IOT BASED THESIS

The Literature Survey section is supportive in examining previous research patterns. It also assists you to find gaps in existing studies. The following is an example literature review for an IoT-related thesis:

IoT-Based Thesis: Literature Survey

1. Introduction

Generally, billions of devices and sensors globally are integrated by Internet of Things (IoT), thereby facilitating them to interact and share data in a perfect manner. Different fields such as smart cities, industrial automation, healthcare, and agriculture, are revolutionized by IoT mechanisms. When emphasizing possible research gaps, this literature survey investigates main research regions and developments in IoT.

2. IoT Architecture and Protocols

• Atzori et al. (2010):
• Title: “The Internet of Things: A survey”
• Important Points:
• In this study, IoT is initiated by combining three significant visions such as internet-oriented, semantic-oriented, and things-oriented.
• Relevant to protection, infrastructure, and standardization, limitations are detected.
• Gubbi et al. (2013):
• Title: “Internet of Things (IoT): A vision, architectural elements, and future directions”
• Important Points:
• A cloud-centric infrastructure is suggested and described IoT protocol stacks.
• This research emphasized the requirement for interoperability and QoS in upcoming IoT models.
• Nitti et al. (2014):
• Title: “IoT Architecture for a Sustainable Tourism Application in a Smart City Environment”
• Important Points:
• For smart tourism applications, depicted an IoT architecture.
• This research highlighted the utilization of lightweight messaging protocols such as CoAP and RESTful APIs.

3. IoT Security and Privacy

• Roman et al. (2011):
• Title: “On the security and privacy of wireless sensor networks”
• Important Points:
• In wireless sensor networks (WSNs), solved protection attacks that are appropriate for IoT.
• Appropriate for resource-limited devices, this study suggested lightweight security protocols.
• Sicari et al. (2015):
• Title: “Security, privacy and trust in Internet of Things: The road ahead”
• Important Points:
• A widespread outline of IoT protection problems are offered in this research.
• Typically, data morality, confidentiality, and trust management are detected and determined as significant limitations.
• Fernandes et al. (2016):
• Title: “Security implications of smart home IoT devices”
• Important Points:
• This research examined the safety impacts of IoT in smart homes.
• Assaults on IoT devices such as baby tracks and smart locks are described in this topic.
• Alrawais et al. (2017):
• Title: “Fog Computing for the Internet of Things: Security and Privacy Issues”
• Important Points:
• In this study, safety and confidentiality issues in fog computing platforms are described in an explicit manner.
• Generally, this research suggested authentication technologies and safer data exchange protocols.

4. IoT in Healthcare (Internet of Medical Things – IoMT)

• Farahani et al. (2018):
• Title: “Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare”
• Important Points:
• For healthcare IoT applications, investigated fog computing.
• The limitations relevant to patient confidentiality, scalability, and data processing are detected in this study.
• Islam et al. (2015):
• Title: “The Internet of Things for health care: A comprehensive survey”
• Important Points:
• In this research IoT applications in healthcare such as chronic disease management and remote tracking are analysed.
• The problems related to device consistency and data interoperability are emphasized in this topic.
• Rahmani et al. (2018):
• Title: “Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach”
• Important Points:
• For processing health data regionally, this study suggested a smart e-health gateway.
• By processing data at the edge, enhanced data confidentiality and decreased delay.

5. IoT in Smart Cities

• Zanella et al. (2014):
• Title: “Internet of Things for Smart Cities”
• Important Points:
• Typically, applications such as waste management, parking models, and smart lighting are described.
• On the basis of CoAP and 6LoWPAN, this study suggested a network infrastructure.
• Celik et al. (2018):
• Title: “MUSTANG: Multiple SiTUation Assessment eNgine for smart buildinG security”
• Important Points:
• Through the utilization of a context-aware situation evaluation engine, this research constructed a smart building model.
• In this study, possible attacks and doubtful action in actual-time are examined.

