Technical Sessions

Session S10

Security, Privacy & Trust (II)

10:10 AM — 11:50 AM JST
Dec 18 Fri, 8:10 PM — 9:50 PM EST

Identifying unfamiliar callers' professions from privacy-preserving mobile phone data

Jiaquan Zhang (University of Goettingen, Germany); Xiaoming Yao (China Telecom Cloud Branch, China); Xiaoming Fu (University of Goettingen, Germany)

Identifying an unfamiliar caller’s profession is important
to protect citizens’ personal safety and property. Due to
limited data protection of many popular online services in some
countries such as taxi hailing or takeouts ordering, many users
encounter an increasing number of phone calls from strangers.
This may aggravate the situation that criminals pretend to be
delivery staff or taxi drivers, bringing threats to the society.
Additionally, many people nowadays suffer from excessive digital
marketing and fraud phone calls because of personal information
leakage. However, previous works on malicious call detection only
focused on binary classification, and do not work for identification
of multiple professions. We observed that web service requests
issued from users’ mobile phones which may show their Apps
preferences, spatial and temporal patterns, and other profession
related information. This offers us a hint to identify unfamiliar
callers. In fact, some previous works already leveraged raw
data from mobile phones (which includes sensitive information)
for personality studies. However, accessing users’ mobile phone
raw data may violate the more and more strict private data
protection policies or regulations (e.g. GDPR 71). Using appropriate
statistical methods to eliminate private information
and preserve personal characteristics, provides a way to identify
mobile phone callers without privacy concern. In this paper, we
exploit privacy-preserving mobile data to develop a model which
can automatically identify the callers who are divided into four
categories of users: normal users (other professions), taxi drivers,
delivery and takeouts staffs, telemarketers and fraudsters. The
validation results over an anonymized dataset of 1,282 users with
a period of 3 months in Shanghai City prove that the proposed
model could achieve an accuracy of 75+%.

Length Matters: Fast Internet Encrypted Traffic Service Classification based on Multi-PDU Lengths

Zihan Chen, Guang Cheng, Bomiao Jiang, Shuye Tang, Shuyi Guo, Yuyang Zhou (Southeast University, China)

Encryption of network traffic has become an inevitable
trend. As an important link to Internet encrypted traffic
analysis, encrypted traffic service classification can provide support
for the coarse-grained network service traffic management
and security supervision. But traditional DPI method cannot be
effectively applied in an encrypted traffic environment, and the
existing methods based on machine learning have two problems
in feature selection. One is the complex feature classification
over costing problem, the other is the TLS-1.2 suited method
is no longer applicable to TLS-1.3 handshake encryption. To
solve these problems, in this paper, we consider the differences
among encryption network protocol stacks and propose a method
of encrypted traffic service classification combining with capsule
neural network in a multi-protocol environment by using multi-
PDU lengths as the features, making full use of Markov property
between PDU length sequences and being suitable to TLS-
1.3 environment. The feature makes our method much faster
than others in feature extraction. Our control experiments on
ISCX VPN-nonVPN dataset show that our method achieves a
satisfactory performance (0.9860 Pr, 0.9856 Rc, 0.9855 F1), which
is superior to the state-of-the-art methods.

Trapdoor Assignment of PEKS-based NDN Strategy in Two-Tier Networks

Kyi Thar Ko, Masahiro Mambo (Kanazawa University, Japan)

Named Data Networking (NDN), where addressable
content name is used, is a candidate of next-generation Internet
architectures. NDN routers use In-Network cache to replicate
and store passing packets to make faster content delivery. NDN
uses human-readable names, and therefore, it is easy for an
attacker to guess which content is requested. To solve this issue,
we have proposed the application of Public Key Encryption
with Keyword Search (PEKS) to NDN and a PEKS-based NDN
strategy has been developed for forwarding packets. Applying
the PEKS scheme produces latency during transmission because
of cryptographic operations. To reduce the number of such
operations, we consider a network environment based on Two-
Tier network design and examine different trapdoor assignments
of PEKS-based NDN. We show some evidences that setting
trapdoor storage only at core routers in Two-Tier network gives
the reduction of the cryptographic operations.

NoPTPeer:Protecting Android Devices from Stealthy Spoofing and Stealing in WLANs without Privilege

Shuying Wei (Tsinghua University, China); Xiaoliang Wang (Capital Normal University, China); Ke Xu (Tsinghua University and Peng Cheng Laboratory, China)

Android devices are prone to spoofing attacks in
Wireless Local Area Networks (WLANs), and many of them
access numerous unknown networks in daily use. Moreover,
because of the weak authentication between Android smartphones,
attackers can steal data in a stealthier way based on Address
Resolution Protocol (ARP) spoofing. These facts bring a gap in the
study of device-side spoofing defense for Android devices. So in
this paper a framework is proposed which requires No Privilege
but can guarantee the True identity of Peers (NoPTPeer), to
protect Android’s device-to-device communication in WLANs. Its
main features include realizing strong authentication between
Android devices, controlling dangerous outgoing connections, and
monitoring suspicious incoming connections. These features are
realized by an Identity-Based-Signature (IBS) scheme, an Android
base class VpnService, and information read from Android system
files, which all require no root privilege and are independent of
network infrastructures. We implement this framework as an
Android smartphone application. The experiments show its
effectiveness in detecting spoofing and monitoring stealing, as well
as acceptable overhead in memory, Central Processing Unit (CPU)
usage and communication latency.

