Low-power Wireless Protocols Research Articles

Lure: A simulator for networks of batteryless intermittent nodes

The emerging paradigm of batteryless intermittent sensor networks (BISNs) presents new challenges for researchers of low-power wireless systems and protocols. The nature of these challenges exacerbates the difficulty of evaluating networks of physical sensor nodes, making simulation an even more important component in evaluating performance metrics, such as communication throughput and delay, for BISN designs. To our knowledge, existing simulators and analytical models do not meet the unique needs of BISN research; therefore, we have created a new open-source BISN simulator named Lure. Lure is designed from the ground-up for simulation of batteryless intermittent systems and networks. Written in Python, Lure is powerful, flexible, highly configurable, and supports rapid prototyping of new protocols, systems, and applications, with a low learning curve. In this paper, we present Lure and validate it with experimental data to show that Lure can accurately reflect the reality of BISNs. We then demonstrate the process of applying Lure to research questions in select case studies.

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.

Understanding Concurrent Transmissions: The Impact of Carrier Frequency Offset and RF Interference on Physical Layer Performance

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

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.

Real-time wireless marine radioactivity monitoring system using a SiPM-based mobile gamma spectroscopy mounted on an unmanned marine vehicle

Marine radioactivity monitoring is critical for taking immediate action in case of unexpected nuclear accidents at nuclear facilities located near coastal areas. Especially when the level of contamination is not predictable, mobile monitoring systems will be useful for wide-area ocean radiation survey and for determination of the level of radioactivity. Here, we used a silicon photomultiplier and a high-efficiency GAGG crystal to fabricate a compact, battery-powered gamma spectroscopy that can be used in an ocean environment. The developed spectroscopy has compact dimensions of 6.5 × 6.5× 8 cm3 and weighs 560 g. We used LoRa, a low-power wireless protocol for communication. Successful data transmission was achieved within 1.4 m water depth. The developed gamma spectroscopy was able to detect radioactivity from a 137Cs point source (3.7 kBq) at a distance of 20 cm in water. Moreover, we demonstrated an unmanned radioactivity monitoring system in a real sea by combining unmanned surface vehicle with the developed gamma spectroscopy. A hidden 137Cs source (3.07 MBq) was detected by the unmanned system at a distance of 3 m. After successfully testing the developed mobile spectroscopy in an ocean environment, we believe that our proposed system will be an effective solution for mobile real-time marine radioactivity monitoring.

Embracing Channel Estimation in Multi-Packet Reception of ZigBee

As a low-power and low-cost wireless protocol, the promising ZigBee has been widely used in sensor networks and cyber-physical systems. Since ZigBee based networks usually adopt tree or cluster topology, the convergecast scenarios are common in which multiple transmitters send packets to one receiver, leading to the severe collision problem. The conventional ZigBee adopts carrier sense multiple access with collisions avoidance to avoid collisions, which introduces additional time/energy overhead. The state-of-the-art methods resolve collisions instead of avoidance, in which mZig decomposes a collision by the collision itself and reZig decodes a collision by comparing with reference waveforms. However, mZig falls into high decoding errors only exploiting the signal amplitudes while reZig incurs high computational complexity for waveform comparison. In this paper, we propose CmZig to embrace channel estimation in multiple-packet reception (MPR) of ZigBee, which effectively improves MPR via lightweight computing used for channel estimation and collision decomposition. First, CmZig enables accurate collision decomposition with low computation complexity, which uses the estimated channel parameters modeling both signal amplitude and phase. Second, CmZig adopts reference waveform comparison only for collisions without chip-level time offsets, instead of the complex machine learning based method. We implement CmZig on USRP-N210 and establish a six-node testbed.

Synchronous Transmissions in Low-Power Wireless

Low-power wireless communication is a central building block of cyber-physical systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude this article with a discussion on open questions and challenges in this research field.

Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols.

Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support has increased the vulnerability of BLE to security threats, since a larger number of devices can participate in a BLE Mesh network. To address these concerns, BLE version 5 enhanced existing BLE security features to deal with various authenticity, integrity, and confidentiality issues. However, there is still a lack of detailed studies related to these new security features. This survey examines the most recent BLE-based mesh network protocols and related security issues. In the first part, the latest BLE-based mesh communication protocols are discussed. The analysis shows that the implementation of BLE pure mesh protocols remains an open research issue. Moreover, there is a lack of auto-configuration mechanisms in order to support bootstrapping of BLE pure mesh networks. In the second part, recent BLE-related security issues and vulnerabilities are highlighted. Strong Intrusion Detection Systems (IDS) are essential for detecting security breaches in order to protect against zero-day exploits. Nonetheless, viable IDS solutions for BLE Mesh networks remain a nascent research area. Consequently, a comparative survey of IDS approaches for related low-power wireless protocols was used to map out potential approaches for enhancing IDS solutions for BLE Mesh networks.

