Power Internet Of Things Devices Research Articles

High Thermoelectric Performance Related to PVDF Ferroelectric Domains in P-Type Flexible PVDF-Bi0.5Sb1.5Te3 Composite Film.

There is increasing demand to power Internet of Things devices using ambient energy sources. Flexible, low-temperature, organic/inorganic thermoelectric devices are a breakthrough next-generation approach to meet this challenge. However, these systems suffer from poor performance and expensive processing preventing wide application of the technology. In this study, by combining a ferroelectric polymer (Polyvinylidene fluoride (PVDF, β phase)) with p-type Bi0.5Sb1.5Te3 (BST) a thermoelectric composite film with maximum is produced power factor. Energy filter from ferroelectric-thermoelectric junction also leads to high Seebeck voltage ≈242µVK-1. For the first time, compelling evidence is provided that the dipole of a ferroelectric material is helping decouple electron transport related to carrier mobility and the Seebeck coefficient, to provide 5× or more improvement in thermoelectric power factor. The best composition, PVDF/BST film with BST 95wt.% has a power factor of 712µW•m-1K-2. A thermoelectric generator fabricated from a PVDF/BST film demonstrated Pmax T 12.02µW and Pdensity 40.8Wm-2 under 50K temperature difference. This development also provides a new insight into a physical technique, applicable to both flexible and non-flexible thermoelectrics, to obtain comprehensive thermoelectric performance.

Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature

Internet of Things (IoT) technology is widely used in new power systems, and it also provides many new modes for network attacks. Illegal terminal device identification is also a significant topic in the field of wireless authentication technology. Some kinds of power network equipment are located in sparsely populated areas and rely on IoT terminals for real-time monitoring. Attackers use illegal terminals to connect power IoT devices for production monitoring and to carry out network attacks, which may cause serious damage, such as power data theft and misoperation of power network equipment. Radio frequency fingerprint (RFF) can extract hardware features from different devices, and is widely used for device identification and authentication. The area over which power network equipment placed is vast, and there are many wireless communication devices and terminals. It is difficult to identify illegal devices through commonly used network management techniques, thus making it difficult to distinguish between the mobile terminals of employees and illegal terminals in general spectrum screening. In response to the above situation, this paper uses the characteristics of the squared spectrum of random access preamble signals to extract hardware device features, proposes an illegal device identification algorithm based on Gaussian distribution theory, and evaluates its performance. The experimental results show that, when the signal-to-noise ratio (SNR) is greater than 15 dB, the average recognition result is greater than 92%. In addition, the algorithm has low computational complexity and high engineering application value.

Blocking probability of massive MIMO: What is the capacity of a massive MIMO IoT system?

In this paper, we provide a methodology to evaluate the capacity of a Massive multiple-input multiple-output (MIMO) supported Internet of Things (IoT) system in which a large number of low cost low power IoT devices transmit and receive sporadic data. Numerous IoT devices are supported by a single cell Massive MIMO base station (BS) with maximum-ratio (MR) processing. Orthogonal reference signals (RSs) or pilots are assigned randomly to all the IoT devices for channel estimation purpose. The number of simultaneously active IoT devices follows Poisson distribution. Due to the tremendous number of IoT devices, orthogonal RSs are heavily reused, which severely degrades the receiver signal quality. One of the most important performance criteria for this kind of system is the blocking probability which shows the percentage of the outage IoT devices, and how we maintain the low blocking probability while supporting all the IoT devices simultaneously is particularly important. Due to RS reuse, we can divide IoT devices into two groups based on their interference levels. We provide detailed theoretical analyses, and show that the blocking primarily happens to the group with higher interference level. Increasing the number of service antennas and/or reducing the number of IoT devices can help to improve the performance of the blocking probability, however there is a regime in which the parameter adjustment helps little to improve the performance. Based on these factors, we provide a useful algorithm to improve the performance of blocking probability. A number of simulation results are also provided to validate the theoretical analysis.

Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveillance

Advances in communication technologies and computational capabilities of Internet of Things (IoT) devices enable a range of complex applications that require ever increasing processing of sensors' data. An illustrative example is real-time video surveillance that captures videos of target scenes and process them to detect anomalies using deep learning (DL). Running deep learning models requires huge processing and incurs high computation delay and energy consumption on resource-constraint IoT devices. In this article, we introduce methods for distributed inference over IoT devices and edge server. Two distinct algorithms are proposed to split the deep neural network layers computation between IoT device and an edge server; the early split strategy (ESS) for battery powered IoT devices and the late split strategy (LSS) for IoT devices connected to regular power source. The evaluation shows that both the ESS and LSS schemes achieve the target inference delay deadline when tested over VGG16 and MobileNet_V2 CNN models. In terms of computational load, the ESS scheme achieves nearly 15–20% reduction whereas LSS scheme achieves up to 60% reduction. The gains in energy saving of IoT devices for both the ESS and LSS schemes are nearly 18% and 52%, respectively.

SEAL-RF: Secure Adiabatic Logic for Wirelessly Powered IoT Devices

Wireless power harvesting has become popular for various Internet-of-Things (IoT)-based devices, such as wireless sensors, radio frequency identification (RFID)-based systems, and implantable devices for healthcare. Since these ubiquitous computing devices are likely to handle confidential information, ensuring their security is critical. In this work, a secure adiabatic logic family, referred to as SEAL-RF, is developed, specifically for RF-powered IoT devices. SEAL-RF is highly energy efficient and resistant against power-based side-channel attacks. Specifically, at the gate-level, the normalized energy deviation (NED) of SEAL-RF is up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$180\times $ </tex-math></inline-formula> lower than conventional (unprotected) adiabatic logic, while consuming up to 39% less average energy per transition. Furthermore, the NED of SEAL-RF is up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.5\times $ </tex-math></inline-formula> lower than an existing secure adiabatic logic, while consuming up to 32% less average energy per transition. A lightweight encryption core based on SIMON algorithm is also designed with the proposed SEAL-RF and conventional adiabatic logic. A correlation power analysis (CPA)-based side-channel attack is mounted on both designs. The secret key for the unprotected SIMON core is retrieved with less than 800 traces whereas the secret key for the SEAL-RF-based SIMON core cannot be retrieved with more than 40K traces. Furthermore, the average power and energy per encryption of the SEAL-RF-based SIMON core is 15.6% lower than the conventional adiabatic logic-based SIMON.

Synergistic multi-source ambient RF and thermal energy harvester for green IoT applications

In a future green Internet of Things (IoT) reality, billions of devices of the IoT infrastructure should be self-powered. Harvesting ambient energy to power IoT devices is an attractive solution that can extend battery life or can completely replace batteries. Considering the global applications of IoT, ubiquitous and continuous availability is an important requirement for ambient energy sources. Radio frequency (RF) energy from mobile phone towers and thermal energy from diurnal cycle temperature fluctuations are good candidates. In this study, we present a synergistic multi-source energy harvester (MSEH) comprising an RF energy harvester (RFEH) and a thermal energy harvester (TEH) integrated through a dual-function component, heatsink antenna. Both harvesters collect ambient energy 24 h a day and are not location specific. The TEH, which is in the shape of a box, collects energy using heatsinks on its sidewalls. The same heatsinks are optimized to also serve as receiving antennas of the RFEH, which collects energy from the GSM900, GSM1800, and 3G bands. Due to the synergistic integration, radiation efficiency of the antenna doubled from 40% to 80% which resulted in ∼10% increase in power conversion efficiency of the RFEH. Similarly, the average power of the TEH without heatsinks 120 μW is doubled to 240 μW for TEH with heatsinks. Field tests have shown that the outputs of the TEH and RFEH have increased 4 and 3 times compared to the independent TEH and RFEH respectively. A temperature and humidity sensor based IoT node has been successfully powered through this energy harvesting system. Overall, the MSEH can collect 3680 μWh of energy per day which is sufficient to obtain the sensors data with a time interval of 3.5 s.

Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The growth in the use of small sensor devices, commonly known as the Internet of Things (IoT), has resulted in unprecedented amounts of data being generated and captured. With the rapidly growing popularity of personal IoT devices, the collection of personal data through such devices has also increased exponentially. To accommodate the anticipated growth in connected devices, researchers are now investigating futuristic network technologies that are capable of processing large volumes of information at much faster speeds. However, the introduction of innovative network technologies coupled with existing vulnerabilities of personal IoT devices and insufficient device security standards is resulting in new challenges for the security of data collected on these devices. While existing research has focused on the technical aspects of security vulnerabilities and solutions in either network or IoT technologies separately, this paper thoroughly investigates common aspects impacting IoT security on existing and futuristic networks, including human-centric issues and the mechanisms that can lead to loss of confidentiality. By undertaking a comprehensive literature review of existing research, this article has identified five key areas that impact IoT security for futuristic next generation networks. Furthermore, by extensively analysing each area, the article reports on conclusive findings and future research opportunities for IoT privacy and security for the next generation of network technologies</i> .