Early Botnet Detection for the Internet and the Internet of Things by Autonomous Machine Learning

Anderson Bergamini de Neira, Alex Medeiros Araujo, Michele Nogueira (Federal University of Paraná, Brazil)

The high costs generated by attacks and the increasing
number of different devices on the Internet and the Internet
of Things (IoT) propel the early detection of botnets (i.e.,
network of infected devices) as a way to gain advantage against
attacks. However, botnet early detection is challenging due
to the continuous mutation, sophistication, and massive data
volume, this last mainly resulted from sensor networks and
IoT. The literature addresses botnets by modeling the behavior
of malware spread, the classification of malicious traffic, and
the analysis of traffic anomalies. This paper presents ANTE,
a system for ANTicipating botnEts signals based on machine
learning algorithms. The ANTE design allows it to adapt to different
scenarios by learning to detect different types of botnets
throughout its execution. Hence, ANTE autonomously selects
the most appropriate machine learning pipeline for each type
of botnet to maximize the correct classification before an attack
effectively begins. The ANTE evaluation follows a comparison
of its results to others from the literature considering three
datasets: ISOT HTTP Botnet, CTU-13, and CICDDoS2019.
Results show an average accuracy of 99.87% and an average
botnet detection precision of 100%.

Session Chair

Haohua Du (Beihang University, China)

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Session S11

Algorithms, Theory, and Protocols (II)

10:10 AM — 11:50 AM JST
Dec 18 Fri, 8:10 PM — 9:50 PM EST

Delay Efficient D2D Communications Over 5G Edge-Computing Mobile Networks with Power Control

Xiaohua Xu (Kennesaw State University, USA); Lixin Wang (Columbus State University, USA)

This talk does not have an abstract.

Efficient Service Entity Chain Placement in Mobile Edge Computing

Yu Liang, Jidong Ge, Sheng Zhang, Changan Niu (Nanjing University, China); Wei Song (Nanjing University of Science and Technology, China); Bin Luo (Nanjing University, China)

Edge service entity placement is a fundamental
issue in mobile edge computing, which tries to place service
entities on edge servers to achieve better economic benefits and
quality of service for users. Most existing studies towards this
issue usually deploy application services separately; however,
we observe that many application services can be broken down
into smaller service components/entities, which may enable us
to share these smaller entities between application services.
Therefore, in this paper, we propose the concept of service
entity chain, which is a chain of ordered service entities
that represent an application service. We study the problem
of placing service entities in the form of chains on edge
servers within a given cost budget, so as to minimize the total
latency experienced by users. We provide a formal problem
formulation and design an efficient algorithm for it. Extensive
simulations are conducted to demonstrate the advantages of
the proposed algorithm compared with two state-of-the-art

Real-time Emotion Recognition for Sales

Si-Ahmed Naas, Stephan Sigg (Aalto University, Finland)

Positive emotion is a pre-condition to any sales
contract. Likewise, the ability to perceive the emotions of
a customer impacts sales performance.
To support emotional perception in buyer-seller interactions,
we propose an audio-visual emotion recognition
system that can recognize eight emotions: neutral, calm,
sad, happy, angry, fearful, surprised, and disgusted. We
reduced noise in audio samples and we applied transfer
learning for image feature extraction based on a pre-trained
deep neural network VGG16. For emotion recognition, we
successfully obtained an audio emotion-recognition accuracy
of 62.51% and 68% and video emotion-recognition accuracy
of 97.13% and 97.77% on the Ryerson Audio-Visual
Database of Emotional Speech and Song (RAVDESS) and
Surrey Audio-Visual Expressed Emotion (SAVEE) datasets
respectively. For the combination of the two models, our
proposed merging mechanism without re-training achieved
an accuracy of close to 100% on both datasets. Finally,
we demonstrated our system for a customer satisfaction
use case in a real customer-to-salesperson interaction using
audio and video models, achieving an average accuracy of

Resilient Caching in Information-Centric Networking

Noriaki Kamiyama, Rin Hamada (Fukuoka University, Japan)

Information-centric networking (ICN), a new network
architecture for efficiently delivering content, has been
widely investigated recently. To be widely spread as a social
infrastructure, ICN is required to sustain not only network
availability, i.e., connectivity between operating routers, but
also content availability, i.e., reachability to content, at network
failures. In ICN, FIBs (forwarding information bases) at routers
are configured so that content requests reach hosts of content
providers having the originals of content. Therefore, requests
for content whose connectivity to originals is lost cannot be
transferred in networks, and the content availability of these
content items is lost. However, copies of unavailable content are
possibly cached at one or more operating routers in ICN, so
content availability can be recovered by promoting one copy
cached at operating routers to the original. Therefore, it is
desirable to cache content at routers located far from its original
to improve the recover probability of unavailable content items.
In this paper, we propose a caching strategy of ICN to achieve this
goal. Through numerical evaluation, we show that the proposed
caching strategy can increase the maximum distance between
the originals and cached copies by several percent to about 20%
compared with the case simply caching content at all routers in

NDN Based Participatory Crowdsensing Framework with Area-focused Interest Forwarding

Hideki Tode, Ashish Man Singh Pradhan, Daishi Kondo, Yosuke Tanigawa (Osaka Prefecture University, Japan)

Mobile crowdsensing is gaining wide popularity, and
its main aim is to collect significant amounts of data by leveraging
mobile terminals such as smartphones that provide a ubiquitous
communication environment. On the other hand, named data networking
(NDN) has become one of the most popular informationcentric
networks. In this paper, as a new paradigm, we propose
an advanced crowdsensing system using NDN. Unlike previous
studies, data, including uncertain information such as people’s
notions, opinions, and knowledge, are gathered from the outer
locations of NDN, thanks to the delay-tolerant feature, which
opens up new possibilities for data collection. The request for the
data is forwarded in an area-focused manner. In addition, with
this approach, we expect to significantly reduce the redundant
data responses.

