Cover
List of Contributors
Preface
1 Industrial Wireless Sensor Networks Overview
1. 1.1 Introduction
2. 1.2 Industry 4.0
3. 1.3 Industrial Wireless Sensor Networks (IWSNs)
4. 1.4 Applications of IWSNs
5. 1.5 Communication Topologies in IWSNs
6. 1.6 Research Developments and Communications Standards for Industry
7. Bibliography
2 Life‐span Extension for Sensor Networks in the Industry
1. 2.1 Introduction
2. 2.2 Wireless Sensor Networks
3. 2.3 Industrial WSNs
4. 2.4 Life‐span Extension for WSNs
5. 2.5 Conclusion
6. Bibliography
3 Multiple Access and Resource Sharing for Low Latency Critical Industrial Networks
1. 3.1 Introduction
2. 3.2 Research Developments
3. 3.3 Priority Based Information Scheduling and Transmission
4. 3.4 Summary
5. Bibliography
4 Narrowband Internet of Things (NB‐IoT) for Industrial Automation
1. 4.1 Introduction
2. 4.2 Overview of NB‐IoT
3. 4.3 NB‐IoT Design Characteristics
4. 4.4 NB‐IoT Frame Structure
5. 4.5 NB‐IoT as an Enabler for Industry 4.0
6. 4.6 Summary
7. Bibliography
5 Ultra Reliable Low Latency Communications as an Enabler For Industry Automation
1. 5.1 Introduction
2. 5.2 Opportunities for URLLC in Industry Automation
3. 5.3 Existing Solutions
4. 5.4 Enabling Technologies
5. 5.5 Conclusion
6. Bibliography
6 Anomaly Detection and Self‐healing in Industrial Wireless Networks
1. 6.1 Introduction
2. 6.2 System Design
3. 6.3 Cell Outage Detection Framework
4. 6.4 Cell Outage Compensation
5. 6.5 Simulation Results
6. 6.6 Conclusion
7. Bibliography
7 Cost Efficiency Optimization for Industrial Automation
1. 7.1 Introduction
2. 7.2 The Evolution of Low Energy Networking Protocols for Industrial Automation
3. 7.3 An Overview of the Costs Involved in Industry 4.0
4. 7.4 Evaluating Costs in an Industrial Environment: A LoRaWAN Case study
5. 7.5 Cost Analysis for Industrial Automation
6. 7.6 Cost Optimization through Energy Harvesting in Industrial Automation
7. 7.7 Conclusion
8. Bibliography
9. Notes
8 A Non‐Event Based Approach for Non‐Intrusive Load Monitoring
1. 8.1 Introduction
2. 8.2 Probabilistic Modelling for Load Disaggregation
3. 8.3 Experimental Evaluations
4. 8.4 Live Deployment
5. 8.5 Conclusion
6. Bibliography
9 Wireless Networked Control
1. 9.1 Introduction
2. 9.2 Industrial Automation
3. 9.3 WNC System Model
4. 9.4 Network and System Control Co‐design
5. 9.5 Conclusion
6. Bibliography
10 Caching at the Edge in Low Latency Wireless Networks
1. 10.1 Introduction
2. 10.2 Living on the Edge
3. 10.3 Classifications of Wireless Caching Networks
4. 10.4 Caching For Low Latency Wireless Networks
5. 10.5 Inter‐cluster Cooperation for Wireless D2D Caching Networks
6. 10.6 Results and Discussions
7. 10.7 Chapter Summary
8. Bibliography
11 Application of Terahertz Sensing at Nano‐Scale for Precision Agriculture
1. 11.1 Introduction
2. Bibliography
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Salient features of the industrial revolutions.
Chapter 2
1. Table 2.1 List of the fixed scenarios with data processing locations and conn...
Chapter 4
1. Table 4.1 NB‐IoT key design features (Ratasuket al., 2017).
2. Table 4.2 NB‐IoT uplink resource unit configuration.
3. Table 4.3 Allowed LTE PRB indices for cell connection in NB‐IoT in‐band opera...
4. Table 4.4 DCI formats defined for NB‐IoT (J. Schlienz and D. Raddino, 2016).
5. Table 4.5 NB‐IoT design modification in relation to LTE.
Chapter 5
1. Table 5.1 Simulation parameters.
Chapter 6
1. Table 6.1 MDT reported measurements.
2. Table 6.2 Simulation parameters.
Chapter 7
1. Table 7.1 LoRaWAN assumed parameters for the life‐time and cost evaluation fo...
2. Table 7.2 Assumptions drawn for evaluating battery replacement cost.
3. Table 7.3 Product categories considered along with associated unit costs and ...
Chapter 8
1. Table 8.1 Test scenarios to evaluate the hidden appliance state estimation pe...
2. Table 8.2 Features for appliance models.
3. Table 8.3 The F scores obtained for the test scenarios using model M₄.
4. Table 8.4 Appliance‐level energy consumption estimation.
Chapter 9
1. Table 9.1 Requirements of key performance indicators for different scenarios.

