology in the Near Future
If people move, radio waves such as WiFi will fluctuate. Bygrasping the fluctuation directly by backscatter communication,
it is possible to create new technology to grasp the behavior ofhumans with higher accuracy than the conventional techniques.
Stimuli-responsive hydrogels exhibiting physical changes in
response to environmental conditions have attracted growing
attention for the past few decades [56]. Recent 3D printers caneasily produce such materials exhibiting physical changes. A
battery-less IoT devices for temperature estimation can beconstructed by constructing a structure that changes the shapeand size according to the temperature change and generates adifferent radio wave fluctuation depending on the shape change.
Furthermore, by creating zero-energy IoT devices that detect
illuminance or by creating zero-energy IoT devices that detectvibration and acceleration using springs, various context
recognition techniques can be created [36][57]. In addition tobattery-less IoT devices, we can also use environmental power
generation technology to gather and use a small amount ofenvironmental energy such as light, vibration, heat and so on incapacitors, and use it for the development of ultra-low power
IoT devices for context recognition of humans and objects.
Using such new technology, we can create several
techniques for context recognition. For example, Ref. [58]presents RF-ECG based on commercial-off-the-shelf (COTS)
RFID, a wireless approach to sense the human heartbeat throughan RFID tag array attached on the chest area in the clothes. Ref.
[7][35] present techniques for detecting fall down of people.
Such techniques can be used for (i) monitoring elderly people’s
sleep and context changes at the elderly facilities. Ref. [59]proposes GRfid, a device-free gesture recognition system by
exploiting phase information of RFID signals with COTS
devices. RF-Kinect in Fig. 2(a) a training-free system whichtracks the body movement in 3D space [60]. Such techniques
can be used for (ii) grasping activities of athletes. Ref. [61]proposes a method to determine the movement direction oftagged object through a Radio Frequency Identification (RFID)based system. Ref. [7] also proposes a method for detecting the
movement of humans using film typed human sensors. Suchtechniques might be able to be used for (iii) grasping the
movement trajectory of people and detecting intrusion of wild
animals. By attaching RFID tags to kindergarten children's
clothes and installing multiple WiFi base stations sending out
WiFi signals that can only reach certain specific areas on playequipment, classrooms, corridors in the kindergarten, each WiFi
base station can collect children’s tag IDs who play together.
Then, we can estimate the friendship of kindergarten’s children
as a graph called sociogram. Some children might interact withvarious friends and others might be isolated. Such relationships
can be represented by (iv) building a sociogram for a target
children group. There are several types of ultra-low power
accelerometers using environmental power. Combining suchdevices and backscatter communication devices, we might beable to construct a monitoring system for (v) grasping windspeeds and ground fluctuation of sloping lands. (vi)Autonomous air conditioning management of commercial
facilities might be also possible.
Although the technology for ambient backscatter andwireless sensing is expected to become promising means toconstruct future smart societies, we need much study forpractical purposes.
IV. OUR CURRENT ON-GOING WORKIn this section, we explain our current on-going work
concerning backscatter communication, channel state
information and context recognition.
A. Integration of Wireless LAN and Ambient Backscatter
We are working on how to integrate ambient backscatter
technology into existing wireless networks. Fig. 4 shows theoverview of the wireless network that we are aiming for. Our
target is wireless networks where IEEE 802.11 nodes andambient backscatter nodes coexist in the same frequency band.
An access point equips the ability of in-band full-duplexing, andcommunicates with PCs and smartphones using IEEE 802.11
signals. IoT devices communicate with the access point withbackscattering the IEEE 802.11 signals. The access pointtransmits and receives data simultaneously using the same
frequency channel, which is performed by utilizing a selfinterference cancellation technique. Therefore, the in-band fullduplexing transmission in wireless communications potentiallydoubles the spectral efficiency relative to the conventional halfduplex operation.
For the integration of IEEE 802.11 and ambient backscatter,
we believe that it is important to provide an environment inwhich the research community easily tests communication
protocols and applications. To this end, we are developing an
open-source ambient backscatter testbed which realizes ZigBeebackscatter in the 2.4 GHz band using commercial-off-the-shelf(COTS) products.

