Enterprise AI Analysis
A comprehensive survey on securing the social internet of things: protocols, threat mitigation, technological integrations, tools, and performance metrics
The integration of social networking concepts with the Internet of Things (IoT) has led to the Social Internet of Things (SIoT)—a paradigm enabling autonomous, context-aware interactions among devices based on social relationships. While this connectivity improves interoperability, it also raises critical challenges in trust management, secure communication, and data protection. This survey reviews 225 papers published between 2014 and 18 September 2025, analyzing advancements in SIoT security. Sources include IEEE Xplore, ACM Digital Library, Springer, ScienceDirect (Elsevier), MDPI, Wiley, Taylor & Francis, and Google Scholar. Blockchain and AI/ML approaches feature prominently, with blockchain referenced in more than 50 papers, AI/ML in over 80, and many adopting both in combination. The literature is examined across architectural foundations, security requirements, and layered defenses, with evaluation most often based on latency, accuracy, scalability, and false-positive rate. The review further highlights existing security and communication protocols, attack mitigation strategies, and the adoption of blockchain, cloud, and edge computing for scalable and decentralized processing. The survey traces the evolution of SIoT research, identifies future directions to strengthen security and transparency, and serves as a reference for researchers and practitioners designing secure and decentralized SIoT environments.
Key Insights & Executive Impact
Our analysis synthesizes the critical advancements and challenges in SIoT security, providing a snapshot of the current research landscape.
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Papers Reviewed
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Time Window
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Blockchain Papers
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AI/ML Papers
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Survey Methodology
Threats & Defenses
Technology Integration
SIoT Applications
Tree-based Taxonomy of Surveyed Research
The survey follows a structured taxonomy to cover the evolution, security, technologies, applications, and future directions of SIoT.
I. Introduction
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II. Survey Methodology
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III. From WSN to SIoT: Evolution, Architecture, and Key Concepts
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IV. Security in SIoT
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V. Emerging trends and applications of SIoT
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VI. Technology
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VII. Security Techniques
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VIII. Tools and Evaluation Metrics in IoT/SIoT Environments
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IX. Conclusion and Discussion
| Relationship type | Semantic definition | Example | Security implications (attack surface & trust signals) |
|---|---|---|---|
| Ownership Object Relationship (OOR) | Objects continue to interact despite ownership changes | Smart car retaining traffic data after resale | Data leakage risk across owners; requires secure data wiping, provenance, and access control |
| Social Object Relationship (SOR) | Objects interact via owners' social connections | Friends' fitness wearables syncing stats | Vulnerable to impersonation or Sybil attacks; trust inferred from social graph strength |
| Sibling Object Relationship (SIBOR) | Objects owned by the same user communicate frequently | Smart home devices (thermostat, lights, sensors) | Lateral compromise risk; trust derives from shared owner identity and credentials |
| Parental Object Relationship (POR) | Sibling devices connected via a parent entity | Fleet of connected vehicles | Centralized control introduces single point of failure; parent trust determines child reliability |
| Co-location Object Relationship (CLOR) | Objects interact due to spatial proximity | Factory robots working together | Susceptible to spoofed/relay attacks; trust based on verified physical presence |
| Co-Work Object Relationship (CWOR) | Objects collaborate to complete a task | Robotic arms and conveyors in packaging | Attack surface in coordination sabotage/DoS; trust validated through task success consistency |
| Guest Object Relationship (GOR) | External objects interact with restricted access | BYOD devices in enterprise | Higher risk of rogue devices; needs strict authentication, sandboxing, and policy enforcement |
| Stranger Object Relationship (STGOR) | Limited interactions with unknown devices | Unknown IoT object in range | High uncertainty and unpredictability; requires anomaly detection and adaptive trust mechanisms |
| Service-Oriented Object Relationship (SVOR) | Objects interact with external service providers | Smart meters subscribed to weather/utility services | Exposure to API/service misuse; trust depends on authentication, SLA compliance, blockchain logging |
| Protocol layer | Common attacks in SIoT environments | Example reference |
|---|---|---|
| Physical layer | Jamming, Eavesdropping, Radio interference, Signal manipulation | 57 |
| Data link/MAC layer | Replay attacks, Collision attacks, Identity spoofing at MAC, Selective forwarding | 58 |
| Network layer | Sybil attack, Sinkhole attack, Wormhole, Blackhole, Routing table poisoning, Selective packet dropping | 59 |
| Transport layer | Flooding (SYN/UDP), Session hijacking, DoS/DDoS, TCP reset attacks | 60 |
