Low-power Wireless Networks Research Articles

Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time.

Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures. The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG. This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.

Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime.

CC2530-Based Wireless Data Long-Distance Communication Simulation Design and Application

With the rapid development of IoT, low-power wireless networks are becoming more and more important in engineering applications. For different IoT applications, a variety of corresponding low-power wireless technologies are studied and used. Among them, ZigBee technology is widely used for wireless data long-distance communication networks that require low-power consumption, low communication rate and large capacity self-organizing networks. However, the standard ZigBee module is limited by the large noise factor of the receiver and the small radiation power of the transmitter in the module, which cannot meet the requirements of long-distance communication. In this paper, we combine RF and communication technologies to design a long-range, low-power transceiver based on ZigBee technology for wireless data long-range communication simulation design and application. Experimental results show that the model in the urban area of 500[Formula: see text]m range, the system’s packet loss rate is kept at a low 0.03 or so, and the average Rssi is kept above and below [Formula: see text][Formula: see text]dBm, which can meet the general communication requirements and achieve the expected communication effect. Under the same packet rate, the model combines the improved AODVjr and Cluster_Tree algorithms, and the packet delivery rate can reach 98.5%, which greatly extends the wireless data long-distance communication life.

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network.

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.

Optimum Piezo-Electric Based Energy Harvesting for Low-Power Wireless Networks with Power Complexity Considerations

Low-power wireless sensing-based networks suffer from many constraints and challenges. In this research work, efficient power source has been designed to provide the need of energy for the Wireless Sensor Networks (WSNs) and Wireless Body Area Networks (WBANs). The energy sources are the main challenge and constraint these wireless networks applications. This paper discusses recent researcher’s works which considered the energy constraints of the WSN and WMSN with their proposed security techniques. The main idea of this presented work is the energy harvesting through extracting the electrical energy from the audio/acoustic signal/energy, this utilized audio/acoustic source in this scenario is the disk jockey. To maximize the produced power from the proposed acoustic energy harvesting Piezo-based several parameters is studied. The parameters are considered in this research work are the method of Piezo-transducers connections, the distance of sound source, the sound intensity variation and the sound concentrating tube length. These tubes are mounted on slim diaphragm two maximize the energy harvesting. The piezoelectric transducer array scenario is designed using four piezoelectric transducers utilizing different connect ion methods, series, parallel and in hybrid. Several practically experiments are performed on the presented different scenarios to evaluate the proposed energy harvesting efficiency. These experiments reveal that the superiority of the proposed acoustic energy harvesting technique with low power complexity wireless networks and suitable with the different presented scenarios.

Optimizing network lifespan through energy harvesting in low-power lossy wireless networks

Optimizing network lifespan through energy harvesting in low-power lossy wireless networks

Deep-Reinforcement-Learning-Aided Loss-Tolerant Congestion Control for 6LoWPAN Networks

The IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) protocol stack is a promising solution to connect Wireless Sensor Networks (WSNs) with the Internet for realizing a ubiquitous network interconnection of all things. However, 6LoWPAN networks face a critical challenge to control congestion caused by the burst of data traffic from wireless sensors. Packet loss will occur when buffer overflows. This paper focuses on the loss-tolerant congestion control problem in 6LoWPAN networks, which has not been addressed in existing works. We formulate the congestion control problem as a non-cooperative Markov game framework and conceive a novel congestion control method, namely Deep reinforcement learning aided Loss-tolerant Congestion Control (DLCC), to alleviate congestion while maintaining a tolerable packet loss imposed by buffer overflow. The proposed DLCC employs Deep Reinforcement Learning (DRL) to solve the curse of state dimensionality, while packet loss constraints are handled by utilizing Lagrange multipliers to integrate the reward with loss constraints. By dynamically updating Lagrange multipliers in an online learning procedure, DLCC finds the optimal congestion control policy. Our simulation results show that DLCC maintains the packet loss rate below the tolerable threshold at the presence of congestion. In contrast to existing hybrid congestion control algorithms, the proposed DLCC algorithm is more energy-efficient and provides higher throughput, lower average delay and better fairness.

An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks

LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.

Eliminating Mapping Error of Link Quality Prediction for Low-Power Wireless Networks

The size of time windows used by low power wireless protocols to compute packet reception ratio (PRR) directly affects the accuracy and agility of link quality prediction. Using small time windows would make the prediction more agile, but its accuracy decreases seriously. To improve the accuracy while maintaining agility, many existing methods generally predict the physical layer parameters within small time windows first and then calculate PRR using mapping models of such parameters and PRR. However, due to the propagation characteristics of wireless signal, these mapping models usually introduce large errors. The mapping error will superimpose on the prediction error of physical layer parameters, which would inevitably degrade the accuracy and reliability of PRR prediction. This paper proposes to eliminate the step of mapping from physical layer parameters to PRR and directly use the historical series of PRR computed within small time windows as input parameters to build prediction model. Then, the PRR within large time windows could be predicted. This effectively avoids the errors introduced by using mapping models while not sacrificing the agility. To verify the proposed method, several machine learning algorithms were chosen to implement the prediction model. Compared with similar methods based on mapping models, the proposed one could achieve higher accuracy under different size of time windows. Specifically, under large time windows which are commonly used to describe PRR in practice, its accuracy is much higher. More importantly, agility of the proposed method is basically equivalent or even superior to existing ones.

