A Short Review on Sleep Scheduling Mechanism in Wireless Sensor Networks

Zhang, Zeyu; Shu, Lei; Zhu, Chunsheng; Mukherjee, Mithun

doi:10.1007/978-3-319-78078-8_7

Cited by 18 publications

(11 citation statements)

References 20 publications

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“…Since the amount of scavenged energy is so low (as Table 1 shows), in most cases the energy harvesting IoT device must operate on amounts of power as low as possible. Several strategies have been proposed for prolonging the lifetime of wireless IoT devices, including duty-cycling [104], sleep scheduling [105], the reduction of the required transmission distance for IoT devices through efficient clustering [106], optimized strategies for adaptively setting the rates of sensor reading and data transmission depending on available energy [107], or the development of scheduling schemes that take into account power consumption when waking up the wireless sensing systems [108]. Some practical techniques for reducing the energy required by an IoT device, also used in the development of low-power embedded systems, include Dynamic Voltage Scaling (DVS) [109], the reduction of the frequency of the processing unit [110], or the appropriate selection of peripherals in the device [111] or of the type of memory involved in data processing and storage (i.e., Flash or RAM -Random access memory) [112], the adaptation of transmission power depending on required communication range and environment [113,114], or logic for deciding the moment and format for sending slow varying data [115 -117].…”

Section: Energy Harvesting Modelingmentioning

confidence: 99%

Energy Harvesting Techniques for Internet of Things (IoT)

et al. 2021

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The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

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Section: Energy Harvesting Modelingmentioning

confidence: 99%

Energy Harvesting Techniques for Internet of Things (IoT)

et al. 2021

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“…Further, duty cycling [78], i.e., scheduling the power on/off states of a RAT for radio communication, can be employed for additional security and improved longevity in devices capable of wireless communication. The ability to toggle the state of the RAT aligns well with concepts such as Collaborative Machine Learning (discussed further in Section 4.2.1), which focuses on improving a global model as a collective.…”

Section: Privacymentioning

confidence: 99%

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

Rajapakse¹,

Karunanayake²,

Nadeem³

2022

Preprint

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The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field proposes to democratize the use of Machine Learning (ML) and Deep Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly energy-efficient pervasive devices capable of operating with less than a few Milliwatts of power. Nevertheless, many solutions assume that TinyML can only run inference. Despite this, growing interest in TinyML has led to work that makes them reformable, i.e., work that permits TinyML to improve once deployed. In line with this, roadblocks in MCU based solutions in general, such as reduced physical access and long deployment periods of MCUs, deem reformable TinyML to play a significant part in more effective solutions. In this work, we present a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation. Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability. In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools. Furthermore, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.

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“…The few survey study of sleep scheduling mechanism is summarized in the study of Zhang et al [42] and observed that most of the studies focused on asynchronous scheduling mechanisms. Moreover, the machine learning approach is widely applied in this field.…”

Section: Related Workmentioning

confidence: 99%

Survey: energy efficient protocols using radio scheduling in wireless sensor network

Mathew¹,

Jones²

2020

IJECE

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An efficient energy management scheme is crucial factor for design and implementation of any sensor network. Almost all sensor networks are structured with numerous small sized, low cost sensor devices which are scattered over the large area. To improvise the network performance by high throughput with minimum energy consumption, an energy efficient radio scheduling MAC protocol is effective solution, since MAC layer has the capability to collaborate with distributed wireless networks. The present survey study provides relevant research work towards radio scheduling mechanism in the design of energy efficient wireless sensor networks (WSNs). The various radio scheduling protocols are exist in the literature, which has some limitations. Therefore, it is require developing a new energy efficient radio scheduling protocol to perform multi tasks with minimum energy consumption (e.g. data transmission). The most of research studies paying more attention towards to enhance the overall network lifetime with the aim of using energy efficient scheduling protocol. In that context, this survey study overviews the different categories of MAC based radio scheduling protocols and those protocols are measured by evaluating their data transmission capability, energy efficiency, and network performance. With the extensive analysis of existing works, many research challenges are stated. Also provides future directions for new WSN design at the end of this survey.

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A Short Review on Sleep Scheduling Mechanism in Wireless Sensor Networks

Cited by 18 publications

References 20 publications

Energy Harvesting Techniques for Internet of Things (IoT)

Energy Harvesting Techniques for Internet of Things (IoT)

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

Survey: energy efficient protocols using radio scheduling in wireless sensor network

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