Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review

Home surveillance system based on LoRa backscattering

This work proposes a battery-free wireless burglar alarm system based on LoRa backscattering. The system consists of multiple wireless nodes that send an alert when its sensor is triggered. The primary goal is to significantly reduce the power consumption of these wireless nodes, eliminating the need for battery replacements and maintenance associated with commercial systems. The paper addresses several crucial aspects of the application, including the hardware design of the tag, which encompasses energy management up to the RF front-end. Additionally, this paper explores the wireless communication protocol by presenting a fully analog modulation approach to optimize power consumption and system responsiveness. This novel method leverages the intrinsic frequency detection function of commercial LoRa transceivers to determine the origin of backscattered LoRa packets, thereby enabling the integration of a large number of sensors within the same frequency channel, all using a single receiver. Finally, the paper provides a proof of concept validated through on-site measurements.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

Micro energy harvesting for IoT platform: Review analysis toward future research opportunities

Micro-energy harvesting (MEH) is a technology of renewable power generation which is a key technology for hosting the future low-powered electronic devices for wireless sensor networks (WSNs) and, the Internet of Things (IoT). Recent technological advancements have given rise to several resources and technologies that are boosting particular facets of society. Many researchers are now interested in studying MEH systems for ultra-low power IoT sensors and WSNs. A comprehensive study of IoT will help to manage a single MEH as a power source for multiple WSNs. The popular database from Scopus was used in this study to perform a review analysis of the MEH system for ultra-low power IoT sensors. All relevant and important literature studies published in this field were statistically analysed using a review analysis method by VOSviewer software, and research gaps, challenges and recommendations of this field were investigated. The findings of the study indicate that there has been an increasing number of literature studies published on the subject of MEH systems for IoT platforms throughout time, particularly from 2013 to 2023. The results demonstrate that 67% of manuscripts highlight problem-solving, modelling and technical overview, simulation, experimental setup and prototype. In observation, 27% of papers are based on bibliometric analysis, systematic review, survey, review and based on case study, and 2% of conference manuscripts are based on modelling, simulation, and review analysis. The top-cited articles are published in 5 different countries and 9 publishers including IEEE 51%, Elsevier 16%, MDPI 10% and others. In addition, several MEH system-related problems and challenges are noted to identify current limitations and research gaps, including technical, modelling, economic, power quality, and environmental concerns. Also, the study offers guidelines and recommendations for the improvement of future MEH technology to increase its energy efficiency, topologies, design, operational performance, and capabilities. This study's detailed information, perceptive analysis, and critical argument are expected to improve MEH research's viable future.

Porous Conductive Textiles for Wearable Electronics

Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.

Scalable Lightweight IoT-Based Smart Weather Measurement System

The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient's location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD).

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor's taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor's categorization enables us to compare the effectiveness of each sensor's system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

Enabling Modular Robotics with Secure Transducer Identification Based on Extended IEEE 21450 Transducer Electronic Datasheets

In robotics, there are many different sensors and actuators mounted onto a robot which may also, in the case of modular robotics, be interchanged during operation. During development of new sensors or actuators, prototypes may also be mounted onto a robot to test functionality, where the new prototypes often have to be integrated manually into the robot environment. Proper, fast and secure identification of new sensor or actuator modules for the robot thus becomes important. In this work, a workflow to add new sensors or actuators to an existing robot environment while establishing trust in an automated manner using electronic datasheets has been developed. The new sensors or actuators are identified via near field communication (NFC) to the system and exchange security information via the same channel. By using electronic datasheets stored on the sensor or actuator, the device can be easily identified and trust can be established by using additional security information contained in the datasheet. In addition, the NFC hardware can simultaneously be used for wireless charging (WLC), thus allowing for wireless sensor and actuator modules. The developed workflow has been tested with prototype tactile sensors mounted onto a robotic gripper.

Soft Fiber Electronics Based on Semiconducting Polymer

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

Energy Optimization of Wireless Sensor Embedded Cloud Computing Data Monitoring System in 6G Environment

With the construction and development of modern and smart cities, people's lives are becoming more intelligent and diversified. Surveillance systems increasingly play an active role in target tracking, vehicle identification, traffic management, etc. In the 6G network environment, facing the massive and large-scale data information in the monitoring system, it is difficult for the ordinary processing platform to meet this computing demand. This paper provides a data governance solution based on a 6G environment. The shortcomings of critical technologies in wireless sensor networks are addressed through ZigBee energy optimization to address the shortage of energy supply and high energy consumption in the practical application of wireless sensor networks. At the same time, this improved routing algorithm is combined with embedded cloud computing to optimize the monitoring system and achieve efficient data processing. The ZigBee-optimized wireless sensor network consumes less energy in practice and also increases the service life of the network, as proven by research and experiments. This optimized data monitoring system ensures data security and reliability.