HEURISTIC GATEWAY PLACEMENT FOR MINIMAL TRANSMISSION POWER & COLLISION PROBABILITY IN AN INTERNET OF THINGS LOW POWER WIDE AREA NETWORK (HGPMTPIoT-LPWAN)

A variety of long-range (LoRa), low-power, and low bit-rate wireless network technologies have been made possible by the Internet of Things' (IoT) rapid growth. As a radio technology for the implementation of numerous Internet of Things applications, the LoRa low-power wide area network (LPWAN) has gained prominence. LoRa is frequently used in conjunction with the Long-Range Wide Area Network Media Access Control (LoRaWAN MAC) protocol and functions in the Industrial, Scientific, and Medical (ISM) bands, which are unlicensed. As a result of receiving a wide variety of different message sizes from diverse applications, LoRa networks experience scalability issues when the number of end nodes connected to one network is more than the shared number of channels. This results in collisions and packet loss. In this paper, heuristic gateway placement for minimal transmission power & collision probability in an internet of things low power wide area network (HGPMTPIOT-LPWAN) is proposed to increase network efficiency, and improve overall performance by measuring collision probability, which in turn can help to reduce the need for retransmissions and packet drop rate. The things network simulator is used to measure the collisions and packet drop rate. An improved performance for HGPMTPIOT as against the efficient graph-based gateway placement (EGBGP) for large-scale LoRaWAN deployments is achieved for packet drop rate by 6%. Likewise, the simulation results show improvements in terms of decreasing the collision probability for 20 to 60 nodes by 20%.

Design of a Reconfigurable Intelligent Surface- Assisted FM-DCSK-SWIPT Scheme With Non-Linear Energy Harvesting Model

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed scheme is developed under a non-linear energy-harvesting (EH) model which can accurately characterize the practical situation. The proposed RIS-FM-DCSK-SWIPT scheme has an appealing feature that it does not require channel state information, thus avoiding the complex channel estimation. We further derive the closed-form theoretical expressions for the energy shortage probability and bit error rate (BER) of the proposed scheme over the multipath Rayleigh fading channel. In addition, we investigate the influence of key parameters on the performance of the proposed transmission scheme in two different scenarios, i.e., RIS-assisted access point (RIS-AP) and dual-hop communication (RIS-DH). Finally, we carry out various Monte-Carlo experiments to verify the accuracy of the theoretical derivation, illustrate the performance advantage of the proposed scheme, and give some design insights for future study.

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

IoT solution for smart water distribution networks based on a low-power wireless network, combined at the device-level: A case study

Wireless systems enable the implementation and deployment of efficient Internet of Things (IoT) solutions in various applications such as smart cities, smart industry, smart transportation, or agriculture 4.0. The low power consumption of battery powered devices provides long system lifetime. Taking advantage of the energy consumption reduction mechanisms of the IEEE 802.15.4g and Narrowband IoT (NB-IoT) standards, this work develops a combined wireless network with application to smart metering for water distribution systems. The end devices used for reading water meters transmit their measurements through cluster heads with dual wireless connectivity. This combined network also finds application in other environments, such as industrial areas with a large geographical extension. Experimental results and simulations results obtained with two demonstrators show an estimate of battery life of more than a decade for those nodes with the highest power requirements.

Implementation of Novel Block and Convolutional Encoding Circuit Using FS-GDI

A novel implementation of Block and Convolutional encoding circuits approach using Gate Input Diffusion-Full Swing (FS-GDI) and Complementary Metal Oxide Semiconductor (CMOS) logic styles has been presented. Performance analysis of the proposed encoding circuits has been performed using Cadence Virtuoso (CV) S/W package. The performance metrics of FS-GDI-based encoding circuits have been compared with traditional CMOS-based designs. The proposed encoding circuits involve Block Hamming (15, 11), Convolutional (2, 1, 3), and Recursive Systematic Convolutional (2, 1, 2) encoding circuits; these presented circuits are simulated and tested using a word length of 11 bits. The simulation experiments revealed that the proposed implemented circuits achieve delay time improving by 27.91% and 33.27% for Hamming (15, 11) and Convolutional (2, 1, 3) codes, respectively. The simple proposed RSC encoder performs better than the Convolutional (2, 1, 3) due to shorter constraint length and less H/W complexity. On the other hand, the Transistor Count-Hardware (TC-H/W) is enhanced by 50% for the Block and Convolutional encoding based on employing area-efficient FS-GDI-XOR gate. From the analysis of the results, the proposed designs achieve efficient power and delay optimization of Hamming and Convolutional encoding utilizing the FS-GDI logic style. Also, it is clear that the characteristics of the proposed encoding circuits are vital and effective in the digital signal processing circuits and encoding/decoding schemes implementation in low-power wireless networks.

