The objectives of this research are: To propose a scheme to estimate the future interference and enable efficient channel switching mechanism to avoid interference for Wireless Sensor Network (WSN) as per the latency requirements specified by Smart Grid (SG). To propose an algorithm for efficient data recovery mechanism so that ZigBee devices can quickly familiarize with the encoding according to the highly dynamic WiFi traffic. To propose an efficient frequency shifting mechanism for different frame sizes of data under interference conditions to guarantee the reliable data transmission within the delay requirements of the SG. To propose an effective mechanism to guarantee the ZigBee communications within the maximum tolerable delay under the coexistence of WiFi for carrying out effective smart home solutions. To propose an algorithm based on effective transmission power control to increase WSN life time under coexistence scenarios for efficient monitoring and controlling purposes at smart homes.
With the introduction of Information and Data-Communication Technology (ICT) to the present electrical power systems, the traditional electrical-grid system is becoming more intelligent and adaptive. The ICT successfully establishes bi-directional communication between Utility companies and the consumer for improving the generation and utilization of power. Wireless Sensor Network (WSN) is efficiently utilized by wide-ranging Smart Grid (SG) applications. Despite many advantages, WSN faces a challenge of avoiding interference experiencing from other coexisting wireless technologies working in the 2.4GHz unlicensed frequency band. Providing support for WSN in terms of avoiding interference is a very challenging issue due to the dynamic wireless communication environment and extremely limited resources. In the present thesis, the problem of interference experienced by WSN in 2.4GHz has been investigated.
The challenges, limitations and requirements for avoiding the interference for WSN working in the vicinity of other technologies like WiFi and Bluetooth have been identified. As a result of the literature survey carried out, it was identified that proper channel se-lection and channel prioritization for WSNs working in the coexisting environment had not been adequately developed till then. Hence, there was a requirement for addressing these issues. The proposed schemes in this thesis are based on simulation results obtained.
