2nd International Workshop on Edge Computing and Artificial Intelligence based Sensor-Cloud System & Workshop on 5G/B5G Security

Session ECAISS-S1

ECAISS & 5G/B5G Session 1

10:00 AM — 11:40 AM JST
Dec 16 Wed, 8:00 PM — 9:40 PM EST

Verifiable Blockchain-Based Searchable Encryption with Forward and Backward Privacy

Ruizhong Du, Yi Wang (Hebei University, China)

Symmetric Searchable Encryption (SSE) is an ideal
technology to secure searches. SSE is dedicated to improving
data confidentiality without sacrificing the searchability of data.
Currently, most SSE schemes work using a semihonest or curious
cloud server model in which the search results are not absolutely
trustworthy. Therefore, a verifiable SSE (VSSE) scheme is
proposed to achieve data integrity verification. Although there
are some solutions to the result verification problem, most of the
schemes concentrate on a static environment and do not consider
the issue of result verification in a dynamic environment.
Therefore, this paper proposes a Verifiable Blockchain-Based
Searchable Encryption with forward and backward privacy
scheme (VBSEFB). First, VBSEFB supports local verification of
the data returned by the cloud server in a dynamic environment.
Second, our solution perfectly realizes forward and backward
privacy and better guarantees data security than other methods.
Finally, under the one-to-many model, the data owner's access
control to data users is realized. A large number of experiments
and evaluations prove the practicality of dynamically updating
encrypted data.

A Game-Theoretical Approach for Task Offloading in Edge Computing

Juan Luo, Qian Qian, Luxiu Yin, Ying Qiao (Hunan University, China)

Edge computing is envisioned as a prominent technology
that provides high computing demand services by offloading
computation-intensive and delay-sensitive task from mobile or Internet
of Things(IoT) devices to nearby edge servers. However, more and
more edge servers are deployed near the terminal devices. The uneven
distribution of devices will lead to insufficient resource utilization of
edge servers, so the social benefits of the edge computing system
decrease. In this paper, we propose an effective task offloading strategy
in the scenario of multi-users and multi-edge servers. Terminal
devices broadcast task offloading request to edge servers, and the
edge servers compete for tasks to improve their resource utilization.
We use a non-cooperative game to describe the competition of edge
servers, and model an optimization problem as the multi-edge servers
resource allocation problem. We then design an iterative algorithm
to solve the optimization problem and prove that the problem has an
unique Nash equilibrium. Simulation results show that the proposed
offloading strategy not only improves the resource utilization of edge
servers, but also guarantees the demand of terminal devices.

A Novel Service Composition Approach for Offloading in Mobile Edge Computing

Dandan Liu (Wuhan University, China), Nitesh Krishna (University of Ottawa, Canada), Amiya Nayak (University of Ottawa, Canada)

Computational offloading in Mobile Edge Computing (MEC) environment can help reduce energy consumption and response time in network, thus improve users’ experience. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. In this paper, we propose to leverage the idea of Software-Defined Networking (SDN) and Service Composition (SC) for such problem. We propose a software-defined service composition model and formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user-centric approach. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. The experimental results demonstrate that our approach can obtain better performance than existing methods.

Dependency-Aware Dynamic Task Scheduling in Mobile-Edge Computing

Mingzhi Wang, Tao Ma, Tao Wu, Chao Chang, Fang Yang, Huaixi Wang (NUDT, China)

With the popularity and development of the Internet
of things (IoT), human life has been deeply affected. Because of
the limitations of computation capability and battery capacity,
it is difficult for IoT devices to support frequent and complex
computing. Motivated by this challenge, many works attempt to
upload tasks of IoT devices to the cloud center for computation.
However, because of the limitation of distance and bandwidth,
cloud computing is difficult to guarantee low latency. As a feasible
solution, Mobile Edge Computing (MEC) has attracted more and
more attention. Most existing works focus on the computation
offloading strategy, while the task scheduling on edge servers
is not studied in depth. The tasks uploaded by IoT devices
are dynamic and random, and there are dependencies between
these tasks. Therefore, it is difficult for edge servers to find a
task scheduling scheme to minimize the task execution delay.
In this paper, to solve the task scheduling problem of edge
server in multi-server and multi-user MEC system, we propose
a heuristic algorithm based on the following three scenarios:
1) Tasks uploaded by IoT devices is dynamic and uncertain.
2) There are dependencies between tasks. 3) The computation
capability of the edge server is limited. Experimental results show
that the proposed algorithm can significantly reduce the overall
completion time of tasks and the average task execution delay in
the edge server.

