Maximizing Network Lifetime of Wireless Sensor-Actuator Networks under Graph Routing

Process industries are adopting wireless sensor-actuator networks (WSANs) as the communication infrastructure. The dynamics of industrial environments and stringent reliability requirements necessitate high degrees of fault tolerance in routing. WirelessHART is an open industrial standard for WSANs that have seen world-wide deployments. WirelessHART employs graph routing schemes to achieve network reliability through multiple paths. Since many industrial devices operate on batteries in harsh environments where changing batteries are prohibitively labor-intensive, WSANs need to achieve long network lifetime. To meet industrial demand for long-term reliable communication, this paper studies the problem of maximizing network lifetime for WSANs under graph routing. We formulate the network lifetime maximization problem for WirelessHART networks under graph routing. Then, we propose the optimal algorithm and two more efficient algorithms to prolong the network lifetime of WSANs. Experiments in a physical testbed and simulations show our linear programming relaxation and greedy heuristics can improve the network lifetime by up to 50% while preserving the reliability benefits of graph routing

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

Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks : A Q-Learning Approach for Graph Routing

Industrial wireless sensor networks 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. Graph routing is used to increase the reliability of communication through path redundancy. Some of the state-of-the-art graph-routing algorithms use weighted cost equations to define preferences on how the routes are constructed. The characteristics and requirements of these networks complicate to find a proper set of weight values to enhance network performance. Reinforcement learning can be useful to adjust these weights according to the current operating conditions of the network. In this article, we present the Q-learning reliable routing with a weighting agent approach, where an agent adjusts the weights of a state-of-the-art graph-routing algorithm. The states of the agent represent sets of weights, and the actions change the weights during network operation. Rewards are given to the agent when the average network latency decreases or the expected network lifetime increases. Simulations were conducted on a WirelessHART simulator considering industrial monitoring applications with random topologies. Results show, in most cases, a reduction of the average network latency while the expected network lifetime and the communication reliability are at least as good as what is obtained by the state-of-the-art graph-routing algorithms

Effect of Unequal Clustering Algorithms in WirelessHART networks

The use of Graph Routing in Wireless Highway Addressable Remote Transducer (WirelessHART) networks offers the benefit of increased reliability of communications because of path redundancy and multi-hop network paths. Nonetheless, Graph Routing in a WirelessHART network creates a hotspot challenge resulting from unbalanced energy consumption. This paper proposes the use of unequal clustering algorithms based on Graph Routing in WirelessHART networks to help with balancing energy consumption, maximizing reliability, and reducing the number of hops in the network. Graph Routing is compared with pre-set and probabilistic unequal clustering algorithms in terms of energy consumption, packet delivery ratio, throughput and average end-to-end delay. A simulation test reveals that Graph Routing has improved energy consumption, throughput and reduced average end-to-end delay when conducted using probabilistic unequal clustering algorithms. However, there is no significant change in the packet delivery ratio, as most packets reach their destination successfully anyway

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

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

Real-Time and Energy-Efficient Routing for Industrial Wireless Sensor-Actuator Networks

With the emergence of industrial standards such as WirelessHART, process industries are adopting Wireless Sensor-Actuator Networks (WSANs) that enable sensors and actuators to communicate through low-power wireless mesh networks. Industrial monitoring and control applications require real-time communication among sensors, controllers and actuators within end-to-end deadlines. Deadline misses may lead to production inefficiency, equipment destruction to irreparable financial and environmental impacts. Moreover, due to the large geographic area and harsh conditions of many industrial plants, it is labor-intensive or dan- gerous to change batteries of field devices. It is therefore important to achieve long network lifetime with battery-powered devices. This dissertation tackles these challenges and make a series of contributions. (1) We present a new end-to-end delay analysis for feedback control loops whose transmissions are scheduled based on the Earliest Deadline First policy. (2) We propose a new real-time routing algorithm that increases the real-time capacity of WSANs by exploiting the insights of the delay analysis. (3) We develop an energy-efficient routing algorithm to improve the network lifetime while maintaining path diversity for reliable communication. (4) Finally, we design a distributed game-theoretic algorithm to allocate sensing applications with near-optimal quality of sensing

Integration of wirelessHART and STK600 development kit for data collection in wireless sensor networks

Offshore industry operates in world’s most challenging environment. Oil and gas facilities aim for continuous production to achieve the desired goals and a robust communication network is required to avoid production loses. The IEEE 802.15.4 specification has enabled low cost, low power Wireless Sensor Networks (WSNs) capable of providing robust communication and therefore utilises as a promising technology in oil and gas industry. The two most prominent industrial standards using the IEEE 802.15.4 radio technology are WirelessHART and ISA100.11a.These are currently the competitors in the automation and offshore industry. In this project, we have worked on Nivis WirelessHART development kit that has some on-board sensors. Our main goal is to integrate WirelessHART with external sensor board so that we can get the readings from external sensors and publish the data over web interface provided by Nivis. Since, Nivis WirelessHART field router is not an open source and un-programmable, therefore it is considered as a black box. Due to lack of such capabilities, we cannot connect external sensor directly to Nivis radio. We have chosen Atmel STK600-Atmega2560 development kit as an external sensor board. In order to establish communication between STK600 and Nivis WirelessHART, we have written an application in AVR studio and flash it to STK600 over the USB connection. We have implemented a serial communication protocol called Nivis simple API and made Nivis board able to get data from sensors interfacing STK600. Nivis radio will then forward this data to WirelessHART through HART gateway. Moreover, we have configured Monitoring Host to visualize the data from external sensors along with built-in sensors over the Monitoring Control System (MCS). Finally, we evaluate our implementation by various experiments and prove that the overall flow is working properly