Table of Contents
1 Introduction
1.1 Introduction
1.2 The Introduction to Smart Grid Communication System Scenario
1.2.1 The Home Area Networks for SG Applications
1.2.2 The Neighbourhood Area Network (NAN) for SG Applications
1.2.3 The WAN for SG Applications
1.3 The WSN Protocol design goals for SG applications
1.3.1 Reliability of WSN
1.3.2 Quality of Service Requirements for WSN
1.3.3 Interference Avoidance
1.3.4 Energy Consumption
1.3.5 Interoperability
1.3.6 Memory Management
1.3.7 Security
1.3.8 Heterogeneous system conditions
1.4 Research Motivation
1.5 Research Objectives
2 A Review on Interference Avoiding Methods for WSN working in the 2.4GHz ISM Band
2.1 Review of Literature
2.2 Interference
2.2.1 Interference from other coexisting technologies working in same frequency band.
2.2.2 Interference from the nodes of same network
2.3 Evaluation and Modeling of Interference
2.3.1 Measurement of Interference
2.3.2 The Identification of Interferer
2.3.3 Modeling Interference
2.4 Observations
2.5 Open Research Issues
2.5.1 Reliability of data communication
2.5.2 Cross-layer dynamics
2.5.3 MAC layer
2.5.4 Channel Selection
2.6 Simulation Environment Utilized for the Research
2.6.1 The Network Simulator(ns) 2.34
2.6.2 Directories of ns-2
2.6.3 Basic Architecture
2.6.4 The Role of C++ and OTcl in ns-2
2.6.5 Main ns-2.34 Simulation Steps
2.6.6 Tracing of the Data Packets
3 Cross-layer based Interference Mitigation and Encoding for Multi-channel ZigBee Networks
3.1 Introduction
3.2 Related Works
3.3 Cross-Layer Multi Channel MAC Protocol
3.3.1 Overview
3.3.2 Estimation of Interference Level
3.3.3 Prediction of future of Hidden Markov Model
3.3.4 Data transmission through channel with least interference
3.4 Forward Error Correction based Encoding Technique for WSN
3.4.1 Transmission Scheme using (CMCMAC-FEC) Encoding.
3.5 Simulation Results of CMCMAC and CMCMAC-FEC
3.5.1 Analysis of network parameters based on varying number of nodes (ZigBee) and fixed data flows (WiFi)
3.5.2 Analysis of network parameters of CMCMAC-FEC based on varying number of nodes and fixed data flows
3.5.3 Analysis of network parameters based on Varying number of flows (WiFi) to the fixed number of nodes(ZigBee)
3.5.4 Analysis of CMCMAC-FEC based network parameters by varying number of flows
3.6 Conclusion
4 Load Aware Channel Estimation and Channel Scheduling for 2.4GHz Frequency Band Based Wireless Networks For Smart Grid Applications
4.1 Introduction
4.2 Related Works
4.3 Partile Swarm Optimization Based Load Aware Channel Estimation and Channel Scheduling for ZigBee Networks Working Under the Influence of WiFi
4.3.1 Pseudorandom-Based Interference Evading Scheme
4.3.2 Load Aware Channel Estimation
4.3.3 Traffic Weight Assignment
4.3.4 Particle Swarm Optimization (PSO) based Load Aware Channel Estimation
4.4 Simulation Results
4.4.1 Verification of various wireless technologies by changing the Number of Nodes
4.4.2 Varying the Data Flows
4.5 Summary
5 A Collaborative Framework for Avoiding Interference between Zigbee and WiFi for Effective Smart Metering Applications
5.1 Introduction
5.2 Related Works
5.3 Proposed Work
5.3.1 The Collaborative Framework for Avoiding Interference between ZigBee and WiFi networks
5.3.2 Realization of the Distance and RSSI
5.3.3 Realization of the Distance and RSSI
5.3.4 Throughput Estimation
5.4 Performance Evaluation
5.5 Conclusion
6 Interference Aware Adaptive Transmission Power Control Algorithm for ZigBee Wireless Networks
6.1 Introduction
6.2 Related Works
6.3 Interference aware Adaptive Transmission Power Control Algorithm
6.3.1 Problem Identification and Objectives
6.3.2 Initialization stage
6.3.3 Operational stage
6.4 Simulation Results
6.4.1 Simulation Parameters
6.4.2 Performance Metrics
6.4.3 Results and Analysis
6.5 Conclusion
7 Conclusion
7.1 Future Scope
Research Objectives and Topics
The primary research objective of this work is to investigate and mitigate the interference challenges faced by Wireless Sensor Networks (WSNs), specifically those based on ZigBee, when operating in the crowded 2.4GHz ISM frequency band alongside high-power technologies like WiFi. This is critical for ensuring reliable communication and meeting the stringent latency requirements of Smart Grid (SG) applications.
- Development of Cross-Layer Multi-Channel MAC protocols to dynamically estimate and avoid interference.
- Implementation of forward error correction (FEC) techniques to improve data recovery under collision scenarios.
- Application of optimization algorithms, such as Particle Swarm Optimization (PSO), for load-aware channel scheduling.
- Design of adaptive transmission power control (TPC) algorithms to enhance network efficiency and node lifetime.
- Extensive performance evaluation using the Network Simulator (ns-2) to validate improvements in packet delivery ratio, energy consumption, and throughput.
Excerpt from the Book
1.1 Introduction
The advanced research on power system issues for almost a decade has introduced the SG to the countries across the world. The SG represents modernized power delivery system. The initiation of advanced Information and Data-Communication Technology (ICT) for SG has significantly improved the quality of power transmission and distribution from power generation plants to end-users. The SG introduces the advanced technologies like modern automation, two-way communication, advanced monitoring, and control to optimize the power quality, efficiency, and reliability of all its interconnected power system elements. With the advent of integrating ICT into the power systems has improved the working capabilities of the utility companies in terms of better asset management and ensures the advanced energy management for the end-user (Gungor et al. 2010).
In the scenario, to execute the desired operations like effective monitoring and controlling the SG assets, it requires a well-established and reliable automation using ICT. The management of the present SG technologies can be done in terms of integrating the heterogeneous entities into the grid network as shown in the Fig.1.1. The power generation, transmission, and distribution are managed accordingly and intelligently based on the proactive scheduling of loads by SG (Gungor et al. 2011).