Game Theory based Joint Task Offloading and Resource Allocation Algorithm for Mobile Edge Computing

Ning Li, Jianen Yan, Zhaoxin Zhang, Xin Yuan (Harbin Institute of Technology, China)

Mobile edge computing (MEC) has emerged for reducing energy consumption and latency by allowing mobile users to offload computationally intensive tasks to the MEC server. Due to the spectrum reuse in the network of MEC, the inner-cell interference has a great effect on MEC’s performance. In this paper, for reducing the energy consumption and latency of MEC, we propose a game theory based approach to join task offloading decision and resource allocation together in the MEC system. In this algorithm, the offloading decision, the CPU capacity adjustment, the transmission power control, and the network interference management of mobile users are regarded as a game. In this game, based on the best response strategy, each mobile user makes their own utility maximum rather than the utility of the whole system. We prove that this game is an exact potential game and the Nash equilibrium (NE) of this game exists. We also investigate the properties of this algorithm, including the convergence, the computational complexity, and the Price of anarchy (PoA). We evaluate the performance of this algorithm by simulation. The simulation results illustrate that this algorithm is effective in improving the performance of the multi-user MEC system.

Session Chair

Haipeng Dai (Nanjing University, China)

Session ECAISS-S2

ECAISS & 5G/B5G Session 2

1:00 PM — 2:40 PM JST
Dec 16 Wed, 11:00 PM — 12:40 AM EST

A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring

Salahadin Seid (Addis Ababa University, Ethiopia), Marco Zennaro (ICTP, Italy), Mulugeta Libse (Addis Ababa University, Ethiopia), Ermanno Pietrosemoli (ICTP, Italy), Pietro Manzoni (Universitat Politècnica de València, Spain)

Traffic congestion is a major problem in many
cities. It happens due to the demand-supply imbalance in the
transportation network and poor management. Traffic flow slows
down when the number of vehicles that travels on the road
increases or the roadway capacity decreases due to various
reasons. In order to solve this problem, different solutions are
proposed to provide reliable, real-time transport management
services in an Intelligent Transportation System (ITS). In this
paper, we propose a novel real-time video analytics using lowcost
IoT devices and LoRaWAN networks to realize new services
and applications that include traffic management through IoT
edge computing. The use of LoRaWAN for such application is our
main contribution.We retrain YOLO v3 object detection machine
learning model (transfer learning) for vehicle detection and
counting, to make it lightweight and fast enough to be able to run
on a Raspberry Pi, a single-board computer with limited RAM.
The edge node, with low-cost smart camera and connectivity
through LoRaWAN networks counts the number of vehicles
using real-time video analytic and report only traffic count to
the server. This experimental work provides insight into the
applicability of a low-cost IoT system to traffic management with
a resource-constrained environment. Real-world video analysis
of vehicle detection and counting show the effectiveness of the
designed solution. The results demonstrate the effectiveness of
the proposed approach.

