Distributed collaboration and anti-interference optimization in edge computing for IoT
Introduction
As the cornerstone of the fourth industrial revolution, the Internet of Things (IoT) uses communications, Artificial Intelligence (AI) [6], edge computing (EC) [5], [16], and big data etc. to interconnect sensors, controllers, machines, people, and things to achieve expected management, coordination and control without human intervention. With the popularization of smart terminal devices, massive data volumes have shown an explosive growth trend, making it difficult to adapt to the cloud computing service model. In order to alleviate the processing pressure of data centers, eliminate computing and communication bottlenecks, and improve the quality of service (QoS), edge computing has emerged. Edge computing aims to provide edge intelligent services near the networks edge close to the source of things or data, meeting the key needs of the industry's digitalization in agile connection, real-time business, data optimization, application intelligence, security and privacy protection.
Edge computing [17], [31] is widely used in the field of IoT [33]. Especially, it is suitable for applications with low latency, high bandwidth, high reliability, massive connection, heterogeneous aggregation and local security privacy protection. At present, the edge computing systems have the characteristics of a huge number of edge devices, heterogeneous device types, dynamic network topology, data quantification and application diversification. However, limited by the processing capacity of existing chips and the level of edge-side storage devices, it is still difficult for edge devices to independently complete complex data processing and AI analysis requirements. While the electromagnetic environment in industrial fields is complex and changeable, the signal transmission is very unstable and vulnerable to interference [12], [30]. Moreover, the contradiction between the scarcity of wireless spectrum resources and the high requirements of industrial-level anti-interference makes the reliable and real-time transmission problem more challenging.
In the complex and harsh electromagnetic environment, in order to solve the interference and fading problems of wireless access and realize reliable data transmission, collaborative communication technology [15], [24] is widely used in IoT systems [32], [34]. In the spatial diversity technology, the transmitter sends data to other users through broadcasting. Then the successfully received users forward data as a relay in collaborative communication. The collaborative communication system is mainly divided into: Decode-and-Forward [29], Amplify-and-Forward [2] and Code Collaboration forwarding [20]. Among them, the Decode-and-Forward method requires the relay node to decode and re-encode the received signal. Amplify-and-Forward methods require the relay node to amplify and forward the signal. The Code Collaboration forwarding methods transmit signals [11] through a certain coding method, and the source node and the relay node respectively send signals in different channels. Although these algorithms solve the problem of reliable communication in deep fading channels, the disadvantage of inextensible bandwidth remains unresolved.
Aiming at the efficient and reliable transmission problem in IoT edge system, this paper proposes an adaptive collaborative anti-interference mechanism. Based on the established EC network model, two interference mitigation algorithms suitable for different transmitting power are proposed. The algorithms combine full-frequency multiplexing and interference mitigation to alleviate the performance deterioration caused by co-frequency interference and deep fading, and effectively improves the anti-interference ability in harsh electromagnetic environment. The contributions of the paper are summarized as follows:
(1) The edge computing system model for IoT is constructed. Field device layer includes sensors, robots and other human-computer interaction terminals. As an access point (AP), the switch sends information to the nodes through broadcasting in the IoT edge system. At the uppermost layer, software defined network (SDN) controller realizes network state awareness, management and decision-making. Moreover, we derive the link failure probability model theoretically.
(2) An Occupy-Interference Mitigation (O-IM) algorithm is proposed, which combines adaptive full-frequency multiplexing and interference mitigation to reduce the dependence of the reliable collaboration on bandwidth and signal-to-noise ratio (SNR). In broadcasting phase, APs serializes messages and broadcasts them to all nodes. In collaboration phase, the decoded node sends data to the un-decoded node in the form of distributed space-time coding.
(3) An anti-interference algorithm based on Interference Mitigation and inter-cell frequency division Interference Avoidance (IM-IA) is proposed to balance bandwidth and SNR. This algorithm adopts interference algorithm scheme in the broadcasting phase. In the collaboration phase, orthogonal frequency multiplexing is adopted to avoid IM-IA scheme, thereby eliminating the lower limit of the O-IM failure probability.
(4) Extensive simulation results on Mininet platform verify that the proposals can obtain lower failure probability, and are less than traditional solutions in transmitting power, bandwidth and SNR requirements. Moreover, O-IM is suitable for low power scenarios, while IM-IA is more suitable for high power case.
The rest of this article is organized as follows. In the second section, related works are introduced. In the third section, the system model is established. In the fourth section, the O-IM and IM-IA algorithm is proposed. In the fifth section, simulation experiment and result analysis are given. In the sixth section, this paper is summarized.