Summary of Chapters
1 Introduction: This chapter provides an overview of Smart Grid (SG) communication, outlining the necessity for reliable WSN integration and identifying key design goals and research objectives.
2 A Review on Interference Avoiding Methods for WSN working in the 2.4GHz ISM Band: This section reviews existing literature on interference in the 2.4GHz band, evaluates current modeling techniques, and identifies critical open research issues.
3 Cross-layer based Interference Mitigation and Encoding for Multi-channel ZigBee Networks: This chapter proposes a cross-layer multi-channel protocol and a forward error correction technique to mitigate WiFi-induced interference in ZigBee networks.
4 Load Aware Channel Estimation and Channel Scheduling for 2.4GHz Frequency Band Based Wireless Networks For Smart Grid Applications: This chapter introduces the PSOLACES algorithm, which uses Particle Swarm Optimization to dynamically estimate network load and schedule channels efficiently.
5 A Collaborative Framework for Avoiding Interference between Zigbee and WiFi for Effective Smart Metering Applications: This chapter details a collaborative framework (CFAI) designed to manage interference and optimize channel usage for smart metering scenarios.
6 Interference Aware Adaptive Transmission Power Control Algorithm for ZigBee Wireless Networks: This chapter presents the IAATPC algorithm, which adaptively controls transmission power based on real-time link quality to reduce interference and energy consumption.
7 Conclusion: The final chapter summarizes the contributions of the proposed novel schemes and outlines future research directions for enhancing WSN reliability in coexisting wireless environments.
Keywords
Home Area Networks, Interference Avoidance, Smart Grid Communications, The 2.4GHz Frequency Band, Wireless Sensor Networks, ZigBee, WiFi, Cross-Layer Protocol, Packet Delivery Ratio, Energy Consumption, Particle Swarm Optimization, Channel Scheduling, Smart Metering, Transmission Power Control, Network Efficiency.
Frequently Asked Questions
What is the core problem addressed in this research?
The work addresses the performance degradation of ZigBee-based Wireless Sensor Networks (WSNs) when operating in the 2.4GHz frequency band alongside high-power coexisting technologies like WiFi, which is a common scenario in modern Smart Grid and smart home applications.
What are the primary research areas?
The research focuses on interference mitigation, load-aware channel estimation, adaptive channel scheduling, and transmission power control within Smart Grid communication environments.
What is the ultimate goal of the proposed methods?
The primary goal is to ensure reliable data transmission, improve network efficiency (throughput and energy consumption), and meet strict latency requirements for critical Smart Grid monitoring and control applications.
Which scientific methods are employed for the research?
The research utilizes statistical modeling (Hidden Markov Models), optimization algorithms (Particle Swarm Optimization), and extensive simulation-based performance analysis using the network simulator ns-2.34.
What does the main body of the work cover?
The main body covers a comprehensive literature review, the proposal and detailed mathematical analysis of five novel algorithms (CMCMAC, CMCMAC-FEC, PSOLACES, CFAI, and IAATPC), and their simulation-based validation against existing protocols.
Which keywords characterize this work?
Key terms include Interference Avoidance, Smart Grid Communications, ZigBee, WiFi, Wireless Sensor Networks, and adaptive protocol design.
How does the CMCMAC protocol improve performance?
CMCMAC improves performance by using a Hidden Markov Model (HMM) to predict future interference patterns and dynamically switching to channels with the least interference, thereby enhancing the packet delivery ratio.
What is the role of the Forward Error Correction (FEC) technique?
The FEC technique (CMCMAC-FEC) is introduced to recover data from partially collided packets at the destination, which significantly boosts the packet delivery ratio and throughput in interference-heavy environments.
- Arbeit zitieren
- Dr. Vikram Kulkarni (Autor:in), 2019, Coexisting problems for wireless sensor networks working in 2.4 GHz frequency band, München, GRIN Verlag, https://www.grin.com/document/509404