Session Chair

Noriaki Kamiyama (Fukuoka University, Japan)

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Session S12

Systems & Tools (II)

10:10 AM — 11:50 AM JST
Dec 18 Fri, 8:10 PM — 9:50 PM EST

Load balancing and resource management in distributed B5G networks

Lu Ma, Weiling Chang, Chao Li, Shanjin Ni, Jia Cui, Ming Liu (National Computer Network Emergency Response Technical Team/Coordination Center of China, China)

The unmanned surface vehicle (USV) cluster, during
marine task execution, works in a challenging communication
environment. The cluster’s network topology and states of
wireless channel change rapidly with time. And the computing
resources of fog nodes may be shared by several task requests
simultaneously. Therefore, it’s necessary to find an effective load
balancing mechanism to cope with the ever-changing adverse
factors. The load balancing problems of vehicular fog computing
of USV cluster are investigated in this work. And furthermore,
the corresponding mathematical models, including marine vehicular
fog computing networks, wireless channels, and several
typical scheduling mechanisms, are established. The analytical
models and simulation results show that the proposed scheduling
algorithm based on minimum response time performs better
than other selected algorithms and can significantly reduce the
response time and blocking probability of task requests.

Research on D2D Data Transmission Protocol in NDN

Bo Cui, Xinmiao Jian, Qi Chen (Inner Mongolia University, China)

Named Data Networking (NDN) is a new revolutionary network architecture and a research hotspot of next generation Internet. As a key candidate technology for 5G, D2D communication technology has attracted much attention. Data sharing is the main application of D2D communication, and data transmission is the core of data sharing. At present, there are few researches on D2D data transmission in NDN data sharing. The researches mainly focus on dataset synchronization and distributed file transmission. In addition, the existing data sharing protocol in NDN shows lower efficiency when directly applied to D2D data transmission. According to the main factors that affect the efficiency of data transmission in D-ChronoChat running in NDN, we design three optimized D2D data transmission protocols, and implement the prototype system. In order to evaluate the performance of each protocol and the main factors affecting the performance, we test the transmission delay of each protocol. The test results show that the Multi Threads Batch Packets (MTBP) protocol shows the best performance among the three optimized data transmission protocols. In MTBP, the data transmission rate can reach 6Mbps, nearly 7.4 times higher than the Single Thread Individual Packet protocol (STIP) in D-ChronoChat.

Efficient Distributed Training in Heterogeneous Mobile Networks with Active Sampling

Yunhui Guo, Xiaofan Yu, Kamalika Chaudhuri, Tajana Rosing (University of California San Diego, USA)

Mobile edge computing is an emerging research
topic which aims at pushing the computation from the cloud
to the edge devices. Most of the current machine learning
(ML) algorithms, such as federated learning, are designed for
homogeneous mobile networks, that is, all the devices collect
the same type of data. In this paper, we address distributed
training of ML algorithms in heterogeneous mobile networks
where the features, rather than the samples, are distributed across
multiple heterogeneous mobile devices. Training ML models in
heterogeneous mobile networks incurs a large communication
cost due to the necessity to deliver the local data to a central
server. Inspired by active learning, which is traditionally used
to reduce the labeling cost for training ML models, we propose
an active sampling method to reduce the communication cost of
learning in heterogeneous mobile networks. Instead of sending all
the local data, the proposed active sampling method identifies and
sends only informative data from each device to the central server.
Extensive experiments on four real datasets, both with numerical
simulation and on a networked mobile system, show that the
proposed method can reduce the communication cost by up to
53% and energy consumption by up to 67% without accuracy
degradation compared with the conventional approaches.

Evaluation environment using edge computing for artificial intelligence-based irrigation system

Takaaki Kawai, Hiroshi Mineno (Shizuoka University, Japan)

It is a concern that Japanese high-level agriculture
represented by high-sugar tomato cultivation will not be inherited
by new farmers and will be lost due to the aging of
Japanese agricultural workers. Artificial intelligence (AI) models
for irrigation control have been proposed to automate high-sugar
tomatoes cultivation. The traditional AI models have been run on
the cloud, which requires a stable communication infrastructure.
However, greenhouses tend not to have such infrastructures. They
use unstable mobile communication, which causes disconnection
and delay. Therefore, it is difficult to control the field irrigation
by the AI models on the cloud.
In this paper, we propose an automatic irrigation system that
is not affected by communication disconnection and delay. To
achieve this, we use edge nodes, which are small computers
operating in farm fields. In our study, we select an edge node
and conduct an experiment to evaluate its suitability for the
irrigation system. Our findings indicate that the system meets
the requirements for an irrigation system, and that it could be
used to evaluate other AI-based systems used in cultivation of
high-sugar tomatoes.