List of Illustrations

Chapter 1
1. Figure 1.1 Enabling technologies, industry 4.0.
2. Figure 1.2 Block diagram of a typical wireless sensor mote.
3. Figure 1.3 Representation of a WSN.
4. Figure 1.4 Star topology.
5. Figure 1.5 Mesh topology.
6. Figure 1.6 Tree topology.
Chapter 2
1. Figure 2.1 General structure of wireless sensor networks.
2. Figure 2.2 A typical wireless sensor node.
3. Figure 2.3 A schematic for a typical energy harvesting system.
4. Figure 2.4 Proposed Q learning assisted two‐stage model.
Chapter 3
1. Figure 3.1 Priority levels and time constraints of different industrial traf...
2. Figure 3.2 MAC LLDN superframe with communication and shared slots.
3. Figure 3.3 Updated superframe to incorporate emergency communications.
4. Figure 3.4 Channel access delay as a function of number of emergency nodes (
5. Figure 3.5 Average delay until successful communication with different chann...
Chapter 4
1. Figure 4.1 NB‐IoT deployment modes.
2. Figure 4.2 NB‐IoT downlink frame structure.
3. Figure 4.3 NB‐IoT uplink frame structure.
Chapter 5
1. Figure 5.1 Example of a control loop topology where only the link between th...
2. Figure 5.2 Example of a control loop topology where only the link between th...
3. Figure 5.3 Example of a control loop topology where both links are wireless....
4. Figure 5.4 Latency aware HARQ.
5. Figure 5.5 Control architecture with a wireless link connecting controller a...
6. Figure 5.6 Proposed scheme, increasing the array gain by using CC‐HARQ in co...
7. Figure 5.7 Required bandwidth for different values of K and z.
Chapter 6
1. Figure 6.1 System model for autonomous COD and COC frameworks.
2. Figure 6.2 An overview of profiling and detection phases in the COD framewor...
3. Figure 6.3 (a) OCSVMD learned network profile for reference scenario, (b) lo...
4. Figure 6.4 OCSVMD ROC curves for shadowing, traffic and ISD cases.
5. Figure 6.5 Network profiling using LOFD.
6. Figure 6.6 LOFD ROC curves for shadowing, traffic and ISD cases.
7. Figure 6.7 Localization of SC based on per cell z scores.
8. Figure 6.8 Radio environment maps and SINR CDF for normal, outage and compen...
Chapter 7
1. Figure 7.1 Evolution of the number of IoT devices and world population in th...
2. Figure 7.2 Comparison of wireless technologies with respect to maximum radio...
3. Figure 7.3 Average energy consumption per day against different sensing inte...
4. Figure 7.4 Cumulative battery replacement cost against variation with respec...
5. Figure 7.5 Damage interval and penalty demonstration when C_u = £450, Rp...
6. Figure 7.6 Total cost summarizing damage penalty and battery replacement cos...
7. Figure 7.7 Comparison of battery replacement cost and damage penalty when ex...
8. Figure 7.8 Interval flexibility and aggregate cost optimization achieved thr...
Chapter 8
1. Figure 8.1 (a) Graphical representation of the hidden Markov model (HMM). S_t
2. Figure 8.2 (a) Graphical representation of an FHMM, which is a combination o...
3. Figure 8.3 An overview of the appliance state decoding process using the fea...
4. Figure 8.4 Performance comparison of feature subgroup models on target test ...
5. Figure 8.5 Individual load recognition performance using M₄ for binary and m...
6. Figure 8.6 (a) Live deployment: mobile application, Plogg unit and the targe...
Chapter 9
1. Figure 9.1 A WNC with multiple closed loops.
2. Figure 9.2 A diagram of a short packet and a long packet.
3. Figure 9.3 The structure of the open system interconnection applied to the W...
4. Figure 9.4 The circumstance where the path of the node needs to be planned i...
5. Figure 9.5 Potential time delay diagram in the WNCS.
Chapter 10
1. Figure 10.1 An illustration of an overlay of a socially interconnected and t...
2. Figure 10.2 An illustration of local caching and content delivery at the wir...
3. Figure 10.3 Schematic diagram of the proposed system model. A cell is divide...
4. Figure 10.4 The devices’ traffic model in a cluster k with cache center VCC ...
5. Figure 10.5 An example of the content cache placement modelled as a bipartit...
6. Figure 10.6 Outage probability of a D2D clustered caching system with cooper...
7. Figure 10.7 Network average delay versus popularity exponent β, under t...
8. Figure 10.8 Network average delay (left hand side y‐axis) and delay reductio...
9. Figure 10.9 Energy consumption per cluster during the local and remote clust...
10. Figure 10.10 Evaluation and comparison of the average delay for the proposed...
Chapter 11
1. Figure 11.1 Various sensing technologies applied at different stages for the...
2. Figure 11.2 Internal morphology of fresh and water stressed leaves using ter...
3. Figure 11.3 Experimental setup for measuring the transmission response by us...
4. Figure 11.4 Samples of different fresh leaves used for the measurement proce...
5. Figure 11.5 Demonstration of measuring the thickness of a leaf using Vernier...
6. Figure 11.6 Transmission response of various leaves in the frequency range 0...
7. Figure 11.7 Path‐loss response of an aromatic leaf considering the effect of...
8. Figure 11.8 Path‐loss response of a sage leaf considering the effect of diff...
9. Figure 11.9 Path‐loss response of a basil leaf considering the effect of dif...

