Smart Garbage Bin Systems – A Comprehensive Survey

Soni, Gulshan; Selvaradjou, Kandasamy

doi:10.1007/978-981-10-7635-0_15

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…However, in these figures (Figs. 13,16), IEEE 802.15.4 GTSoff shows moderate improvements (as much as ≈ 20%) over the proposed FSGPS. The primary reason behind this improvement is IEEE 802.15.4 GTSoff has CAP interval is extended throughout SD.…”

Section: Analysis Of Breakdown Of Packet (Pkt) Transmission (Tx)mentioning

confidence: 95%

“…Low rate wireless personal area network (LRWPAN) gained substantial attention among the researchers in recent decades. The elevation of micro-miniature sensor technologies and communication protocols offer a fertilized ground to develop the latest and novel technologies, such as the internet of things (IoT, smart home, smart cities and wireless body area network (WBAN) [12,16,[18][19][20]. One of the widespread IEEE standards is IEEE 802.15.4 that is intended for trivial energy consumption, less data rate, small communication area and real-time traffic.…”

Section: Introductionmentioning

confidence: 99%

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The Finest Superframe Interval and GTS Provision Scheme (FSGPS) for Time-Sensitive IEEE 802.15.4 Applications

Soni

Selvaradjou

2021

SN COMPUT. SCI.

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The IEEE 802.15.4 medium access control (MAC) is a widely used protocol for low rate wireless personal area network because of its vital features, such as low power consumption, low data rate, short-range and real-time communications. Its unique guaranteed time slot (GTS) mechanism makes it an appropriate communication protocol in healthcare monitoring and other critical applications. Nevertheless, this GTS mechanism has few a constraints. One of the key limitations is the scarcity of GTS slots and the static nature of the GTS allocation mechanism. In this paper, we propose a scheme called the Finest Superframe interval and GTS provision scheme (FSGPS) that aims to reduce the limitations of IEEE 802.15.4 MAC to make it more suitable for time-sensitive services. To address the impacts of the quasi-static nature of WBAN, we in this paper consider temporal path loss variation in our simulations that accounts for real-time variation associated with the nodes. Through extensive simulations, we show that our proposed FSGPS outperforms IEEE 802.15.4 MAC in respect of several key performance metrics.

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Section: Analysis Of Breakdown Of Packet (Pkt) Transmission (Tx)mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

The Finest Superframe Interval and GTS Provision Scheme (FSGPS) for Time-Sensitive IEEE 802.15.4 Applications

Soni

Selvaradjou

2021

SN COMPUT. SCI.

Self Cite

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“…MMU in bins. The use of multiple sensors in "smart bins" for tasks such as detecting the waste level or reporting their geographical position have been considered over the years [52,102]. In [9,53,54] a computer vision system is investigated to process internal images of bins; in particular, in these works: (1) the camera is installed on the truck (i.e.…”

Section: Computer Vision Focusing On Foodmentioning

confidence: 99%

“…What if the camera is installed directly on each bin? What if we integrate the variables currently monitored in bins [102] with the knowledge of the class and mass of material recognized by an MMU? MMU in autonomous sorting systems.…”

Section: Computer Vision Focusing On Foodmentioning

confidence: 99%

Material Measurement Units for a Circular Economy: Foundations through a Review

Zocco¹,

McLoone²,

Smyth³

2021

Preprint

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Long-term availability of minerals and industrial materials is a necessary condition for sustainable development as they are the constituents of any manufacturing product. In particular, technologies with increasing demand such as GPUs and photovoltaic panels are made of critical raw materials. To enhance the efficiency of material management, in this paper we make three main contributions: first, we identify in the literature an emerging computer-vision-enabled material monitoring technology which we call Material Measurement Unit (MMU); second, we provide a survey of works relevant to the development of MMUs; third, we describe a material stock monitoring sensor network deploying multiple MMUs.

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“…In an Internet of Things application suite, there are several options for physical components and software behaviors for the design of a physical agent [10]. According to existing experiments [16] and our experience with the IoT domain [2], [4], [17], we introduce possible variants of an IoT embodied agent and we use a FODA notation to explore this variability.…”

Section: A Variability In Iot Embodied Agentsmentioning

confidence: 99%

An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning

Nascimento

Alencar

Lucena

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and be accessed remotely. Such applications have been proposed in several domains including health care, smart cities and agriculture. However, despite their increased adoption, deploying these applications in specific settings has been very challenging because of the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a modeling approach for IoT analytics based on learning embodied agents (i.e. situated agents). The approach involves: (i) a variability model of IoT embodied agents; (ii) feedback evaluative machine learning; and (iii) reconfiguration of a group of agents in accordance with environmental context. The proposed approach advances the state of the art in that it facilitates the development of Agent-based IoT applications by explicitly capturing their complex and dynamic variabilities and supporting their self-configuration based on an context-aware and machine learning-based approach.

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Smart Garbage Bin Systems – A Comprehensive Survey

Cited by 17 publications

References 22 publications

The Finest Superframe Interval and GTS Provision Scheme (FSGPS) for Time-Sensitive IEEE 802.15.4 Applications

The Finest Superframe Interval and GTS Provision Scheme (FSGPS) for Time-Sensitive IEEE 802.15.4 Applications

Material Measurement Units for a Circular Economy: Foundations through a Review

An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning

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