A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Application of reinforcement learning with Q-learning for the routing in industrial wireless sensors networks

Industrial Wireless Sensor Networks (IWSN) usually have a centralized management approach, where a device known as Network Manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. The routing algorithms need to ensure path redundancy while reducing latency, power consumption, and resource usage. Graph routing algorithms are used to address these requirements. The dynamicity of wireless networks has been a challenge for tuning and developing routing algorithms, and Machine Learning models such as Reinforcement Learning have been applied in a promising way in Wireless Sensor Networks to select, adapt and optimize routes. The basic concept of Reinforcement Learning is the existence of a learning agent that acts and changes the state of the environment, and receives rewards. However, the existing approaches do not meet some of the requirements of the IWSN standards. In this context, this thesis proposes the Q-Learning Reliable Routing approach, where the Q-Learning model is used to build graph routes. Two approaches are presented: QLRR-WA and QLRR-MA. QLRR-WA uses a learning agent that adjusts the weights of the cost equation of a state-of-the-art routing algorithm to reduce the latency and increase the network lifetime. QLRR-MA uses several learning agents so nodes can choose connections in the graph trying to reduce the latency. Other contributions of this thesis are the performance comparison of the state-of-the-art graph-routing algorithms and the evaluation methodology proposed. The QLRR algorithms were evaluated in a WirelessHART simulator, considering industrial monitoring applications with random topologies. The performance was analyzed considering the average network latency, network lifetime, packet delivery ratio and the reliability of the graphs. The results showed that, when compared to the state of the art, QLRR-WA reduced the average network latency and improved the lifetime while keeping high reliability, while QLRR-MA reduced latency and increased packet delivery ratio with a reduction in the network lifetime. These results indicate that Reinforcement Learning may be helpful to optimize and improve network performance.As Redes Industriais de Sensores Sem Fio (IWSN) geralmente têm uma abordagem de gerenciamento centralizado, onde um dispositivo conhecido como Gerenciador de Rede é responsável pela configuração geral, definição de rotas e alocação de recursos de comunicação. Os algoritmos de roteamento precisam garantir a redundância de caminhos para as mensagens, e também reduzir a latência, o consumo de energia e o uso de recursos. O roteamento por grafos é usado para alcançar estes requisitos. A dinamicidade das redes sem fio tem sido um desafio para o ajuste e o desenvolvimento de algoritmos de roteamento, e modelos de Aprendizado de Máquina como o Aprendizado por Reforço têm sido aplicados de maneira promissora nas Redes de Sensores Sem Fio para selecionar, adaptar e otimizar rotas. O conceito básico do Aprendizado por Reforço envolve a existência de um agente de aprendizado que atua em um ambiente, altera o estado do ambiente e recebe recompensas. No entanto, as abordagens existentes não atendem a alguns dos requisitos dos padrões das IWSN. Nesse contexto, esta tese propõe a abordagem Q-Learning Reliable Routing, onde o modelo Q-Learning é usado para construir os grafos de roteamento. Duas abordagens são propostas: QLRR-WA e QLRR-MA. A abordagem QLRR-WA utiliza um agente de aprendizado que ajusta os pesos da equação de custo de um algoritmo de roteamento de estado da arte, com o objetivo de reduzir a latência e aumentar a vida útil da rede. A abordagem QLRR-MA utiliza diversos agente de aprendizado de forma que cada dispositivo na rede pode escolher suas conexões tentando reduzir a latência. Outras contribuições desta tese são a comparação de desempenho das abordagens com os algoritmos de roteamento de estado da arte e a metodologia de avaliação proposta. As abordagens do QLRR foram avaliadas com um simulador WirelessHART, considerando aplicações de monitoramento industrial com diversas topologias. O desempenho foi analisado considerando a latência média da rede, o tempo de vida esperado da rede, a taxa de entrega de pacotes e a confiabilidade dos grafos. Os resultados mostraram que, quando comparado com o estado da arte, o QLRR-WA reduziu a latência média da rede e melhorou o tempo de vida esperado, mantendo alta confiabilidade, enquanto o QLRR-MA reduziu a latência e aumentou a taxa de entrega de pacotes, ao custo de uma redução no tempo de vida esperado da rede. Esses resultados indicam que o Aprendizado por Reforço pode ser útil para otimizar e melhorar o desempenho destas redes

Enhancing SDN WISE with Slicing Over TSCH

[EN] IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.This work has been supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU and has been possible thanks to the collaboration of the Instituto Tecnologico de Informatica (ITI) of Valencia.Orozco-Santos, F.; Sempere Paya, VM.; Albero Albero, T.; Silvestre-Blanes, J. (2021). Enhancing SDN WISE with Slicing Over TSCH. Sensors. 21(4):1-29. https://doi.org/10.3390/s21041075S12921

TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers

[EN] Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them to operate with high determinism. For this reason, the IEEE 802.15.4e standard incorporates the Time Slotted Channel Hopping (TSCH) protocol which reduces interference and increases the reliability of transmissions. This standard does not specify how time resources are allocated in TSCH scheduling, leading to multiple scheduling solutions. Schedulers can be classified as autonomous, distributed and centralised. The first two have prevailed over the centralised ones because they do not require high signalling, along with the advantages of ease of deployment and high performance. However, the increased QoS requirements and the diversity of traffic flows that circulate through the network in today's Industry 4.0 environment require strict, dynamic control to guarantee parameters such as delay, packet loss and deadline, independently for each flow. That cannot always be achieved with distributed or autonomous schedulers. For this reason, it is necessary to use centralised protocols with a disruptive approach, such as Software Defined Networks (SDN). In these, not only is the control of the MAC layer centralised, but all the decisions of the nodes that make up the network are configured by the controller based on a global vision of the topology and resources, which allows optimal decisions to be made. In this work, a comparative analysis is made through simulation and a testbed of the different schedulers to demonstrate the benefits of a fully centralized approach such as SDN. The results obtained show that with SDN it is possible to simplify the management of multiple flows, without the problems of centralised schedulers. SDN maintains the Packet Delivery Ratio (PDR) levels of other distributed solutions, but in addition, it achieves greater determinism with bounded end-to-end delays and Deadline Satisfaction Ratio (DSR) at the cost of increased power consumption.This work has been supported by DAIS (https://dais-project.eu/) which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 101007273. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, Netherlands, Denmark, Norway and Turkey. It has also been funded by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial-IVACE". Furthermore, has been supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU.Orozco-Santos, F.; Sempere Paya, VM.; Silvestre-Blanes, J.; Albero Albero, T. (2022). TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers. Applied Sciences. 12(1):1-19. https://doi.org/10.3390/app1201011911912

Industrial Wireless Sensor Networks

Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in this Special Issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs

Enhancing graph routing algorithm of industrial wireless sensor networks using the covariance-matrix adaptation evolution strategy

The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication

Congestion control in wireless sensor networks

Information-sensing and data-forwarding in Wireless Sensor Networks (WSN) often incurs high traffic demands, especially during event detection and concurrent transmissions. Managing such large amounts of data remains a considerable challenge in resource-limited systems like WSN, which typically observe a many-to-one transmission model. The result is often a state of constant buffer-overload or congestion, preventing desirable performance to the extent of collapsing an entire network. The work herein seeks to circumvent congestion issues and its negative effects in WSN and derivative platforms such as Body Sensor Networks (BSN). The recent proliferation of WSN has emphasized the need for high Quality-of-Service (QoS) in applications involving real-time and remote monitoring systems such as home automation, military surveillance, environmental hazard detection, as well as BSN-based healthcare and assisted-living systems. Nevertheless, nodes in WSN are often resource-starved as data converges and cause congestion at critical points in such networks. Although this has been a primal concern within the WSN field, elementary issues such as fairness and reliability that directly relate to congestion are still under-served. Moreover, hindering loss of important packets, and the need to avoid packet entrapment in certain network areas remain salient avenues of research. Such issues provide the motivation for this thesis, which lead to four research concerns: (i) reduction of high-traffic volumes; (ii) optimization of selective packet discarding; (iii) avoidance of infected areas; and (iv) collision avoidance with packet-size optimization. Addressing these areas would provide for high QoS levels, and pave the way for seamless transmissions in WSN. Accordingly, the first chapter attempts to reduce the amount of network traffic during simultaneous data transmissions, using a rate-limiting technique known as Relaxation Theory (RT). The goal is for substantial reductions in otherwise large data-streams that cause buffer overflows. Experimentation and analysis with Network Simulator 2 (NS-2), show substantial improvement in performance, leading to our belief that RT-MMF can cope with high incoming traffic scenarios and thus, avoid congestion issues. Whilst limiting congestion is a primary objective, this thesis keenly addresses subsequent issues, especially in worst-case scenarios where congestion is inevitable. The second research question aims at minimizing the loss of important packets crucial to data interpretation at end-systems. This is achieved using the integration of selective packet discarding and Multi-Objective Optimization (MOO) function, contributing to the effective resource-usage and optimized system. A scheme was also developed to detour packet transmissions when nodes become infected. Extensive evaluations demonstrate that incoming packets are successfully delivered to their destinations despite the presence of infected nodes. The final research question addresses packet collisions in a shared wireless medium using distributed collision control that takes packet sizes into consideration. Performance evaluation and analysis reveals desirable performance that are resulted from a strong consideration of packet sizes, and the effect of different Bit Error Rates (BERs)