Network Scalability Comparison of IEEE 802.15.4 and Receiver-Assigned CDMA

Petrosky, Eric E.; Michaels, Alan J.; Ridge, Devin

doi:10.1109/jiot.2018.2884455

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…In the MAC layer, different devices have different requirements for E2E communications according to their specific functions. Although many different MAC protocols have been proposed (e.g., CSMA-Carrier Sense Multiple Access) [115], CDMA-Code Division Multiple Access) [116], TDMA-Time Division Multiple Access) [117] and their improved versions, these methods still lack flexibility, and do not fulfill the emerging requirements of industrial applications. Generally, industrial E2E communications can be divided into two categories: periodic communications and aperiodic communications.…”

Section: B End-to-end Communicationmentioning

confidence: 99%

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

et al. 2021

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The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models.This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

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Section: B End-to-end Communicationmentioning

confidence: 99%

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

et al. 2021

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“…In the following, we detail the procedure we used to derive the values v i,k involved in calculation of Equation (1) and shown in Figure 17. These values, as previously highlighted, have been obtained through a literature analysis on several research works [79][80][81][82][83][84][85][86][87]). In each of these papers, at least one technology and one performance metric were considered.…”

Section: Fundingmentioning

confidence: 99%

“…The values shown in Figure 17 have been obtained analyzing relevant works appeared in the literature-namely [79][80][81][82][83][84][85][86][87]-and normalizing each data in order to directly compare (through adimensional values) the communication technologies presented in the paper. In detail, each axis of the chart in Figure 17 corresponds to each of the 9 metrics.…”

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confidence: 99%

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Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies

et al. 2019

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The Internet of Things (IoT), being a “network of networks”, promises to allow billions of humans and machines to interact with each other. Owing to this rapid growth, the deployment of IoT-oriented networks based on mesh topologies is very attractive, thanks to their scalability and reliability (in the presence of failures). In this paper, we provide a comprehensive survey of the following relevant wireless technologies: IEEE 802.11, Bluetooth, IEEE 802.15.4-oriented, and Sub-GHz-based LoRa. Our goal is to highlight how various communication technologies may be suitable for mesh networking, either providing a native support or being adapted subsequently. Hence, we discuss how these wireless technologies, being either standard or proprietary, can adapt to IoT scenarios (e.g., smart cities and smart agriculture) in which the heterogeneity of the involved devices is a key feature. Finally, we provide reference use cases involving all the analyzed mesh-oriented technologies.

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“…Enhancements for reduced-precision correlations, optimally pruned coefficients, and variable-length operations are all considered in Section III. Measurable results from hardware prototypes built on an Intel Arria 10 field-programmable gate array (FPGA) are presented in Section IV, offering simpler methods for asynchronous reception, underlay-based watermark validation [14], and receiver-assigned code-division multiple access (CDMA) operations [15] in IoT-caliber devices. Finally, conclusions can be found in Section V.…”

Section: Introductionmentioning

confidence: 99%

Fallthrough Correlation Techniques for Arbitrary-Phase Spread Spectrum Waveforms

2019

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The use of practically non-repeating spreading codes to generate sequence-based spread spectrum waveforms is a strong method to improve transmission security, by limiting an observer's opportunity to cross-correlate snapshots of the signal into a coherent gain. Such time-varying codes, particularly when used to define multi-bit resolution arbitrary-phase waveforms, present significant challenges to the intended receiver, who must synchronize acquisition processing to match the time-varying code each time it changes. This paper presents a series of options for optimizing the traditional brute-force matchedfilter preamble correlator for burst-mode arbitrary-phase spread spectrum signals, achieving significant computational gains and flexibility, backed by measurable results from hardware prototypes built on an Intel Arria 10 Field Programmable Gate Array (FPGA). The most promising of which requires no embedded multipliers and reduces the total hardware logic by more than 76%. Extensions of the core fallthrough correlator techniques are considered to support low-power asynchronous reception, underlay-based physical layer firewall functions, and Receiver-Assigned Code Division Multiple Access (RA-CDMA) protocols in Internet of Things (IoT)-caliber devices.

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Network Scalability Comparison of IEEE 802.15.4 and Receiver-Assigned CDMA

Cited by 17 publications

References 18 publications

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies

Fallthrough Correlation Techniques for Arbitrary-Phase Spread Spectrum Waveforms

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