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List of Contributors

Hasan T. Abbas
James Watt School of Engineering
University of Glasgow
UK

Muhammad Mahtab Alam
Thomas Johann Seebeck
Department of Electronics
Tallinn University of Technology
Estonia

Akram Alomainy
School of Electronic Engineering and Computer Science
Queen Mary University of
London
UK

Ramy Amer
CONNECT Centre for Future
Networks
Trinity College Dublin
Ireland

Anas Amjad
Staffordshire University
UK

Gennaro Boggia
Department of Electrical and
Information Engineering
Politecnico di Bari
Italy

M. Majid Butt
Nokia Bell Labs
France
and
CONNECT Centre for Future
Networks
Trinity College Dublin
Ireland

Luigi Alfredo Grieco
Department of Electrical and
Information Engineering
Politecnico di Bari
Italy

Mona Jaber
School of Electronic Engineering
and Computer Science
Queen Mary University of
London
UK

Alar Kuusik
Thomas Johann Seebeck
Department of Electronics
Tallinn University of Technology
Estonia

Yannick Le Moullec
Thomas Johann Seebeck
Department of Electronics
Tallinn University of Technology
Estonia

Hassan Malik
Thomas Johann Seebeck
Department of Electronics
Tallinn University of Technology
Estonia

Nicola Marchetti
CONNECT Centre for Future
Networks
Trinity College Dublin
Ireland

