ISSN 2186-7437

NII Shonan Meeting Report

No. 2018-6

RESILIENT MACHINE-TO-MACHINECOMMUNICATION
http://shonan.nii.ac.jp/seminar/114/

Stephan Sigg
Mayutan Arumaithurai
Toru Hasegawa
March 26 - 29, 2018

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, JapanRESILIENT MACHINE-TO-MACHINECOMMUNICATION
http://shonan.nii.ac.jp/seminar/114/
Organizers:
Stephan Sigg (Aalto University, Finland)
Mayutan Arumaithurai (University of Goettingen, Germany)
Toru Hasegawa (Osaka University, Japan)
March 26 - 29, 2018

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1

Focus of the meeting and Rationale

Machine-to-machine (M2M) interactions such as wearables, vehicular networks
and smart homes will constitute more than a third of the total internet connections1 . These Internet of Things (aka Industrial Internet, aka Industry 4.0)
networks are rapidly growing in complexity and continuing to extend into thepersonal and private domain. Fuelled by the numerous sensors interconnected,
massive amounts of Big Data need to be managed, routed and processed efficiently, for instance, supported by the network edge or fog computing concepts.
These tremendous device and data amounts envisioned will be supportedby the upcoming 5G wireless systems which shall support data rates of tens of
megabits per second for tens of thousands of connections for massive wirelesssensor networks and 1 Gb per second simultaneously. This becomes possible
through mm-wave communication, flexible spectrum use, massive MIMO andFemtocells.
At the same time, networking technology is shifting towards virtualization,with Software Defined Networking (SDN) and Network Function Virtualization(NFV) likely to change the infrastructure landscape. Networking paradigms
are witnessing a shift from location oriented networking to content/information
orientation (e.g. ICN, NDN,). The cloud concept transforms the Internet to a
network of data centers, featuring computer-to-cloud-to-computer interactions.
The potential benefits of combining the massive environmental perception
based on M2M with the control power available in upcoming network paradigms
is huge, as is the opportunity of number of research issues opened. Some of thepressing research issues are listed below.

1.1

Networking support for M2M

ICN and SDN have been primarily designed for fixed networks but recent work
proposes extensions to wireless networking. While these technologies have thepotential to cater to the needs to M2M based applications, there remains a lotof unresolved issues. This concerns, for instance, the largely unsolved questionof scalability of ICN routing schemes, orchestration of NFV based services, aswell as the location and actual implementation of SDN controllers.

1.2

Wireless support for M2M

5G is envisioned to support M2M scenarios with higher data rate and massive
device count. Dynamic spectrum sharing will be required to support optimally
adapt to different traffic types and highly variable QoS requirements in M2M
scenarios. Further research issues regard cloud radio access networks to move
RAN functionality to the cloud for on-demand creation of cloud-based virtual
mobile networks exploiting NFV. Vehicular communication is envisioned as one
major aspect of M2M.
1 http://www.cisco.com/c/en/us/solutions/collateral/service-provider/
ip-ngn-ip-next-generation-network/white_paper_c11-481360.html

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1.3

Security and Privacy for M2M

Security and access control are key concerns for M2M and expand also to secure
distributed data structures and privacy preserving data distribution schemes
as well as usable security for constrained devices. Attribute-based proxy reencryption could enable group conditioned access control for M2M content, butalso Blockchain holds the potential to serve as a secure distributed databaseacross IoT and Edge devices. Another approach to increase privacy on sharedencrypted data is homomorphic encryption for M2M networks Forward andBackward secrecy gains increased importance with the duration of an M2M
instrumentation.

1.4

Data support for M2M

While the data produced for M2M is exploding, the nature of the data shifts
towards multimedia and video content while at the same time its personal linkis intensifying covering health and fitness related data, emotions and data fromthe private domain. Novel sensing modalities and communication means are
exploited with RF based recognition, visible light communication and Intrabody communication. Resource restricted M2M devices require resource sharing
mechanisms among devices and the network edge. Several technical solutions
have been proposed for this but proper incentives for such collaborative approaches are lacking.

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Meeting Schedule

The meeting was organized over four productive seminar days with a strong
focus on presentations by international experts from academia and industry.
In addition, Tutorials, working groups and breakout sessions were organized
for in-depth discussion on selected topics. The meeting schedule is depicted infigure 1.

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Overview of Talks and discussions

Mobile Augmented Reality
Prof. Yu Xiao, Aalto University, Finland
Prof. Yu Xiao introduces their mobile AR systems, e.g., an AR navigation
system in a supermarket, an AR app for object recognition, an AR gaming
system, and an AR navigation system, and main research challenges for realizing the systems in mobile networks, focusing on difficulties in realizing AR
with the current technologies, such as bandwidth consumption or computing.
She concludes that mobile AR systems, in general, require high frame rate andhigh resolution image processing and transportation via networks, and this requires high bandwidth and low latency communications. The network throughput/bandwidth and latency is critical for mobile AR applications and those of
5G systems are not sufficient.

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25.03.2018

26.3.201827.3.201828.3.2018

7:30

Breakfast

8:00

Breakfast

8:30
9:00

Introduction, 2minintro pitches

9:30
10:00
10:30
11:00
11:30
12:00

Tutorials: (1) Narrowband IoT (2) Adaptation & Self Awaren.

Break

Break

Break

Breakout Discussions(3rd day topics)

Closing
Wrap-up and outlook

Lunch

Lunch

Group Photo

13:30

Applications
Talks (20-30min)

Future Opportunities
Talks (20-30min)

Networking, wireless, security
and data support for M2M

15:00
15:30

Break

16:00

Xiaoming Fu
Breakout Discussions(1st day topics andalong 3-4 verticals)

16:30
17:00

Check-in

17:30
18:00

Dinner

18:30
19:00

Summary of the
Seminar, collaborationand continuation

Future Opportunities
Talks (20-30min)

Lunch

14:30

Networking, wireless, security
and data support for M2M

Breakfast

Break

Lunch

14:00

Challenges
Talks (20-30min)

Seminar theme,
identifying challenges

13:00

12:30

29.3.2018

Breakfast

Breakout Discussions(2nd day topics andalong 3-4 verticals)

Excursion to Kamakura
Great Buddah and Hase
temple, Hachimangu
shrine, sakura path,
Breakshopping/souvenir
Tutorials: (3) IoT comm.
streetstandards & Security
Dirk Kutscher

