A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scal-ability

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Most Common NB-IoT Applications and Associated Net-work Resources Challenges

Smart metering

One of the most suitable uses for NB-IoT is smart metering. NB-IoT is commonly used for water, gas and electricity metering (Adhikary et. al., 2016). The unique-ness of NB-IoT systems as used for water and gas metering as opposed to electricity metering is the limited energy availability for NB-IoT nodes. Most water and gas meters are battery operated. The limited energy availability poses a challenge on their design because the design has to ensure a long network lifetime (Yeoh et. al., 2018). Due to the energy limitations, water and gas meters are often constrained to using low energy consuming communication technologies, unlike electricity meter designs which can still explore other power-hungry communication technologies such as cellular (LTE, GPRS etc) (Carlesso et. al., 2015). Battery-powered NB-IoT mod-ules do not need a power connection, deliver deep indoor penetration, and thereby, establish a reliable connection even in areas where mobile reception is poor (Laurid-sen et. al., 2018). The provider can read the meter remotely and the end customer does not have to stay at home to wait for the meter reader to take meter readings at the user’s premises (Pennacchioni et. al., 2017).
Another case scenario is the one of application of the NB-IoT for remote smart energy metering. In the energy sector, the developing of smart metering networks allows operators and companies to improve the production e ciency and to o er an enhanced service to customers (Kaipainen, 2018).
The authors in (Pennacchioni et. al., 2017) propose a deployment analysis of the NB-IoT system for several metering systems to assess its coverage and capacity performance. Several case scenarios are set-up (Pennacchioni et. al., 2017) to assess the performance. This can be summarised as follows:
• Energy metering (Gas metering for example): User equipment is placed in a deep indoor location sometimes in an underground area. This means that they are a ected by extra path-loss compared to units in an outdoor situation such as water meters. These devices are static.
• Air quality metering: These type of smart NB-IoT meters are also static (not mobile) and would normally be placed in a household location. Although these devices are placed indoors, they are not often in an area of deep indoor location like the gas meters for example.
• Smart yard water meters: These water consumption counters and loggers are often deployed in an outdoor location. This makes them experience almost no path-losses.
It is important to note that this particular set of application scenarios mostly require very reliable, scalable and energy-e cient performance than it does require the high data rate performance. This is because all the devices in these three case scenarios are assumed to communicate daily to an NB-IoT Base Station on a periodic inter-val to send their daily consumption values. This makes their data rate requirement quite low. They are however required to have very reliable uplink communication as very important smart cities management decisions are based on the data from these devices. They are also required to be of very energy-e cient designs across the entire network stack as most of these NB-IoT devices are battery powered. Finally, because these applications are mostly related to smart utilities management, they often in-volve very large numbers of devices and therefore require the ability to e ciently scale without degrading their energy e ciency and network reliability performance.

Smart Cities

In Smart Cities, NB-IoT systems can be used in street lighting as discussed in (Chen et. al., 2018). Lamp posts tted with appropriate modules can be switched on and o or dimmed remotely and can trigger an alarm if they malfunction (Abinaya, 2017). If a city connects its parking spaces using NB-IoT, better utilisation is achieved of available parking. Motorists are directed by a smart parking guidance system to the nearest free parking space by the shortest route (Shi et. al., 2017) (Lin et. al., 2017). In waste disposal, garbage cans tted with NB-IoT modules alert a control centre when they are full. As a consequence, waste disposal companies can optimise vehicle routes and reduce costs (Sinha, 2017).
A typical case scenario for the use of NB-IoT in smart cities applications is described and analysed in (El Soussi et. a., 2018). The scenario consists of NB-IoT devices placed in various parts of a city in heterogeneous environments with di erent network coverage conditions. These devices are used for parking management, tra c control, waste management and many other day-to-day city management operations. They primarily serve to log data for predictive and reactive city management planning (El Mahjoubi et. al., 2017). The experience has demonstrated a clear energy performance di erence between various types of devices depending on whether they are mobile or static, indoors or outdoors, urban or suburban. Some key challenges related to the deployment of NB-IoT systems in a smart city application have been identi ed. These include network planning and optimization to ensure reliable and long coverage, network latency and as well as the localisation of nodes.