A Systematic Review of Internet of Things in Clinical Laboratories: Opportunities, Advantages, and Challenges

The Internet of Things (IoT) is the network of physical objects embedded with sensors, software, electronics, and online connectivity systems. This study explores the role of IoT in clinical laboratory processes; this systematic review was conducted adhering to the PRISMA Statement 2020 guidelines. We included IoT models and applications across preanalytical, analytical, and postanalytical laboratory processes. PubMed, Cochrane Central, CINAHL Plus, Scopus, IEEE, and A.C.M. Digital library were searched between August 2015 to August 2022; the data were tabulated. Cohen's coefficient of agreement was calculated to quantify inter-reviewer agreements; a total of 18 studies were included with Cohen's coefficient computed to be 0.91. The included studies were divided into three classifications based on availability, including preanalytical, analytical, and postanalytical. The majority (77.8%) of the studies were real-tested. Communication-based approaches were the most common (83.3%), followed by application-based approaches (44.4%) and sensor-based approaches (33.3%) among the included studies. Open issues and challenges across the included studies included scalability, costs and energy consumption, interoperability, privacy and security, and performance issues. In this study, we identified, classified, and evaluated IoT applicability in clinical laboratory systems. This study presents pertinent findings for IoT development across clinical laboratory systems, for which it is essential that more rigorous and efficient testing and studies be conducted in the future.

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.

A Comprehensive Survey on RF Energy Harvesting: Applications and Performance Determinants

There has been an explosion in research focused on Internet of Things (IoT) devices in recent years, with a broad range of use cases in different domains ranging from industrial automation to business analytics. Being battery-powered, these small devices are expected to last for extended periods (i.e., in some instances up to tens of years) to ensure network longevity and data streams with the required temporal and spatial granularity. It becomes even more critical when IoT devices are installed within a harsh environment where battery replacement/charging is both costly and labour intensive. Recent developments in the energy harvesting paradigm have significantly contributed towards mitigating this critical energy issue by incorporating the renewable energy potentially available within any environment in which a sensor network is deployed. Radio Frequency (RF) energy harvesting is one of the promising approaches being investigated in the research community to address this challenge, conducted by harvesting energy from the incident radio waves from both ambient and dedicated radio sources. A limited number of studies are available covering the state of the art related to specific research topics in this space, but there is a gap in the consolidation of domain knowledge associated with the factors influencing the performance of RF power harvesting systems. Moreover, a number of topics and research challenges affecting the performance of RF harvesting systems are still unreported, which deserve special attention. To this end, this article starts by providing an overview of the different application domains of RF power harvesting outlining their performance requirements and summarizing the RF power harvesting techniques with their associated power densities. It then comprehensively surveys the available literature on the horizons that affect the performance of RF energy harvesting, taking into account the evaluation metrics, power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting. Finally, it summarizes the available literature associated with RF powered networks and highlights the limitations, challenges, and future research directions by synthesizing the research efforts in the field of RF energy harvesting to progress research in this area.

Swarm Intelligence Techniques for Mobile Wireless Charging

This paper proposes energy-efficient swarm intelligence (SI)-based approaches for efficient mobile wireless charging in a distributed large-scale wireless sensor network (LS-WSN). This approach considers the use of special multiple mobile elements, which traverse the network for the purpose of energy replenishment. Recent techniques have shown the advantages inherent to the use of a single mobile charger (MC) which periodically visits the network to replenish the sensor-nodes. However, the single MC technique is currently limited and is not feasible for LS-WSN scenarios. Other approaches have overlooked the need to comprehensively discuss some critical tradeoffs associated with mobile wireless charging, which include: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall available network energy. These important tradeoffs are investigated in this study. Thus, this paper aims to investigate some of the critical issues affecting efficient mobile wireless charging for large-scale WSN scenarios; consequently, the network can then be operated without limitations. We first formulate the multiple charger recharge optimization problem (MCROP) and show that it is N-P hard. To solve the complex problem of scheduling multiple MCs in LS-WSN scenarios, we propose the node-partition algorithm based on cluster centroids, which adaptively partitions the whole network into several clusters and regions and distributes an MC to each region. Finally, we provide detailed simulation experiments using SI-based routing protocols. The results show the performance of the proposed scheme in terms of different evaluation metrics, where SI-based techniques are presented as a veritable state-of-the-art approach for improved energy-efficient mobile wireless charging to extend the network operational lifetime. The investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.