6. IoT in Agriculture (Smart Farming)

• Wolfert et al. (2017):
• Title: “Big data in smart farming – A review”
• Important Points:
• This research investigated the utilization of big data analytics in accurate farming.
• The significance of interoperability among heterogeneous IoT devices are highlighted in this topic.
• Rabah et al. (2019):
• Title: “Wireless sensor networks for precision agriculture in cold arid regions: a case study for Inner Mongolia, China”
• Important Points:
• Mainly, for tracking weather situations and soil dampness, this study implemented wireless sensor networks.
• For enhancing irrigation plans, constructed a decision assistance model.

7. Edge and Fog Computing in IoT

• Bonomi et al. (2014):
• Title: “Fog computing and its role in the Internet of Things”
• Important Points:
• In this study, the concept of fog computing is initiated as an expansion for the cloud.
• Generally, this research described its contribution in enhancing delay and data processing for IoT applications.
• Chiang et al. (2016):
• Title: “Fog and IoT: An overview of research opportunities”
• Important Points:
• The limitations and chances in combining fog computing along with IoT are emphasized.
• This study concentrated particularly on mobility, delay, and protection problems.

8. IoT Standards and Interoperability

• Vermesan et al. (2013):
• Title: “Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems”
• Important Points:
• The requirement for open principles and interoperability in IoT are highlighted.
• This research described previous IoT principles such as MQTT, IEEE 802.15.4, and CoAP.
• Granjal et al. (2015):
• Title: “Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues”
• Important Points:
• In this study safety protocols such as Zigbee, DTLS, and 6LoWPAN are analysed.
• Open research limitations in device authentication and safe data exchange are detected.

9. IoT Data Analytics

• Krishnan et al. (2017):
• Title: “Big Data Analytics in IoT: Challenges, Techniques, and Opportunities”
• Important Points:
• Limitations in examining huge numbers of IoT data are investigated.
• This research depicted stream processing systems and big data analytics models.
• Ray et al. (2018):
• Title: “Internet of Things for smart agriculture: Technologies, practices, and future direction”
• Important Points:
• For enhancing farming efficiency, investigated IoT data analytics approaches.
• In this study, data preprocessing and feature extraction are highlighted for efficient forecasting modelling.

What research topic should I select for a PhD in IoT based fog computing?

The process of selecting a research topic for a PhD in terms of IoT fog computing is determined as both challenging and fascinating. We provide few captivating research topics that might assist as possible guidelines:

1. Resource Management and Optimization in Fog Computing for IoT:

• Explanation: To stabilize storage, computation, and bandwidth utilization in fog computing platforms, aim to construct resource allocation methods.
• Research Queries:
• How can dynamic resource allotment reduce energy utilization and delay in fog computing networks?
• What contribution does reinforcement learning play in enhancing task offloading policies?
• Potential Research Guidelines:
• The major direction is the reinforcement learning-related resource allotment and task scheduling.
• Multi-objective enhancement of resource usage such as bandwidth, storage, computation is considered as the key guidelines.

2. Security and Privacy Challenges in IoT-Based Fog Computing:

• Explanation: Specifically, in healthcare and industrial IoT applications, solve data protection and confidentiality problems relevant to fog computing.
• Research Queries:
• In what way can distributed identity management enhance device authentication in fog networks?
• What confidentiality-preserving data collection approaches can be utilized to secure user data in fog computing?
• Potential Research Guidelines:
• The essential instruction is effective and safer data collection protocols.
• Blockchain-related authentication and access control for fog devices.

3. Scalability and Interoperability in Fog Computing:

• Explanation: Scalable fog computing infrastructures have to be formulated that assure consistent interoperability among heterogeneous IoT devices.
• Research Queries:
• How can fog arrangement models assure interoperability among various IoT communication protocols?
• What data federation systems can assist consistent data sharing in extensive fog networks?
• Potential Research Guidelines:
• For extensive fog computing, data federation systems and data-sharing protocols are the key instructions.
• Arrangement models for heterogeneous IoT protocols such as LoRaWAN, Zigbee, etc.