Fig. 4. Integration of Wireless LAN and Ambient Backscatter

There are three reasons for implementing backscatterdevices using ZigBee (IEEE 802.15.4). The first reason is a goodbalance between communication distance and communication
speed. Since IEEE 802.15.4 realizes 250 kbps communication
speed using direct sequence spread spectrum, communication
distance is long due to spread gain. The low-power 250 kbps
communication is suitable for many sensor-based applications.
For example, the communication speed is too slow when LoRa
is used, and the communication distance is short and power
consumption is large when using Wi-Fi for ambient backscatter.
The second reason is that there are many IEEE 802.15.4
development environments which are commercially available.
The transceivers of IEEE 802.15.4 are sold at less than $100,
and many development and analysis tools are provided. On theother hand, the history of LoRa has less history than IEEE
802.15.4, resulting in few transceiver choices. Wi-Fi has many
products that cannot access the physical layer and the MAC
layer, and it is not suitable for research and development. The
third reason is 2.4 GHz band. As mentioned above, we are trying
to integrate wireless LAN and backscatter communication.
Thanks to the long history of 2.4 GHz communication, we caneasily get 2.4 GHz COTS products and build the experimental
environment.
We are developing an experimental environment for
protocol study. Fig. 5 shows the experimental apparatus forFig. 5. Experimental Apparatus for 2.4 GHz Backscatterprotocol study on 2.4 GHz backscatter. The experimental
apparatus is constructed to enable researchers to easily modify
the protocols of the physical layer and MAC layer, and consistsof a continuous wave transmitter, 2.4 GHz backscatter node, andbackscatter receiver. The continuous wave transmitter consistsof a frequency synthesizer and power amplifier. The 2.4 GHz
backscatter node consists of an STM32 Nucleo board [62] andan RF switch. The backscatter receiver consists of an STM32
Nucleo board, a switch capacity filter, an orthogonal transducer,
and a local oscillator. The total cost of these components is lessthan $1,000. The software on STM32 Nucleo board can be
implemented using free online IDE called as mbed compiler [63].
We are also developing a ZigBee backscatter node forapplication researcher: small size (2cm × 4.25cm) and only
support IEEE 802.15.4. Fig. 6 shows the ZigBee backscatternode. The ZigBee backscatter node consists of STM32 CPU, an
RF switch, a chip antenna, and a button battery. The size of thenode is constrained by the button battery and the chip antenna.
We use CR2032 as a button battery which diameter is 2cm. The
chip antenna has restrictions that the area of the ground must be
sufficient and a metal plate must not be near the chip antenna.
The chip antenna has restrictions that the area of the ground mustbe sufficient and there is no characteristic if there is a metal platein the vicinity. The transmitter side of ZigBee backscatter is assame as the continuous wave transmitter in above mentioned
experimental apparatus for protocol study. We can use
commercial IEEE 802.15.4 modules as the receiver side.
In order to integrate wireless LAN and ambient backscatter
communication, it is not just a matter of the hardware testbed.
Since the existing IEEE 802.11 MAC protocol cannot be used
for the integrated networks, it is necessary to design a newcommunication protocol. For example, when backscatter
communication is generated every time wireless LAN
communication is performed, and the communication capacity
is consumed. As a result, the communication performance of thewireless LAN is deteriorated. Additionally, since thecommunication speed of backscatter communication is muchslower than that of wireless LAN, the packet error rate ofbackscatter communication increases when there is not enoughwireless LAN traffic.