Optimal Task Offloading and Resource Allocation for C-NOMA Heterogeneous Air-Ground Integrated Power Internet of Things Networks

By combining information communication technology with power grid, the smart grid-oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the safe and reliable power grid operation and improve system energy efficiency. Nevertheless, PIoT devices have only limited communication and computing resources since they are mostly deployed in remote areas that may be out of service coverage of existing terrestrial 5G networks. To overcome the resource limitation, we leverage Air-Ground Integrated C-NOMA Heterogeneous PIoT Networks (PAGIC HetNets), and study the core challenges in PAGIC HetNets. As PIoT devices are normally powered by battery, we aim at minimizing the energy consumption of PIoT devices and thoroughly investigate the problem of task offloading and resource allocation with minimal energy consumption. This problem belongs to a mixed integer nonlinear programming (MINLP) with extra difficulty that the long-term queuing delay and short-term constraints are coupled. To tackle the difficulty, we use Lyapunov optimization to transform this hard problem into three subproblems. The first subproblem is task splitting and local computing resource assignment at the PAGIC user side, which we solve with the Lagrangian multiplier method. The second subproblem is queue-aware channel reusing, and matching theory is adopted to solve it. The third subproblem is optimizing the aerial server resource allocation, for which we propose a greedy-based solution. Numerical simulations demonstrate that our approach can obtain excellent performance in terms of energy consumption, spectrum efficiency, task backlog, and queuing delay with lower complexity compared with several benchmark methods.

An Ambient RF Powered Wireless Sensor System

The development of an ambient radio frequency (RF) powered Internet of Things (IoT) wireless sensor system is reported. The system integrates all major components including multiple antennas, rectifiers, a power management unit (PMU), a microcontroller unit (MCU) with RF transceiver module and sensing unit. A novelty of this applied engineering innovation is the design and use of multi-band dual-polarized multiple antennas to handle the low ambient RF power density found in the everyday environment. Our prototype demonstrates that it is possible to sense temperature, pressure and humidity and wirelessly transmit these signals to a central server with a duty cycle of approximately 15 minutes, when the background ambient RF signals are as low as -28.6 dBm at 950 MHz. The proposed system can address the important issue of energy supply in the development of IoT providing an alternative energy source to batteries or solar cells. Ambient RF powered IoT devices do not require periodic replacement of their batteries or to be placed in visible regions which makes them suitable for wall, ceiling and floor applications. Our ambient RF powered IoT wireless sensor system provides concrete proof, through demonstration, that harnessing ambient RF energy for powering IoT sensor systems is feasible.

A CPK-Based Identity Authentication Scheme for IoT

As the power Internet of Things (IoT) enters the security construction stage, the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and practicality. The existing public key infrastructure (PKI)-based security authentication scheme is currently difficult to apply in many terminals in IoT. Its key distribution and management costs are high, which hinders the development of power IoT security construction. Combined Public Key (CPK) technology uses a small number of seeds to generate unlimited public keys. It is very suitable for identity authentication in the power Internet of Things. In this paper, we propose a novel identity authentication scheme for power IoT. The scheme combines the physical unclonable function (PUF) with improved CPK technology to achieve mutual identity authentication between power IoT terminals and servers. The proposed scheme does not require third-party authentication and improves the security of identity authentication for power IoT. Moreover, the scheme reduces the resource consumption of power IoT devices. The improved CPK algorithm solves the key collision problem, and the third party only needs to save the private key and the public key matrix. Experimental results show that the amount of storage resources occupied in our scheme is small. The proposed scheme is more suitable for the power IoT.

Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-Based Power IoT With Massive Connectivity

Integrating edge computing and non-orthogonal multiple access (NOMA) into the power Internet of Things (PIoT) provides a promising way to meet the stringent demands of communication delay, energy consumption, and massive connectivity. The service tasks can be partitioned into independent subtasks, and can be processed locally by PIoT devices or offloaded to the edge servers through reusing radio resource blocks (RBs). In this paper, we propose a Multi-timescale multi-dimEnsion Resource allocatIon and Task Splitting algorithm, namely MERITS, for NOMA-edge computing-based PIoT. First, Lyapunov optimization is exploited to decompose the long-term stochastic optimization problem into three short-term deterministic subproblems, i.e., RB allocation in a large timescale, task splitting and computation resource allocation in a small timescale. The first subproblem is modeled as a one-to-many matching problem with externalities, and solved by the proposed swap matching-based RB allocation algorithm. The second subproblem is relaxed into a continuous convex problem which is easily to be solved, and the third subproblem is solved by Lagrange dual decomposition. Simulation results demonstrate that MERITS outperforms the existing resource allocation and task splitting algorithms in terms of energy consumption, queuing delay, queue backlog, and connection success ratio in the massive connectivity scenario.

Energy Harvesting and Power Management for IoT Devices in the 5G Era

The fifth-generation (5G) network is a fast-growing technology that impacts personal devices for both society and the economy. With the widespread Internet of Things (IoT) devices in such networks, powering and exploring energy to operate them is vital in the 5G era. Therefore, projecting and forecasting how to power IoT devices in the 5G network have already begun, with an aspiration to provide self-powered and green future communication services. Here, we provide a vision for energy harvesting technologies for IoT devices (e.g., emerging wearable and implantable devices employed in healthcare and consumer technologies) that can aid the research line in the 5G era. We propose that various power harvesting and management techniques at the circuit, device, and system levels will be the most crucial segment of IoT devices. Thus, self-power and sustainability should be key features of IoT devices in the 5G network, and ought to be given close attention by electronics and communications communities. To envision this concept, we provide a systematic outline in which prospective power harvesting scenarios of IoT devices in the 5G network are expected. We successively describe vital potential landscapes of 5G and evaluate the energy harvesting and power management requirements. We additionally investigate the challenges beyond energy technologies that could impede the 5G enhancement.

Indoor light energy harvesting for battery-powered sensors using small photovoltaic modules.

As interest in Internet-of-Things (IoT) devices like wireless sensors increases, research efforts have focused on finding ways for these sensors to self-harvest energy from the environment in which they are installed. Photovoltaic (PV) cells or mini-modules are an intuitive choice for harvesting indoor ambient light, even under low light conditions, and using it for battery charging and powering of these devices. Characterizations of battery charging, for small rechargeable batteries from low charge to full charge, have been investigated using PV mini-modules of equal area. We present battery charging results using three different PV technologies, monocrystalline silicon (c-Si), gallium-indium-phosphide (GaInP) and gallium-arsenide (GaAs) under a warm color temperature (3000 K) LED lighting at an illuminance of 1000 lx. Battery charging times are shortest for the more efficient GAInP and GaAs mini-modules whose spectral response are a better match to the LED test source, which contains mostly visible photons, and longest for the less efficient Si cells. As a demonstration, a wireless temperature sensor mote was attached to the charging circuit and operated to determine its power consumption in relation to the available charging power. The mote's maximum power draw was less than the charging power from the least efficient c-Si mini-module. Our findings affirm the feasibility of utilizing PV under typical indoor lighting conditions to power IoT devices.

Optimization of Ultra-Dense Wireless Powered Networks.

The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

UAP: A unified authentication platform for IoT environment

The Internet of Things (IoT) is a network that encompasses every physical object being connected to the Internet. The IoT objects face several issues and challenges due to the large number and the heterogeneity of the interconnected devices. Many researchers have shown that security is among the most important challenges that the IoT faces. Security requirements such as confidentiality, integrity and authentication are fundamental, but the last one is considered as the cornerstone security service.This paper considers authentication as its core and presents an extensive review of the already proposed authentication solutions for the IoT. Subsequently, it investigates new authentication requirements and challenges introduced in the context of the embedded IoT devices. Furthermore, it unveils the proposed authentication platform. The platform is based on a reduced signature generated on a distributed way from the devices’ personal keys. Also, it allows a receiving entity to verify the identity of the sender entity and obliges the sender not to deny the emission action. The analysis and simulation comparison between the literature and the proposed solution is presented.The analysis and simulation study provide that the proposed platform respects the requirements of any IoT device and is recommended for low power IoT devices. In the sequel, simulation results validate the analytical study and shows that the proposal is recommended for any IoT embedded device. For instance, the average energy and communication costs are no more than 0.03 microjoules and 2.73 message exchange, respectively.