DinnerBanquetWelcome Banquet
Talks
26.03. 13:30

Speaker

Topic

Networking support
Wireless support

Matthias WaehlischYu Xiao

Security and Privacy

AsokanName to MAC address mapping in NDN
Mobile Augmented Reality
Privacy preserving oblivious neural network and adversarial Machine
Learning

Data support

Keijo Heljanko

Latency-Constency trade-offs in Distributed Databases

27.03. 11:00
Networking support
Wireless support

Ferdinand Peper
Riku Jantti

Security and Privacy

Lars Wolf

Communication protocols for highly restricted nodes
Ambient and Quantum backscatter
PotatoNet - Real-world condition WSN(Challenges and lessons learned)

Paul Lukowikcz

Cyber-Groups - Activity Recognition in Groups

Networking support

Jiannong Cao

Edge Mesh:enabling scalable connectivity and distributed intelligence for IoT

Wireless support
Security and Privacy

Yusheng Ji
Yuki Koizumi

Vehicular edge/cloudPrivacy issues about ICN and IoT

Takuya Maekawa

Recent studies with relatively new sensing modalities(CSI, IR camera, Sound probing)

Data support
28.03. 9:00

Data support

Tutorials, working groups and Talks
Tutorial
Carsten Bormann
Interest group Mayutan Arumaithurai
Christian Becker,
Tutorial Wolfgang Schroeder
Preikschat
Tutorial
Riku Jantti
Talk
Thomas Schmidt
Talk
Xiaoming Fu
Talk
Dirk Kutscher
Interactive
Olga Streibel

Constrained application protocol
Blockchain for M2M applications
Adaption and Self-Awareness in M2M for Predictability
Narrowband IoT
IoT Networking in RIOT
Geosocial Networks

Research Directions for Industrial IoT and Edge Computing
M2M at Bayer

Figure 1: Meeting Schedule4Discussion: Current evaluations of the system were conducted on 4G since
5G is still not in provision, so that further evaluation on 5G might be necessary. Several configurations for LTE systems have been considered and evenin the best case, LTE is insufficient to support HD sharing. In simulations,
VeinsLTE (SUMO and OMnet++) have been utilized and parameters have beenchosen that achieved best performance. The simulator does not support LTE
D2D though. In the Hololens demonstration, images were fetched from the
FrameReader and converted into jpeg images, which are then sent to the server
for processing. Coding technologies will be considered in the future. Regarding wireless support for AR, 5G NR and WiGig capacity should be enough to
support image transmission even in dense deployments.

The Need for a Name to MAC Address Mapping in NDN:
Towards Quantifying the Resource Gain
Prof. Matthias Wählisch, Freie Universität Berlin, Germany
In this talk, we start from two observations. First, many application scenarios that benefit from ICN involve battery driven nodes connected via shared
media. Second, current link layer technologies are completely ICN agnostic,
which prevents filtering of ICN packets at the device driver level. Consequently,
for any ICN packet, interest as well as data, is processed by the CPU. This sacrifices local system resources and disregards link layer support functions such as
wireless retransmission. We argue for a mapping of names to MAC addresses
to efficiently handle ICN packets, and start exploring dynamic face-based mapping schemes. We analyze the impact of this link-layer adaptation in real-world
experiments and quantitatively compare to different configurations. Our findings on processing, reliability, and energy consumptions on constrained devices
indicate significant gains in larger networks.
Further Reading:• P. Kietzmann, C. Gündogan, T. C. Schmidt, O. Hahm, and M. Wählisch,“The Need for a Name to MAC Address Mapping in NDN: Towards
Quantifying the Resource Gain,” in Proc. of 4th ACM Conference on
Information-Centric Networking (ICN). New York, NY, USA: ACM,
September 2017, pp. 36–42.
Discussion: Regarding routing tables and maintenance provided at that layer,
there are solutions that also interact with lower levels. A key point though isthat the mapping should be independent. The mapping functions needed forinterest broadcast use link-layer features, only. It must not be intermixed withhigher-layer features such as routing tables. For a compromise between broadand unicast, multicast MAC addresses and their respective handling is doable
as well.
For researchers that want to test their protocols, IoT lab is usable in general,
however, it might get challenging for certain applications or test scenarios (e.g.,fine-grained energy measurements).

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Privacy preserving oblivious neural network and adversial
Machine Learning
Prof. N. Asokan, Aalto University, Finland
Abstract: Applications of machine learning (ML) are becoming pervasivein all aspects of human endeavor. Security and privacy applications are no
exception. The stunning advances in accuracy and performance of ML-based
systems underpins this development. Recent research has brought significant
improvements to various applications like detecting malware, steering users awayfrom phishing websites, and helping users by inferring sensible security/privacy
configurations based on context.
Any successful system needs to consider and defend against possible adversarial interference. ML-based systems are no exception. Recent work has
shown how an adversary could influence the training of ML-models, fool MLmodels into incorrect conclusions , or extract information by querying MLmodels (”model inversion” and ”model stealing”). For example, an adversary
can manipulate a traffic sign so that the tampering is not evident to humans,
but can fool an autonomous car into incorrectly interpreting it (e.g., left-turninto a right-turn). This is called an ”adversarial example”. Recent research
has shown that it is relatively easy to find adversarial examples against a variety of ML-based systems. Consider another example: cloud-based ML-models
are increasingly popular. But these require users to reveal their input data to
the cloud server; Input data may be sensitive and can be misused for other purposes; for example, a cloud-based malware detection system results in the server
learning about all the applications on a user’s device. But this information canbe used to completely profile the users by, e.g., inferring their gender, income,hobbies, political/religious affiliations etc.
The potential for adversarial behavior also has an opportunity cost: if multiple organizations can pool their sensitive data, they can build more effective
ML-models which can benefit all organizations involved, but will be prevented
from doing so, if the sensitive data cannot cross organizational boundaries because of regulation or concerns about potential adversarial behavior. Consider
two hospitals in different jurisdictions that can build a better diagnosis modelby pooling their patient data, but cannot afford to reveal their data to eachother.
Depending on where who the adversary is (e.g., data provider, model trainer,model user) and what its target are (e.g., influencing model training, foolingthe model, extracting information about the model or the training data), different security and privacy concerns arise. A number of different techniques
can be used to defend against adversarial behavior. Advanced cryptographic
techniques like multi-party computation and homomorphic encryption can help
design techniques allowing mutually distrusting parties to make joint computations without revealing their private input data to each other. Statisticaltechniques like differential privacy can enable data sharing by provably ensuring that shared data will not lead to unintended information disclosure. Both ofthese can provide strong guarantees, but are often expensive. Use of hardwarebased security mechanisms, like trusted execution environments, can provide
efficient solutions the problem of joint computations on private data but requirea leap-of-faith in trusting the integrity of the hardware-based security mech6

