A 1.36 μW 312–315 MHz synchronized-OOK receiver for wireless sensor networks using 65 nm SOTB CMOS technology

Hoang, Minh-Thien; Sugii, Nobuyuki; Ishibashi, Koji

doi:10.1016/j.sse.2015.11.016

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Band sub-GHz [31,52,54,87] 2.4 GHz [31,57,64,82,85] 5 GHz [70] 60 GHz [65] Others [62,71,81] Technique MCU [51,52,56,61,62] Correlator [85,[88][89][90][91] AS3930 [28,59,63,80,84] Others [58,60,68,92] Modulation OOK [60,71,87,93,94] ASK [51,64,68,70] FSK [69,90,93] Others [57] was advanced in [57]. This WuRx front-end's major building blocks are composed of a matching network, an antenna and a LO generator for minimizing the frequency.…”

Section: Wur Categorization Classification Referencementioning

confidence: 99%

An overview on low energy wake-up radio technology: Active and passive circuits associated with MAC and routing protocols

Zaraket¹,

Murad²,

Yazdani³

et al. 2021

Journal of Network and Computer Applications

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Section: Wur Categorization Classification Referencementioning

confidence: 99%

An overview on low energy wake-up radio technology: Active and passive circuits associated with MAC and routing protocols

Zaraket¹,

Murad²,

Yazdani³

et al. 2021

Journal of Network and Computer Applications

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“…WSN-based IoT applications often use low-cost, lowpower modulation techniques e.g., On-Off Keying (OOK), Amplitude Shift Keying (ASK), and Phase Shift Keying/ Frequency Shift Keying (PSK/FSK) [18], [19] at the physical layer. Sharma et al [20] provide a performance review of numerous narrow-band digital modulation methods used in WSNs.…”

Section: Introductionmentioning

confidence: 99%

DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

Majumder,

Chatterjee,

Mallik

et al. 2023

IEEE Access

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Nowadays, data is ubiquitous and has a significant influence on our day-to-day activities due to the emergence of high speed internet and widespread use of sensor-enabled Internet of Things (IoT) devices. Owing to major improvement in sampling rate of sensors in recent years, a low variation of the sensed physical parameters is witnessed during a small time interval of observation, which in turn shows high correlation in time domain. Data-critical applications like personal healthcare monitoring, video surveillance, and other applications where data dropping creates significant barriers are attracted by high correlation data. However, due to their power-constrained nature, such applications do not benefit much from the transmission of redundant data. The ideal solution to this problem could possibly be achieved by adopting typical lossless source coding strategy with low-complex design. This paper presents a novel encoding scheme termed as Dual Run Distribution based Encoding (DRDE) scheme by exploiting high correlation of sensor data to suitably encode them using symbol run statistics, leading to a reduced length of data with a very large percentage of 0s. Employing silent symbol based communication, the transmitter can be kept in silent state during periods of the most dominant symbol '0' in the encoded messages and using a hybrid FSK − ASK modulation/demodulation technique for communication with a non-coherent receiver results in a significant reduction in transmitter energy. We simulate the proposed sensor data encoding technique on real-life data with low-cost, low data-rate transceivers like CC2420. Simulation results show about 88% (theoretical) and 79-82% (practical) savings in transmitter and 12% (theoretical) 23.5% (practical) savings in receiver energy over conventional BFSK with real-life sensor dataset. Furthermore, our proposed method outperforms in terms of overall energy savings and reduction of CO 2 footprint, generating 1.48 − 0.041 mg/day, which is 78% lesser than conventional BFSK modulation scheme, making our proposed scheme suitable for sustainable IoT applications in WSN compared to existing schemes. Furthermore, we investigate the influence of various data compression algorithms on computation time, CPU power consumption, and transmission cost on an LPC2148 microcontroller built upon a 16-bit/32-bit ARM7TDMI chipset.

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“…However, the received power cannot be directly stored on a battery or fed to an application to be powered. This is because, batteries require a specific voltage level to start charging [29] and even some of the low power consuming applications require a specific voltage level for operation [1] [2] [3] [5] . The amplifier circuit performs the function of increasing the voltage and provide a path to store the power.…”

Section: Amplifier Circuitmentioning

confidence: 99%

“…Hence the current is given by equation 5. 5. The amount of current depends directly on the Width to Length ratio of the transistor.…”

Section: Output Current Analysismentioning

confidence: 99%

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Asic Design of RF Energy Harvester Using 0.13UM CMOS Technology

Zaveri¹

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ASIC Design of RF Energy Harvester using 0.13um CMOS Technology Jainish Zaveri Recent advances in wireless sensor nodes, data acquisition devices, wearable and implantable medical devices [1] [2] [3] have paved way for low power (sub 50uW) devices. These devices generally use small solid-state or thin film batteries for power supply which need replacement or need to be removed for charging. RF energy harvesting technology can be used to charge these batteries without the need to remove the battery from the device, thus providing a sustainable power supply. In other cases, a battery can become unnecessary altogether. This enables us to deploy wireless network nodes in places where regular physical access to the nodes is difficult or cumbersome. This thesis proposes a design of an RF energy harvesting device able to charge commercially available thin film or solid-state batteries. The energy harvesting amplifier circuit is designed in Global Foundry 0.13um CMOS technology using Cadence integrated circuit design tools. This Application Specific Integrated Circuit (ASIC) is intended to have as small a footprint as possible so that it can be easily integrated with the above-mentioned devices. While a dedicated RF power source is a direct solution to provide sustainable power to the harvesting circuit, harvesting ambient RF power from TV and UHF cellular frequencies increases the possibilities of where the harvesting device can be placed. The biggest challenge for RF energy harvesting technology is the availability of adequate amount of RF power. This thesis also presents a survey of available RF power at various ultra-high frequencies in San Luis Obispo, CA. The idea is to determine the frequency band which can provide maximum RF power for harvesting and design a harvester for that frequency band. v

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A 1.36 μW 312–315 MHz synchronized-OOK receiver for wireless sensor networks using 65 nm SOTB CMOS technology

Cited by 9 publications

References 12 publications

An overview on low energy wake-up radio technology: Active and passive circuits associated with MAC and routing protocols

An overview on low energy wake-up radio technology: Active and passive circuits associated with MAC and routing protocols

DRDE: Dual Run Distribution Based Encoding Scheme for Sustainable IoT Applications

Asic Design of RF Energy Harvester Using 0.13UM CMOS Technology

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