| Application layer | False data injection, Malicious code injection, Privacy leakage, Malware/botnet attacks, Unauthorized service access | 6 |
| Work | Integration type | Purpose | Strengths | Limitations |
|---|---|---|---|---|
| 140 | Blockchain + AI/ML + Cloud or Edge/Fog | Enhance trust and decision-making through verifiable data and intelligent analytics | Ensures immutable trust history + intelligent behavior learning | High complexity and model drift; blockchain latency; requires frequent model updates |
| 146 | Cloud + Edge/Fog + AI/ML | Enable real-time anomaly detection with local edge processing (on Raspberry Pi) and cloud-based visualization | Real-time ML on Raspberry Pi- Non-wearable passive sensing, Privacy-aware design with dashboard | Used simulated sensor data (not live), Caregiver mobile app not yet developed |
| 146 | Digital Twin + Edge/Fog/Cloud + Blockchain | Enable cyber-physical mirroring, predictive analytics, and secure control | Real-time digital mirroring, decentralized analytics and traceability | High synchronization overhead; model mismatch; digital twin setup cost |
| 142 | Blockchain + Federated Learning + Edge-Fog-Cloud Computing | Privacy-preserving ECG anomaly detection and real-time decision making | Low latency, enhanced privacy, decentralized model training, tamper-proof storage via smart contracts | Increased cost, execution time, energy use due to blockchain overhead; partial energy modeling only |
| 148 | AI/ML + IoT | Improve waste classification accuracy using deep learning and optimized ensemble learning in IoT-enabled environments | Low-complexity model with 85% accuracy; CSO optimization improves performance; outperforms traditional models (SVM, XGBoost) | No explicit use of edge/cloud; real-time deployment and scalability not discussed; limited to image-only input |
Smart Healthcare: ML-RASPF Framework
The ML-RASPF framework provides a machine learning-based, rate-adaptive approach for dynamic resource allocation in smart healthcare IoT environments. It integrates a mist-edge-cloud architecture with LSTM for traffic prediction, GBDT for delay estimation, and Deep Q-Network (DQN) for adaptive control.
Domain: Smart Healthcare IoT (IoMT)
Ref: 120
Technique: LSTM (Traffic), GBDT (Delay), DQN (Rate), ML-RASPF
Outcome/Benefit: Up to 20% lower latency, 18% higher throughput, and 19% reduced energy consumption, making it suitable for dynamic, QoS-critical healthcare applications.
Limitations: Simulation-only; no adversarial evaluation.
Smart Cities: Urban Water Pressure Anomaly Prediction
An IoT-enabled LSTM-based model optimizes urban water supply systems by predicting pressure anomalies in real-time, leveraging sensor data and seasonal features.
Domain: Smart City
Ref: 115
Technique: IoT, LSTM, MQTT
Outcome/Benefit: Achieved a MAPE of 4.79%, enabling early accident detection and faster emergency response.
Limitations: Limited dataset; no external simulation tools.
Smart Agriculture: AI-Driven Agricultural Intelligence Model
This model combines IoT sensors, cloud analytics, edge computing, and blockchain to enable precision farming, pest control, and supply chain transparency.
Domain: Smart Agriculture
Ref: 129
Technique: AI, IoT, Edge/Cloud, Blockchain, Drones
Outcome/Benefit: Improved 30% water savings and 20% increase in crop quality.
Limitations: Connectivity issues; high adoption cost; limited expertise.
Calculate Your Potential ROI with Secure SIoT
Estimate the potential savings and efficiency gains for your enterprise by implementing secure Social Internet of Things solutions.
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Your Secure SIoT Implementation Roadmap
A phased approach to integrating advanced security, trust, and intelligence into your SIoT environment, ensuring a smooth and successful deployment.
Phase 1: Strategic Assessment & Planning
Identify key SIoT use cases, assess current security posture, define trust models, and outline privacy requirements. Develop a phased implementation plan with clear KPIs and resource allocation. This involves a detailed audit of existing IoT infrastructure and social interaction points.
Phase 2: Architecture Design & Protocol Selection
Design a resilient SIoT architecture incorporating decentralized identity, trust management (e.g., blockchain), and intelligent decision-making (AI/ML). Select appropriate communication protocols (e.g., CoAP/DTLS, MQTT/TLS) and layered security mechanisms tailored for resource-constrained devices.
Phase 3: Pilot Deployment & Validation
Implement a pilot SIoT system in a controlled environment, focusing on a critical use case (e.g., smart home security, asset tracking). Validate the chosen security protocols, trust mechanisms, and AI/ML models using simulation tools (e.g., NS-3, iFogSim) and real-world testbeds. Collect performance metrics like latency, accuracy, and false-positive rates.
Phase 4: Scalable Integration & Optimization
Expand the SIoT deployment to a larger scale, integrating with existing enterprise systems and cloud/edge infrastructure. Continuously monitor performance, security events, and trust dynamics. Implement adaptive trust updates, anomaly detection, and decentralized policy enforcement. Optimize resource utilization and ensure interoperability across diverse device types.
Phase 5: Continuous Monitoring & Ethical Governance
Establish ongoing monitoring and auditing processes to ensure data integrity, privacy, and accountability. Regularly update AI/ML models, smart contracts, and security policies. Address ethical and regulatory concerns, ensuring explainability and user consent. Prepare for future evolutions in SIoT technology and attack vectors.
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