anism. A promising architectural alternative to centralized model building isfederated learning where multiple data owners can jointly build a model without
having to disclose their datasets to the others.
Security and privacy concerns in ML-based systems are multilateral. Therefore solutions are multilateral as well. There will be no one technique that
addresses all the privacy and security concerns. Other factors like cost of deployment, usability, and performance also need to be accounted for. As istypical in any large system, a suite of techniques need to be used to provide
defense-in-depth.
MiniONN, short for minimalistic oblivious neural networks, is an example
of recent efforts to address one such problem. We show how to transform an
existing neural network model to an ”oblivious neural network” model so thatit can be used in a cloud-hosted setting to provide prediction services to clients
without compromising the privacy of client inputs or the confidentiality of theserver’s model. MinoONN is more general and is significantly faster than previous approaches for oblivious neural networks. The work was presented at ACM
CCS 2017.
• The full version of the paper is at https://eprint.iacr.org/2017/452• Slides for the talk are available at https://asokan.org/asokan/research/
ML-and-security.pdf
Discussion: Machine learning is ubiquitous, and growing 44 percent annually
over the next 5 years. With regard to security, it can be used for (1) AccessControl and also for (2) Deception Detection. However, just using machinelearning is not sufficient. For example, with face recognition, it is not sufficientto have a high recognition rate, because the adversary could try to mimic theface of authorized persons (mask, makeup). In addition, classification can bethrown off easily by adding carefully selected noise, like for example adding 10
percent of noise to an image to a bus classifies it as an ostrich in some machinelearning algorithm, or adding 0.07 percent of noise to a panda makes it a gibbon.
This kind of things are problems also in relevant applications, such as with traffic
signs in autonomous driving. Another example is that iPhone can be unlocked
through ultra-sound. A human would not be fooled, but machine learning is.
Distributed machine learning has a problem if there is a malicious actor. This
actor may learn information from other actors without their consent. It is evennot clear where to search for the adversary and what is his target. In particular,
this is problematic if the adversarial actor resides within the machine learning
pipeline, e.g. when the input is compromised. The adversary could, however,also be client that tries to model inference (invert model, infer membership). For
example, an adversary could try to steal or approximate the model of Google
translate. Furthermore, the prediction service could be malicious. When you
have to reveal your set of applications, the service provider could abuse thisinformation. Then, the adversary might reside inside the training pipeline. If
a model is optimized for two things at the same time, the person providing
the training date may not be aware that some of the data is extracted, eventhough he/she assumes the data is anonymized. Finally, the data owner could
be malicious. For example, SPAM mails hold a lot of hidden information that
confuses the training of the SPAM filter. Other example is Microsoft’s on-line

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learning chat-bot that was provided with incorrect/false data, making the model
eventually behave in unintentional ways. In summary, if there is a reason forsomeone to subvert your model, you have a problem. Therefore, cloud-based
machine learning might be problematic. A natural question is therefore whether
is is possible to make a Neural Network oblivious, i.e., that it is basically unawareof the data. The speaker did this by using an interactive protocol: MiniONN.
This proof-of-concept implementation achieved an overhead of 1 second, which
may be still too much for real Machine Learning applications that might expect
microsecond delays.
Another possibility is, to use machine learning to defend against such attacks, i.e. to distinguish between adversarial and benign behavior. The caveat
is that you only get probabilistic guarantees, so that such solutions are potentially susceptible to evasion themselves. Overall, system level defenses areprobably the best way, at least in the short-term, to defend against adversarial
behavior The holy grail is to understand how humans do inferences and have
Machine Learning do it like that. But in the short- to medium-term, we stillneed to defend current machine learning techniques from adversarial behavior.
There are approaches to add noise to data (differential privacy) or warn users
when they have revealed enough information to a service to allow leakage of unintended information. Problem with the first is that adding excessive noise may
harm utility (i.e., make the resulting dataset not useful for the purpose it was
intended for) and with the second is that the adversary may be smarter andcould make meaningful inferences with less data than the defender expected.
Also adding noise might cost you, e.g. in terms of bandwidth.

Latency-Consistency trade-offs in Distributed Databases
Assoc. Prof. Keijo Heljanko, Aalto University, Finland
Abstract: One of the main approaches to coordinate distributed systems
is to use a fault tolerant distributed database to store the data needed for
coordination between nodes. The CAP theorem by Brewer has shown that under
network partitions such distributed databases can only have two of the following
three properties: Consistency (C), Availability (A), and Partition tolerance (P).
Thus the user of databases needs to make a concious choice on whether to havea database that is centralized (CA) leading to limited scalability, consistent andpartition tolerant (CP) leading to a database that will be consistent but will
not be available for writes during network partition, or available and partition
tolerant (AP) leading to inconsistencies during network partition. Practicalapplications are often combinations of CP database systems for data that needs
consistency and AP database systems for data that needs low latency. The talkalso discusses the use of immutable data and conflict free replicated datatypes(CRDTs) as application patterns allowing the use of AP datastores without
problems with data inconsistencies. The use of highly syncronized atomic clocks
to do distributed transactions between database shards was discussed as animplementation technique used by the Google Spanner database. This would
require reliable high precision clock syncronization, a feature that would be mostwelcome to also commercial cloud database servers.