Section snippets
Related works
Diversity technology is an effective solution to suppress wireless communication interference. Researchers have designed a variety of spatial and frequency domain diversity schemes suitable for fading channels. Dai et al. [4] proposed a forward link capacity analysis model to compare macro and micro diversity, and proved that they can increase forward link capacity. Shankar et al. [26] calculated the interrupt probability of the shadow fading channel when using diversity, and evaluated the
System model
The edge computing network connects many smart devices in the smart factory to form a three-layer network structure. The bottom layer is intelligent wireless devices such as sensors, handsets, and robots in the industrial field. Wireless devices are not necessarily mobile devices. Some fixed devices are limited to a narrow space or are not suitable for wired connection, and still use wireless access. As an AP for wireless devices, wireless switches are expected to provide stable and reliable
Distributed collaborative anti-interference scheme
For the collaborative communication in the edge computing network, it is proposed two anti-interference algorithms for different transmission powers: the collaborative O-IM algorithm based on full frequency multiplexing and the IM-IA algorithm based on the frequency division in cell. O-IM introduces full-bandwidth multiplexing and continuous decoding into the broadcast and collaboration phase, which is suitable for low transmit power scenarios. IM-IA reduces the frequency multiplexing in the
Experimental settings
The performance of the O-IM and IM-IA algorithms is verified in terms of link delay, bandwidth, and SNR. Delay is used to reflect the overhead and the throughput of the protocol. The required bandwidth and SNR are used to reflect the performance of the protocol. If the bandwidth and SNR required for the protocol to meet the performance requirements are low, the protocol performance is good. The failure probability of the protocol refers to the probability of unsuccessful reception under the
Conclusion
In edge computing networks, the access layer is expected to provide real-time and reliable data transmission services for massive devices. In order to achieve ultra-reliable data transmission in the industrial IoT system, this paper established an SDN-based edge computing network architecture. The O-IM algorithm based on full frequency multiplexing and the IM-IA algorithm based on frequency multiplexing are proposed. The algorithm uses the broadcast-collaborative approach to achieve
CRediT authorship contribution statement
Yuhuai Peng: Funding acquisition, Project administration, Resources, Supervision. Chenlu Wang: Data curation, Visualization, Writing – review & editing. Qiming Li: Conceptualization, Methodology, Writing – original draft. Lei Liu: Formal analysis, Validation. Keping Yu: Investigation, Software.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1702000, in part by the National Natural Science Foundation of China under Grant 61871107, in part by the Fundamental Research Funds for the Central Universities under Grant N2116013, and in part by the Key Projects of Science and Technology of Henan Province under Grants 222102210043 and 222102210173.
Yuhuai Peng received the Ph.D. degree in communication and information systems from Northeastern University in 2013. He is now an associate professor in the same university. His research interests include Internet of Things (IoT), industrial communication networks, health monitoring, etc.
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Yuhuai Peng received the Ph.D. degree in communication and information systems from Northeastern University in 2013. He is now an associate professor in the same university. His research interests include Internet of Things (IoT), industrial communication networks, health monitoring, etc.
Chenlu Wang received the M.E. degree in information and communication engineering from Shenyang Aerospace University, Shenyang, China, in 2020. He is currently working toward the Ph.D. degree in the school of Computer Science and Engineering at Northeastern University, Shenyang, China. His research interests include artificial intelligence, industrial Internet of things and satellite Internet.
Qiming Li received the B.S. degree in Communication Engineering from University of Science and Technology Liaoning, Anshan, China, in 2018. She is currently working toward the M.E. degree in the School of Computer Si iA Science and Engineering at Northeastern University, Shenyang, China. Her research interests include software defined network and industrial Internet of things.
Lei Liu (Member, IEEE) received the B.Eng. degree in communication engineering from Zhengzhou University, Zhengzhou, China, in 2010, and the M.Sc. and Ph.D degrees in communication engineering from Xidian University, Xian, China, in 2013 and 2019, respectively. From 2013 to 2015, he was employed a subsidiary of China Electronics Corporation. From 2018 to 2019, he was supported by China Scholarship Council to be a visiting Ph.D. student with the University of Oslo, Oslo, Norway. He is currently a Lecture with the Department of Electrical Engineering and Computer Science, Xidian University. His research interests include vehicular ad hoc networks, intelligent transportation, mobile-edge computing, and Internet of Things.
Keping Yu received the M.E. and Ph.D. degrees from the Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo, Japan, in 2012 and 2016, respectively. He was a Research Associate and a Junior Researcher with the Global Information and Telecommunication Institute, Waseda University, from 2015 to 2019 and 2019 to 2020, respectively, where he is currently a Researcher (Assistant Professor).
Dr. Yu has hosted and participated in more than ten projects, is involved in many standardization activities organized by ITU-T and ICNRG of IRTF, and has contributed to ITU-T Standards Y.3071 and Supplement 35. He is an Associate Editor of IEEE Open Journal of Vehicular Technology, Journal of Intelligent Manufacturing, Journal of Circuits, Systems and Computers. He has been a Lead Guest Editor for Sensors, Peer-to-Peer Networking and Applications, Energies, Journal of Internet Technology, Journal of Database Management, Cluster Computing, Journal of Electronic Imaging, Control Engineering Practice, Sustainable Energy Technologies and Assessments and Guest Editor for IEICE Transactions on Information and Systems, Computer Communications, IET Intelligent Transport Systems, Wireless Communications and Mobile Computing, Soft Computing, IET Systems Biology. His research interests include smart grids, information-centric networking, the Internet of Things, artificial intelligence, blockchain, and information security.