Demo: Simulating a 6TiSCH Network using Connectivity Traces from Testbeds

International audienceThe 6TiSCH simulator is an existing Python-based simulation tool that captures the full behavior of 6TiSCH, the Industrial IoT protocol stack standardized by the IETF. The existing 6TiSCH simulator uses a radio propagation model. In this demo, we present an extension to the 6TiSCH simulator which allows a simulation to be run against connectivity traces previously gathered on testbeds and real-world deployments. We demonstrate four elements. First, Mercator, the OpenWSN-based tool we developed to collect connectivity traces from different testbeds. Second, K7, the generic format we defined for these connectivity traces. Third, the set of 17 connectivity traces we gathered from testbeds and real-world deployments, and which are publicly available. Fourth, the extension of the 6TiSCH simulator which enables it to replay K7 connectivity traces rather than using a propagation model. Using connectivity traces for simulation is a way to increase the confidence the result are representative of a real-world deployment. Furthermore, it allows better repeatability than re-running an experiment on a testbed where the connectivity necessarily changes over time

A Lesson in Scaling 6LoWPAN -- Minimal Fragment Forwarding in Lossy Networks

This paper evaluates two forwarding strategies for fragmented datagrams in the IoT: hop-wise reassembly and a minimal approach to directly forward fragments. Minimal fragment forwarding is challenged by the lack of forwarding information at subsequent fragments in 6LoWPAN and thus requires additional data at nodes. We compared the two approaches in extensive experiments evaluating reliability, end-to-end latency, and memory consumption. In contrast to previous work and due to our alternate setup, we obtained different results and conclusions. Our findings indicate that direct fragment forwarding should be deployed only with care, since higher packet transmission rates on the link-layer can significantly reduce its reliability, which in turn can even further reduce end-to-end latency because of highly increased link-layer retransmissions.Comment: If you cite this paper, please use the LCN reference: M. S. Lenders, T. C. Schmidt, M. W\"ahlisch. "A Lesson in Scaling 6LoWPAN - Minimal Fragment Forwarding in Lossy Networks." in Proc. of IEEE LCN, 201

A dynamic distributed multi-channel TDMA slot management protocol for ad hoc networks

With the emergence of new technologies and standards for wireless communications and an increase in application and user requirements, the number and density of deployed wireless ad hoc networks is increasing. For deterministic ad hoc networks, Time-Division Multiple Access (TDMA) is a popular medium access scheme, with many distributed TDMA scheduling algorithms being proposed. However, with increasing traffic demands and the number of wireless devices, proposed protocols are facing scalability issues. Besides, these protocols are achieving suboptimal spatial spectrum reuse as a result of the unsolved exposed node problem. Due to a shortage of available spectrum, a shift from fixed spectrum allocation to more dynamic spectrum sharing is anticipated. For dynamic spectrum sharing, improved distributed scheduling protocols are needed to increase spectral efficiency and support the coexistence of multiple co-located networks. Hence, in this paper, we propose a dynamic distributed multi-channel TDMA (DDMC-TDMA) slot management protocol based on control messages exchanged between one-hop network neighbors and execution of slot allocation and removal procedures between sender and receiver nodes. DDMC-TDMA is a topology-agnostic slot management protocol suitable for large-scale and high-density ad hoc networks. The performance of DDMC-TDMA has been evaluated for various topologies and scenarios in the ns-3 simulator. Simulation results indicate that DDMC-TDMA offers near-optimal spectrum utilization by solving both hidden and exposed node problems. Moreover, it proves to be a highly scalable protocol, showing no performance degradation for large-scale and high-density networks and achieving coexistence with unknown wireless networks operating in the same wireless domain

Optimal Number of Message Transmissions for Probabilistic Guarantee in the IoT

International audienceThe Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R% of messages generated by sensor nodes are delivered to the sink with a latency ≤ L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. M F air is very easy to compute, whereas M Opt minimizes the total number of transmissions necessary for a message to reach the sink. M F air and M Opt are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions M axT rans is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M axT rans is fixed. For both TSCH networks, we show that M Opt provides a better reliability and a longer lifetime than M F air, which provides a shorter average end-to-end latency. M Opt provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler

Bringing life out of diversity: Boosting network lifetime using multi‐PHY routing in RPL

International audienceIn this article, we propose a routing mechanism based on the RPL protocol in a wireless network that is equipped with a mix of short-range and long-range radios. We introduce Life-OF, an objective function for RPL which uses a combination of metrics and the diverse physical layers to boost the network's lifetime. We evaluate the performance of Life-OF compared to the classical MRHOF objective function in simulations. Two key performance indicators (KPIs) are reported: network lifetime and network latency. Results demonstrate that MRHOF tends to converge to a pure long-range network, leading to short network lifetime. However, Life-OF improves network lifetime by continuously adapting the routing topology to favor routing over nodes with longest remaining lifetime. Life-OF combines diverse radios and balances power consumption in the network. This way, nodes switch between using their short-range radio to improve their own battery lifetime and using their long-range radio to avoid routers that are close to depletion. Results show that using Life-OF improves the lifetime of the network by up to 470% that of MRHOF, while maintaining similar latency

Performance Evaluation of Source Routing Minimum Cost Forwarding Protocol over 6TiSCH Applied to the OpenMote-B Platform

The aim of this work is the development of Source Routing Minimum Cost Forwarding (SRMCF) protocol over IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH), evaluating the performance of these protocols for the Internet of Things (IoT). To perform this evaluation, this work is making use of the OpenWSN project platform, which implements IEEE 802.15.4e in an open source environment. The evaluation process is also being done in the most recent version of the OpenMote-B platform. Another goal of this collaboration is to give contribution to the investigation of the applicability of quality of service (QoS) applied to the IEEE 802.15.4e standard. In the present stage of development, the efforts are concentrated on the programming of the required code, and the adaptation of the OpenWSN stack. Once the programming code is implemented, the team will investigate the possibilities to apply quality of service over the stack developed. Next, the team will also investigate the possibilities to explore long range routing techniques using the OpenMote platforms. In this task, we will use xBee, LoraWAN, Raspberry PI and Arduino platforms.info:eu-repo/semantics/publishedVersio

Industry 4.0: Industrial IoT Enhancement and WSN Performance Analysis

L'abstract è presente nell'allegato / the abstract is in the attachmen