In [64], the authors have proposed a MAC protocol ofwireless LAN supporting ambient backscatter. The proposed
MAC protocol utilizes IoT specific features: many applications
have their own constant communication cycles which varydepending on target applications. Only by registering the dataacquisition cycle of each IoT device to the access point, theproposed MAC protocol enables the wireless LAN
communication and backscatter communication to coexist withlow overhead. Scheduling is performed according to thecommunication cycle and band occupation time to reduce theband-utilization efficiency and communication error rate. The
scheduling includes which IoT device's backscatter
communication is permitted, and whether the access point sends
a dummy packet for backscattering.
B. Wireless Sensing
One of our wireless sensing works is congestion estimationin train cars based on Bluetooth RSSI among smartphones [65](Fig. 7). It is natural that human body causes signal attenuation.
However, in train cars, RF is also affected by other complex
factors such as train car body itself, distribution of people,
distance between the phone pair, and so on. This makes the carlevel congestion estimation non-trivial. We have overcome thischallenge by aggregating measurements from multiple users
estimated to be in the same train car. We note that the user
positions are also estimated by relying on RSSI combined withreference nodes whose positions are given. Since doors between
train cars significantly attenuate the signal, our method estimates
car-level positions regardless of congestion levels based on
RSSI measured among users. Then, we estimate car-level
congestion by majority voting weighted by the reliability of
estimated positions. Our method is based on the likelihood
functions for both of the congestion and position estimation.
These functions are built according to our preliminary
experiments. In [65], we have conducted real experiments andachieved car-level positioning with the accuracy of 83%. Also,
we have confirmed that our method achieves the estimation of
three-level congestion (low/medium/high) with the F-measureof 0.82.

Fig. 7.

Fig. 6. ZigBee Backscatter Node for Application Study

Snapshots of Different Congestion Levels

We are also working on wireless sensing utilizing alreadydeployed wireless networks. In [66], we have proposed acongestion estimation using synchronized RSSIs measured byIEEE 802.15.4. First, we have proposed a mechanism tomeasure two kinds of RSSIs: the inter-node RSSI andsurrounding RSSI. The inter-node RSSI is the strength of a radio
signal when a sensor node receives the signal that another sensor
node transmits. The surrounding RSSI is the strength of radio
waves when a sensor node receives signals that other sensor
nodes do not transmit. Both of these RSSIs are strictlysynchronized using “Choco” which is a wireless sensor network
platform utilizing simultaneous transmission [66]. The proposed
method enables us to estimate the congestion just by adding
RSSI acquirement features to IEEE 802.15.4 wireless sensor
networks already-deployed for a different purpose. The
examples of different purpose include structural healthmonitoring and smart meters. Next, we have also proposedcongestion estimation algorithms that estimate the number ofpeople from the inter-node RSSI and the number of devices fromthe surrounding RSSI. An experiment conducted in ourlaboratory has confirmed that the algorithm succeeds to estimate
the number of people with approximately 79% accuracy, witherrors up to two people.
In [8], we have proposed an IEEE 802.11ac explicit
feedback-based channel state information (CSI) learning system.
The proposed CSI learning system automatically collects IEEE
802.11ac CSI feedback frames among a Wi-Fi access point anddevices with Wi-Fi capture interface. A CSI feedback frame
includes compressed angles information, and our system
extracts 624 features from the frame. In the learning phase, theuser inputs label information to the learning system, and thelearning system automatically relates the captured CSI with the
input label information. In the estimation phase, our learning
system infers a label using captured CSI frames at that time. We
have evaluated our learning system for device-free user location
estimation with six patterns: different combinations of the
behavior of a user and the antenna orientation of an access point.
The evaluation results show that our CSI learning systemachieves approximately 96% accuracy for seven positions when
the behavior of the user is walking and the orientations of theantennas have divergence.
C. Deep Learning on IoT device networks
Distributed machine learning is nowadays becoming more
popular. Most of the existing frameworks that support
distributed machine learning adopt a data parallelization concept
where multiple learning instances are launched and the trained
parameters in DNN are aggregated and shared for more
lightweight, faster training [50]. For example, in Distributed
Machine Learning Toolkit (DMTK) framework operated byMicrosoft supports inter-process communication libraries (MPIand ZMQ) for exchanging data block among processes.
Distributed TensorFlow [51] by Google adopts a parameterserver and workers model. ChainerMN, which is an add-on
package, also supports MPI such as OpenMP for the same
purpose. However, the data parallelization model is basically atechnique for a set of parallel, networked servers with richcomputational and networking resources, which is notapplicable to our case.
The fundamental problem which we aim in this paper is how
to manage and utilize data in in-situ environment [52]. We will
have a number of small or tiny edge (or end-user) devices infuture, each of which has very limited capability of computationand communication. But delivering the data stream from thoseedge devices to a remote server is not reasonable. We need toarrange a communication channel, including expensive cellularcommunication, for each sensing location. For example, forremote monitoring and tracking of daily life of elderly, who live
alone in mountainous area (this is a big issue in Japanesesociety), 3G is still an important way of communication due tothe limitation of LTE/4G service in such areas. Consuming themost of the limited channel capacity by delivering raw sensor
data, most of which is useless after training or classification, is
not a feasible solution.
For more ambient, energy-aware processing, we haveproposed a framework called MicroDeep [7]. The key conceptof MicroDeep lies in embedding deep learning (bothtraining/testing functions) onto wireless sensor networks, which
are composed of tiny IoT processors with sensors. Since thosesensors will be embedded in our environment in future,leveraging their processing power and communication devices
will benefit energy-efficient sensing and feedback. Particularly,
additional investment for deep learning is not necessary as tiny
sensor nodes cooperate with each other to conduct the trainingtask.
The idea is to appropriately distribute the neurons of CNN(Convolutional Neural Network) to wireless nodes, each ofwhich has limited processing capability but can have somepower when they are united. We use a distributed version of
CNNs which are designed for this purpose. Normally, thewireless sensors nodes are installed in 2D (or 3D) space and they
are close to each other to form a mesh-like network. We regard
each block of sensing data from those sensor nodes at the sametime as an image data, and apply CNN to each data block. For
this purpose, we assign each sensor node onto XY-coordinates
as shown in Fig. 8. The forward propagation of data that includes
convolution, pooling and fully-connect layers (units) is carriedout based on the assigned coordinates, where a unit executed ona sensor node takes the sensing data owned by theneighboring/surrounding sensor nodes as inputs.