Low-Cost Assistive Body Temperature Screening System to Combat Communicable Infectious Diseases Leveraging Edge Computing and Long-Range and Low-Power Wireless Networks

In recent days, due to the emergence of communicable infectious diseases, healthcare, and medical technologies are expected to play a critical role. The advancement of communication and sensor network technologies has accelerated mass screening systems to combat the disease. Human temperature detection is one of the measurements for crowd screening in public places. Nevertheless, it is challenging to design a fast, lightweight, and easy-to-deploy contact-less crowd screening system in the outdoor environment due to several factors, such as environmental effect, background temperature, deployment cost, and remote operation. The state-of-the-art is mainly based on either hand-held devices or high-cost infrared cameras in only designated places. This article presents an end-to-end contactless assistive method for human body temperature screening systems, starting from collecting raw temperature data using a thermal camera to identify the suspected individual for combating communicable infectious diseases. We leverage the computing, storage, and communication resources offered by edge computing. In particular, we deploy a lightweight version of MobileNet v2 in resource-constrained Raspberry Pi 4B to detect the human’s head and body from the thermal image and use a classifier to determine the temperature from the raw temperature data. Moreover, we leverage a low-power and long-range wireless network for the exchange of model parameters between Raspberry Pi and the remote server. The experiments show that although the detection accuracy is not very high, we can reduce the bottleneck from screening time and reduce the exposure for the individuals because of the reduced bottleneck. Our proposed solution is implemented in Python and is available under the open-source MIT License at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mitunhub/HAWK-i</uri> .

Machine Learning Applied to LoRaWAN Network for Improving Fingerprint Localization Accuracy in Dense Urban Areas

In the area of low-power wireless networks, one technology that many researchers are focusing on relates to positioning methods such as fingerprinting in densely populated urban areas. This work presents an experimental study aimed at quantifying mean location estimation error in populated areas. Using a dataset provided by the University of Antwerp, a neural network was implemented with the aim of providing end-device location. In this way, we were able to measure the mean localization error in areas of high urban density. The results obtained show a deviation of less than 150 m in locating the end device. This offset can be decreased up to a few meters, provided that there is a greater density of nodes per square meter. This result could enable Internet of Things (IoT) applications to use fingerprinting in place of energy-consuming alternatives.

Autonomous Traffic-Aware Scheduling for Industrial Wireless Sensor-Actuator Networks

Recent years have witnessed rapid adoption of low-power Wireless Sensor-Actuator Networks (WSANs) in process industries. To meet the critical demand for reliable and real-time communication in harsh industrial environments, the industrial WSAN standards make a set of specific design choices, such as employing the Time-Slotted Channel Hopping (TSCH) technique. Such design choices distinguish industrial WSANs from traditional Wireless Sensor Networks, which were designed for best-effort services. Recently, there has been increasing interest in developing new methods to enable autonomous transmission scheduling for industrial WSANs that run TSCH and the Routing Protocol for Low-Power and Lossy Networks (RPL). Our study shows that the current approaches fail to consider the traffic loads of different devices when assigning time slots and channels, which significantly compromises network performance when facing high data rates. In this article, we introduce a novel Autonomous Traffic-Aware transmission scheduling method for industrial WSANs. The device that runs ATRIA can detect its traffic load based on its local routing information and then schedule its transmissions accordingly without the need to exchange information with neighboring devices. Experimental results show that ATRIA provides significantly higher end-to-end network reliability and lower end-to-end latency without introducing additional overhead compared with a state-of-the-art baseline.

A Survey of Small, Low-Frequency Antennas: Recent designs, practical challenges, and research directions

Robust, secure, low-power wireless communications and networking are needed to support emerging applications in emergency and disaster response, robotics, autonomous vehicles, and the Internet of Things (IoT) in physically complex environments, such as indoor/outdoor, dense urban, and other challenging scenarios. These applications require a variety of wireless communications modalities, including high and very-high frequency (HF and VHF) bands. To effectively deploy compact wireless communications systems at these low frequencies, high-performance electrically small antennas (ESAs) are needed. In this article, we present a survey of state-of-the-art low-frequency ESAs, including passive resonant, tunable, multiband, and actively matched designs. While passive designs are subject to classic tradeoffs in size and efficiency, active and platform-integrated techniques offer potential performance-enhancing alternatives. And, though single ESAs tend to omnidirectional beam patterns, multiantenna arrays can achieve directionality, and designs include parasitic, biomimetic, and distributed approaches.