This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system design that facilitates a more effective learning process for the parity check matrix. We simplify the input dataset, restrict the number of parameters to learn and improve the gradient back-propagation within the model. We also introduce novel optimizers specifically designed for discrete-valued weights. Based on conventional gradient computation, these optimizers provide discrete weights updates, enabling finer control and improving explainability of the learning process. Through these changes, we consistently achieve improved code performance, provided appropriately chosen hyper-parameters. To rigorously evaluate the performance of learned codes in the context of short to medium block lengths, we propose a comprehensive code performance assessment framework. This framework enables a fair comparison between our learning methodology and random search approaches, ensuring statistical significance in our results. The proposed model pave the way for a new approach to the efficient learning of linear block codes tailored to specific decoder structures.

Reducing the number of bytes transmitted by a low-power wireless device greatly reduces its power consumption. While header compression is a well-studied topic with solutions such as SCHC that are well-established standards, very little work exists on compressing the payload. This is all the stranger that the payload typically contains more bytes than the headers. This demonstration introduces Dixy, a payload compression technique which can be used alongside SCHC. We implement SCHC and Dixy on the nRF52840, a popular micro-controller. We have them compress packets collected from a real-world deployment by startup company Falco. We show how the resulting joint header and payload compression reduces the number of bytes exchanged between two boards by 74%. The demonstration allows visitors to understand SCHC and Dixy, trigger packets being compressed and transmitted, and observe the number of bytes and the charge consumed with enabling header and/or payload compression.

The Industrial Internet of Things (IIoT), integral to Industry 4.0, leverages wireless low-power and lossy networks (LLNs) for critical applications. These networks fulfill demanding industrial requirements, ensuring limited end-to-end latency and high reliability. The Internet engineering task force (IETF) has developed the 6TiSCH protocol stack, a network architecture comprising cutting-edge standards to support this IIoT paradigm. Particularly, the 6TiSCH architecture introduces “tracks” to deliver deterministic Quality of Service (QoS) across diverse networks, aligning with stringent service level agreements (SLAs). Furthermore, the reliable and available wireless (RAW) work group, now part of the deterministic networking (DetNet) group, enhances this concept for wireless-specific challenges. The RAW framework effectively balances redundancy against the constraints like spectrum and battery usage. It incorporates reliability mechanisms, including tracks and specialized control routines. This article focuses on applying tracks within deterministic 6TiSCH networks, in line with IETF standard, to demonstrate the ability of tracks to tend toward deterministic performance in wireless mesh networks. We provide a new remote network management interface, evolved from current 6TiSCH protocols, particularly 6top and constrained application protocol, designed to install and maintain tracks in 6TiSCH networks. Our approach, implemented in the OpenWSN open-source protocol stack, is evaluated on the real OpenMote-B hardware for two real-life use cases. The performance evaluation shows drastic improvement in latency (up to 4.5 times).

In 5G New Radio (NR), beam management entails periodic and continuous transmission and reception of control signals in the form of synchronization signal blocks (SSBs), used to perform initial access and/or channel estimation. However, this procedure demands continuous energy consumption, which is particularly challenging to handle for low-cost, low-complexity, and battery-constrained devices, such as RedCap devices to support mid-market Internet of Things (IoT) use cases. In this context, this work aims at reducing the energy consumption during beam management for RedCap devices, while ensuring that the desired Quality of Service (QoS) requirements are met. To do so, we formalize an optimization problem in an Indoor Factory (InF) scenario to select the best beam management parameters, including the beam update periodicity and the beamwidth, to minimize energy consumption based on users' distribution and their speed. The analysis yields the regions of feasibility, i.e., the upper limit(s) on the beam management parameters for RedCap devices, that we use to provide design guidelines accordingly.

The growing number of Internet of Thing (IoT) and Ultra-Reliable Low Latency Communications (URLCC) use cases in next generation communication networks calls for the development of efficient Forward Error Correction (FEC) mechanisms. These use cases usually imply using short to mid-sized information blocks and requires low-complexity and/or fast decoding procedures. This paper investigates the joint learning of short to mid block-length coding schemes and associated Belief-Propagation (BP) like decoders using Machine Learning (ML) techniques. An interpretable auto-encoder (AE) architecture is proposed, ensuring scalability to block sizes currently challenging for ML-based linear block code design approaches. By optimizing a coding scheme w.r.t. the targeted decoder, the proposed system offers a good complexity/performance trade-off compared to various codes from literature with length up to 128 bits.