Fig. 8. Assignment of CNN to XY-Coordinates

The backpropagation process is carried out in a distributed
fashion, which is originally designed as a centralized process.
Weights of units are updated independently by each sensor nodeto avoid communication overhead, sacrificing some accuracy.
We conducted two experiments using real data. One is
performed using the temperature data measured in an over1,400m2 lounge space using 50 temperature sensors. The lounge
space is divided into 25 x 17 cells, and the temperature measured
within a cell is associated with its coordinates. Each sensor
measured the temperature every 30 minutes, and we finallycollected 2,961 temperature samples from August 26th toOctober 27th in 2016. We trained CNN to detect discomfort in
the space, and as a result of our MicroDeep, the accuracy was
about 95\% while 97% by the standard CNN with the optimized
hyperparameters. The maximal communication cost comparedwith this standard version is just 13%, which means MicroDeep
can reduce the peak traffic concentrated onto a single node.

Although the current version of Micro Deep uses general 2.4
GHz Wi-Fi, we can reduce the electric power of wirelesscommunication by using ambient backscatter communication.
This is our on-going future work.

(a) with Optimal Parameter Set(b) with Feasible Parameter Set

Fig. 10. Communication Cost of Sensor Node (Motion)

Fig. 9. Prototyped IR-sensor Array for Motion Detection

Another experiment is for narrow space, where a sensor
array is built using thin-, energy-efficient film-type infra-red
sensors with microprocessors. Fig. 9 shows a prototyped IRsensor array. This is used for activity recognition, particularly,
fall detection of elders. Every sensor transfers its measured databy 2.4GHz wireless communication. We have collected 55 gait
samples from five (young) subjects who imitated falling down
behavior of elders. We regard each sample as a stream of 66
images where the frame rate is five frames per second. Although
the walking speed is not uniform among different persons, we
empirically determined two seconds (= 10 frames) window tocover a single passage. We gave 6,610 3D arrays as inputs to
CNN. We used CNN consisting of one convolutional layer, one
pooling layer and two fully-connected layers.
We have compared the following two cases: (a) best
accuracy by using standard CNN with optimal parameter settingand (b) heuristic assignment to maximize the correspondence of
CNN links and WSN links equalizing the number of units
assigned to each sensor node. Fig. 10 shows the communication
costs of the sensor nodes for both cases. We have carried out ten
trials of learning and validation with a randomly split pair oftraining and validation dataset. For each trial, the weightparameters have been randomly initialized. We have evaluatedthe best accuracy by using standard CNN with optimal
parameter setting where the accuracy is 91.875% and the
maximal communication cost becomes 360 as shown in Fig.
10(a). On the other hand, for the case of heuristic assignment tomaximize the correspondence of CNN links and WSN links, wehave found 89.7275% accuracy and the maximal
communication cost becomes 210 as shown in Fig. 10(b). Hence,the performance of our prototype is 2% lower than that of the
optimized CNN while we can achieve 40% mitigation of the
maximal communication cost. For designing energy harvesting
IoT sensor systems, it is very important to equalize the numberof units assigned to each sensor node and to minimize themaximal communication costs of the sensor nodes so that all the
sensor nodes can be alive and work well using a small amountof energy. We believe our approach is one of the promising
approaches for constructing tiny IoT device networks andproviding effective deep learning mechanisms on such tiny IoT
device networks.