A QoE-based 360° Video Adaptive Bitrate Delivery and Caching Scheme for C-RAN

Shunyi Wang, Xiaobin Tan, Simin Li, Xiang Xu, Jian Yang, Quan Zheng (University of Science and Technology of China, China)

With the development of Virtual Reality (VR) technology,
the growing number of VR users puts tremendous pressure
on network bandwidth. The tile-based scheme is proposed
to reduce the transmission size of 360 video and improve
bandwidth utilization. However, when the Field of View (FoV) of
the user changes unexpectedly, the tile-based scheme will cause
video distortion and quality switching by unacceptable delay.
Therefore, many methods are proposed to cache the tiles that
users are most likely to playback in Cloud/Edge to decrease
delay. However, the dynamic adaptive bitrate delivery and the
caching decision is a complex joint optimization problem, which
will be a dimensional explosion problem when the scale of users
and videos is large. In this paper, we design a QoE-based 360
video adaptive bitrate delivery and caching scheme aiming to
maximize the quality of experience (QoE) of multi-user and
ensure the fairness of users. To solve this optimization problem
which is proved to be NP-Hard, we propose a bitrate selection
and caching decision algorithm by greedy strategy. Numerical
simulation results demonstrate that our algorithm significantly
improves cache hit rate and QoE performance compared with
other algorithms with fairness guaranteed.

Session Chair

Celimuge Wu (The University of Electro-Communications, Japan)

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Session S13

Mobile & Wireless Networks (II)

1:50 PM — 4:10 PM JST
Dec 18 Fri, 11:50 PM — 2:10 AM EST

A FAIR Extension for the MQTT Protocol

Dariush Salami (Aalto University, Finland); Olga Streibel, Marcus Rhenius (Bayer AG); Stephan Sigg (Aalto University, Finland)

We address the realization of the Findability,
Accessibility, Interoperability, and Reusability (FAIR) data
principles in an Internet of Things (IoT) application through a
data transfer protocol. In particular, we propose an architecture
for the Message Queuing Telemetry Transport (MQTT) protocol
that validates, normalizes, and filters the incoming messages
based on the FAIR principles to improve the interoperability
and reusability of data. We show that our approach can
significantly increase the degree of FAIRness of the system by
evaluating the architecture using existing maturity indicators in
the literature. The proposed architecture successfully passes 18
maturity indicators out of 22. We also evaluate the performance
of the system in 4 different settings in terms the latency and
dropped messages in a simulation environment.We demonstrate
that the architecture not only improves the degree of FAIRness
of the system but also reduces the dropped messages rate.

AirQ: A Privacy-Preserving Truth Discovery Framework for Vehicular Air Quality Monitoring

Rui Liu, Jianping Pan (University of Victoria, Canada)

Air pollution has become an important health
concern. The recent developments of vehicular networks and
crowdsensing systems make it possible to monitor fine-grained
air quality with vehicles and road-side units. On account of the
different precisions of onboard sensors and malicious behaviors
of participants, sensory data usually vary in quality. Thus, truth
discovery has been a crucial task which targets at better utilizing
the data. However, in urban cities, there is a significant difference
in traffic volumes of streets or blocks, which leads to a data
sparsity problem for truth discovery. To tackle the challenge,
we present a truth discovery algorithm incorporating spatial
and temporal correlations. Besides, to protect the privacy of
participating vehicles, we develop the algorithm into a privacypreserving
truth discovery framework by adopting the technique
of masking. The proposed framework is lightweight than the
existing cryptography-based methods. Simulations are conducted
to show that the proposed framework has a good performance.
Although the framework is presented for air quality monitoring,
we fully discuss the possible applications and extensions.

Jointly Video Bitrate Adaptation and Multicast Resource Allocation in Mobile Edge Networks

Simin Li, Xiaobin Tan, Shunyi Wang, Jian Yang, Quan Zheng (University of Science and Technology of China, China)

Current schemes for Dynamic Adaptive Streaming
over HTTP (DASH) are mainly client-driven. Thus, in the
scenario of multiple users watching the same video, repeated
subscription and data transmission results in an under-utilization
of network bandwidth resources. Additionally, competition for
limited network resources of individual users may motivate selfish
behaviors, which leads to unfairness and sub-optimal utility of
video services. In this paper, Multimedia Broadcast Multicast
Service (MBMS) in mobile edge networks for multi-bitrate video
sessions is applied to overcome these limitations. We formulate
a non-linear integer programming (NLIP) model, which jointly
optimize bitrate adaptation and resource allocation for multiple
users. This model takes video quality, playback interruptions, and
quality oscillations as linear constraints to maximize multicast
users’ Quality of Experience (QoE). Due to NP-Hardness of this
problem, we propose a heuristic greedy algorithm, which can
work out the optimal or near-optimal solution with low time
complexity. The evaluation results demonstrate that our method
can achieve Pareto Optimality of the system utility, and maximize
users’ QoE while ensuring fairness.

Data Collection Strategy Based on Drone Technology in Wireless Sensor Networks

Bo Yang, Xiangyu Bai (Inner Mongolia University, China)

In recent years, drone technology has developed rapidly. Drone’s low cost, fast and flexible deployment, as well as strong mobility have made it possible to use drone-assisted sensor networks for data collection tasks. In this way, data collection nodes can break through the movement path restriction of traditional nodes, broaden the spatial movement range of nodes, and it is more suitable for data collection in complex environments. In this paper, we proposed a data collection strategy based on drone technology in Wireless Sensor Networks. Kmeans++ clustering method is used for auxiliary clustering and cluster head election in the initial state, which significantly improves the final error of the clustering result. Then, we used drone to assist cluster head election and data collection, which comprehensively considering the relative distance of every sensor node in the cluster and their relative remaining energy. In addition, for some nodes that have not been elected in the previous specified round, a reasonable priority is set to make the energy consumption of sensor nodes in the entire network more balanced. At the same time, we excluded the influence of dead nodes. Compared with many new methods proposed in recent years, the data collection strategy proposed delays the death time of the sensor nodes, reduces the overall energy consumption of the sensor nodes, and has a better performance. This work provides new ideas for the future work.

