Power Internet Of Things Applications Research Articles

High Capacitance Porous Ruthenium Nitride Films with High Rate Capability for Micro-Supercapacitors.

The demand for high-performance energy storage devices to power Internet of Things applications has driven intensive research on micro-supercapacitors (MSCs). In this study, RuN films made by magnetron sputtering as an efficient electrode material for MSCs are investigated. The sputtering parameters are carefully studied in order to maximize film porosity while maintaining high electrical conductivity, enabling a fast charging process. Using a combination of advanced techniques, the relationships among the morphology, structure, and electrochemical properties of the RuN films are investigated. The films are shown to have a complex structure containing a mixture of crystallized Ru and RuN phases with an amorphous oxide layer. The combination of high electrical conductivity and pseudocapacitive charge storage properties enabled a 16µm-thick RuN film to achieve a capacitance value of 0.8Fcm-2 in 1m KOH with ultra-high rate capability.

Room Temperature Lattice Thermal Conductivity of GeSn Alloys.

CMOS-compatible materials for efficient energy harvesters at temperatures characteristic for on-chip operation and body temperature are the key ingredients for sustainable green computing and ultralow power Internet of Things applications. In this context, the lattice thermal conductivity (κ) of new group IV semiconductors, namely Ge1-xSnx alloys, are investigated. Layers featuring Sn contents up to 14 at.% are epitaxially grown by state-of-the-art chemical-vapor deposition on Ge buffered Si wafers. An abrupt decrease of the lattice thermal conductivity (κ) from 55 W/(m·K) for Ge to 4 W/(m·K) for Ge0.88Sn0.12 alloys is measured electrically by the differential 3ω-method. The thermal conductivity was verified to be independent of the layer thickness for strained relaxed alloys and confirms the Sn dependence observed by optical methods previously. The experimental κ values in conjunction with numerical estimations of the charge transport properties, able to capture the complex physics of this quasi-direct bandgap material system, are used to evaluate the thermoelectric figure of merit ZT for n- and p-type GeSn epitaxial layers. The results highlight the high potential of single-crystal GeSn alloys to achieve similar energy harvest capability as already present in SiGe alloys but in the 20 °C-100 °C temperature range where Si-compatible semiconductors are not available. This opens the possibility of monolithically integrated thermoelectric on the CMOS platform.

Performance measurements for indoor photovoltaic devices: Classification of a novel light source

There is an increasing interest in using indoor photovoltaic (IPV) devices to power Internet of Things applications, low power communications, and indoor environmental sensing. For the commercialization of IPV technologies, device performance measurements need to conform to the relevant standardized specifications. We present a novel IPV device measurement system that incorporates digital light processing (DLP) to deliver a spectrally invariant light source at all required illuminance levels, as specified by the indoor standard testing conditions in IEC TS 62607-7-2:2023. We evaluated the DLP system according to requirements for spectral coincidence, temporal stability, and non-uniformity at the sample plane. We demonstrate the measurements to define the classification status of the system and the unique benefits of the DLP system that allow a stable spectral profile and high levels of uniformity across all illuminance levels. This is the first reported measurement system for IPV device testing based on DLP technology, and the classification methodology of this work can be used as an example for the classification of indoor light simulators in laboratory environments based on the latest IEC TS 62607-7-2:2023.

A 8.1-nW, 4.22-kHz, −40–85 °C relaxation oscillator with subthreshold leakage current compensation and forward body bias buffer for low power IoT applications

This paper presents a relaxation oscillator (RxO) utilizing subthreshold transistor leakage current compensation (SLC) technology to expand the operating range from 70 °C to 85 °C at high temperatures. The compensated RxO outperforms the uncompensated counterpart by 25 times for temperature coefficient (TC). Additionally, forward body bias (FBB) technology optimizes the digital buffer delay with temperature drift at low supply voltage, extending the oscillator’s low-temperature range from −20 °C to −40 °C. Fabricated in a 65 nm CMOS process, the oscillator consumes a mere 8.1 nW at a 0.4 V supply voltage. At room temperature, it operates at 4.22 kHz with an energy efficiency of 1.92 nW/kHz. The oscillator demonstrates a temperature coefficient (TC) of 114.01 ppm/°C within the range of −40 °C to 85 °C, rendering it a prospective choice for low-power Internet of Things (IoT) applications.

