Goodput optimization via dynamic frame length and charging time adaptation for backscatter communication

Li, Yanjun; Fu, Lingkun; Ying, You; Yong, Sun; Chi, Kaikai; Zhu, Yi-hua

doi:10.1007/s12083-016-0480-1

Cited by 10 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider that the CRFID has 7400-bit data in buffer that need to be sent to the reader. Our proposed scheme is simulated to obtain the goodput performance and the corresponding frame length and number of redundant frames, which are compared with the theoretical optimal solution obtained from Reference [36]. The theoretical optimal value is used as a benchmark under specific energy-harvesting and channel conditions.…”

Section: Discussionmentioning

confidence: 99%

Fast and Reliable Burst Data Transmission for Backscatter Communications

Zhao

Liu

2019

Sensors

View full text Add to dashboard Cite

Computational radio frequency identification (CRFID) sensors are able to transfer potentially large amounts of data to the reader in the radio frequency range. However, the existing EPC C1G2 protocol is inefficient when there are abundant critical and emergency data to be transmitted and cannot adapt to changing energy-harvesting and channel conditions. In this paper, we propose a fast and reliable method for burst data transmission by fragmenting large data packets into blocks and we introduce a burst transmission mechanism to optimize the EPC C1G2 communication procedure for burst transmission when there are critical and emergency data to be transmitted. In addition, we utilize erasure codes to reduce Acknowledgement (ACK) delay and to improve system reliability. Our results show that our proposed scheme significantly outperforms the current fixed frame length approach and the dynamic frame length and charging time adaptation scheme (DFCA) and that the goodput is close to the theoretically optimal value under different energy-harvesting and channel conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Fast and Reliable Burst Data Transmission for Backscatter Communications

Zhao

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Power management schemes such as Dynamic Power Management (DPM), On-Demand Medium Access Control, joint energy-harvesting and operation scheduling, energy synchronized 10:17 communication and the harvesting-aware utility-based sensing rate allocation algorithm (Gu et al 2009;Fafoutis and Dragoni 2011;Sharma et al 2010b;Zhang et al 2011;Shu et al 2017) enable task (e.g., communication or sensing tasks) scheduling to meet ENO. Furthermore, techniques such as duty cycling and adaptive sensing (Alippi et al 2009;Hsu et al 2006), adaptive control (Vigorito et al 2007), topology control (Tan et al 2015;Yoon et al 2015;Wang et al 2016b), cooperative transmission (Zhang et al 2016a), frame length optimization (Li et al 2017), adaptive throughput, and mean delay optimal energy-neutral policies have been proposed for EH-WSNs to maximize the performance of the network given the rate of energy that is harvested by the sensor nodes (Sharma et al 2010b;Fafoutis and Dragoni 2011).…”

Section: Power Managementmentioning

confidence: 99%

Energy-Harvesting Wireless Sensor Networks (EH-WSNs)

Adu-Manu

Adam

Tapparello

et al. 2018

ACM Trans. Sen. Netw.

288

162

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

show abstract

“…[23] devised a special communication protocol that cut the data frame into very small data units to accommodate environments in which low energy use is paramount. However, this work does not take into account the situation where the channel quality is poor and bit errors and retransmission occur [24] .…”

Section: Related Workmentioning

confidence: 99%

Optimal data transmission in backscatter communication for passive sensing systems

Zhao

et al. 2020

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

Computational Radio Frequency IDentification (CRFID) is a device that integrates passive sensing and computing applications, which is powered by electromagnetic waves and read by the off-the-shelf Ultra High Frequency Radio Frequency IDentification (UHF RFID) readers. Traditional RFID only identifies the ID of the tag, and CRFID is different from traditional RFID. CRFID needs to transmit a large amount of sensing and computing data in the mobile sensing scene. However, the current Electronic Product Code, Class-1 Generation-2 (EPC C1G2) protocol mainly aims at the transmission of multi-tag and minor data. When a large amount of data need to be fed back, a more reliable communication mechanism must be used to ensure the efficiency of data exchange. The main strategy of this paper is to adjust the data frame length of the CRFID response dynamically to improve the efficiency and reliability of CRFID backscattering communication according to energy acquisition and channel complexity. This is done by constructing a dynamic data frame length model and optimizing the command set of the interface protocol. Then, according to the actual situation of the uplink, a dynamic data validation method is designed, which reduces the data transmission delay and the probability of retransmitting, and improves the throughput. The simulation results show that the proposed scheme is superior to the existing methods. Under different energy harvesting and channel conditions, the dynamic data frame length and verification method can approach the theoretical optimum.

show abstract

Goodput optimization via dynamic frame length and charging time adaptation for backscatter communication

Cited by 10 publications

References 26 publications

Fast and Reliable Burst Data Transmission for Backscatter Communications

Fast and Reliable Burst Data Transmission for Backscatter Communications

Energy-Harvesting Wireless Sensor Networks (EH-WSNs)

Optimal data transmission in backscatter communication for passive sensing systems

Contact Info

Product

Resources

About