Wireless Automation as an Enabler for the Next Industrial Revolution

MUHAMMAD A. IMRAN, PHD, is Professor of Communication Systems at the James Watt School of Engineering and the Dean, University of Glasgow, UESTC. He is also an Affiliate Professor at the University of Oklahoma, USA, and a Visiting Professor at 5G Innovation Centre, University of Surrey, UK.SAJJAD HUSSAIN, PHD, is an Assistant Professor at the University of Glasgow in the James Watt School of Engineering, UK. Dr. Hussain is an IEEE Senior Member and Senior Fellow Higher Education Academy.QAMMER H. ABBASI, PHD, is an Assistant Professor at the University of Glasgow in the James Watt School of Engineering in addition to Visiting Assistant Professor with Queen Mary University of London. Dr. Abbasi is an IEEE Senior Member and URSI Young Scientist Award winner.

• List of Contributors xiiiPreface xvii1 Industrial Wireless Sensor Networks Overview 1Mohsin Raza and Huan X. Nguyen1.1 Introduction 11.2 Industry 4.0 31.3 Industrial Wireless Sensor Networks (IWSNs) 61.4 Applications of IWSNs 81.4.1 Feedback Control Systems 81.4.2 Motion and Robotics 91.4.3 Safety Applications 91.4.4 Environmental Monitoring 91.4.5 Machine/Structural Health Monitoring 101.5 Communication Topologies in IWSNs 101.6 Research Developments and Communications Standards for Industry 111.6.1 IEEE 802.15.4 121.6.2 IEEE 802.15.4e 131.6.3 Zigbee 131.6.4 WirelessHART 141.6.5 ISA100.11a 141.6.6 6LoWPAN 14Bibliography 152 Life-span Extension for Sensor Networks in the Industry 19Metin Ozturk, Mona Jaber, and Muhammad A. Imran2.1 Introduction 192.2 Wireless Sensor Networks 212.3 Industrial WSNs 242.3.1 Requirements and Challenges 252.3.2 Protocols and Standards 262.3.3 IWSN Applications 272.4 Life-span Extension for WSNs 282.4.1 Energy Harvesting 292.4.1.1 Solar Energy Harvesting 312.4.1.2 Wind Energy Harvesting 312.4.1.3 Radio Frequency Energy Harvesting 322.4.1.4 Piezoelectric Energy Harvesting 322.4.1.5 Thermal Energy Harvesting 332.4.2 Energy Conservation 332.4.2.1 Duty Cycling 342.4.2.2 Data Driven Approaches 352.4.2.3 Mobility Based Approaches 352.4.2.4 Q Learning Assisted Energy Efficient Smart Connectivity 362.5 Conclusion 40Bibliography 413 Multiple Access and Resource Sharing for Low Latency Critical Industrial Networks 47Mohsin Raza, Anas Amjad, and Sajjad Hussain3.1 Introduction 473.2 Research Developments 513.2.1 CSMA/CA Based MAC Schemes 533.2.2 TDMA Based MAC Schemes 533.2.3 Multichannel MAC Schemes 543.2.4 Priority Based MAC Schemes 553.3 Priority Based Information Scheduling and Transmission 563.4 Summary 61Bibliography 614 Narrowband Internet of Things (NB-IoT) for Industrial Automation 65Hassan Malik, Muhammad Mahtab Alam, Alar Kuusik, Yannick Le Moullec, and Sven Pärand4.1 Introduction 654.2 Overview of NB-IoT 654.3 NB-IoT Design Characteristics 684.3.1 Low Device Complexity and Low Cost 684.3.2 Coverage Enhancement (CE) 704.3.3 Long Device Battery Lifetime 704.3.4 Massive Device Support 714.3.5 Deployment Flexibility 724.3.6 Small Data Packet Transmission Support 744.3.6.1 Control Plane CIoT EPS Optimization (CP) 744.3.6.2 User Plane CIoT EPS Optimization (UP) 764.3.7 Multicast Transmission Support 764.3.8 Mobility Support 764.4 NB-IoT Frame Structure 774.4.1 Downlink Transmission Scheme 784.4.1.1 Narrowband Reference Signal (NRS) 784.4.1.2 Narrowband Primary and Secondary Synchronization Signals (NPSS and NSSS) 784.4.1.3 Narrowband Physical Broadcast Channel (NPBCH) 794.4.1.4 Narrowband Physical Downlink Control Channel (NPDCCH) 794.4.1.5 Narrowband Physical Downlink Shared Channel (NPDSCH) 804.4.2 Uplink Transmission Scheme 804.4.2.1 Demodulation Reference Signal (DMRS) 804.4.2.2 Narrowband Physical Random Access Channel (NPRACH) 814.4.2.3 Narrowband Uplink Shared Channel (NPUSCH) 814.4.3 NB-IoT Design Modification in Relation to LTE 814.5 NB-IoT as an Enabler for Industry 4.0 814.5.1 Process Automation 834.5.2 Human–Machine Interfaces 844.5.3 Logistics and Warehousing 844.5.4 Maintenance and Monitoring 854.6 Summary 85Bibliography 865 Ultra Reliable Low Latency Communications as an Enabler For Industry Automation 89João Pedro Battistella Nadas, Guodong Zhao, Richard Demo Souza, and Muhammad A. Imran5.1 Introduction 895.2 Opportunities for URLLC in Industry Automation 915.2.1 URLLC Industrial Applications 915.2.2 New Business Models 935.3 Existing Solutions 945.3.1 LTE 945.3.2 WirelessHART and ISA100.11a 955.4 Enabling Technologies 965.4.1 Faster Channel Coding 965.4.2 Latency Aware HARQ 975.4.3 Joint Design 985.4.3.1 Communication Model 1005.4.3.2 Proposed Solution 1005.4.3.3 Numerical Results and Conclusion 1035.5 Conclusion 104Bibliography 1046 Anomaly Detection and Self-healing in Industrial Wireless Networks 109Ahmed Zoha, Qammer H. Abbasi, and Muhammad A. Imran6.1 Introduction 1096.2 System Design 1136.2.1 COD Stage 1136.2.2 COC Stage 1156.3 Cell Outage Detection Framework 1156.3.1 Profiling Phase 1156.3.1.1 Local Outlier Factor Based Detector (LOFD) 1196.3.1.2 One-Class Support Vector Machine based Detector (OCSVMD) 1206.3.2 Detection and Localization Phase 1226.4 Cell Outage Compensation 1226.5 Simulation Results 1246.5.1 Simulation Setup 1246.5.1.1 Parameter Estimation and Evaluation 1246.5.2 Cell Outage Detection Results 1276.5.3 Localization 1356.5.4 Compensation 1366.6 Conclusion 138Bibliography 1387 Cost Efficiency Optimization for Industrial Automation 141Hafiz Husnain Raza Sherazi, Luigi Alfredo Grieco, Gennaro Boggia, and Muhammad A. Imran7.1 Introduction 1417.2 The Evolution of Low Energy Networking Protocols for Industrial Automation 1447.2.1 Radio Frequency Identification and Near Field Communication 1447.2.2 Bluetooth 1457.2.3 Zigbee 1457.2.4 Bluetooth Low Energy (BLE) 1457.2.5 Wi-Fi 1467.2.6 IPv6 Over Low Power Wireless Personal Area Networks (6LoWPAN) 1467.2.7 Low Power Wide Area Networks (LPWAN) 1467.2.7.1 Long Range Wide Area Networks (LoRaWAN) 1487.2.7.2 Sigfox 1497.2.7.3 Narrowband IoT (NB-IoT) 1507.3 An Overview of the Costs Involved in Industry 4.0 1517.3.1 Battery Replacement Cost 1527.3.2 Damage Penalty 1527.3.3 Cost Relationships and Trade-off Analysis 1527.4 Evaluating Costs in an Industrial Environment: A LoRaWAN Case study 1537.4.1 Battery Lifetime of Monitoring Nodes 1557.4.2 Battery Replacement Cost 1567.4.3 Damage