Table 10 A comparison between the articles studied on the web service in detail
From: Social Internet of Things: vision, challenges, and trends
Article | Main Topic | Strengths | Weakness | New Idea |
|---|---|---|---|---|
Chen et al. [41] | SOA-based IoT service composition | High survivability Minimize convergence time | Just consider a few attacks | Adaptive trust-based approach |
Kouicem et al. [55] | Dynamic service selection for service composition | Decreasing response time Improving the success rate | Not any exact percentage for QoS metrics | Optimization technique |
Chen et al. [56] | A social network model for service composition in IoT | Improve service management Efficient service search approach High availability | High cost | Model |
Garg et al. [57] | QoS-aware approach for semantic web-service | Increasing user satisfaction Improving response time and throughput | Not any functional properties considered | An integrated framework |
Butt et al. [58] | Adaptive and context-aware service discovery | Minimize service invocation delay High scalability Improving energy efficiency | – | Context-aware Service Discovery Protocol |
Wei and Jin [59] | A context-awareness aspect of service discovery | High adaptability to a dynamic environment Providing an efficient service discovery process | – | A context-aware service discovery architecture |
Xia et al. [60] | Efficient Social-Like Semantic-Aware Service Discovery Mechanism | High scalability Considering adaptability Improving search efficiency | Not considering security and privacy and keep it as a future research title | SLSAa The mechanism for Large-Scale IoT |
Klauck and Kirsche [61] | DNS-Based service discovery | Improving interoperability Low memory consumption | Not any large scale network considered and keep it as a future issue | Service discovery standard |
Shamszaman and Ali [62] | Object Selection | High flexibility in resource sharing | Not consider the entire system in terms of QoS | Platform |
Baker et al. [63] | Multi-cloud IoT service composition | High energy-efficient service composition Low execution time | High cost | An energy-efficiency algorithm |
Pallec et al. [64] | Physical-Interface-Based IoT Service | High availability | Low scalability Not any simulation or implementation | An IoT service composition approach |
Ma et al. [65] | QoS-aware and GP-based web service composition | Low time More efficiency | Not considering any simulation of development | GP-based greedy algorithm |
Liu et al. [100] | Service Composition recommendation | High performance High accuracy Strong adaptability | Not any exact service composition classification in the nonfunctional aspect | Model Algorithm |
Mayer and Guinard [101] | Semantic discovery service in web-enabled IoT | Semantic Discovery High flexibility | Not considering the heterogeneity of devices | DiscoWoT interface implementation |