Wireless Information and Energy Provision with Practical Modulation in Energy Self-Sustainable Wireless Networks

Jie Hu, Li Zhang, Qin Yu (University of Electronic Science and Technology of China, China); Kun Yang (University of Essex, UK)

Due to its long-distance propagation, radiofrequency
(RF) signals have been relied upon for remotely
charging low-power communication devices, which significantly
extends the lifetime of battery-powered devices. Delivering both
wireless information provision (WIP) and wireless energy provision
(WEP) services in the RF band requires a holistic design of
wireless information and energy provision (WIEP) for achieving
energy self-sustainability in the next generation of wireless
networks, such as 6G. However, most of the existing works
studied the integrated WIEP performance by exploiting Gaussian
distributed signals with the infinite alphabet, which cannot
be realised in a practical communication system. Therefore,
we investigate the WIEP performance by considering practical
modulation schemes having finite alphabet in a well-known
Nakagami-m wireless fading channel. Furthermore, we jointly
optimise the transmit power allocation and the transmission
switching threshold of a WIEP transmitter and the power
splitting ratio of a WIEP receiver in order to maximise the
attainable spectrum efficiency for WIP, while satisfying both the
WEP requirement and the WIP reliability constraint. Numerical
results are provided for characterising the rate-energy-reliability
trade-off of different modulation schemes in various wireless
fading conditions.

A Priority Task Scheduling Algorithm based on Residual Energy in EH-WSNs

Wuyungerile Li, Gao Haode, Yingcong Liu, Bing Jia, Baoqi Huang (Inner Mongolia University, China)

Energy Harvesting Wireless Sensor Networks
(EHWSNs) have been widely studied in recent years. In solar
charged EH-WSNs, the Sun illumination changes with the
changes of environment, in consequence the collected energy of
the sensor node is unstable, especially in rainy day, windy day or
the angle of the solar panel changes. Therefore, the reasonable
assignment of energy in EH-WSNs becomes critical important. In
This paper, based on the solar energy charateristics, we propose
a priority task scheduling algorithm that suitable for EH-WSNs,
that is, the transmission method and order of collected data
are determined according to task priority and the remaining
energy of the node. The simulation results show that the priority
task scheduling algorithm guarantees the fairness of node energy
distribution, the timeliness of sending urgent tasks and the high
processing rate of common tasks when the energy provided by
the environment is small.

Energy-efficient Trajectory Planning and Speed Scheduling for UAV-assisted Data Collection

Weidu Ye, Wenjia Wu, Feng Shan, Ming Yang, Junzhou Luo (Southeast University, China)

Unmanned aerial vehicle (UAV) assisted data collection
is a promising technology, where a base station (BS) is
mounted on a UAV to collect data from ground sensors (GSs).
However, it is very challenging to save the energy of UAV while
completing the tasks of data collection. In this work, a novel
energy consumption model of UAV is adopted, where the UAV flies
at a proper speed is the most energy efficient, i.e., the UAV will
cost more energy when it flies faster or slower. According to this
model, we investigate the Energy-efficient Trajectory Planning
and Speed Scheduling (ETPSS) problem, aiming at minimizing
the total energy consumption of UAV by determining flight trajectory
and speed of UAV while completing the task of data collection
for each GS. To solve this problem, we decompose it into two
sub-problems, i.e., trajectory design and speed scheduling, and
propose a three-step scheme named Energy-efficient Trajectory
and Speed Optimization (ETSO). Moreover, the second step of
ETSO optimally solves the speed scheduling sub-problem. Finally,
we conduct simulation experiments, and the results demonstrate
that the ETSO performs well on energy efficiency.

Session Chair

Muhammad Aslam (Wuhan University, China)

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Session S14

Edge and Fog Computing (II)

1:50 PM — 4:10 PM JST
Dec 18 Fri, 11:50 PM — 2:10 AM EST

Joint Service Caching and Computation Offloading to Maximize System Profits in Mobile Edge-Cloud Computing

Qingyang Fan (Harbin Engineering University, China); Junyu Lin (Institute of Information Engineering, Chinese Academy of Sciences, China); Guangsheng Feng, Zihan Gao (Harbin Engineering University, China); Huiqiang Wang (Harbin Engineering University and Peng Cheng Laboratory, China); Yafei Li (Harbin Engineering University, China)

Considering the advantages of mobile edge computing
(MEC), such as low latency, high bandwidth, etc., more
and more mobile services are cached to mobile edge servers.
However, due to limited computing resources and storage capacity
of mobile edge servers, it is hard to guarantee that all
services are cached and all computation offloading requests are
satisfied. In this paper, we jointly optimize service caching and
computation offloading to maximize system profits in mobile
edge-cloud computing (MECC). The problem is formalized as
a nonconvex optimization problem with discrete variables. We
propose a Dynamic Joint computation Offloading and Service
Caching algorithm (DJOSC) to solve the problem. Specifically, a
regularization technique and Lyapunov optimization theory are
used to transform the problem into two subproblems, which are
solved by convex optimization techniques. Numerical evaluations
show that the maximum system profits can be achieved under
different computing resources, storage capacities and bandwidth

Urban Mobility Prediction based on LSTM and Discrete Position Relationship Model

Ming Tao, (Dongguan University of Technology, China); Geng Sun (Guangdong University of Technology and Guangdong University of Technology, China); Tian Wang (Huaqiao University, China)

In the context of edge and fog computing, urban mobility prediction acting as an important role in urban planning, traffic prediction, and resource reservation has making a great contribution for the construction of smart cities, and has been considered to be a challenging research and industrial topic for many years. Generally, those popular prediction methods abstract trajectories into independent points in the manner of gridding, clustering and others. However, these data processing methods make the position representation vector lose the connection relationship between geographic locations which is very important for the mobility prediction. To address this issue, this paper proposes a discrete position relationship model to represent the connection between geographic locations, on the basis, a Long Short Term Memory (LSTM) prediction model is established to predict the next position of the mobile target. Experiments and numerical analyses show that these investigations can take full advantage of the relationship between relative positions and reduce the prediction relative error.