Exploiting Tensor-Based Bayesian Learning for Massive Grant-Free Random Access in LEO Satellite Internet of Things

With the rapid development of Internet of Things (IoT), low earth orbit (LEO) satellite IoT is expected to provide low power, massive connectivity and wide coverage IoT applications. In this context, this paper provides a massive grant-free random access (GF-RA) scheme for LEO satellite IoT. This scheme does not need to change the transceiver, but transforms the received signal to a tensor decomposition form. By exploiting the characteristics of the tensor structure, a Bayesian learning algorithm for joint active device detection and channel estimation during massive GF-RA is designed. Theoretical analysis shows that the proposed algorithm has fast convergence and low complexity. Finally, extensive simulation results confirm its better performance in terms of error probability for active device detection and normalized mean square error for channel estimation over baseline algorithms in LEO satellite IoT. Especially, it is found that the proposed algorithm requires short preamble sequences and support massive connectivity with a low power, which is appealing to LEO satellite IoT.

Cooperative Offloading and Resource Management for UAV-Enabled Mobile Edge Computing in Power IoT System

The lack of the computation services in remote areas motivates power Internet of Things (IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) technology. However, the computation services will be significantly affected by the UAVs' capacities, and distinct power IoT applications. In this paper, we firstly propose a cooperative UAV-enabled MEC network structure in which the UAVs are able to help other UAVs to execute the computation tasks. Then, a cooperative computation offloading scheme is presented while considering the interference mitigation from UAVs to devices. To maximize the long-term utility of the proposed UAV-enabled MEC network, an optimization problem is formulated to obtain the optimal computation offloading decisions, and resource management policies. Considering the random devices' demands and time-varying communication channels, the problem is further formulated as a semi-Markov process, and the deep reinforcement learning based algorithms are proposed in both of the centralized and distributed UAV-enabled MEC networks. Finally, we evaluate the performance of the proposed DRL-based schemes in the UAV-enabled MEC framework by giving numerical results.

Design of the Baseband Physical Layer of NarrowBand IoT LTE Uplink Digital Transmitter

With the new age of technology and the release of the Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this paper, a digital transmitter of NarrowBand Internet of Things (NB-IoT) is proposed targeting very low power and delay-insensitive IoT applications with low throughput requirements. NB-IoT is a new cellular technology introduced by 3GPP in release 13 to provide wide-area coverage for the IoT. The low-cost receivers for such devices should have very low complexity, consume low power and hence run for several years. In this paper, the implementation of the data path chain of digital uplink transmitter is presented. The standard specifications are studied carefully to determine the required design parameters for each block. And the design is synthesized in UMC 130-nm technology.

A Residual Phase Noise Compensation Method for IEEE 802.15.4 Compliant Dual-Mode Receiver for Diverse Low Power IoT Applications

The Internet of Things (IoT) with its plethora of applications brings up new challenges in optimizing power consumption, error performance, latency, and throughput in its communication devices. Recently, there has been increasing necessity of reconfigurable and multistandard transceivers in order to bring adaptability in the IoT devices to suit the nature of the application and satisfy the aforementioned performance metrics as well. In this paper, we focus on the efficient design of receivers compliant with IEEE 802.15.4 which is a prominent protocol for low power IoT applications. We propose a robust phase noise compensation method which efficiently removes the residual phase noise remaining after the coarse frequency offset compensation due to direct-sequence spread spectrum operation on large packets. We also propose a dual-mode receiver using the proposed compensation method and showcase its ability to cater to the diverse nature of IoT applications. The detailed architecture of proposed dual-mode receiver is presented along with its FPGA prototyping and ASIC implementation. We have analyzed overall power consumption by the proposed dual-mode receiver considering the packet error rate and retransmission scenario. The results show that the proposed receiver saves significant energy consumption by changing its mode in favorable channel environments.