Current Issue : April-June
Volume : 2023
Issue Number : 2
Articles : 5 Articles
Public cloud computing has become increasingly popular due to the rapid advancements in communication and networking
technology. As a result, it is widely used by businesses, corporations, and other organizations to boost the productivity.
However, the result generated by millions of network-enabled IoT devices and kept on the public cloud server, as well as the
latency in response and safe transmission, are important issues that IoT faces when using the public cloud computing. These
concerns and obstacles can only be overcome by designing a robust mutual authentication and secure cross-verification
mechanism. Therefore, we have attempted to design a cryptographic protocol based on a simple hash function, xor operations,
and the exchange of random numbers. The security of the proposed protocol has formally been verified using the ROR model,
ProVerif2.03, and informally using realistic discussion. In contrast, the performance metrics have been analyzed by looking
into the security feature, communication, and computation costs. To sum it up, we have compared our proposed security
mechanism with the state-of-the-art protocols, and we recommend it to be effectively implemented in the public cloud
Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such
as natural language processing. Edge cloud computing systems are in an asymmetric structure, where
task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation
requirements. In such a computing system, cloud servers have no energy limitations, since
they have unlimited energy resources. Edge computing systems, however, are resource-constrained,
and the energy consumption is thus expensive, which requires an energy-efficient method for RNN
job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process
tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement.
Based on the QoS requirements, the runtime manager dynamically assigns RNN inference
tasks to edge and cloud computing systems and performs energy optimization on edge systems using
dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud
system indicate that in edge systems, our method can reduce the energy up to 45% compared with
the state-of-the-art approach....
Recently, IT technologies related to the Fourth Industrial Revolution (4IR), such as artificial intelligence (AI), Internet of things
(IoT), cloud computing, and edge computing have been studied. Although there are many used clothing occurrences with 61
trillion worn of clothing consumption per year in Korea, it is not properly collected due to the efficiency of the used clothing
collection system, and the collected used clothing is not properly recycled due to insufficient recycling system, lack of skilled labor
force, and health problems of workers. To solve this problem, this study proposes a deep learning clothing classification system
(DLCCS) using cloud and edge computing. The system proposed is to classify clothing image data input from camera terminals
installed in various clothing classification sites in various regions into two classes, as well as nine classes, by deep learning using
convolution neural network (CNN). And the classification results are stored in the cloud through edge computing. The edge
computing enables the analysis of the data of the Internet of Things (IoT) device on the edge of the network before transmitting it
to the cloud. The performance evaluation parameters that are considered for the proposed research study are transmission velocity
and latency. Proposed system can efficiently improve the process and automation in the classification and processing of recycled
clothing in various places. It is also expected that the waste of clothing resources and health problems of clothing classification
workers will be improved....
With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute
content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in
the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle
edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to
reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end
delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on
the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles.
Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy
consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced;
second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced;
then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading
collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment
not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the
effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental
results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is
increased by 0.32%, which improves the practicability of the system....
In the research of searchable encryption, fine-grained data authorization is a convenient way to manage the search rights for users.
Recently, Liu et al. proposed a fine-grained searchable scheme with verification, which can control the search authorization and
verify the results. In this paper, we first present a forgery attack against Liu et al.’s scheme and then propose a novel scheme of
verifiable data search with fine-grained authorization in edge environment. Based on the key aggregate mechanism and Merkle
hash tree, our proposed scheme not only achieves file-oriented search permission management but also implements the correctness
and completeness verification of search results. In addition, with the assistance of edge server, resource-constrained users
can easily perform the tasks of search and verification. Finally, we prove our scheme is secure based on the decision l-bilinear
Diffie–Hellman exponent problem. The performance analysis and experiment results demonstrate that our proposed scheme has
lower computation, communication, and storage costs contrast to the existing schemes....
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