SDN Based Computation Offloading for Industrial Internet of Things

Gen Li, Shutian Hua, Liang Liu, Xiaolong Zheng, Huadong Ma (Beijing University of Posts and Telecommunications, China)

As a new type of highly collaborative and shared
intelligent network between producers and production environments,
Industrial Internet of Things (IIOT) has been taken
an important part of the fourth industrial revolution. IIOT
generates large amounts of sensory data which need to be
processed rapidly. However, the cloud-based data processing
method consumes a long time and huge network overhead, which
further affects the quality of service. On the other hand, the
emerging edge computing also cannot process data efficiently
because of limited compute and network resource. In this paper,
we propose a four-layer network architecture based on SDN
for the industrial internet of things scenario. Through effective
transmission and computation coupling, the processing response
efficiency is improved. We present a three-level computation
offloading method to realize the optimization of network delay
and power consumption. Theory and experiments show that
the method proposed in this paper can effectively reduce the
computation power consumption and response time.

Analysing Ballistocardiography for Pervasive Healthcare

Roni Hytönen, Alison Tshala, Jan Schreier, Melissa Holopainen, Aada Forsman, Minna Oksanen, Rainhard Dieter Findling, Le Ngu Nguyen, Stephan Sigg (Aalto University, Finland); Nico Jähne-Raden (Hannover Medical School, Germany)

We describe a methodology to measure ballistocardiography
(BCG) signals from the body surface, using body-worn
digital accelerometers to extract medically relevant information
for Pervasive Healthcare. We are able to measure measuring
heart rate with an 95% accuracy as well as other cardiac metrics,
such as the S1-S2 interval, deviating from ECG by only 1.3%.
Our results show that BCG can be a viable alternative to an
electrocardiogram to provide complementary information on the
heart’s condition in mobile and pervasive use cases. We further
show that BCG information can be detected from arm as reliably
as from chest, which is especially convenient for measuring from
supine positions in Pervasive healthcare applications.

Energy-saving Strategy for Edge Computing by Collaborative Processing Tasks on Base Stations

Zhongjun Ma, Zhenchun Wei, Wenjie Zhang, Zengwei Lyu, Junyi Xu, Benhong Zhang (Hefei University of Technology, China)

This talk does not have an abstract.

Real-time Task Offloading for Data and Computation Intensive Services in Vehicular Fog Computing Environments

Chunhui Liu, Kai Liu, Xincao Xu, Hualing Ren, Feiyu Jin, Songtao Guo (Chongqing University, China)

Recent advances in wireless communication, sensing,
and computing technologies have paved the way for the development
of a new era of Internet of Vehicles (IoV). Nevertheless,
it is challenging to process data and computation intensive tasks
with strict time constraints due to heterogeneous communication,
storage, and computation capacities of IoV network nodes, spotty
wireless connections in vehicles and infrastructures, unevenly
distributed workload, and high vehicles mobility. In this paper, we
propose a two-layer vehicular fog computing (VFC) architecture
to explore the synergistic effect of the cloud, the fog nodes, and
the terminals on processing data and computation intensive IoV
tasks. Then, we formulate the real-time task offloading model,
aiming at maximizing the task service ratio. Further, considering
the dynamic requirements and resource constraints, we propose
a real-time task offloading algorithm to adaptively categorize
all tasks into four types, and then cooperatively offload them.
Finally, we build the simulation model and give a comprehensive
performance evaluation, which validates the performance of the
proposed method.

MEC-Assisted FoV-Aware and QoE-Driven Adaptive 360° Video Streaming for Virtual Reality

Chih-Ho Hsu (National Taiwan University, Taiwan)

Virtual reality (VR) has been envisioned as the
killer-application in the 5G mobile networks. Among numerous
VR services, 360 video streaming is the most promising one.
Nevertheless, its wide adoption is hindered by large latency
incurred in cloud-based video delivery and insufficient bandwidth
resource in Radio Access Network (RAN). Fortunately, the
emergence of Multi-access Edge Computing (MEC) become an
enabler to fulfill the potential of VR by providing caching and
computing resources at network edges. Also, since a user can view
only a part of the entire 360 video frame due to the limitation
of eye vision, users’ Quality of Experience (QoE) can be further
enhanced if we can predict his Field of View (FoV). In this
paper, we propose a novel MEC-assisted FoV-aware and QoEdriven
Adaptive Streaming (MFQAS) scheme for 360 videos.
Specifically, we first provide a comprehensive QoE model for
360 video streaming. Second, we adopt AutoRegression Moving
Average (ARMA) model in FoV prediction. Finally, we propose
a heuristic algorithm to optimize the caching and computing
decision at MEC server based on predicted FoV so that users’
QoE can be enhanced. The simulation results show that our
proposed method can provide much better prediction accuracy
and QoE compared with baseline algorithms.