8

Discussion: An interesting detail described in the Google Spanner paper isthat it suffices that Google has time synchronization guarantee as high as 6
ms. Unfortunately, commercial cloud vendors are not yet giving high-precision
clocks as a service. Such more precise real-time information for applications
would allow the same techniques as Google Spanner has implemented using theirproprietary TrueTime service to be also used in commercial cloud services.
Further reading:• Corbett et al. Spanner: Google’s Globally Distributed Database. ACM
Trans. Compt. Syst. 31(3):8:1-8:22 (2013)

DeepScan: Exploiting deep learning for malicious accountdetection in location-based social networks
Xiaoming Fu, University of Göttingen
An adversary may give harmful effects for PoI-centric location-based social
network applications. For example, malicious users may check-in within minutes
to multiple different locations. The presenter focuses on the detection of malicious users in LBSN. Machine learning is used and detecting malicious accounts
in Dianping (Chinese version of Yelp) is presented as an example. For data annotations, 15 volunteers are recruited, and more than 1/3 were malicious. The
method is based on the analysis of time series activities (e.g. user profiles, checkin information, reviews). DeepScan uses Long-Short-Term-Memory (LSTM) tojudge whether an account is malicious or not. Results show high accuracy of
98%. If time-series features are not considered, it becomes 95%. Comparedwith the other approaches, the proposed one overwhelms them. In summary,by fully-utilizing tempo-spatial features, high accuracy could be achieved.
Discussion: The proposal did not yet consider individual account identification, so that an attacker could use multiple accounts (Sybill attack). The
presenter stated that, currently, students are hired to annotate the malicious
accounts.

Tutorial: Adaptation and Self-Awareness
Christian Becker, University of Mannheim, Wolfgang Schroeder-Preikschat, University of Erlangen-Nuremberg
Ant colonies are an example of a self-organizing system. In general, selforganizing (Software) Systems feature the five aspects (system state, evaluation
criteria, acceptance space, disturbances, control mechanisms). For such systems, system objectives vs. low-level control need to be considered as well as
robustness (dead space, survival space, acceptance space, target space), flexibility and limitations of adaptivity (adaptation vs. awareness). In particular,
recovery is possible only from a survival space but disturbance might lead intoa dead space. A use case for such self-organizing system is platooning, where9different approaches can be used for control: (de)centralization and distribution. Overall, systems become more and more context-aware, communicate,
and autonomous. Adaptation and self-awareness are key concepts.
For M2M, always some kind of OS is involved, especially in interprocess
communication, and device programming. Several design decisions impact system performance: One stack per instance gives lower latency while one stack
per kernel gives higher latency. For sharing common resources among systems
process scheduling is one aspect for which we have to come up with a sequenceof actions.
Energy efficiency is a major problem: A single bitcoin generation uses lotsof energy and therefore large-scale IoT means large-scale energy problems. Predicting energy demand is good but the hardware converts energy however thesoftware determines how much. What can be done is static program analysis(worst case execution analysis) to accumulate knowledge. Essentially, this way
we are counting the Joules of a single instruction. An application where thiswas used is the flying sensor net, a project with biologists and flying bats. They
designed a sensor(2 g weight) for the 20 g bats, which consists of a microcontroller with own energy profile. Energy awareness is not only a technical but
also an economical and ecological issue.
Discussion: It appears that in computer science, known ideas from control
theory are re-invented. What seems missing so far are asynchronous interactions, deadlocks and so forth. The impact of latency is typically severe andnetwork and latency is critical.

Performance of Narrowband IoTProf. Riku Jäntti, Aalto University
LoRa and SigFox are examples of technologies for low bandwidth long range
communication technologies. They are now being complemented with Narrowband IoT (NB-IoT) which is a non-backwards compatible extension of LTE
targeted for cellular based IoT applications. Strong beamforming is used toconcentrate energy in the radio to specific target devices. The system is reliablethrough multiple retransmissions.
Two major classes of IoT communication are Massive IoT & Critical IoT.
The main difference is that Critical IoT is time critical. The goal is to allow for
up to 10 years battery lifetime for NB-IoT devices, depending on usage pattern.
For comparison: SigFox is uplink only without any downlink, no acknowledgements. LoRa has some downlink but is mainly focused on uplink.
Aalto has implemented a NB-IoT prototype. They have a base station and
IoT module implemented in a software defined radio. All the base band processing in the base station is done in a Linux PC. Regarding virtualization, NB-IoT
is easier to virtualize than regular LTE as there is less front-haul traffic. The low
bandwidth also reduces the time needed for computation, which allows longer
distances to computing resources or slower computers.
It seems clear that NB-IoT will actually be deployed. Basically all operators
have deployment plans and it is already deployed in China.

10Discussion: Chip size of NB-IoT devices can be 26x16x2.5 mm and PHY layer
performance is collision-free (different to LoRA). It also scales much better than
LoRA (https://tools.ietf.org/html/draft-ietf-lpwan-overview).
Further reading:• M. R. Palattella et al., ”Internet of Things in the 5G Era: Enablers,Architecture, and Business Models,” in IEEE Journal on Selected Areasin Communications, vol. 34, no. 3, pp. 510-527, March 2016.
• Comparison of Wi-Sun with other technology, done by Wi-Sun alliance:
https://www.wi-sun.org/index.php/tcwp-en/file

Communication protocols for highly restricted nodes
Ferdinand Peper, NICT-CiNet
Possible applications of neural dust are medical and intelligent materials.
Medical applications are wearable, implantable devices. For instance, a dust
device in brain can communicate to the external device attached to the skull.
Neural dust is also used for muscle control. Regarding intelligent materials,
biological systems tightly integrate sensing, actuation, and control. Also, engineering applications exits that could benefit from a similar approach.
Operating conditions for high-density sensor networks are Energy-autonomity(make nodes simple, make operation simple, use energy-efficient signaling), Expendability (low cost, flexible), Ubiquitous (high density. small size, low cost),
Non-interference (wireless range small, difference modes of wireless). Therefore,
in the group of the presenter, spiking is used for low-power consumption. The
general philosophy is that there are no node IDs, no routing tables, no multi-hop
routing, not device-oriented, but location-oriented. For instance: communication through silence (Interval of spike is used).
Discussion: The devices are operating in MHz frequency and the propagation
delay of two spikes are identical. The nodes, however, never know that theyreached consensus.