Piezoelectric energy harvester with double cantilever beam undergoing coupled bending-torsion vibrations by width-splitting method

We propose piezoelectric energy harvester (PEH) with double-cantilever-beam (DCB) undergoing coupled bending-torsion vibrations by combining width-splitting method and asymmetric mass, in order that more ambient energy could be harvested from environmental vibration with multiple-frequency excitation. The geometrical dimensions are optimized for PEHDCB, when the maximum of output peak voltages U_p-max and resonance frequency difference (Δf₀) between the first and second modes are chosen as optimization objectives based on orthogonal test method. The energy harvesting efficiency is evaluated by the proportion of half-power bandwidth and quality factor, and the experimental and simulation results are compared to verify reliability. The U_p-max1 and P_p-max1 are increased 25.2% and 57.3% for PEHDCB under the multi-frequency excitation, when the split-width method is applied into PEH with single-cantilever-beam (SCB) undergoing coupled bending-torsion vibrations. The deviations of U_p-max1 and f₀ are at the ranges of 4.9–14.2% and 2.2–2.5% for PEHDCB under the different mass ratios, and the measurement reliability is acceptable considering incomplete clamping, damping and inevitable assembly effects. The energy harvesting efficiency of PEHDCB presented is much higher than that of the conventional PEHSCB from environmental vibration with multiple-frequency excitation.

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application ‘MQTT dashboard’ is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.

FAJIT: a fuzzy-based data aggregation technique for energy efficiency in wireless sensor network

Wireless sensor network (WSN) is used to sense the environment, collect the data, and further transmit it to the base station (BS) for analysis. A synchronized tree-based approach is an efficient approach to aggregate data from various sensor nodes in a WSN environment. However, achieving energy efficiency in such a tree formation is challenging. In this research work, an algorithm named fuzzy attribute-based joint integrated scheduling and tree formation (FAJIT) technique for tree formation and parent node selection using fuzzy logic in a heterogeneous network is proposed. FAJIT mainly focuses on addressing the parent node selection problem in the heterogeneous network for aggregating different types of data packets to improve energy efficiency. The selection of parent nodes is performed based on the candidate nodes with the minimum number of dynamic neighbors. Fuzzy logic is applied in the case of an equal number of dynamic neighbors. In the proposed technique, fuzzy logic is first applied to WSN, and then min–max normalization is used to retrieve normalized weights (membership values) for the given edges of the graph. This membership value is used to denote the degree to which an element belongs to a set. Therefore, the node with the minimum sum of all weights is considered as the parent node. The result of FAJIT is compared with the distributed algorithm for Integrated tree Construction and data Aggregation (DICA) on various parameters: average schedule length, energy consumption data interval, the total number of transmission slots, control overhead, and energy consumption in the control phase. The results demonstrate that the proposed algorithm is better in terms of energy efficiency.

Prospective Efficient Ambient Energy Harvesting Sources for IoT-Equipped Sensor Applications

In the past few years, the internet of things (IoT) has garnered a lot of attention owing to its significant deployment for fulfilling the global demand. It has been seen that power-efficient devices such as sensors and IoT play a significant role in our regular lives. However, the popularity of IoT sensors and low-power electronic devices is limited due to the lower lifetime of various energy resources which are needed for powering the sensors over time. For overcoming this issue, it is important to design and develop better, high-performing, and effective energy harvesting systems. In this article, different types of ambient energy harvesting systems which can power IoT-enabled sensors, as well as wireless sensor networks (WSNs), are reviewed. Various energy harvesting models which can increase the sustainability of the energy supply required for IoT devices are also discussed. Furthermore, the challenges which need to be overcome to make IoT-enabled sensors more durable, reliable, energy-efficient, and economical are identified.

Sixth Generation (6G) Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research directions.

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

Energy efficiency is a major concern in the emerging mobile cellular wireless networks since massive connectivity is to be expected with high energy requirements from the network operators. Non-orthogonal multiple access (NOMA) being the frontier multiple access scheme for 5G, there exists numerous research attempts on enhancing the energy efficiency of NOMA enabled wireless networks while maintaining its outstanding performance metrics such as high throughput, data rates and capacity maximized optimally.The concept of green NOMA is introduced in a generalized manner to identify the energy efficient NOMA schemes. These schemes will result in an optimal scenario in which the energy generated for communication is managed sustainably. Hence, the effect on the environment, economy, living beings, etc is minimized. The recent research developments are classified for a better understanding of areas which are lacking attention and needs further improvement. Also, the performance comparison of energy efficient, NOMA schemes against conventional NOMA is presented. Finally, challenges and emerging research trends, for energy efficient NOMA are discussed.

Channel Prediction Based on BP Neural Network for Backscatter Communication Networks

Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.

Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.