Understanding the limits of LoRaWAN

The quick proliferation of LPWAN networks, being LoRaWAN one of the most adopted, raised the interest of the industry, network operators and facilitated the development of novel services based on large scale and simple network structures. LoRaWAN brings the desired ubiquitous connectivity to enable most of the outdoor IoT applications and its growth and quick adoption are real proofs of that. Yet the technology has some limitations that need to be understood in order to avoid over-use of the technology. In this article we aim to provide an impartial overview of what are the limitations of such technology, and in a comprehensive manner bring use case examples to show where the limits are

Combining distributed queuing with energy harvesting to enable perpetual distributed data collection applications

This is the peer reviewed version of the following article: Vazquez-Gallego F, Tuset-Peiró P, Alonso L, Alonso-Zarate J. Combining distributed queuing with energy harvesting to enable perpetual distributed data collection applications. Trans Emerging Tel Tech. 2017;e3195 , which has been published in final form at https://doi.org/10.1002/ett.3195. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This paper presents, models, and evaluates energy harvesting–aware distributed queuing (EH-DQ), a novel medium access control protocol that combines distributed queuing with energy harvesting (EH) to address data collection applications in industrial scenarios using long-range and low-power wireless communication technologies. We model the medium access control protocol operation using a Markov chain and evaluate its ability to successfully transmit data without depleting the energy stored at the end devices. In particular, we compare the performance and energy consumption of EH-DQ with that of time-division multiple access (TDMA), which provides an upper limit in data delivery, and EH-aware reservation dynamic frame slotted ALOHA, which is an improved variation of frame slotted ALOHA. To evaluate the performance of these protocols, we use 2 performance metrics: delivery ratio and time efficiency. Delivery ratio measures the ability to successfully transmit data without depleting the energy reserves, whereas time efficiency measures the amount of data that can be transmitted in a certain amount of time. Results show that EH-DQ and TDMA perform close to the optimum in data delivery and outperform EH-aware reservation dynamic frame slotted ALOHA in data delivery and time efficiency. Compared to TDMA, the time efficiency of EH-DQ is insensitive to the amount of harvested energy, making it more suitable for energy-constrained applications. Moreover, compared to TDMA, EH-DQ does not require updated network information to maintain a collision-free schedule, making it suitable for very dynamic networks.Peer ReviewedPostprint (author's final draft

Delay and energy consumption analysis of frame slotted ALOHA variants for massive data collection in internet-of-things scenarios

This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirementsPeer ReviewedPostprint (published version

Combining low-code programming and SDL-based modeling with snap! in the industry 4.0 context

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.One of the main challenges to implement Industry 4.0 technologies within the industrial fabric is the lack of suitable concrete models and tools that demonstrate the potential benefits of embracing the digital transformation process. To overcome this challenge, over the past years, various Industry 4.0 automation and robotics providers have presented solutions based on visual block programming languages, which follow an emerging low-code approach to reduce the entry barriers of digital technologies. However, block-based low-code tools typically lack the formality introduced by specification languages, limiting their ability to model the industrial processes formally. Taking this into account, in this article, we present the combination of specification languages and visual block programming languages to enable industrial users to test and/or build their own Digital Twin models at a suitable abstraction level and with low entry barriers. In particular, we combine SDL and Snap! to create SDL4Snap!, an opensource and web-based tool that facilitates the implementation and validation of Digital Twin prototypes. Overall, the resulting tool has the potential to reduce the entry barrier to Digital Twins in small and medium enterprises, which are part of the late majority and laggard groups regarding the adoption of digital technologies in the context of Industry 4.0.Peer ReviewedPostprint (published version

LPDQ: a self-scheduled TDMA MAC protocol for one-hop dynamic lowpower wireless networks

