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

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

BurstProbe: Debugging Time-Critical Data Delivery in Wireless Sensor Networks

In this paper we present BurstProbe, a new technique to accurately measure link burstiness in a wireless sensor network employed for time-critical data delivery. Measurement relies on shared probing slots that are embedded in the transmission schedule and used by nodes to assess link burstiness over time. The acquired link burstiness information can be stored in the node's flash memory and relied upon to diagnose transmission problems when missed deadlines occur. Thus, accurate diagnosis is achieved in a distributed manner and without the overhead of transmitting rich measurement data to a central collection point. For the purpose of evaluation we have implemented BurstProbe in the GinMAC WSN protocol and we are able to demonstrate it is an accurate tool to debug time-critical data delivery. In addition, we analyze the cost of implementingBurstProbe and investigate its effectiveness

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

Wireless Sensor Technology Selection for I4.0 Manufacturing Systems

The term smart manufacturing has surfaced as an industrial revolution in Germany known as Industry 4.0 (I4.0); this revolution aims to help the manufacturers adapt to turbulent market trends. Its main scope is implementing machine communication, both vertically and horizontally across the manufacturing hierarchy through Internet of things (IoT), technologies and servitization concepts. The main objective of this research is to help manufacturers manage the high levels of variety and the extreme turbulence of market trends through developing a selection tool that utilizes Analytic Hierarchy Process (AHP) techniques to recommend a suitable industrial wireless sensor network (IWSN) technology that fits their manufacturing requirements.In this thesis, IWSN technologies and their properties were identified, analyzed and compared to identify their potential suitability for different industrial manufacturing system application areas. The study included the identification and analysis of different industrial system types, their application areas, scenarios and respective communication requirements. The developed tool’s sensitivity is also tested to recommend different IWSN technology options with changing influential factors. Also, a prioritizing protocol is introduced in the case where more than one IWSN technology options are recommended by the AHP tool.A real industrial case study with the collaboration of SPM Automation Inc. is presented, where the industrial systems’ class, communication traffic types, and communication requirements were analyzed to recommend a suitable IWSN technology that fits their requirements and assists their shift towards I4.0 through utilizing AHP techniques. The results of this research will serve as a step forward, in the transformation process of manufacturing towards a more digitalized and better connected cyber-physical systems; thus, enhancing manufacturing attributes such as flexibility, reconfigurability, scalability and easing the shift towards implementing I4.0

An architecture framework for enhanced wireless sensor network security

This thesis develops an architectural framework to enhance the security of Wireless Sensor Networks (WSNs) and provides the implementation proof through different security countermeasures, which can be used to establish secure WSNs, in a distributed and self-healing manner. Wireless Sensors are used to monitor and control environmental properties such as sound, acceleration, vibration, air pollutants, and temperature. Due to their limited resources in computation capability, memory and energy, their security schemes are susceptible to many kinds of security vulnerabilities. This thesis investigated all possible network attacks on WSNs and at the time of writing, 19 different types of attacks were identified, all of which are discussed including exposures to the attacks, and the impact of those attacks. The author then utilises this work to examine the ZigBee series, which are the new generation of wireless sensor network products with built-in layered security achieved by secure messaging using symmetric cryptography. However, the author was able to uniquely identify several security weaknesses in ZigBee by examining its protocol and launching the possible attacks. It was found that ZigBee is vulnerable to the following attacks, namely: eavesdropping, replay attack, physical tampering and Denial of Services (DoS). The author then provides solutions to improve the ZigBee security through its security schema, including an end-to-end WSN security framework, architecture design and sensor configuration, that can withstand all types of attacks on the WSN and mitigate ZigBee’s WSN security vulnerabilities

WirelessHART: Wireless System for Process Automation

WirelessHART is a wireless communication protocol for process automation applications. WirelessHART was first introduce in September 2007 by the HART Communication Foundation, and the main aim was to add wireless capabilities to the already existing HART technology. WirelessHART has five main components namely Gateway, Field device, Network manager, adapter, Security Manager, and Router. The WirelessHART protocol was built based on the Open System Interconnection (OSI) of seven layers, but the WirelessHART has five layers; Physical, Data-Link, Network, Transport, and Application Layer. It employs many processes to ensure data; Confidentiality, Authenticity, Integrity, Availability, and Data freshness. Key Management is very critical in securing the WirelessHART network. The Security manager is responsible for the key management in the network. The Network Manager is responsible for assigning and allocating the keys in the network. The WirelessHART is not different from attack on any other wireless technologies. Attacks like Interference, Jamming, Tampering, Sybil, Collusion, Spoofing, Exhaustion, Wormhole, De-synchronization, Selective Forwarding, Traffic analysis, Misdirection, Hello flooding and Sinkhole. WirelessHART uses the Advance Encryption Standard (AES) to secure the network against attackers. The AES is made up of three parts, Cipher, Inverse cipher and key expansion.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Neighbor discovery for industrial wireless sensor networks with mobile nodes

Industrial wireless sensor networks can facilitate the deployment of a wide range of novel industrial applications, including mobile applications that connect mobile robots, vehicles, goods and workers to industrial networks. Current industrial wireless sensor standards have been mainly designed for static deployments, and their performance significantly degrades when introducing mobile devices. One of the major reasons for such degradation is the neighbor discovery process. This paper presents and evaluates two novel neighbor discovery protocols that improve the capability of mobile devices to remain connected to the industrial wireless sensor networks as they move. The proposed protocols exploit topology information and the nature of devices (static or mobile) to reliably and rapidly discover neighbor devices. This is achieved in some cases at the expense of increasing the number of radio resources utilized and the energy consumed in the discovery process. The proposed solutions have been designed and evaluated considering the WirelessHART standard given its widespread industrial adoption. However, they can also be adapted for the ISA100.11a and IEEE 802.15.4e standards.This work was supported in part by the Spanish Ministry of Economy and Competitiveness and FEDER funds under the project TEC2014-57146-Rby the Local Government of Valencia with reference ACIF/2013/060 and by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 723909 (AUTOWARE project)