Energy harvesting aware protocol for 802.11-based Internet of Things network

Rosyidi, Lukman; Sari, Riri Fitri

doi:10.1109/tencon.2016.7848228

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Multi‐hop communication could be an excellent way to improve the coverage area of IoT networks 2 . On the other hand, energy constraint is a critical issue in IoT applications because a remote device could be located in an area that battery replacement or recharge is impossible or difficult 3 …”

Section: Introductionmentioning

confidence: 99%

“…2 On the other hand, energy constraint is a critical issue in IoT applications because a remote device could be located in an area that battery replacement or recharge is impossible or difficult. 3 In recent years, energy harvesting has attracted significant attention in order to prolong the lifetime of remote devices. [4][5][6] Energy can be harvested from different sources like wind, thermal, solar, and wireless radio-frequency (RF), but the efficiency of each method depends on its availability.…”

Section: Introductionmentioning

confidence: 99%

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A novel hybrid backscatter and conventional algorithm for multi‐hop Internet of Things networks

Raeisi

Mahdavi

Hosseini

2022

Trans Emerging Tel Tech

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This article investigates a multi‐hop cognitive radio network in terms of end‐to‐end bit delivery. The network exploits backscatter communication (BackCom) and harvest‐then‐transmit (HTT) mode in a hybrid manner. Such a network can be used in Internet of Things (IoT) applications in which IoT users coexist with a primary network and use the primary spectrum to transmit data in both BackCom and HTT modes. Besides, such users can harvest energy from the primary signals. A novel hybrid backscatter and conventional transmission (HBCT) algorithm is proposed in order to maximize end‐to‐end bit delivery by jointly optimizing time and power allocations. For this goal, we formulate a nonconvex optimization problem. Next, we transform the problem into a convex one and develop a new analytical formulation by which we calculate the optimal power and time allocation in closed‐form equations. The numerical results demonstrate the superiority of HBCT compared with current schemes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel hybrid backscatter and conventional algorithm for multi‐hop Internet of Things networks

Raeisi

Mahdavi

Hosseini

2022

Trans Emerging Tel Tech

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“…While the current state of the art offers numerous studies on energy models for IoT devices [18,19] and Cloud infrastructures [20,21], to the best of our knowledge, none of them provides the overall picture. It is thus hard to estimate the energy consumption induced by the increase of IoT devices on Cloud infrastructures for instance.…”

Section: Introductionmentioning

confidence: 99%

End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

Orgerie

Rodero³

et al. 2018

Future Generation Computer Systems

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Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.

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“…As a representative example, a system has been developed that periodically monitors and manages information about a target environment (e.g., temperature, humidity, and illumination) around the sensor [2][3][4]. There is a critical problem that the lifetime of the WSN is limited because of limited sensor batteries [5]. Also, it cannot be assumed that all sensors are easily physically accessible.…”

Section: Introductionmentioning

confidence: 99%

REACH: An Efficient MAC Protocol for RF Energy Harvesting in Wireless Sensor Network

Kim

Park

Kim

et al. 2017

Wireless Communications and Mobile Computing

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This paper proposes a MAC protocol for Radio Frequency (RF) energy harvesting in Wireless Sensor Networks (WSN). In the conventional RF energy harvesting methods, an Energy Transmitter (ET) operates in a passive manner. An ET transmits RF energy signals only when a sensor with depleted energy sends a Request-for-Energy (RFE) message. Unlike the conventional methods, an ET in the proposed scheme can actively send RF energy signals without RFE messages. An ET determines the active energy signal transmission according to the consequence of the passive energy harvesting procedures. To transmit RF energy signals without request from sensors, the ET participates in a contention-based channel access procedure. Once the ET successfully acquires the channel, it sends RF energy signals on the acquired channel during Short Charging Time (SCT). The proposed scheme determines the length of SCT to minimize the interruption of data communication. We compare the performance of the proposed protocol with RF-MAC protocol by simulation. The simulation results show that the proposed protocol can increase the energy harvesting rate by 150% with 8% loss of network throughput compared to RF-MAC. In addition, the proposed protocol can increase the lifetime of WSN because of the active energy signal transmission method.

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Energy harvesting aware protocol for 802.11-based Internet of Things network

Cited by 7 publications

References 9 publications

A novel hybrid backscatter and conventional algorithm for multi‐hop Internet of Things networks

A novel hybrid backscatter and conventional algorithm for multi‐hop Internet of Things networks

End-to-end energy models for Edge Cloud-based IoT platforms: Application to data stream analysis in IoT

REACH: An Efficient MAC Protocol for RF Energy Harvesting in Wireless Sensor Network

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