Ambient and Quantum Re/Back-scatter Communications
Prof. Riku Jäntti, Aalto University
Backscatter of radio waves from an object has been a subject of active study
since the development of radar back in the 1930s, and the use of backscatteredradio for communications since Harry Stockman’s work in 1948. Backscatter
Communications (BC) is widely used in RFID where a reader device generates
an unmodulated carrier signal, a passive tag absorbs the energy of this signal
and then sends back the modulated signal to the reader. BC devices do notneed a power-hungry transceiver and can achieve up to 1000 times lower power
consumption and 10 to 100 times lower device cost than contemporary activetransceiver-based solutions. In traditional BC solutions, a reader device needs tospend power transmitting unmodulated carrier that will be then modulated by

11the BC device. In Ambient backscatter communications, the BC devices modulates the ambient signal impinging at their antenna. It reuses the power andradio spectrum of other wireless systems to transmit it’s information withoutcausing harmful interference to these systems.
Microwave quantum technology is becoming more mature as more and morecomponents have been demonstrated in laboratory environments. One of theforeseen applications of microwave quantum technology is the quantum radar(QR). Similarly as backscatter communications bear close resemblance to classical radar technology, the Quantum Backscatter Communications is closely
related to the QR. In QBC, quantum phenomenon are utilized to improve thesystem performance beyond the physical limits of their classical counterparts.
Discussion: In backscatter communications you pay the price in the ambient
sender power consumption. You have to use some coding to detect backscattersignals from multiple devices. What would help though is to know the sent
FM signal (e.g. receiving the network radio broadcast). In theory, quantum
backscatter can be implemented but it needs cooling to a very low temperature(mK level). It would be needed to reach ultimate sensitivity by minimizing thenumber of photons detected.

PotatoNET - Real-world condition WSN
Lars Wolf, Technische Universiät Braunschwig
Our PotatoNet has been deployed on an agricultural area in 2015 to performseveral WSN outdoor experiments while measuring the stress of potato crops. It
was extended a year later by the PotatoMesh, a solar panel-based mesh networkof nodes, and another deployment in 2017. Throughout all these deployments
we experienced problems and failures at different stages of the projects. We derive key problems and some important concepts when it comes to outdoor WSN
deployments. Also the storing of the crop is an important part of the overall agricultural production and logistics chain. Monitoring and adjusting theenvironmental conditions in storage are important tasks to achieve an optimaland efficient storability. Therefore, we are currently investigating the communication part of such a system where information about current conditions ofstored potatoes can be collected from sensor nodes which are in the middle ofstored potatoes. Among others, we study suitable frequencies and networking
structures.
Discussion: Agriculture is an important application domain for WSNs. The
battery should optimally last for at least one season, the measurement frequency
was roughly 1 sample per minute. The voltage scaling was made fail save byusing a separate module and by internal checking (matrix computations).
Further reading:• Jakob Juu, Wireless Sensor Networks and Localization for Biomass Storages, Aarhus University, Denmark 2015.

12

Cyber Borg and Cyber-Groups: Activity recognition ingroupsPaul Lukowicz, German Research Center for AI, DFKI
Access to digital domain is increasingly more frequent and becomes the center of our life: any time any place. Positive aspects that evolve from this aree.g. Assistants that are listening all the time, dietary monitoring by classifying chewing sounds, smart phones predicting divorces. We are at the verge ofany real life event becoming, instantly globally connected to the digital domain.
Anything that happens in physical life leaves a digital imprint. Physical objects
have digital shadows. You can use devices to personalize your spam and tointeract with the digital shadow
Discussion: A great application would be noise-cancelling headphones to cancel advertisements – one of Steve Mann’s favourite applications: alternative reality. Regarding privacy, the right balance is important. Neither ’forget about
privacy’ nor putting people with flags in front of cars is appropriate. In any case,companies already know details about people. Health insurance company canknow your health data because you can trust them – they would go bankrupt ifthey handled your data like Facebook. We do have social norms that support
here too. Comment: Privacy violations by careless third parties. GDPR – ’yourdata can be moved outside of the EU’.

Research Directions for IoT Edge Computing
Dirk Kutscher, CTO Huawei Munich (Presented remotely)
Computing is shifting to distributed perspectives. Industrial IoT use cases
include TSN/Profinet/CAN which require video feeds, real-time analytics andreal-time control loops processing. IoT data streams are directed to the cloudfrom many IoT domains (Smart City IoT, Industrial IoT, Home IoT). Current
in-network computing with C/S protocols has many limitations (scalability, efficiency, performance and robustness). To address the challenges, Unikernels,
Light-weight scripting, Trusted execution environments, Data-oriented communications and programming abstracts (ICN/NFN), Distributed consensus protocol (blockchains), are studied.
NFV adds computation to networks. Networked computation is a consequence of adding more and more to the network leading networks to become
distributed systems themselves.
Data silos can be established and should be avoided. This can be hardenedby the constant flow of data from sensors and IoT applications. Named Data
Networks are described as a client server infrastructure where the name spaces
are managed by servers. Obviously C/S architectures have some problems here,eg. firewalls, mapping to underlying L2 protocols. As a consequence, networks
should be built where computation is a first-order service.
Opportunities for ICN were sketched and the link to IoT was highlighted.
An extension of ICN is named function networking. Similar to stored procedures
NFN can be used for data intensive applications.

13Named functions can be used to virtualize execution. In contrast to a RPC
the network decides the placement. This is complemented by SPOC, a protocol
to play for decentralized computations as nano payments.
Opportunities for NFN research are, e.g., transport based on MQTT, client
authentication, auto-scaling, error semantics and QoS, and abstractions for programming are needed.
Summarizing, challenging topics in ICN/NFN research are
ICN security, opportunistic caching and in-network caching, pub/sub, custodian storage/forwording/processing, multi-tenancy.
Named function networking dynamic computation (for edge computing big
data, streaming processing, service chaining, Broker function, scalable
computation (auto-scaling), efficiency, deterministic computation (performance aspects), programming models).
Some related pointers to further reading:• Sifalakis et al.: An Information Centric Network for Computing the Distribution of Computations, 1st International ACM Conference in Information Centric Networking (ACM ICN 2014), September 2014, Paris, France.
• Krol et al.: NFaaS: Named Function as a Service. In Proceedings ofthe 4th ACM Conference on Information-Centric Networking (ICN ’17).
ACM, New York, NY, USA, 134-144.
• Krol et al.: ”SPOC: Secure Payments for Outsourced Computations”,
NDSS’18 Workshop on Decentralized IoT Security and Standards (DISS).
• https://trac.ietf.org/trac/irtf/wiki/icnrg• https://datatracker.ietf.org/rg/dinrg/about/
Discussion: Towards the future of edge computing, the environment is dynamically changing. Potential players are going into good positions, e.g., theMobile China approach. Here, chances for mid sized enterprises exist. Edge
computing can be used for mission critical applications, such as vehicular networks, but problems exist with extensibility of car functionality. Here, good
separation of computing can help.