Indoor and Outdoor Localisation Applications

The NB-IoT is suitable for locating pets or valuables both indoor and outdoor sce-narios. To not lose sight of a pet or an expensive personal item, an NB-IoT module can be a low-cost alternative to a GSM tracker. NB-IoT presents an entirely new set of opportunities for low power, low-cost localisation of both moveable and xed assets such as cars, sensor nodes (Song et. al., 2017). As use case scenario is the use of triangulation to establish localisation of NB-IoT nodes between three nearest base stations can be a low energy approach as compared to each nodes having its GPS module. Based on the Signal-to-Noise Ratio (SNR) of the packet received from the three nearby BSs with well-known GPS locations, the NB-IoT node can reasonably be located (Ribeiro et. al., 2018). Many other possible techniques could be used for localization from NB-IoT device-driven communications. These techniques include Observed Time Di erence of Arrival (OTDoA) (Hu et. al., 2017) and Received Signal Strength Indicator (RSSI) (Sallouha et. al., 2017) for example.


Farming and forestry: Monitoring livestock

NB-IoT technology is also suitable for agricultural use where there is no power sup-ply or where network coverage is poor (CanLong et. al., 2018). In irrigation of elds or plantations, tank levels, pump pressure, and ow rates are measured. The location and health of livestock can be monitored as well. In forestry, low-cost sen-sors can be distributed in large numbers to report information such as temperature, smoke development, or wind direction (Elijah et. al., 2018). With the advent of the NB-IoT technology, the IoT agriculture sensors have become more accessible than ever before, helping farmers maximize yields, conserve resources such as water and fertilizers, reduce waste and enhance productivity (Digital Matter, 2020). The In-ternet of Things is allowing agriculture, here speci cally arable farming, to become data-driven, leading to more timely and cost-e ective production and management of farms, and at the same time reducing their environmental impact. An example is the one of farm management, traceability of produce and forecasting. Forecast-ing employs NB-IoT sensors as source of data and the collected data can be used for prediction of some of the key farming events through precisely designed analytic methods (Villa-Henriksen et. al., 2020).

Industry: NB-IoT on pallets and pipelines

In a use case scenario that there is a need to monitor oil and gas pipelines, sensors relay important information about pressure, ow rate, or possible leaks. There is often no external power source for pipelines in inaccessible areas (Zhang et. al., 2018). NB-IoT could nd appropriate applications since modules have a long service life, require no maintenance, and have a 20 dB wider range than conventional mobile network connections (Spajic, 2017).

Table of contents :