Lightweight and Secure Publish-Subscribe System for Cloud-Connected Ultra Low Power IoT Devices

The Internet of Things (IoT) enables the development of innovative applications in various domains such as healthcare, transportation, and Industry 4.0. The integration of the cloud platform's large processing and storage capacity with the ubiquitous sensing and actuation provided by the devices creates an IoT architecture that provides vast raw data. The IoT devices send the data to the cloud platform with the developed IoT applications, which can use publish-subscribe systems. However, messages with sensitive content require end-to-end security. Besides that, IoT devices may present processing, memory, payload, and energy restrictions. In this sense, messages in an IoT architecture need to achieve both energy-efficiency and secure message delivery. Thus, this article's main contribution refers to a system that standardizes the publish-subscribe topic and payload used by the cloud platform and the IoT devices. Our system also provides end-to-end security while surpassing the energy-efficiency to send data than the main related works in the literature regarding the use of publish-subscribe systems in IoT.

Capture Aware Sequential Waterfilling for LoRaWAN Adaptive Data Rate

LoRaWAN (Long 1 Range Wide Area Network) is an attractive network infrastructure and protocol suite for ultra low power Internet of Things devices. Even if the technology itself is quite mature and specified, the currently deployed wireless resource allocation strategies are still coarse and based on rough heuristics. This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices. Our design relies on three complementary approaches: i) equalize the Time-on-Air of packets transmitted by the system’s End Devices in each spreading factor’s group; ii) balance the spreading factors across multiple gateways and iii) keep into account the channel capture, which our experimental results show to be very substantial in LoRa. While retaining an extremely simple and scalable implementation, this strategy yields a significant improvement (up to 38%) in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech, and appears to be extremely robust to different operating/load conditions and network topology configurations.

Mini Wind Harvester and a Low Power Three-Phase AC/DC Converter to Power IoT Devices: Analysis, Simulation, Test and Design

Wind energy harvesting is a widespread mature technology employed to collect energy, but it is also suitable, and not yet fully exploited at small scale, for powering low power electronic systems such as Internet of Things (IoT) systems like structural health monitoring, on-line sensors, predictive maintenance, manufacturing processes and surveillance. The present work introduces a three-phase mini wind energy harvester and an Alternate Current/Direct Current (AC/DC) converter. The research analyzes in depth a wind harvester’s operation principles in order to extract its characteristic parameters. It also proposes an equivalent electromechanical model of the harvester, and its accuracy has been verified with prototype performance results. Moreover, unlike most of the converters which use two steps for AC/DC signal conditioning—a rectifier stage and a DC/DC regulator—this work proposes a single stage converter to increase the system efficiency and, consequently, improve the energy transfer. Moreover, the most suitable AC/DC converter architecture was chosen and optimized for the best performance taking into account: the target power, efficiency, voltage levels, operation frequency, duty cycle and load required to implement the aforementioned converter.

Quad-Band 3D Rectenna Array for Ambient RF Energy Harvesting

This paper presents a quad-band, 3D mountable rectenna module for ambient energy harvesting. With the aim of powering up Internet of Things (IoT) nodes in practical ambient environments, a hybrid approach of combining power, both at RF and DC, is adopted using 98 MHz FM band, GSM900 (Global System for Mobile Communications), GSM1800, and Wi-Fi 2.4 GHz band. A dual polarized cross-dipole antenna featuring asymmetric slots as well as central ring structure enables multiband response and improved matching at the higher three frequency bands, whereas a loaded monopole wire antenna is used at the lower FM band. Four identical multiband antennas are placed in a 3D cubic arrangement that houses a 4-to-1 power combiner and matching circuits on the inside and the FM antenna on the top. In order to maintain stable rectenna output at varying input power levels and load resistances, a novel transmission line based matching network using closed form equations is proposed. Integrated in form of a 10 × 10 × 10 cm3 cube using standard FR4 substrate, the rectenna generates a peak output voltage of 2.38 V at −10 dBm input power. The RF to DC conversion efficiency is 70.28%, 41.7%, 33.37%, and 27.69% at 98 MHz, 0.9 GHz, 1.8 GHz, and 2.4 GHz, respectively, at −6 dBm. The rectenna also exhibits a measured conversion efficiency of 31.3% at −15 dBm for multitone inputs in ambient environment. The promising results in both indoor and outdoor settings are suitable to power low power IoT devices.

Enabling Low-Power OFDM for IoT by Exploiting Asymmetric Clock Rates

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT’s TX power when the AP sets $8\times $ clock rate.