Machine learning
Prof. Paul Lukowicz, German Research Center for Artificial Intelligence, DFKI
The basic Machine learning approach is to find features, collect data andfind separation boundary between classes. The underlying assumption is that
there is a correspondence between the structure of data and semantics. Thereis also an assumption of smoothness: if a test data point is closer to a training
data point, it will be classified correctly. The goal of machine learning then is
to find a computational form of the mapping from feature space to class space.
The naive approach is to try all possible functions. Machine learning retains
uncertainty (there can be several possible boundaries between classes; this is14

what leads to the existence of adversarial examples) and will have to model
noise. This is essentially a search problem. With infinite time/memory, andnoise-free training data it is possible to build a perfect model. The goal of practical machine learning is to find a reasonable approximation with finite data andfinite resources. The underlying approach to train a model is to find a family ofparametrized functions, define an error function, and find values for parametersthat minimize the error. Different approaches to do this lead to different training
algorithms: decision trees, linear classifiers (separated by a plane), perceptrons(weighted inputs, sum, and apply to an activation function). Fundamentally
this is a multi-dimensional minimization problem that is not solvable analytically. Machine learning tries to find approximations. The number of parameters
should be minimized because more parameters implies the need for more training data. A simple approach is to use one parameter set per region (every region
has its own parameter). Shared parameter sets, where the same parameter candifferentiate between multiple classes are more powerful. This enables the useof exponentially fewer parameters. Deep neural networks work by hierarchical representation: e.g., in handwriting recognition, intuitively, the first layer
discovers shapes in fixed positions, the next layer does position-independent
shape detection (self-organized feature detector layers). Nested representation
can capture any manifold with distributed representation. The renaissance ofdeep neural networks is due to two reasons: (1) people didn’t know how to train
deep networks, such that local minima can be avoided and (2) the computational complexity. The breakthrough came with the realization that training
layer by layer first is the key (unsupervised training of autoencoders that build
hidden layers as compact representations allowing restoration of data without
loss; similar to dimensionality reduction but without constraints).
Discussion: Message/Take away: Machine learning algorithms are by nomeans intelligent or even really learning. It is more that a function is optimized with respect to some underlying data and there is no real intelligence orlearning in this. In popular culture and also by popular people, the ’risk’ of machines taking over due to Machine learning is drastically exaggerated. It is notcomparable with the abstraction capabilities that humans have: Show a humana single picture of a cat and he can abstract from this and knows how cats look
like. This is by no means possible with today’s Machine learning approaches.
On the other hand, in popular text mining literature, it is pointed out thatthe approach taken by Machine learning can indeed be described as learning:
We show examples and train the system on these examples and from theseexamples, the recognition function becomes better optimized. This is, on somelevel of abstraction similar to the human learning process.
Implementing a feedback loop for Machine Learning approaches might in
the optimum case lead to the construction of a turing machine. This is also
related to control theory and feedback control loops in that discipline. Indeed,
the Machine learning approaches are running on a Turing machine, so that is
the most capability we can expect from them. It is impossible to achieve any
computation model superior to a Turing machine when this was the computation
model where the algorithm was executed on.

15

Edge Mesh: Enabling scalable connectivity and distributed
intelligence for IoT
Prof. Jiannong Cao
Smart IoT and edge computing can help to make IoT smarter. Edge computing research can be divided into parts: Interaction with IoT devices; Architecture and Management. Most research is focused on single edge nodes. Edgemesh is, a solution to leverage mesh network and system architecture of edge
devices to enable collaboration and integration of edge devices to support large
scale IoT.
Edge mesh scenarios constitute connecting machines in a construction site,
machines in an industry, vehicular networks, healthcare, intelligent eco system. The design principles applied cover decentralized architecture, bottom up
coordination, multi hop connectivity, gateway for interoperability, pushing intelligence from cloud to edge devices. This can create a distributed intelligence
where Ede devices collaborate for distributed decision making. The presenterhas distributed intelligent SDN controllers (Distributed task computation; Howto assign tasks to distributed edge nodes). Future work constitutes extending
SDN for M2M communications, device mobility, Edge mesh for 5G.
Discussion: To set up a testbed for Edge mesh, the presenter started from awireless mesh networks and used and added edge devices to connect heterogeneous networks. They integrated the edge devices into these wireless networks.
Integrating vehicular networks creates security and privacy issues. Anotherquestion is whether the cloud is actually needed with so many edges in the
future. There might be a situation where the application switches between
centralized and distributed.

Vehicular Edge/Cloud: Offloading Framework & SchedulingProf. Yusheng Ji, National Institute of Informatics, Japan
Considering the future of smart vehicles and their high computing demand,
this talk treats the problem of job assignment and offloading to neighboring vehicles. To solve this problem, the speaker introduced two computing schemes:
AVE (Autonomous vehicular edge) and HVC (Hybrid vehicle cloud) schemes.
For AVE, no external infrastructure (like cloud servers) is used and jobs generated at a vehicle are directly offloaded to its neighbor vehicles with job scheduling based on Ant-Colony optimization (ACO). The speaker also showed that the
ACO rapidly converges. For HVC, external infrastructure, i.e., road side units(edge nodes) and cloud servers are implemented to support the job offloading
to neighbor vehicles and to meet the real-time requirements to computation.
Due to the uncertainty of future job arrival and resource availability, online
scheduling is adopted and job queueing in AVE is removed. The speaker also
described that thanks to the location estimation with GPS, the success rate ofjob offloading (successful finish of offloaded jobs) increases.

16

Discussion: The computation is performed in an on-line manner or by using
the ACO-based algorithm and the time complexity of the scheduling is polynomial in the number of jobs, and the number of nodes. It hence completes thecomputation in a short time. The problems considered by the speaker include
that beacons are not relayed, and offloading requests can only be forwarded till
the second hop. So the problem is not very large, i.e., two hops at most.