Session Chair

Hideki Tode (Osaka Prefecture University, Japan)

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Session S15

Security, Privacy & Trust (III)

1:50 PM — 4:10 PM JST
Dec 18 Fri, 11:50 PM — 2:10 AM EST

Markov Probability Fingerprints: A Method for Identifying Encrypted Video Traffic

Luming Yang, Shaojing Fu, Yuchuan Luo, Jiangyong Shi (National University of Defense Technology, China)

Detecting illegal video plays an important role in
preventing and countering crime in daily life. It is effective
for supervisors to monitor the network by analyzing traffic
from devices. In this way, illegal video can be detected when
it is played on the network. Most Internet traffic is encrypted,
which brings difficulties to traffic analysis. However, many
researches suggest that even if the video traffic is encrypted, the
segmentation prescribed by Dynamic Adaptive Streaming over
HTTP (DASH) causes content-dependent fragments, which can
be used to identify the encrypted video traffic without decryption.
This paper presents Markov probability fingerprint for video,
and then designs an algorithm for encrypted video streaming
title identification. We demonstrate that an external attacker can
identify the video title by analyzing the fragment sequence of
encrypted video traffic. Based on the m-order Markov chain, we
use the transition tensor of the fragment sequence generated
by the video traffic as the video fingerprint, and prove its
effectiveness. Then we explore approaches that can further
improve the performance of methods in terms of discrimination
accuracy. We make promising observations that the higher-order
Markov chain, larger training set, and more detailed binning of
fragments contribute to encrypted video traffic discrimination.
We run a thorough set of experiments that illustrate that our
method can achieve an outstanding accuracy rate up to 97.5%,
which is superior to previous work.

An Efficient Data Aggregation Scheme with Local Differential Privacy in Smart Grid

Na Gai, Kaiping Xue, Peixuan He, Bin Zhu (University of Science and Technology of China, China); Jianqing Liu (University of Alabama in Huntsville, USA); Debiao He (Wuhan University, China)

Smart grid achieves reliable, efficient and flexible
grid data processing by integrating traditional power grid with
information and communication technology. The control center
can evaluate the supply and demand of the power grid through
aggregated data of users, and then dynamically adjust the power
supply, price of the power, etc. However, since the grid data
collected from users may disclose the user’s electricity using
habits and daily activities, the privacy concern has become
a critical issue. Most of the existing privacy-preserving data
collection schemes for smart grid adopt homomorphic encryption
or randomization techniques which are either impractical
because of the high computation overhead or unrealistic for
requiring the trusted third party. In this paper, we propose a
privacy-preserving smart grid data aggregation scheme satisfying
local differential privacy (LDP) based on randomized response.
Our scheme can achieve efficient and practical estimation of
the statistics of power supply and demand while preserving
any individual participant’s privacy. The performance analysis
shows that our scheme is efficient in terms of computation and
communication overhead.

Detection of malicious nodes in drone ad-hoc network based on supervised learning and clustering algorithms

Shanshan Sun, Zuchao Ma, Liang Liu, Hang Gao, Jianfei Peng (Nanjing University of Aeronautics and Astronautics, China)

Multi-drone swarm has been widely used in disaster
monitoring, mapping and remote sensing, national defense
military and other fields, and has become a research hotspot in
recent years. Due to the openness of its operating environment,
attackers can invade the control system to capture drone, and
then carry out data attacks such as tamper attack, drop attack
and replay attack in drone ad-hoc network, which causes a great
threat to the security of drone network. Existing malicious nodes
detection algorithms are not efficient when applied to drone
ad-hoc network, for the following reasons: (1) The malicious
node detection algorithms based on reputation usually adopt
a static threshold to determine whether a node is malicious,
which is inefficient in dynamic drone network. (2) Mutual
cooperation based malicious node detection algorithms rely
on the high meeting probability of nodes. In order to solve
the above problems, we propose a Malicious Drones Detection
Algorithm(MDA) based on supervised learning and clustering
algorithms. The ground station calculates the reputation value
of each routing path according to the received packets from
different source nodes, and then evaluates the reputation value
of drones with linear regression algorithm. Finally, gaussian
clustering algorithm is used to cluster drones and find out
malicious drones. Experiments were conducted in indoor and
outdoor drone network. The experimental results indicate that
the accuracy of MDA outperforms the existing methods by 10%-
20%. And in the case of fewer malicious nodes, the accuracy can
reach more than 90%, and the error rate is less than 10%.

Lifetime Improvements of Smart Sensors Maintenance Protocol in Prospect of IoT-based Rampal Power Plant

Syed Bilal Hussain Shah, Wang Lei (Dalian University of Technology, China); Md Ershadul Haque (Feni University, Bangladesh); Md Jahirul Islam (Z. H. Sikder University of Science & Technology Shariatpur, Bangladesh), Anil Carie (VIT-AP Amaravati, India); Neeraj Kumar (Thapar Institute of Science and Technology Patiala, India)

In the 21st century, the power quality and availability
with customer demands to the society is the main challenging
factor right now. Therefore, the gird monitoring system
become a vital issue to monitor power grid system. The current
smart grid system mainly focuses on smart metering system
and improving the customer utility communication system.
On customer management side, although those advancement
provides an extra benefit, in spite of, the management of a grid
system is one of the major dominating field in the era of Internet
of Thing (IoT). From the field of industry and academia
researches, Wireless sensor networks (WSNs) is getting to
much popularity for monitoring power grid. Moreover, saving
node energy enhance the lifespan of whole monitoring network.
Due to lack of energy making policy, unnecessary activating
all participate nodes consume node energy drastically, which
is the main reason for shortening the lifetime of monitoring
system. To solve this issue, maintenance technology provides
the best opportunity to preserve node energy. This study
investigates the issues that are associated with energy consumption
using maintenance protocols in prospect of Rampal,
Bangladesh power plant data. The modelling data has been
collected through literature survey. Extensive simulation work
has done for monitoring Rampal power using WSN. Finally, a
comparative study of maintenance protocols were performed to
maintain optimal network correction and thereafter extending
the lifetime of monitoring network.