Penalty 1577.5 Cost Analysis for Industrial Automation 1587.5.1 Statistics for Energy Consumption 1587.5.2 Statistics for Battery Replacement Cost 1597.5.3 Statistics for Damage Penalty in a Plain Industrial Environment 1617.5.4 The Cumulative Cost 1637.6 Cost Optimization through Energy Harvesting in Industrial Automation 1647.6.1 Extending the Battery Lifetime 1657.6.2 Tuning the Sensing Interval 1657.7 Conclusion 168Bibliography 1688 A Non-Event Based Approach for Non-Intrusive Load Monitoring 173Ahmed Zoha, Qammer H. Abbasi, and Muhammad A. Imran8.1 Introduction 1738.2 Probabilistic Modelling for Load Disaggregation 1758.2.1 Model Definition 1778.2.2 Inference 1788.3 Experimental Evaluations 1808.3.1 Experiment Design 1818.3.2 Feature Sub-Groups 1828.3.3 Performance Evaluation 1838.3.3.1 Binary and Multi-State Classification 1838.4 Live Deployment 1878.4.1 Energy Estimation 1888.5 Conclusion 190Bibliography 1919 Wireless Networked Control 193Zhen Meng and Guodong Zhao9.1 Introduction 1939.2 Industrial Automation 1949.3 WNC System Model 1969.3.1 WNC Model 1969.3.1.1 Wireless Networks 1979.3.1.2 Control System 1989.3.2 WNC System Requirements 1999.3.2.1 System Structure 1999.3.2.2 Real-Time Performance 2009.3.2.3 High Reliability 2019.3.2.4 Determinism 2019.3.2.5 Sample Data Traffic and Event Order 2019.3.3 Analysis of Influencing Factors 2029.3.3.1 Sampling Period 2029.3.3.2 Time Delay 2029.3.3.3 Packet Loss 2039.4 Network and System Control Co-design 2039.5 Conclusion 204Bibliography 20410 Caching at the Edge in Low Latency Wireless Networks 209Ramy Amer, M. Majid Butt, and Nicola Marchetti10.1 Introduction 20910.2 Living on the Edge 21110.3 Classifications of Wireless Caching Networks 21410.3.1 Wireless Caching Architecture 21510.4 Caching for Low Latency Wireless Networks 21710.5 Inter-cluster Cooperation for Wireless D2D Caching Networks 21810.5.1 Proposed Network Model 21910.5.2 Content Placement and Traffic Characteristics 22210.5.3 Caching Problem Formulation 22410.5.3.1 Arrival and Service Rates 22410.5.3.2 Network Average Delay 22510.5.4 Proposed Caching Schemes 22610.5.4.1 Caching Popular Files 22610.5.4.2 Greedy Caching Algorithm 22710.5.4.3 Outage Probability 22810.6 Results and Discussions 23010.7 Chapter Summary 234Bibliography 23511 Application of Terahertz Sensing at Nano-Scale for Precision Agriculture 241Adnan Zahid, Hasan T. Abbas, Aifeng Ren, Akram Alomainy, Muhammad A. Imran, and Qammer H. Abbasi11.1 Introduction 24111.1.1 Limitations of Conventional Methods 24311.1.2 Transformation from Micro- to Nanotechnology 24311.1.3 Evolution of Nanotechnology 24511.1.4 Potential Benefits of Nanotechnology in Agriculture 24511.1.5 Challenges in Nanotechnology 24611.1.5.1 Health and Environmental Impacts 24611.1.5.2 High Production Costs 24611.1.5.3 Risk Assessment 24711.1.6 Evolving Applications of Terahertz (THz) Technology 24711.1.7 Materials and Methods 24911.1.7.1 Experimental Setup 24911.1.7.2 Sample 24911.1.7.3 Thickness of Leaves 25011.1.8 Measurement Results 25011.1.8.1 Transmission Response 25011.1.8.2 Path-loss Response of Leaves 25311.1.9 Conclusion 254Bibliography 255Index 259