A Survey on LPWAN-5G Integration: Main Challenges and Potential Solutions

Yu, Chiang; Sambo, Yusuf A.; Onireti, Oluwakayode; Imran, Muhammad Ali

doi:10.1109/access.2022.3160193

Cited by 34 publications

(28 citation statements)

References 102 publications

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“…Physical objects are no longer cut off from the virtual world; they may now be operated remotely and serve as physical access points to Internet services. This progress is creating enormous potential for the economy as well as for people [20], [63], [78], [115], [116], [134], [146], [188], [189], [196], [197], [232]- [234].…”

Section: Obstacles and Challengesmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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Section: Obstacles and Challengesmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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“…( 7) is the standard clustering objective, which aims to assign K clients to L clusters so that the sum of the squared l 2 -norm distance between the class distribution vector V k and its nearest cluster centroid C l is minimized. Constraint (8) ensures that cluster has the same size K L . Constraint (9) ensures that each client can only be assigned to one cluster at a time.…”

Section: Icg: Inter-cluster Groupingmentioning

confidence: 99%

“…(c) Limited Bandwidth: Wide wireless coverage is often required in open spaces such as mines and blasting sites. The cellular low-power wide-area networks (LPWANs) [8], including NarrowBand IoT, LTE-M, etc., feature long coverage range and low power consumption, and thus become especially favored by the industry. As a price, LPWAN has a low bit rate (e.g., up to 220kbps for NarrowBand IoT and up to 1Mbps for LTE-M [9]), and results in high latency when transmitting ML models and therefore becomes the efficiency bottleneck of FL.…”

Section: Introductionmentioning

confidence: 99%

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Zeng¹,

Li²,

Yu³

et al. 2022

Preprint

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Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a highperformance and efficient system named HFEDMS for incorporating practical FL into Industrial Metaverse. HFEDMS reduces data heterogeneity through dynamic grouping and training mode conversion (Dynamic Sequential-to-Parallel Training, STP). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters (Semantic Compression and Compensation, SCC). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies (Layer-wise Alternative Synchronization Protocol, LASP) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that HFEDMS improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.

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“…Low-power wide-area (LPWA) communication technologies have evolved rapidly in recent years with the development of the Internet of Things [ 19 , 20 ]. The benefits of the LPWA technique, as the name suggests, are low power consumption and stable and qualified connection capability over long range (i.e., a few kilometres) at low cost [ 21 , 22 , 23 ], and it can operate in different frequency ranges. Based on their different coverages, the relationship between LPWA technologies and general wireless networks is shown in Figure 6 .…”

Section: Communication Requirementsmentioning

confidence: 99%

Survey of Collision Avoidance Systems for Underground Mines: Sensing Protocols

Qian

Zhao

et al. 2022

Sensors

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With the growing number of unintentional interactions occurring in underground mines, Collision Avoidance System (CAS) establishment and maintenance has become an urgent need for mining industries to enhance their risk profile and improve construction safety. Usually, most collision accidents can be divided into three different categories in line with the involved participants and infrastructure condition. The accidents pose a great risk of financial cost to mining companies and even cause casualties. In detail, this paper presents an intensive study survey of positioning techniques, including ranging algorithms, to accommodate the demands of various proximity sensors and improve the capability of situational awareness. Then, we exploit the importance of the communication system, prevalent low-power wide-area technologies and related communication protocols. The effectiveness of communication systems decides and facilitates the success of the final integrated system that can be used to fundamentally address the problem of collision avoidance. For the purpose of collaboration between communication systems and other executive departments, a series of systematic comparisons of pertinent technologies and algorithms is given near the end, followed by a brief discussion on the best choice among these options. In the proposed solution, the overall end-to-end delay can be minimised to a few nanoseconds and the localisation accuracy can achieve centimetre level when operating in the range of 100 m.

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A Survey on LPWAN-5G Integration: Main Challenges and Potential Solutions

Cited by 34 publications

References 102 publications

Insights on the internet of things: past, present, and future directions

Insights on the internet of things: past, present, and future directions

HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Survey of Collision Avoidance Systems for Underground Mines: Sensing Protocols

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