V. RESEARCH CHALLENGE
As mentioned above, understanding humans and its
environment is a key enabler for providing smart and intelligent
applications and services in future smart society. In order todeploy such services in our ambient environment, it is essential
to fully utilize battery-less and maintenance-free IoT devices
and their corresponding technologies. A significant challenge is
how to make use of inferior, less-powerful zero-energy IoT
devices to achieve processing of interest, i.e., accurate
recognition of humans and objects, while a single device does
not work. We need to make such distributed tiny IoT devices
orchestrate for both sensing and communications.
From the communication point of view, it is important to beable to successfully use general wireless communication such as
WiFi and ZigBee and ambient backscatter communication inparallel as shown in Fig. 1. In general, there are cases where
many zero-energy IoT devices need to be placed in a target area.
In such a case, it is important to avoid the collision ofcommunication IoT devices. If we use multiple channels inparallel, we need to consider practical channel assignments
among those IoT devices according to data size, transmission
frequency, and channel quality as well as application
requirements. In a given IoT device network, it is cumbersome
for the system designer to individually specify the setting oftransmission/reception timing, channel assignment and recovery
procedure at the time of transmission/reception errors. It is
important to devise mechanisms to automatically generatenecessary information collection algorithms from (a) 3D map
and obstacle information of a given IoT device network, (b) thenecessary information collection cycle and (c) the recovery
methods when errors occur.
From the sensing point of view, as shown in Section III.B,
distributed processing on zero-energy IoT device networks willachieve highly promising sensing in ambient environments. In
Section IV.C, we show that the development of machinelearning mechanisms on tiny IoT device networks with low
communication costs is an important research issue. Also, thenature of radio wave propagation differs considerably dependingon the location and the presence of obstacles. The efficiency ofmachine learning strongly depends on the initial values and/or
teaching data. The radio wave propagation evaluation tools andnetwork simulators can be used together to generate appropriate
initial values depending on given location environments. A partof tiny IoT devices may be broken. The development of resilient
distributed machine learning mechanisms in the environments
containing such broken IoT devices is also important.

VI. CONCLUSION
In this paper, we focus on ambient backscatter
communication technology capable of communication using
radio waves in the environments and survey the latest
technology of backscatter communication, wireless sensing andcontext recognition of humans and objects. In general, sinceeach zero-energy IoT device does not have enough recognition
capability by itself, we need to construct an edge computingbased network with a large number of zero-energy IoT devices
in order to acquire diverse sensing information. Here, we present
issues to be considered in distributed processing on zero-energy
IoT device networks and their solutions. we also explain ourcurrent on-going work concerning backscatter communication,
channel state information and context recognition. We show thatAI technology such as Convolutional Neural Networks (CNNs)
on wireless sensor networks (WSNs) can integrate ambient
backscatter based direct sensing with ultra-low power IoT
devices and wireless sensing based indirect sensing withexisting wireless networks in order to create energy harvested
context recognition technology of humans and objects. We hopezero-energy IoT device networks using backscatter
communication become more popular and they will be used forconstructing our smart societies in the near future.
ACKNOWLEDGMENT
The research in this paper is partly supported by JSPS
Grants-in-Aid for Scientific Research (Grant Numbers:
26220001, 18H05393, 19H01102, 16KT0106, 16H01718 and
17KT0042). The research is also partly supported by NICT
research grant (#19103).
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