Current Medium Access Control (MAC) protocols for data collection scenarios with a large number of nodes that generate bursty traffic are based on Low-Power Listening (LPL) for network synchronization and Frame Slotted ALOHA (FSA) as the channel access mechanism. However, FSA has an efficiency bounded to 36.8% due to contention effects, which reduces packet throughput and increases energy consumption. In this paper, we target such scenarios by presenting Low-Power Distributed Queuing (LPDQ), a highly efficient and low-power MAC protocol. LPDQ is able to self-schedule data transmissions, acting as a FSA MAC under light traffic and seamlessly converging to a Time Division Multiple Access (TDMA) MAC under congestion. The paper presents the design principles and the implementation details of LPDQ using low-power commercial radio transceivers. Experiments demonstrate an efficiency close to 99% that is independent of the number of nodes and is fair in terms of resource allocation.Peer ReviewedPostprint (author’s final draft

Experimental energy consumption of Frame Slotted ALOHA and Distributed Queuing for data collection scenarios

Data collection is a key scenario for the Internet of Things because it enables gathering sensor data from distributed nodes that use low-power and long-range wireless technologies to communicate in a single-hop approach. In this kind of scenario, the network is composed of one coordinator that covers a particular area and a large number of nodes, typically hundreds or thousands, that transmit data to the coordinator upon request. Considering this scenario, in this paper we experimentally validate the energy consumption of two Medium Access Control (MAC) protocols, Frame Slotted ALOHA (FSA) and Distributed Queuing (DQ). We model both protocols as a state machine and conduct experiments to measure the average energy consumption in each state and the average number of times that a node has to be in each state in order to transmit a data packet to the coordinator. The results show that FSA is more energy efficient than DQ if the number of nodes is known a priori because the number of slots per frame can be adjusted accordingly. However, in such scenarios the number of nodes cannot be easily anticipated, leading to additional packet collisions and a higher energy consumption due to retransmissions. Contrarily, DQ does not require to know the number of nodes in advance because it is able to efficiently construct an ad hoc network schedule for each collection round. This kind of a schedule ensures that there are no packet collisions during data transmission, thus leading to an energy consumption reduction above 10% compared to FSA.Peer ReviewedPostprint (published version

Standardized low-power wireless communication technologies for distributed sensing applications

Recent standardization efforts on low-power wireless communication technologies, including time-slotted channel hopping (TSCH) and DASH7 Alliance Mode (D7AM), are starting to change industrial sensing applications, enabling networks to scale up to thousands of nodes whilst achieving high reliability. Past technologies, such as ZigBee, rooted in IEEE 802.15.4, and ISO 18000-7, rooted in frame-slotted ALOHA (FSA), are based on contention medium access control (MAC) layers and have very poor performance in dense networks, thus preventing the Internet of Things (IoT) paradigm from really taking off. Industrial sensing applications, such as those being deployed in oil refineries, have stringent requirements on data reliability and are being built using new standards. Despite the benefits of these new technologies, industrial shifts are not happening due to the enormous technology development and adoption costs and the fact that new standards are not well-known and completely understood. In this article, we provide a deep analysis of TSCH and D7AM, outlining operational and implementation details with the aim of facilitating the adoption of these technologies to sensor application developers.Peer ReviewedPostprint (published version

Teaching Communication Technologies and Standards for the Industrial IoT? Use 6TiSCH!

The IETF 6TiSCH stack encompasses IEEE802.15.4 TSCH, IETF 6LoWPAN, RPL, and CoAP. It is one of the key standards-based technologies to enable industrial process monitoring and control, and unleash the Industrial Internet of Things (IIoT). The 6TiSCH stack is also a valuable asset for educational purposes, as it integrates an Internet-enabled IPv6-based upper stack with stateof- the-art low-power wireless mesh communication technologies. Teaching with 6TiSCH empowers students with a valuable set of competencies, including topics related to computer networking (medium access control operation, IPv6 networking), embedded systems (process scheduling, concurrency), and wireless communications (multipath propagation, interference effects), as well as application requirements for the IIoT. This article discusses how the 6TiSCH stack can be incorporated into existing and new curricula to teach the next generation of electrical engineering and computer science professionals about designing and deploying such networks. It also gives a comprehensive overview of the 6TiSCH stack and the tools that exist to support a course based on it.status: publishe