1.2 Fertilizing AIoT from Roots to Leaves

Loh, K. Lawrence

doi:10.1109/isscc19947.2020.9062950

Cited by 22 publications

(9 citation statements)

References 5 publications

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“…Ref. [21] set the scene on the future of IoT, while exploring the applications and challenges associated with running artificial intelligence tasks on power-constrained devices. Also, considering the hardware point of view, an analysis of the trade-off between power efficiency and programming flexibility for different types of processors was laid out.…”

Section: From Edge Computing To Edge Intelligencementioning

confidence: 99%

An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection

Gomes,

Soares,

Torres

et al. 2024

Sensors

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In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system’s success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system’s maintenance intervals.

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Section: From Edge Computing To Edge Intelligencementioning

confidence: 99%

An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection

Gomes,

Soares,

Torres

et al. 2024

Sensors

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“…As discussed in more detail in [64], [125], the Schreier's toolbox can be used to synthesize the NTF for a given value of f n . Once the ideal NTF and STF have been determined, the best values of the loop-filter coefficients are selected in order to optimize the performance of the BP CT-Σ∆M in terms of robustness and stability, while maximizing DR and the SNDR with the minimum power consumption 3 .…”

Section: B Widely-tunnable Ct Bp-σ∆ms For Rf Digitizersmentioning

confidence: 99%

“…IoT implies the interconnection of billions of cyberphysical entities, which can either be physical or virtual, with a hybrid software/hardware structure, capable of communicating with each other, sometimes without the need of human intervention thanks to Machine-to-Machine (M2M) communication protocols. Moreover, IoT is expected to have a potential impact on the global economy of 11.1 trillion dollars in 2025, what means above 10% of the gross domestic product globally, about 30 billion connected devices in year 2023 and 350 billion by 2030 [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…It is therefore necessary to address a number of design challenges in order to build a solid bridge between the physical environment and its corresponding virtualized version. Efficient IoT nodes will require also the development of hybrid software/hardware platforms, new computation paradigms and communication protocols, as well as highly adaptive and programmable circuitry, and very specially in the analog side of the system and the analog/digital interface [2], [3], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…The so-called Cognitive Radio (CR) [14] allows communication systems to make a more efficient use of the frequency spectrum, by dynamically modifying the transceiver specifications according to the information sensed from the electromagnetic environment [15]. An efficient implementation of CR-based terminals may benefit from embedding AI engines in their main building blocks, which in turn will need new Circuits and Systems (CAS) strategies with a high degree of programmability and reconfigurability in order to dynamically select the optimum set of performance metrics and transmission bands [3], [9], [13], [16]- [19].…”

Section: Introductionmentioning

confidence: 99%

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AI-Managed Cognitive Radio Digitizers

Rosa

2022

IEEE Circuits Syst. Mag.

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Embedding Artificial Intelligence (AI) in integrated circuits is one of the technology pillars of the so-called digital transformation. Nowadays, the vast majority of electronic devices benefits from digital signal processing to implement more and more functionalities, which can be further enhanced by the action of AI algorithms and artefacts. Moreover, as the analog/digital interfaces are moving closer and closer to the point where the information is either acquired or transmitted, the so-called AImanaged data converters are becoming key building blocks in an increasingly number of interconnected cyberphysical systems -made up of both software and hardware components. Software Defined Radio (SDR) and Cognitive Radio (CR) systems intended for 5G/6G communications are good examples which can benefit from an early digitization managed by AI engines.In this context, this paper presents an overview of circuits and systems techniques for AI-managed analog/digital interfaces with application in SDR/CR mobile telecom systems. Some design trends and challenges are discussed, going from new communications and computing paradigms for AIoT devices and networks, to digital-based/scaling-friendly analog circuit techniques for an efficient digitization. The state of the art on Analogto-Digital Converters (ADCs) is surveyed, putting emphasis on highly-programmable Sigma-Delta Modulators (Σ∆Ms) as one of the best ADC candidates for SDR/CR transceivers. Some chip examples are shown to illustrate their potential application in AI-enhanced CR end-devices.

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Artificial Intelligence of Things (AIoT) for Intelligent Data Design

Gandhi,

Kumar

2024

Reshaping Intelligent Business and Industry

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1.2 Fertilizing AIoT from Roots to Leaves

Cited by 22 publications

References 5 publications

An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection

An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection

AI-Managed Cognitive Radio Digitizers

Artificial Intelligence of Things (AIoT) for Intelligent Data Design

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