Zhen Meng
James Watt School of Engineering
University of Glasgow
UK

Sven Pärand
Telia Estonia Ltd.
Estonia

João Pedro Battistella Nadas
James Watt School of Engineering
University of Glasgow
UK

Metin Ozturk
James Watt School of Engineering
University of Glasgow
UK

Mohsin Raza
Faculty of Science and
Technology
Middlesex University
UK

Aifeng Ren
James Watt School of Engineering
University of Glasgow
UK

Huan X. Nguyen
Faculty of Science and
Technology
Middlesex University
UK

Hafiz Husnain Raza Sherazi
Department of Electrical and
Information Engineering
Politecnico di Bari
Italy

Richard Demo Souza
Universidade Federal de Santa
Catarina‐Florianópolis
Brazil

Adnan Zahid
James Watt School of Engineering
University of Glasgow
UK

Guodong Zhao
James Watt School of Engineering
University of Glasgow
UK

Ahmed Zoha
James Watt School of Engineering
University of Glasgow
UK

Preface

Over the past three centuries, industrial processes have evolved from steam operated mechanical looms (industry 1.0), electrically run machines (industry 2.0) to programmable logic controller (PLC) based manufacturing (industry 3.0). However, in the 21st century, we are talking about the industrial revolution through to industry 4.0. Industry 4.0 is about digitizing all industrial processes and making them intelligent, efficient and autonomous through sensing, connectivity, big data analytics, and control. With advancements in enabling technologies, industrial processes are likely to build further on industry 4.0, resulting in even smarter manufacturing solutions.

Besides the technical challenges, the industrial revolution will face challenges like overhauling of company culture, and hiring and training people with appropriate skills to run the digital processes. Moreover, the companies will need to develop new business models in order to maximize their outputs and take full advantage of the industrial revolution.

With regards to the technical challenges, cybersecurity is an extremely important issue since any sort of cyber attack could result in financial as well as human loss. Interoperability is another important aspect that has to be ensured in the form of the use of open source software and solutions, privacy and accessibility.

The key enablers for industrial revolution are advanced electronics and information and communication technologies (ICT), e.g. energy‐efficient sensing, cloud and mobile‐edge computing, reliable and low latency communication, big data analytics through machine learning and artificial intelligence, and the Industrial Internet of Things (IIoT).

To enable flexible and scalable connectivity solutions for industry 4.0, the role of wireless automation is pivotal. To achieve industrial wireless automation there are stringent requirements of low latency and high reliability in the presence of harsh industrial environments. Specifically, to enable industrial revolution, the end‐to‐end latency should be less than 0.5 ms while the reliability requirements of 10–9 or even less block error ratio (BLER).

To address these issues, the fifth generation of mobile communications (5G) proposes meeting the industrial wireless automation requirements through ultra‐reliable low‐latency communication (URLLC) services.

Integrating 5G URLLC with machine learning and artificial intelligence solutions will result in proactive anomaly detection followed up by self‐healing enabled by a reliable feedback control loop.

This book is presented in a way that it gradually builds on the knowledge introduced in the previous chapters, thus providing a seamless integration of various topics. The book chapters can be broadly divided into the following parts:

Industrial wireless sensor networks, application, challenges, and solutions (Chapters 1–3)
- Industrial wireless sensor networks and their applications
- Life‐span extension for sensor networks in the industry
- Multiple access and resource sharing for low latency critical industrial networks
Industrial automation via 5G URLLC and IIoT (Chapters 4 and 5)
- IIoTs and narrow‐band IoT for industrial automation
- URLLC as an enabler for industry automation
Machine learning and industrial automation optimization (Chapters 6–8)
- Anomaly detection and self‐healing in industrial wireless networks
- Cost efficiency optimization for industrial automation
- Non‐intrusive load monitoring
Advanced topics on wireless network control, nano‐scale communication and wireless caching (Chapters 9–11)
- Wireless networked control
- Wireless caching for industrial automation
- Nano‐scale communication for agriculture industrial automation.

Muhammad A. Imran, Sajjad Hussain,
Qammer H. Abbasi
University of Glasgow, UK