Privacy Issues in ICN and IoTProf. Yuki Koizumi, Osaka University, Japan
Information Centric Networking (ICN) inherently provides many benefits
that IP cannot offer, such as in-network caching, client mobility, multicast, andsecurity, thanks to its name-based forwarding, i.e., packets are forwarded/routed
according to application-meaningful names. Despite the benefits of ICN, privacy in ICN may be harder than that in IP because ICN uses meaningful namesof packets, which are visible to all forwarders. An existing study has proposedname obfuscation, which encrypts meaningful application data names into obfuscated meaningless network names and sends Interest packets to the obfuscatednames. Although name obfuscation may mitigate the threat, a certain degree of
information might be leaked if attackers use auxiliary information, such as popularity of content. The speaker has proposed a method based on k-anonymity
and l-diversity to mitigate the threat to privacy in ICN. With the proposed
method, a consumer sends an Interest packet with k − 1 dummy Interest packets, which have l diverse properties, so that attackers can neither distinguish
the Interest packet of the consumer from the other k − 1 Interest packets nor
estimate a certain degree of information from the Interest packets.
Discussion: The Key point of ICN is that it breaks up the informationmonopoly of Google and the likes. Names of data pieces are stored together
with the data. Data pieces are encrypted so that only authorized consumers
can decrypt them by using any of existing cryptography techniques, such as
public-key cryptography, and hence they cannot be seen by attackers. Second,
it is difficult to get names by attackers because they are encrypted. That is, it
is difficult to get data pieces in the cache by specifying the names of the data
pieces.

Recent Studies with Relatively New Sensing Modalities
Prof. Takuya Maekawa, Osaka University, Japan
Several activity recognition projects have been presented. One example isstate change detection by using Wi-Fi CSI. The goal of the project is to monitor
elderly people at home in a non-invasive way. Only a single receiver is used,
and movement of objects in a room, such as doors, furniture and windows, isdetected. CNN is used, and a high f-measure was achieved (>0.8). Also several
indoor/outdoor positioning methods are introduced. From them, a method toestimate indoor location semantics. The idea is to identify the location featuresby smartphone sensors and active sound probing (wall material classification).
The accuracy was not sufficiently high. Finally, a bio-navigation project is17

presented. The issue is to save energy of video camera attached to animals,
and the idea is to trigger video recording (and GPS) by accelerometers. In field
experiment, sensor loggers are attached to wild birds in Japan, and the videofrom bird-eye view is presented. They are also attached to cormorant.
Discussion The speaker trained the model using all the data and test themodel by cross validation.

A dozen years of standardizing the Internet of Things
Prof. Carsten Bormann, University of Bremen, Germany
The slides of the talk are available here: https://drive.google.com/open?
id=14umUCrsYZHbZHCZDwik7ibrXY7rgKicE

Networking the IoT with RIOT
Thomas C. Schmidt, HAW Hamburg, Germany
The Internet of Things (IoT) is rapidly evolving from large numbers of embedded devices that gradually connect to the Internet. Such nodes are often
constrained and limited to battery-powered low power lossy radio links. RIOT,
the friendly operating system for the IoT, is an open source initiative for fuelingan IoT ecosystem that is not locked in with vendors or service operators.
This talk introduces the networking architecture that turns RIOT into a
powerful IoT system, and enables low-power wireless deployment. RIOT networking offers (i) a modular architecture with generic interfaces for plugging
in drivers, protocols, or entire stacks, (ii) support for multiple heterogeneousinterfaces and stacks that can concurrently operate, and (iii) GNRC, its cleanly
layered, recursively composed default network stack. Focussing on deployability,
we discuss and analyse several IoT networking approaches including 6LowPan
and Information Centric Networking.
Further Reading:• M. Lenders, P. Kietzmann, O. Hahm, H. Petersen, C. Gündoğan, E. Baccelli, K. Schleiser, T. C. Schmidt, and M. Wählisch, “Connecting theWorld of Embedded Mobiles: The RIOT Approach to Ubiquitous Networking for the Internet of Things,” Open Archive: arXiv.org, Technical
Report arXiv:1801.02833, January 2018.

4

Overview of Interest Group Discussions

In break-out sessions, we have worked on selected topics that evolved from thediscussions. The results are briefly summarized in the following.

18

4.1

Break-out topic ’Machine Learning’

4.1.1

Group 1

• Machine Learning is not about learning, it is about multi-dimensional data
fitting• data fitting benefits from additional data points and plausability checks;
quality of input is essential• Humans would like to understand why ML-based decisions were taken (andin which manner). For control theory approaches, stability was always
important, here is significant difference (at the moment?).
• we now have a lot of systems that sense data and use machine learning;they may contribute to a global view (and go from lower layer data tohigher layer semantics)• assume that every object can communicate with every object in a cheap
way, we can easily enable such a global view
• you can combine these sources, this combination (fusion) can provide fora kind of sanity check (of ML decisions)• this interconnection is a game changer, similar to atomic clock in distributed systems domain• low latency communication is one requirement to enable specific application scenarios• trust problems arise when you incorporate additional ’sensors’• global view could lead to information overload (edge computing may help?)• selection of information to be provided to others probably needed, also to
avoid network congestion
4.1.2

Group 2• Potential example applications of machine learning are traffic control, attack detection, autonomous vehicles, production/maintenance of smart
grid systems and so on.
• Among them, autonomous vehicles have attracted attention. Resiliency
against malicious vehicles is important.
• Identifying communication patterns of IoT devices and attackers is hopeful.
• Distributed machine learning is a good example of edge computing. Protecting privacy is its advantage.
• Edge computing (machine learning) under unreliable wireless networks isdifficult.
• The other issues include bad effects due to compromised machine learning
nodes, roles of humans, feasibility of brain computing, learning based on
simulations and so on.
4.1.3

Group 3

• We discussed if machine learning can provide guarantees, similar to realtime systems. We discussed how airplanes can provide good guarantees,
but at a high cost and high redundancy. Eventually, what we arrived at isthat what we can provide is confidence pertaining to certain environmental
19

conditions. E.g., an autonomous train could provide higher confidencesoon since the environment is better controlled.
• Metrics: We are probably looking at the wrong metrics or comparison.
E.g., probably machines cannot detect an apple with 100% guarantee, but
can probably detect an apple while you are drawing due to the amountof data that they have access to. For instance, take a look at quickdraw (https://quickdraw.withgoogle.com/) and its database (https://quickdraw.withgoogle.com/data).
• when can machines start to program? That might take a while, however, it might be interesting to see if machines can automatically generate
documentation by looking at the code written.

