Textiles have been concomitant of human civilization for thousands of years. With the advances in chemistry and materials, integrating textiles with energy harvesters will provide a sustainable, environmentally friendly, pervasive, and wearable energy solution for distributed on-body electronics in the era of Internet of Things. This article comprehensively and thoughtfully reviews research activities regarding the utilization of smart textiles for harvesting energy from renewable energy sources on the human body and its surroundings. Specifically, we start with a brief introduction to contextualize the significance of smart textiles in light of the emerging energy crisis, environmental pollution, and public health. Next, we systematically review smart textiles according to their abilities to harvest biomechanical energy, body heat energy, biochemical energy, solar energy as well as hybrid forms of energy. Finally, we provide a critical analysis of smart textiles and insights into remaining challenges and future directions. With worldwide efforts, innovations in chemistry and materials elaborated in this review will push forward the frontiers of smart textiles, which will soon revolutionize our lives in the era of Internet of Things.

Smart Textiles for Electricity Generation.

Energy harvesting technologies have been explored by researchers for more than two decades as an alternative to conventional power sources (e.g. batteries) for small-sized and low-power electronic devices. The limited life-time and necessity for periodic recharging or replacement of batteries has been a consistent issue in portable, remote, and implantable devices. Ambient energy can usually be found in the form of solar energy, thermal energy, and vibration energy. Amongst these energy sources, vibration energy presents a persistent presence in nature and manmade structures. Various materials and transduction mechanisms have the ability to convert vibratory energy to useful electrical energy, such as piezoelectric, electromagnetic, and electrostatic generators. Piezoelectric transducers, with their inherent electromechanical coupling and high power density compared to electromagnetic and electrostatic transducers, have been widely explored to generate power from vibration energy sources. A topical review of piezoelectric energy harvesting methods was carried out and published in this journal by the authors in 2007. Since 2007, countless researchers have introduced novel materials, transduction mechanisms, electrical circuits, and analytical models to improve various aspects of piezoelectric energy harvesting devices. Additionally, many researchers have also reported novel applications of piezoelectric energy harvesting technology in the past decade. While the body of literature in the field of piezoelectric energy harvesting has grown significantly since 2007, this paper presents an update to the authors' previous review paper by summarizing the notable developments in the field of piezoelectric energy harvesting through the past decade.

https://iopscience.iop.org/article/10.1088/1361-665X/ab36e4/pdf

A review of energy harvesting using piezoelectric materials: state-of-the-art a decade later (2008–2018)

A comprehensive review of Thermoelectric Generators: Technologies and common applications

The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.

https://www.mdpi.com/2079-9292/8/7/768/pdf

A Survey on Internet of Things and Cloud Computing for Healthcare

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.

/pdf/a-survey-on-security-and-privacy-of-5g-technologies-1hq5wqw2pb.pdf

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Content addressable memory (CAM) for search and match operations demands high speed and low power for near real-time decision-making across many critical domains. Resistive RAM (RRAM)-based in-memory computing has high potential in realizing an efficient static CAM for artificial intelligence tasks, especially on resource-constrained platforms. This paper presents an XNOR-based RRAM-CAM with a time-domain analog adder for efficient winning class computation. The CAM compares two operands, one voltage and the second one resistance, and outputs a voltage proportional to the similarity between the input query and the pre-stored patterns. Processing the summation of the output similarity voltages in the time-domain helps avoid voltage saturation, variation, and noise dominating the analog voltage-based computing. After that, to determine the winning class among the multiple classes, a digital realization is utilized to consider the class with the longest pulse width as the winning class. As a demonstrator, hyperdimensional computing for efficient MNIST classification is considered. The proposed design uses 65 nm CMOS foundry technology and realistic data for RRAM with total area of 0.0077 mm2, consumes 13.6 pJ of energy per 1 k query within 10 ns clock cycle. It shows a reduction of ~ 31 × in area and ~ 3 × in energy consumption compared to fully digital ASIC implementation using 65 nm foundry technology. The proposed design exhibits a remarkable reduction in area and energy compared to two of the state-of-the-art RRAM designs.

/pdf/rram-based-cam-combined-with-time-domain-circuits-for-4kavd89uvq.pdf

RRAM-based CAM combined with time-domain circuits for hyperdimensional computing.

This paper comprises two new methodologies to improve yield and reduce system-on-a-chip power. The first methodology is based on faulty static random-access memory (SRAM) cells detections and cache resizing . The key advantage of this approach is that it enables the end user to control the system’s parameters to be error tolerant. Furthermore, this technique enables aggressive voltage scaling which causes parametric (soft) failures in SRAM-based memory. As such, the proposed methodology can be utilized to exchange cache size for lower power or better yield . In the second methodology, data from faulty cells are treated as imposed noise . Depending on the application, this error percentage (imposed noise) can be mitigated through three options. First, ignore error if the percentage of the error is tolerable. Second, simple hardware filtration is needed. Finally, software-based filtration is required. The viability of this approach is that it allows aggressive voltage scaling below the traditional to be a 100% correct approach for SRAM supply, which results in substantial reduction of power, trading off quality for power . For both approaches, BIST is used as part of the powerup sequence to identify the faulty memory addresses per voltage level and compute the faulty cells percentage. Furthermore, the proposed methodologies help in improving reliability and counteracting long-term effects on memory cell stability and lifetime degradation caused by negative bias temperature instability.

Design Methodologies for Yield Enhancement and Power Efficiency in SRAM-Based SoCs

Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for light image regions and dark image regions within the intensity image that match a given intensity threshold during a single processing pass. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER areas. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Architecture and method for real-time parallel detection and extraction of maximally stable extremal regions (MSERS)

This paper presents a single stage power management unit that includes an enhanced stage-switch Dickson charge pump (DCP) to boost and regulate a low input voltage. A new switching mechanism is presented to significantly reduce the losses encountered in conventional DCP switches. Frequency and stage modulation are utilized in the proposed design. The stage modulation provides different gain levels (coarse) and the frequency modulation tunes the voltage level and regulates the output voltage based on a pre-determined reference voltage. Using four stages charge pump, silicon measurement results in 65 nm CMOS technology show a maximum efficiency of 66% at input voltage of 0.7 V and output power of 27 μW. The system supports a range of load current between 0.1 μA − 34 μA with a maximum operating frequency of 1.8MHz. The proposed system supports an input voltage range from 0.55 to 0.7 V which can be used in energy harvesting applications such as solar and thermal harvesting.

A Charge Pump Based Power Management Unit With 66%-Efficiency in 65 nm CMOS

DC-DC converters are important building block of most of today's System-on-Chips (SoCs). This is true because the need to modulate the voltage of different parts of the chip to operate at the optimum level is essential to achieve high power efficiency. This paper investigates two topologies of the DC-DC converter that are widely used. The two topologies are Low Dropout regulator and Switched Inductor DC-DC buck converter. The operation for both topologies is explained as well as the advantages and disadvantages of each topology. Spice simulation with 65nm low power foundry process technology is used to compare the two topologies in terms of energy efficiency, settling time, area, and design complexity. Detail study of voltage level conversion and output load current and its impact on efficiency is also reported.

LDO regulator versus switched inductor DC-DC converter

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