An Optimal Wireless Transmission Strategy based on Coherent Beamforming and Successive Interference Cancellation for Edge Computing

Zhehao Li (HeFei University of Technology, China), Lei Shi (HeFei University of Technology, China), Yi Shi (Intelligent Automation Inc, United States), Yuqi Fan (HeFei University of Technology, China), Zhenchun Wei (HeFei University of Technology, China)

In general, edge devices and edge servers in edge
computing environment communicate with each other by wireless
network, which put forward a high requirement for end-to-end
wireless communication performance. In this paper, we propose
an optimal strategy by combining the coherent beamforming
(CB) technique and the successive interference cancellation (SIC)
technique for improving the performance of the edge device
communications. CB technique can be used for expanding the
transmitter’s transmitting range, while SIC technique can be
used for improving the receiver’s receiving ability. However, when
these two techniques are used jointly, interference will occur
between transmitters and receivers, which makes the CB-SIC
strategy hard to be designed. We first give the mathematical
model based on CB-SIC and show it is difficult to solve directly.
Then, we design a heuristic algorithm called time slot loop
allocation (TSLA) algorithm. TSLA is based on greedy strategy
to obtain an approximate optimal solution. By using TSLA, the
whole scheduling time will be divided into many time slots. In
each time slot, we try to make as many edge devices as possible
to transmit data to the server. These can increase the overall data
throughput. In simulation, we compare CB-SIC wireless network
with CB only, SIC only, and traditional multi-hop network.
Simulation results show that the TSLA algorithm can improve
the end-to-end communication performance in edge computing

Virtual Machine Security Migration Strategy Based on the Edge Computing Environment

Ruizhong Du, Wangkai He, Junfeng Tian (Hebei University, China)

For mobile edge computing, the migration time between traditional cloud servers and edge devices is long, and there are security issues such as man-in-the-middle attacks in the process. In this regard, a migration scheme centered on edge nodes is proposed. The edge node is closer to the edge device, which can shorten the migration time. The solution uses the Transport Layer Security (TLS) protocol for key exchange to establish a session-secure communication channel, and virtual machine migration between edge devices is carried out in the channel by dynamic migration. The simulation results show that compared with that of other schemes, the migration time of the virtual machines is shortened. Security analysis shows that this solution can not only meet the requirements of data confidentiality and integrity but also resist man-in-the-middle attacks.

Load Balancing Mechanisms of Unmanned Surface Vehicle Cluster Based on Marine Vehicular Fog Computing

Kuntao Cui, Wenli Sun, Bin Lin, Wenqiang Sun (Dalian Maritime University, China)

The traffic explosion and the rising of diverse
requirements lead to many challenges for the mobile networks on
flexibility, scalability, and deployability. To meet the challenges,
end-to-end network slicing and mobile edge computing (MEC)
are introduced into beyond fifth-generation (B5G) mobile communication
networks. However, the edge servers are limited by
computing and data processing capabilities, how to implement
efficient load balancing and resource management in data center
are still challenges for B5G network slices. In this paper, we
first propose a model of load balancing for B5G network slices
and formulate a total cost minimization problem, considering
the energy cost, processing delay, backhaul bandwidth cost,
and revenue loss associated with the backhaul delay. Then, a
distributed algorithm based on Proximal Jacobian Alternating
Direction Method of Multipliers (PJ-ADMM) is proposed to solve
the formulated problem. Simulations are conducted to show that
the proposed algorithm

Accurate IoT Device Identification from Merely Packet Length

Yizhen Sun (State Grid Information & Communication Company of Hunan Electric Power Corporation, China), Shupo Fu (Central South University, China), Shigeng Zhang (Central South University, China), Yuxi Wu (State Grid Information & Communication Company of Hunan Electric Power Corporation, China), Jingchuan Feng (State Grid Information & Communication Company of Hunan Electric Power Corporation, China)

With the massive deployment of IoT devices, the
management of IoT devices becomes more and more important.
In this paper, We only need the packet length the device sent to
serves in 180s to identify the device.We evaluated the algorithms
K-Nearest Neighbor,Random Forest,Suport Vector Machine and
Multilayer Perceptron for classification. The results show that the
Random Forest is the best and can achieve 99.6% if accuracy
in the identification of devices. We also ranked the importance
of 10 features related to packet length. Using the five most
important features (media, mean, skewness, absolute energy,
standard deviation and of packet length), we can achieve 99.5%
accuracy on the public dataset and 99.29% accuracy on our

Session Chair

Yang Liu (Beijing University of Posts and Telecommunications, China)

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