BIA: A Blockchain-based Identity Authorization Mechanism

Xiaodong Ren, Feilong Lin, Zhongyu Chen, Changbing Tang, Zhonglong Zheng, Minglu Li (Zhejiang Normal University, China)

The abuse of personal identity information is one
of the most serious problems worldwide. Most social services
or businesses use the identity authorization to confirm their
validity and legality and the copies of users’ identity certification
are usually recorded by the service providers. It is easy to
leak the users’ identity information due to the untrustworthy
service provider or single-point security failure, and various
social problems are then caused. To deal with such problems,
this paper proposes a Blockchain-based Identity Authorization
mechanism (BIA). First, an Identity Authorization Module (IAM)
is devised, which reads the identity certificate and transform the
identity plaintext to ciphertext under the authorization by the
user’s identity certificate entity and password. IAM guarantees
the security of identity information by keeping its plaintext offline.
Second, a Business Contract Module (BCM) is designed,
which provides a general smart contract framework for identity
authorization that can be adopted by most of social services
or businesses. Third, a double-chain blockchain infrastructure
is developed, whereby the encrypted identity information and
service smart contracts are respectively recorded in the tamperresistant,
non-repudiable, and publicly verifiable way. Finally,
a prototype system has been developed to verify the security,
feasibility and effectiveness of the proposed BIA.

Weighted Local Outlier Factor for Detecting Anomaly on In-Vehicle Network

Yuan Linghu, Ming Xu, Xiangxue Li and Haifeng Qian (East China Normal University, China)

Modern vehicles are generally equipped with dozens
of (or even hundreds of) electronic and intelligent devices
and bloom into more involved information hub in enabling
V2X networking. Protecting this increasingly complex vehicle
ecosystem can be an arduous task, especially as the proliferation
of data across distinct connected devices makes them more
vulnerable than ever before. Intrusion detection systems (IDSs)
have been found extremely rewarding in monitoring in-vehicle
network traffic and detecting potential intrusions. The paper
presents WLOF-InV, a novel unsupervised method based on
local density for IDS on in-vehicle network. Given historical invehicle
data of message identifiers, WLOF-InV first segments the
traffic into a slice of (e.g., m) sliding windows. For each sliding
window, WLOF-InV exerts information gain to select features
for dimensionality reduction and squeezes out n features which
are then bundled together to form a row vector and eventually
gets an mn matrix. WLOF-InV then adaptively determines the
hyperparameters for local outlier factor (LOF) model (optimizing
the scores for ranking the training data and the cutoff position
for anomalies). In online detection, WLOF-InV determines the
features by the information gain and invokes abnormal score
weighting mode (which weights the LOF value of each dimension
data by entropy method) to obtain the complete LOF score (of
the overall traffic), and thereby grabs the anomaly traffic by
resorting to the adjusted model. WLOF-InV is validated on the
real data of three attack types (DoS, fuzzy, and impersonation).
Experimental results demonstrate that WLOF-InV contrives next
to optimal performance.

SASAK: Shrinking the Attack Surface for Android Kernel with Stricter "seccomp" Restrictions

Yingjiao Niu, Lingguang Lei, Yuewu Wang, Jiang Chang, Shijie Jia, Chunjing Kou (Institute of Information Engineering, Chinese Academy of Sciences, China)

The increasing vulnerabilities in Android kernel
make it an attractive target to the attackers. Most kernel-targeted
attacks are initiated through system calls. For security purpose,
Google has introduced a Linux kernel security mechanism
named “seccomp” since Android O to constrain the system calls
accessible to the Android apps. Unfortunately, existing Android
seccomp mechanism provides a fairly coarse-grained restriction
by enforcing a unified seccomp policy containing more than
250 system calls for Android apps, which greatly reduces the
effectiveness of seccomp. Also, it lacks an approach to profile
the unnecessary system calls for a given Android app. In this
paper we present a two-level control scheme named SASAK,
which can shrink the attack surface of Android kernel by strictly
constraining the system calls available to the Android apps with
seccomp mechanism. First, instead of leveraging a unified seccomp
policy for all Android apps, SASAK introduces an architecturededicated
system call constraining by enforcing two separate
and refined seccomp policies for the 32-bit Android apps and
64-bit Android apps, respectively. Second, we provide a tool to
profile the necessary system calls for a given Android app and
enforce an app-dedicated seccomp policy to further reduce the
allowed system calls for the apps selected by the users. The
app-dedicated control could dynamically change the seccomp
policy for an app according to its actual needs. We implement
a prototype of SASAK and the experiment results show that
the architecture-dedicated constraining reduces 39.6% system
calls for the 64-bit apps and 42.5% system calls for the 32-bit
apps. 33% of the removed system calls for the 64-bit apps are
vulnerable, and the number for the 32-bit apps is 18.8%. The
app-dedicated restriction reduces about 66.9% and 62.5% system
calls on average for the 64-bit apps and 32-bit apps, respectively.
In addition, SASAK introduces negligible performance overhead.

Session Chair

Kaigui Bian (Peking University, China)

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