4. Edge Intelligence and Federated Learning in Fog Computing:

• Explanation: It is approachable to construct federated learning systems that contain the ability to disseminate machine learning missions among edge devices in fog networks.
• Research Queries:
• How can federated learning systems be enhanced for resource-limited fog devices?
• What confidentiality-preserving approaches can protect federated learning systems in fog computing?
• Potential Research Guidelines:
• For federated learning, differential privacy and safer collection approaches are examined as major directions.
• Optimization methods for disseminated model training on resource-limited devices.

5. Latency and Reliability in Real-Time IoT Applications:

• Explanation: In what way fog computing can align the delay and consistency necessities of actual-time IoT applications such as smart grids or automated vehicles has to be investigated.
• Research Queries:
• What contribution do network slicing and TSN (Time-Sensitive Networking) protocols play in delay mitigation?
• How can fog computing infrastructures assure high consistency and ultra-low delay for actual-time applications?
• Potential Research Guidelines:
• Network slicing and Quality of Service (QoS) models are determined as possible research instructions.
• The main guideline is Time-Sensitive Networking (TSN) protocols for fog computing.

6. Energy Efficiency and Green Computing in Fog Networks:

• Explanation: The energy-effective computing models have to be constructed in such a manner that decrease power utilization in fog computing without convincing effectiveness.
• Research Queries:
• How can fog devices deploy dynamic power management to enhance energy utilization?
• What energy-aware scheduling methods can prolong the lifespan of battery-operated fog devices?
• Potential Research Guidelines:
• Energy-aware task scheduling and load balancing.
• The key research direction is dynamic power management and sleep scheduling approaches.

7. IoT Data Analytics and Stream Processing in Fog Computing:

• Explanation: For processing high-velocity IoT data streams at the fog layer, focus on creating actual-time data analytics models.
• Research Queries:
• What contribution does data pre-processing such as collection, filtering play in enhancing data analytics?
• How can stream processing models effectively manage high-velocity IoT data at the fog layer?
• Potential Research Guidelines:
• In-network data collection and filtering approaches are the essential research instructions.
• High-velocity data stream processing models such as Storm, Apache Flink.

8. Digital Twins in IoT-Based Fog Computing:

• Explanation: Specifically, for process improvement and forecasting maintenance, research in what way digital twins can design and handle IoT devices in fog computing platforms.
• Research Queries:
• What forecasting maintenance frameworks can be constructed by employing digital twin data?
• How can digital twins efficiently depict and track IoT devices in fog computing networks?
• Potential Research Guidelines:
• Utilizing digital twin data, forecasting maintenance methods.
• The possible research directions are actual-time designing and tracking models for digital twins.

9. Fog Computing for Industrial IoT (IIoT):

• Explanation: For industrial IoT applications such as predictive maintenance and manufacturing computerization, it is appreciable to model fog computing infrastructures.
• Research Queries:
• What fault-tolerant technologies can assure consistency in major industrial IoT models?
• In what way can fog computing infrastructures enhance computerization and forecasting maintenance in industrial IoT applications?
• Potential Research Guidelines:
• The main research guidelines are forecasting maintenance and process computerization methods.
• Consistency models and fault-tolerant technologies for IIoT.

10. Interoperability Standards and Protocols for Fog Computing:

• Explanation: Focus on exploring interoperability protocols and principles that are able to enable consistent data exchange and interaction among fog computing and devices.
• Research Queries:
• What contribution do ontologies and semantic data systems play in data interoperability?
• How can progressing interoperability principles such as OPC UA and oneM2M enhance fog computing?
• Potential Research Guidelines:
• The key research instruction is semantic interoperability models and ontologies.
• For consistent data exchange, protocol conversion technologies are determined as major research directions.