1. Introduction
1.1 Introduction
1.2 Background and motivation
1.3 Problem Statement
1.4 Sub-problems
1.4.1 Sub-problem 1: Channel coding with high energy cost
1.4.2 Sub-Problem 2: Network scalability
1.4.3 Sub-problem 3: Low modulation data rates
1.5 Hypotheses
1.5.1 A channel-aware adaptive channel coding approach
1.5.2 An adaptive transmission repetition number selection
1.5.3 Spread spectrum and clustering approach
1.6 Importance and benets of the study
1.7 Delimitation and assumptions of the study
1.8 Research Methodology
1.9 Contributions and Outputs of the Study
1.10 Thesis Outline
2. The Narrowband Internet of Things State of Art, Challenges, and Opportunities
2.1 Introduction
2.1.1 Background and motivation
2.2 An overview on the LPWANs and the NB-IoT
2.2.1 Comparison of the NB-IoT and other IoT Technologies
2.2.2 NB-IoT and LoRa PHY layer comparison
2.3 NB-IoT design objectives
2.4 Most Common NB-IoT Applications and Associated Network Re-sources Challenges
2.4.1 Smart metering
2.4.2 Smart Cities
2.4.3 Indoor and Outdoor Localisation Applications
2.4.4 Farming and forestry: Monitoring livestock
2.4.5 Industry: NB-IoT on pallets and pipelines
2.5 The NB-IoT as part of 5G cellular IoT
2.6 SDN and NFV for NB-IoT within 5G Systems
2.7 Energy ecient NB-IoT Channel Coding (CC) schemes
2.7.1 Why Is Energy Ecient Channel Coding Important for NB-IoT 52
2.7.2 Existing NB-IoT Energy-Ecient Channel Coding (CC) ap-proaches
2.8 Data rate enhanced NB-IoT modulation selection schemes
2.9 Link Adaptation Schemes for enhanced NB-IoT scalability
2.10 An overview on the NB-IoT spectrum sharing and clustering
2.10.1 Spectrum Sharing Techniques in licensed band IoT systems
2.10.2 Spread Spectrum challenges in NB-IoT systems
2.10.3 Clustering Approaches for energy-ecient NB-IoT
2.10.4 Energy-Ecient Network coding techniques for NB-IoT systems
2.11 The NB-IoT performance Challenges and open issues
2.11.1 NB-IoT Energy Eciency Challenges and opportunities
2.11.2 Data Rates enhancement and network reliability challenges
2.11.3 Network Scalability issues
2.12 Conclusion
3. A Modelling Approach of the Narrowband IoT (NB-IoT) PHY Layer Performance
3.1 Introduction
3.2 NB-IoT Operation Modes
3.3 The Downlink (DL) interface Model
3.3.1 The narrowband physical broadcast channel (NPBCH)
3.3.2 The narrowband physical downlink control channel (NPDCCH) 98
3.3.3 The narrowband physical downlink shared channel (NPDSCH) 98
3.3.4 A Prediction Model for the NB-IoT DL Processing Delay
3.3.5 The NB-IoT DL versus the LTE DL: Fundamental dierences 102
3.4 NB-IoT data rate versus energy eciency mathematical modelling and analysis
3.5 Performance analysis
3.5.1 Simulation set-up
3.5.2 Results Obtained
3.6 Conclusion
4. An Energy-Ecient and Adaptive Channel Coding Approach for Narrowband Internet of Things (NB-IoT) Systems
4.1 Introduction
4.2 Background and Motivation
4.3 Methods and Experimental Approach
4.3.1 Power Consumption Model
4.4 An Overview of Existing Energy Eciency Techniques for NB-IoT Systems
4.4.1 Existing NB-IoT Energy-Ecient Channel Coding (CC) Ap-proaches
4.4.2 Ecient Selection of Modulation Coding Scheme (MCS)
4.4.3 Repetition-Dominated Channel Coding Approaches
4.4.4 The NBLA and Its Open-Loop Power Control Approaches
4.4.5 NBLA Open-Loop Power Control
4.5 The Proposed Adaptive Channel Coding Technique
4.5.1 The Inner Loop Approach
4.5.2 The Outer Loop Link Adaptation Approach
4.6 Performance Evaluation
4.6.1 Evaluation Setup
4.6.2 EEACC MATLAB Simulation
4.6.3 Obtained Results and Discussion
4.7 Conclusion
5. A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scal-ability
5.1 Introduction
5.2 Background, Motivation and Objectives
5.3 The proposed Intelligent Mixed Approach
5.3.1 Hypothesis of the proposed approach
5.3.2 The Mixed Frequency Hopping Spread Spectrum and Cluster- ing Algorithm
5.4 Performance Evaluation and Obtained Results
5.4.1 Evaluation set-up
5.4.2 Real-Life Application Scenario under Simulation
5.4.3 Obtained Results, Analysis & Discussion
5.5 Conclusion
6. Conclusions and Recommendations for future work
6.1 Introduction
6.2 The stated research objectives and achievements of the Study
6.3 Benets of the study
6.4 Recommendations for Future study
6.5 Overall Conclusions


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