TSCH Networks for Health IoT

The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference. We enhance and experimentally evaluate the Time-Slotted Channel Hopping protocol from the IEEE 802.15.4 standard to address these challenges. Our contributions are a new schedule and an adaptive channel selection mechanism to increase the performance of time-slotted channel hopping in this domain. Evaluation in a test house shows that the enhanced system is suitable for our e-Health application and compares favorably with state-of-the-art options. The schedule provides higher reliability compared with the minimal scheduling function from the IETF 6TiSCH Working Group and has a better energy-efficiency/reliability tradeoff than the Orchestra scheduler. Results from 29 long-term residential deployments confirm the suitability for the application and show that the system is able to adapt and avoid channels used by WiFi. In these uncontrolled environments, the system achieves 99.96% average reliability for networks that generate 7.5 packets per second on average.

Green industrial networking: recent advances, taxonomy, and open research challenges

The consciousness of environmental problems has attracted the industry's attention toward the reduction of unnecessary energy emission by enabling green industrial networking. The reduction of unnecessary energy emitted by industrial networks can be a possible solution to many environmental issues. Green industrial networking is in its infancy, and an overview of the domain is still lacking. In this article, we discuss recent advances in industrial and green networking paradigms to investigate the impact on global communities. We also classify the literature by devising a taxonomy based on networking technologies, machines, network types, topologies, field bus types, transmission media, and hierarchical levels. Moreover, we identify and discuss key enablers (adaptive links, resource-based energy conservation, energy-efficient scheduling, energy-aware systems, energy-aware proxying, energy-conservative approaches, and low-power wireless protocols) for green industrial networking. Furthermore, we discuss challenges that remain to be addressed as future research directions.

(Invited) Challenges and Solutions for Future Internet-of-Things Semiconductor Technologies

Emerging IoT edge-node applications demand new capabilities from semiconductor technologies including unparalleled energy efficiency, reduced package form factor via increased integration, wireless connectivity across a range of low-power wireless protocols, embedded memory for operating systems with smaller footprint and longer battery life, and low cost. This paper will detail these challenges and show why future fully-depleted silicon-on-insulator technologies, such as 22FDX technology, possess the salient features to satisfy these requirements and enable future generations of IoT devices.

Early Scavenger Dimensioning in Wireless Industrial Monitoring Applications

The industrial Internet era is pushing for even more miniaturized, powerful, and energy efficient devices that seamlessly integrate to the Internet and aim to improve efficiency of industries by monitoring, actuating, or sampling data from machines, infrastructures, and systems. Industrial low power wireless protocols are one of the key enablers of that revolution but still energy consumption is what is limiting ubiquitous deployments of perpetual and unattended devices. The adoption of energy harvesting technologies is enabling autonomously powered control and monitoring systems on industries, infrastructures, and cities. Yet, putting these systems together require a clear understanding of their capabilities and behavior in order to dimension their energy needs and to contribute to the development of a new generation of self-powered ubiquitous devices. Therefore, this paper discusses, through a use case, the tradeoff to reliably dimension scavenger properties to network requirements and application needs, with the main objective to enable industries to optimize the adoption of that technologies while keeping low technical risks.

(Invited) Challenges and Solutions for Future Internet-of-Things Semiconductor Technologies

Emerging IoT edge-node applications demand new capabilities from semiconductor technologies including unparalleled energy efficiency, reduced package form factor via increased integration, wireless connectivity across a range of low-power wireless protocols, embedded memory for operating systems with smaller footprint and longer battery life, and low cost. This talk will detail the salient features of 22FDXTM technology, the industry’s first 22nm fully-depleted silicon-on-insulator technology, which give designers the tools to satisfy these challenging requirements. 22FDXTM has capabilities to meet the low-static leakage requirements of edge-node MCUs with low duty-cycles and low-static leakage and the performance and low active power requirements of wearable application processors. This is accomplished via a next generation FDSOI transistor architecture, a novel body-biasing capability to dynamically adjust threshold voltages, and a wide-range of Vt options to achieve over 50% total power reduction compared to 28nm and device leakages as low as 1pA/um. To meet the low-cost requirements of IoT, 22FDXTM was architected to minimize costly double patterning steps while maximizing the overall die shrink. Over 40% area reduction relative to 28nm is observed in an ARM A9 Neon core. In addition, the overall BoM and package form factors can be reduced to lower system costs by integrating RF functionality. To satisfy the wireless connectivity requirements, 22FDX provides an IP offering that satisfies a range of low-power wireless protocols such as WiFi, Bluetooth®, and ZigBeeTM and interface network IPs useful for IoT applications. Finally, next generation embedded memories, such as MRAM, could be an energy efficient solution that will provide advances to system designs. 22FDXTM is the right solution to meet the requirements of emerging IoT applications.

Toward HW/SW integration

Traditional Computer Science curricula focus on the training for logic reasoning and programming skills. System integration is often not covered in computer science curricula. As the embedded platforms migrate from 8-bit microprocessors to 32-bit microprocessors, the engineers require different skills to design modern embedded systems. The Computer Science faculties at several universities in Taiwan have collaborated to design a new course to meet such needs. In this course, we focus on the capability of integrating hardware and software in networked embedded systems. In particular, the students learn the skills for embedded real-time programming, multi-thread programming, low-power network protocols, security, and wireless networks protocol. In this article, we report the design rationale and current status of this course.