6TiSCH is a working group at the IETF, which is standardizing how to combine IEEE802.15.4 time‐slotted channel hopping (TSCH) with IPv6. The result is a solution that offers both industrial performance and seamless integration into the Internet and is therefore seen as a key technology for the Industrial Internet of Things. This article presents the 6TiSCH simulator, created as part of the standardization activity, and which has been used extensively by the working group. The goal of the simulator is to benchmark 6TiSCH against realistic scenarios, something which is hard to do using formal models or real‐world deployments. This article discusses the overall architecture of the simulator, details the different models it uses (ie, energy and propagation), compares it to other simulation/emulation platforms, and presents five published examples of how the 6TiSCH simulator has been used.
The recent increase of connected devices has triggered countless Internet-of-Things applications to emerge. By using the Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e MAC layer, wireless multi-hop networks enable highly reliable and low-power communication, supporting mission-critical and industrial applications. TSCH uses channel hopping to avoid both external interference and multi-path fading, and a synchronization-based schedule which allows precise bandwidth allocation. Efficient schedule management is crucial when minimizing the delay of a packet to reach its destination. In networks with recurrent sensor data transmissions that repeat after a certain period, current scheduling functions are prone to high latencies by ignoring this recurrent behavior. In this article, we propose a TSCH scheduling function that tackles this minimal-latency recurrent traffic problem. Concretely, this work presents two novel contributions. First, the recurrent traffic problem is defined formally as an Integer Linear Program. Second, we propose the Recurrent Low-Latency Scheduling Function (ReSF) that reserves minimal-latency paths from source to sink and only activates these paths when recurrent traffic is expected. Extensive experimental results show that using ReSF leads to a latency improvement up to 80 % compared to state-of-the-art low-latency scheduling functions, with a negligible impact on power consumption of at most 6 %.
The advent of Internet-of-Things (IoT) applications, such as environmental monitoring, smart cities, and home automation, has taken the IoT concept from hype to reality at a massive scale. However, more mission-critical application areas such as energy, security and health care do not only demand low-power connectivity, but also highly reliable and guaranteed performance. While fulfilling these requirements under controlled conditions such as urban and indoor environments is relatively trivial, tackling the same obstacles in a more challenging and dynamic setting is significantly more complicated. In environments where infrastructure is sparse, such as rural or remote areas, specialized infrastructure-less ad-hoc solutions are needed, which provide long-range multi-hop connectivity to remote sensors and actuators. In this paper we propose a new general-purpose IoT platform based on a combination of Low-power Wireless Personal Area Network (LoWPAN) and multi-hop Wireless Sensor Network (WSN) technology. It supports reliable and guaranteed realtime data dissemination and analysis, as well as actuator control, in dynamic and challenging infrastructure-less environments. In this paper, we present the IoT platform architecture and an initial hard-and software prototype. Moreover, a use case based on realtime monitoring and training adaptation for cyclists is presented. Based on this case study, evaluation results are presented that show the ability of the proposed platform to operate under challenging and dynamic conditions.
IoT-based solutions for sport analytics aim to improve performance, coaching and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g. LTE, Wi-Fi). In this paper, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multi-hop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g. a moving car following the cyclists). In this work we present a detailed technical description of this solution, describing its requirements, options and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.
The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.
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