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Secure and Energy-Aware Computation Offloading of IoT Sensors in Mobile Edge Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 21195

Special Issue Editor

Prof. Dr. Robert Hsu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
Interests: cloud computing; IoT; RFID; big data; edge & fog computing; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing technology is fast becoming a part of our lives. It is often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put a significant demand on improving communication security and reducing the power consumption of the system. As a consequence, traditional computing and mobile communication primarily designed for human being-oriented applications are facing tremendous challenges. Mobile Edge Computing and Communication (MECC) integrates the radio access network, the software defined network, device to device communications, and cloud/edge technologies. With MECC, devices or nodes with storage, computing, and caching capabilities can be deployed in close proximity with sensing devices and act as middleware between cloud and local networks. Computation-excessive and latency-stringent applications can be offloaded to nearby devices through device to device communications or to nearby edge nodes through cellular or other wireless technologies. It is expected that together with MECC, critical issues faced by sensing devices such as short battery life, limited computing capability, and stringent latency can be greatly alleviated.

This Special Issue aims to attract contributions covering both the theory and practice of any of the aforementioned challenges, from the management software stack to domain-specific applications, bringing together state-of-the-art technical solutions and prototype implementations for future MECC. In particular, it focuses on system modelling, design, architecture, implementation, assessment, adaptation, and management of MECC applications and services with wearable devices, together with communication protocols and sharing mechanisms. Possible topics include, but are not limited to, the following:

  • Communications among edges, communication between edges and central cloud, mobile and wearable communications, wearable sensor networks, smart communication technologies;
  • Sensing and wearable devices, implantable devices, wearable sensors;
  • Powering wearable devices, energy harvesting techniques, power management, and constraints optimization;
  • Architecture of MECC, system design, ambient intelligence-driven system, systems designs combining wearable MECC features, and ubiquity;
  • Transmission/networking technologies;
  • Deployment of wearable devices;
  • Trends in mobile/wearable/implantable devices;
  • Trends in mobile/wearable/implantable services and technologies;
  • Assistive, patient body-driven technology;
  • Novel application models;
  • Service migration in Edge Computing systems;
  • Reliability and availability;
  • Security, privacy, and QoS/QoE.

Dr. Robert Hsu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT Sensors
  • Mobile Computing
  • Edge Computing
  • Computation offloading
  • Energy-Aware
  • wearable communication

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Published Papers (4 papers)

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Research

21 pages, 1619 KiB  
Article
Mode Selection and Spectrum Allocation in Coexisting D2D and Cellular Networks with Cooperative Precoding
by Yu-Wei Chan, Feng-Tsun Chien and Chao-Tung Yang
Sensors 2019, 19(24), 5417; https://doi.org/10.3390/s19245417 - 9 Dec 2019
Cited by 1 | Viewed by 2704
Abstract
In this paper, we investigate the mode selection strategies for a new device-to-device (D2D) pair becoming active in a network with a number of existing D2D sensors or users coexisting with cellular users in a D2D-enabled heterogeneous network. Specifically, we propose two selection [...] Read more.
In this paper, we investigate the mode selection strategies for a new device-to-device (D2D) pair becoming active in a network with a number of existing D2D sensors or users coexisting with cellular users in a D2D-enabled heterogeneous network. Specifically, we propose two selection rules, the signal-to-interference-plus-noise-ratio (SINR)-based and the capacity-based, combined with two sets of different precoding schemes and discuss their impacts on the system under a variety of scenarios. While the cooperative block diagonalization (BD) among the cellular users combined with the zero-forcing (ZF) precoding among D2D users can eliminate interference observed at the new D2D receiving sensor, the maximum signal-to-leakage-and-noise-ratio (SLNR) precoding is often a preferred option due to low-complexity implementations and comparable performance. We note that the two selection rules, the SINR-based and the capacity-based, considered in this paper impact on the system differently, with interesting tradeoff from different perspectives. Finally, we provide insights by simulations into the best selection among the three modes depending on a variety of use cases in the network. Full article
(This article belongs to the Special Issue Secure and Energy-Aware Computation Offloading of IoT Sensors in Mobile Edge Computing)
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12 pages, 1869 KiB  
Article
Efficient Continuous Skyline Query Processing in Wireless Sensor Networks
by Yingyuan Xiao, Xu Jiao, Hongya Wang, Ching-Hsien Hsu, Li Liu and Wenguang Zheng
Sensors 2019, 19(13), 2902; https://doi.org/10.3390/s19132902 - 30 Jun 2019
Cited by 4 | Viewed by 2457
Abstract
Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and [...] Read more.
Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of the popular queries for multi-criteria decision making, has received considerable attention due to its numerous applications. In this paper, we study how to process a continuous skyline query over a sensor data stream in WSNs. We present an energy-efficient continuous skyline query method called EECS. EECS can avoid the transmission of invalid sensor data and prolong the lifetime of WSNs. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Secure and Energy-Aware Computation Offloading of IoT Sensors in Mobile Edge Computing)
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17 pages, 2968 KiB  
Article
A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition
by Dong-Wei Chen, Rui Miao, Wei-Qi Yang, Yong Liang, Hao-Heng Chen, Lan Huang, Chun-Jian Deng and Na Han
Sensors 2019, 19(7), 1631; https://doi.org/10.3390/s19071631 - 5 Apr 2019
Cited by 72 | Viewed by 6462
Abstract
Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, [...] Read more.
Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity. Full article
(This article belongs to the Special Issue Secure and Energy-Aware Computation Offloading of IoT Sensors in Mobile Edge Computing)
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16 pages, 3095 KiB  
Article
SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
by Lei Yan, Suzhi Cao, Yongsheng Gong, Hao Han, Junyong Wei, Yi Zhao and Shuling Yang
Sensors 2019, 19(4), 831; https://doi.org/10.3390/s19040831 - 18 Feb 2019
Cited by 56 | Viewed by 9085
Abstract
As outlined in the 3Gpp Release 16, 5G satellite access is important for 5G network development in the future. A terrestrial-satellite network integrated with 5G has the characteristics of low delay, high bandwidth, and ubiquitous coverage. A few researchers have proposed integrated schemes [...] Read more.
As outlined in the 3Gpp Release 16, 5G satellite access is important for 5G network development in the future. A terrestrial-satellite network integrated with 5G has the characteristics of low delay, high bandwidth, and ubiquitous coverage. A few researchers have proposed integrated schemes for such a network; however, these schemes do not consider the possibility of achieving optimization of the delay characteristic by changing the computing mode of the 5G satellite network. We propose a 5G satellite edge computing framework (5GsatEC), which aims to reduce delay and expand network coverage. This framework consists of embedded hardware platforms and edge computing microservices in satellites. To increase the flexibility of the framework in complex scenarios, we unify the resource management of the central processing unit (CPU), graphics processing unit (GPU), and field-programmable gate array (FPGA); we divide the services into three types: system services, basic services, and user services. In order to verify the performance of the framework, we carried out a series of experiments. The results show that 5GsatEC has a broader coverage than the ground 5G network. The results also show that 5GsatEC has lower delay, a lower packet loss rate, and lower bandwidth consumption than the 5G satellite network. Full article
(This article belongs to the Special Issue Secure and Energy-Aware Computation Offloading of IoT Sensors in Mobile Edge Computing)
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