4.2

Break-out topic ’IoT’

We first structured the topic into (Radios (PHY/MAC); Networking (IP, ICN);
Application Layer/Transfer; Structural interop/serialization; Semantic interoperability; Data aggregation, machine learning). In parallel to these building
blocks, we have security and privacy aspects. Naming, discovery, and selfdescription relate sometimes to protocols, sometimes not. With discovery wemean neighbor discovery (lower layer) but also service discovery etc. One open
question is where computation is performed, but we did not discuss this further.
In the second part of this breakout, we focussed on different directions in radio
development. We distinguished between (i) wide area radio and (ii) ultra low
resource radio.
Wide area radio We discussed in more detail LoRA, SIGFOX, WY-SUN,
NarrowBand IoT. SIGFOX is considered a very proprietary service, which is
the reason why many people started to consider LoRa. We agreed that you
cannot build a full network based on SIGFOX, but you may want to support
some kind of client functionality. The range of radio depends on the deployment
details. In general, there are two ways of improving coverage, either by a very
good radio (power strength), or by designing a cellular model. Then, we discussed the problem of coexistence of different administrative radio domains (seework by Laura Feeney). These different domains might be because of different
radio technologies but might also exist when the same technology is deployed.
Severeness of the problem depends on upcoming IoT deployment. We agreed
that further research should be conducted to avoid future problems. We also
discussed support of IP layer convergence for the different radio technologies.
LoRa, NB-IoT, and WY-SUN already provide IP layer convergence, SIGFOX
is getting there slowly (main probleme is very small packet sizes). Basically, for
all wide area technologies, an IP convergence layer can be designed. This is incontrast to ultra low resource radios, for which we do not know how to designa convergence layer.
Ultra low resource radio We identified LoRa on backscatter, Inter-onlyinterval, BRZZ (work by Lars Wolf), power line communication, and ultrasound communication. Most of these technologies are more on the researchside. After discussing radio technologies, we briefly discussed the networking
layer, including IP, ICN, DTN, and deterministic networking. We highlighted

20

that IP can be used in very different ways. In particular in the IoT, where wehave very heterogenous link layers, tweaking IP can be useful to improve they
way how networks are constructed (e.g., bootstrapping, management). Further
details on this topic will be presented in the tutorial given by Carsten Bormann
and in the talk given by Thomas Schmidt.

4.3

Break-out topic ’Blockchain for M2M’

We discussed whether blockchain is really useful for M2M/IoT networks, where
we can use it in M2M/IoT, and why we use it from perspectives of its heavy andslow computation and requirements on high computing resources. The following
issues/points arises during the discussion:• To circumvent the heavy computation of blockchain, constructing a hierarchical structure, where blockchain is not applied to check transactions
among IoT devices in the lower layer and it is applied to check groups ofthe transactions in higher layers.
• The blockchain technology can prove that registered transactions are surely
carried but it cannot prove that data pieces provided to the transactions
are surely generated by authorized IoT devices.
• Blockchain is useless if all data of IoT/M2M systems is gathered in thecloud. That is, blockchain can be used to disconnect the relation of thecloud and the IoT/M2M systems.
• Inserting a block into blockchain might be fast enough for registering newtrusted IoT devices, and hence blockchain can be applied for managing
IoT devices instead of managing each transaction between the devices.
Finally, we have concluded that blockchain can be used to prove trusts of nodes
participating in M2M/IoT systems, to retrieve reputations of the nodes, and torealize incentives/rewards for nodes to supporting the distributed system.

5

List of Participants

1. Ioannis Psaras, UCL, UK, (i.psaras@ucl.ac.uk)
2. Thomas C. Schmidt, HAW Hamburg, Germany (thomas.schmidt@idsmannheim.de)3. Matthias Wählisch, Freie Universität Berlin, Germany (waehlisch@ieee.org)
4. Lars Wolf, Technische Universität Braunschweig, Germany, (wolf@ibr.cs.tubs.de)
5. Carsten Bormann, Universitt Bremen, Germany (cabo@tzi.org)
6. Hong-Linh Truong, TU Wien, Austria, (hong-linh.truong@tuwien.ac.at)
7. Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan (lrd@nict.go.jp)
8. Jun Kurihara, Zettant Inc., Japan, (kurihara@ieee.org)
9. Yuki Koizumi, Osaka University, Japan, (ykoizumi@ist.osaka-u.ac.jp)
10. Prof. Shingo Ata, Osaka City University, Japan (ata@info.eng.osakacu.ac.jp)
11. Prof. Christian Becker, University of Mannheim, Germany (christian.becker@unimannheim.de)21

Figure 2: Participants of the Shonan Seminar 114 (Resilient Machine-toMachine Communication)
12. Prof. Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong
(jiannong.cao@polyu.edu.hk)
13. Prof. Keijo Heljanko, Aalto University, Finland (keijo.heljanko@aalto.fi)
14. Prof. Riku Jntti, Aalto University, Finland (riku.jantti@aalto.fi)
15. Yusheng Ji, National Institute of Informatics, Japan, (kei@nii.ac.jp)
16. Prof. Paul Lukowicz, German Research Center for Artificial Intelligence,
DFKI , Germany (paul.lukowicz@dfki.de)
17. Prof. Kazuya Murao, Ritsumeikan University, Japan (murao@cs.ritsumei.ac.jp)
18. Prof. Jrg Nolte, Brandenburg University of Technology, Germany (jon@informatik.tucottbus.de)
19. Dr. Borje Ohlman, Ericsson Research, Sweden (borje.ohlman@ericsson.com)
20. Prof. Ren Ohmura, Toyohashi University of Technology, Japan (ren@tut.jp)
21. Dr. Olga Streibel, Bayer, Germany (olga.streibel@bayer.com)
22. Prof. Xiaoyan Wang, Ibaraki University, Japan (wangxy@nii.ac.jp)
23. Prof. Yu Xiao, Aalto University, Finland (yu.xiao@aalto.fi)
24. Prof. Hirozumi Yamaguchi, Osaka University, Japan (h-yamagu@ist.osakau.ac.jp)
25. Prof. Xiaoming Fu, University of Goettingen, Germany (fu@cs.uni-goettingen.de)
26. Prof. Takuya Maekawa, Osaka University, Japan (maekawa@ist.osakau.ac.jp)
27. Prof. Ferdinand Peper, NICT-CiNet, Japan (peper@nict.go.jp)
28. Prof. Wolfgang Schrder-Preikschat, Friedrich-Alexander-Universitt ErlangenNrnberg, Germany (wolfgang.preikschat@t-online.de)22