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PhD LITERATURE REVIEW AND GAP ANALYSES
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- PhD LITERATURE
What do you consider the major research themes or solutions in the existing literature?
- Traditional Routing Protocols: AODV, DSR, OLSR dominate early solutions
- ML-based Security Models: SVM, Decision Tree and shallow neural nets applied for Intrusion Detection
- Trust-Based Frameworks: Much explored, node reputation, and behavior-based trust mechanisms
- AI/DRL Approaches: DRL is showing promise regarding adaptive, real-time secure routing
- Federated Learning Models: Emerging but limited – Proposed for privacy-aware IDS
- Swarm Intelligence and Bio-inspired Models: Ant colony optimization and PSO-based routing
- Hybrid Frameworks: Merging trust models with DRL is coming up
Research review services and PhD literature review help are often used to critically evaluate these themes.
Gap Insight: Nothing but outdated ML/Trust schemes; hardly any field deployment of FL and DRL in dynamic MANETs.
Where are the apparent gaps in the research provided in the current literature?
- Theoretical Gaps: Theoretical frameworks for unified trust-anomaly are missing
- Methodological gaps: Real testbeds are few and simulations (NS2/NS3) dominate
- Empirical Gaps: Most studies rely on fabricated or reduced-scale test cases
- Practical Gaps: Most models are not validated in energy-constrained mobile node environments
- Application Gaps: MANETs in disaster relief or vehicular scenarios are not well studied
These observations are critical for those seeking gap identification for thesis and how to identify gaps in existing research.
Gap Insight: There is a very substantial justification that needs to be addressed by research in multiplying gaps-effective integrating novel methods-field validation-more use cases.
Which theoretical models or frameworks are most widely applied in this field?
- Reinforcement Learning (Q-Learning/DRL): Applied for routing but does not scale efficiently
- Trust Models: Heuristically designed trust update rules; lacks formal theory
- Game Theory: The application to model adversarial behaviour and cooperation but lacks integration in learning
- Graph Theory/Graph neural networks: seldom used especially considering their potential in modelling dynamic topology
- Federated Trust Models: emerging but conceptually underdeveloped
Such reviews align with academic literature review standards and can benefit from research gap analysis service inputs.
Gap Insight: The existing theoretical instruments are highly obsolete or too narrowly applied and, thus, there is a strong case for creating new frameworks through developing hybrids or formalized new frameworks.
How compatible are the different assessment measures and benchmarking methods in comparison across studies?
- Metrics Vary Widely: Packet Delivery Ratio, End-to-End Delay, Throughput, Detection Accuracy are common terms
- Not a Common Benchmarking Suite: Comparison found across studies are not standardized
- Tools Taken into Use: For example, NS2, NS3, OMNeT++ MATLAB and Python were utilized for ML/DRL; however, Tensorflow was incorporated for the AI models
- Problems on Reproducibility: Lack of open-source codes or datasets makes it difficult to repeat
Systematic literature review practices and professional literature review writing for PhD often highlight this limitation.
Gap Insight: Inconsistency of evaluation damages comparative study; unified benchmarking and open testbeds are critical needs.
What has been tried in various applications or use cases to address the issue?
- Military Use Cases: Really fast detection, very high security, and minimal delays
- Civil/IoT: Power-efficient, very low-cost routing, user privacy
- VANET: Mobility-aware routing is almost never tied to a secure protocol
- Disaster Relief: A real-time and infrastructure-less but under-explored aspect.
- MANETs for Healthcare: A few studies reveal the concept of sensitive data and emergency communication in the area.
These use cases are essential for domain-specific literature review services and support identifying literature gaps for doctoral research.
Gap Insight: Most solutions are generic and not tailored; an urgent need exists for domain-specific secure routing strategies.
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What are the limitations of the existing AI/ML applications in MANETs?
- Scalability Limitations: DRL agents often come to failure within extremely high mobility of nodes or very large networks.
- Interpretability Issues: The black-box models predominated by the absence of justifications behind the decisions.
- Deployment Limitations: Memory, power, and compute capabilities are limited from the MANET nodes.
- Training Problems: All these require very large datasets which are often not available for MANETs.
- Adaptability Issues: Most of the AI models do not generalize across scenarios or topological changes within the model.
These challenges are often explored during research gap analysis in literature review and discussed in literature review writing service contexts.
Gap Insight: This drives research toward lightweight, interpretable, and hardware-adaptable models for real MANET environments.
How privacy and data security were handled in previous collaborative or distributed detection systems?
- Centralized Detection System: Still Adopted but risks data aggregate and leakages
- Federated Learning (FL): Very promising but never adopted for a MANET IDS
- Differential Privacy: Conceptual yet lacks practical applicability
- Secure Aggregation Techniques: Very few in collaborative frameworks
- Trust Calculation Models: Common sharing of behavior data, at times compromising with privacy of user.
Future researchers seeking PhD research writing support and writing a structured literature review chapter for PhD should consider these points.
Gap Insight: This is another subject with much desired privacy versus security addressed issue and calls for features that will hence advance decentralized methods in privacy-preserving manner.
Which aspects of industry standards or protocols are ignored in academic research?
- IEEE 802.11s (Wireless Mesh): At best, highly sparingly considered in either simulation or protocol
- 5G/6G Compatibility: With that said, one can fairly say it lacks greatly when modern MANETs are concerned.
- Routing Standards (RFC 3561/AODV): Research with few studies that used these in their newer versions or compliant ones
- Edge Devices/IoT Hardware: Engagement with hardware limitations does not go far.
- Industry Deployment Studies: Few collaborations engage academia and industry with each other.
These areas often surface in literature mapping service reports and review of literature services for research scholars.
Gap Insight: A severe disconnect exists between theoretical models and standard-compliant deployable systems.
How inclusive are datasets in current studies?
- The Synthetic Data Could Be More: Traffic generated randomly generally does not reflect real-world characteristics
- Publicly Available Datasets Are Rarities: It would appear that there exist no gold-standard datasets for MANET attack detection
- Unlabeled data: Restriction on designing supervised learning architectures
- Simulated Scenarios: Do not incorporate communication errors and node failures that happen in real life
- Data Diversity: Most datasets show a mono-click; they span neither military nor vehicular nor civil domains.
This concern is central to help with finding research gaps in PhD topics and literature review and synthesis for PhD dissertation.
Gap Insight: Strong need for publicly available, labeled, and realistic dataset for AI-supported MANET research.
Is there vagueness in concepts or definitions in the literature?
- Trust Is an All-Inclusive Term: Definitions and calculation methods vary widely, and there is no consensus
- Ambiguities In Attack Terminology: Gray hole, wormhole, and Sybil attacks are often taken as one
- Confusion Between Trust and Anomaly: Half-hearted enlistment of trust with overlapping definition with anomaly
- Misuse Of Resilience: To refer often to redundancy or robustness without any precision
- Lack Of a Unified Taxonomy: No one classification granted right on threats, behavior, or trust levels
These ambiguities are major discussion points in tips for writing a critical literature review and demonstrate the importance of gap analysis in academic research.
Gap Insight: Another major point is the creation of formal definitions and taxonomies to enable generalization and interoperability of the models.