In wireless networks, interferences between transmissions are modelled either in time or frequency domain. In this article, we jointly analyze interferences in the time-frequency domain using a stochastic geometry model assuming the total time-frequency resources to be a two-dimensional plane and transmissions from Internet of Things (IoT) devices time-frequency patterns on this plane. To evaluate the interference, we quantify the overlap between the information packets: provided that the overlap is not too strong, the packets are not necessarily lost due to capture effect. This flexible model can be used for multiple medium access scenarios and is especially adapted to the random time-frequency access schemes used in Low-Power Wide-Area Networks (LPWANs). By characterizing the outage probability and throughput, our approach permits to evaluate the performance of two representative LPWA technologies Sigfox®and LoRaWAN®.

Efficiently monitoring a network requires full observability of each node. The main challenge lies in correlating the monitoring logs coming from all the nodes and reconstructing useful network-wide knowledge such as routing topology or end-to-end performance. We propose a monitoring framework in which nodes report events that are significant to the life of the network, following clear specified semantics. Clear semantics allows maintaining a network model, run on a computer on the side of the network, mirroring the state of the real network. The successive states of this model are archived in a database and can be browsed by the investigation tools instead of querying the real physical network. This is especially beneficial in the case of low bandwidth networks or resource-constrained devices encountered in the Internet of Things. With two different examples, LoRaWAN and OpenWSN, we illustrate how this monitoring framework allows using the same generic tool suite to debug, monitor and tweak a diversity of networks.

This poster presents the QoS Oriented Opportunistic Routing (QOR) protocol, a novel opportunistic routing protocol for energy constrained Wireless Sensor Networks. QOR builds a joint routing structure and addressing scheme and proposes a cascading forwarding scheme that guarantees reliable, replication-free data transmissions and collision-free acknowledgements even in presence of long-range asymmetric links. QOR makes the opportunistic routing scheme a practical reality and our preliminary simulations show that QOR outperforms the IETF state-of-the-art routing protocol RPL.

For energy constrained wireless sensor networks, lifetime is a critical issue. Several medium access control protocols have been proposed to address this issue, often at the cost of poor network capacity. To address both capacity and energy issues, we propose a novel medium sharing protocol for Wireless Sensor Networks named Cascading Tournament (CT-MAC). CT-MAC is a synchronous, localized, dynamic, joint contention/allocation protocol. Relying on cascading iterations of tournaments, CT-MAC allocates multiple time slots to nodes that compete for accessing the medium. CT-MAC offers an unprecedented trade-off between traffic delay, network capacity and energy efficiency and stands out as a solid candidate for energy constrained sensor networks that must support heterogeneous traffic loads. Our simulations show that CT-MAC significantly outperforms the state-of-the-art SCP-MAC protocol.

This paper presents the Self Adapting Receiver Initiated MAC protocol (SARI-MAC), a novel asynchronous MAC protocol for energy constrained Wireless Sensor Networks. SARI-MAC self-adapts to the traffic load to meet specified Quality of Service requirements at the lowest energy cost possible. To do so, SARI-MAC relies on traffic estimation, duty-cycle adaptation and acknowledgement mechanisms. Our performance evaluation assesses that SARI-MAC meets given QoS requirements in a energy efficient manner and outperforms the state of the art protocol RI-MAC in a broad range of traffic scenarios.

Wireless Sensor Networks (WSN) need to be organized in order to be more resource-efficient and scalable. Directed Acyclic Graphs (DAG) are of interest for WSN organization because of their route redundancy to the root. A recent routing protocol for WSNs coming from the IETF ROLL Working Group specifies the use of DAGs and proposes to improve the route redundancy even further by routing through sibling nodes as well. In this paper, we investigate the effectiveness of routing through sibling nodes and we propose a new use of siblings which preserves most of the performance but reduces algorithmic complexity and memory requirements.