A SOA-Based Service Discovery Framework in Internet of Things

2011 ◽  
Vol 6 (9) ◽  
pp. 310-315 ◽  
Author(s):  
Peng Li ◽  
Junping Dong ◽  
Junhao Wen ◽  
Wei Zhou
Keyword(s):  
Internet Of Things ◽  
Service Discovery

scholarly journals Optimal Placement of Social Digital Twins in Edge IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6181
Author(s):  
Olga Chukhno ◽  
Nadezhda Chukhno ◽  
Giuseppe Araniti ◽  
Claudia Campolo ◽  
Antonio Iera ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Data Exchange ◽  
Service Discovery ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Digital Twins ◽  
The Social

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

scholarly journals A Comparative Analysis of Service Discovery Approaches for the Internet of Things

2017 ◽  
Vol 3 (1) ◽  
pp. 17 ◽  
Author(s):  
Meriem Aziez ◽  
Saber Benharzallah ◽  
Hammadi Bennoui
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Service Discovery ◽  
The Internet ◽  
Service Description ◽  
Smart Healthcare ◽  
Healthcare It ◽  
Multiple Domains ◽  
The Internet Of Things ◽  
Significant Attention

Abstract—The Internet of Things (IOT) has gained a significant attention in the last years. It covers multiple domains and applications such as smart home, smart healthcare, IT transportation...etc. the highly dynamic nature of the IOT environment brings to the service discovery new challenges and requirements. As a result, discovering the desirable services has become very challenging. In this paper, we aim to address the IoT service discovery problem and investigate the existing solutions to tackle this problem in many aspects, therefore we present a full comparative analysis of the most representative (or outstanding) service discovery approaches in the literature over four perspectives: (1) the IoT service description model, (2) the mechanism of IoT service discovery, (3) the adopted architecture and (4) the context awareness.

scholarly journals Automated Service Discovery for Social Internet-of-Things Systems

2020 ◽  
Author(s):  
Abdullah Khanfor ◽  
Hakim Ghazzai ◽  
Ye Yang ◽  
Mohammad Rafiqul Haider ◽  
Yehia Massoud
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Social Internet Of Things

scholarly journals Virtualization Based Efficient Service Matching and Discovery in Internet of Things

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1007
Author(s):  
Zulfiqar Ali Khan ◽  
Israr Ullah ◽  
Muhammad Ibrahim ◽  
Muhammad Fayaz ◽  
Ayman Aljarbouh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Processing Time ◽  
Service Selection ◽  
Geographic Area ◽  
Residual Energy ◽  
Service Matching ◽  
Domain Specific ◽  
Proposed Model ◽  
Iot Devices

Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through actuators. Dynamically discovering and integrating different data streams from different sensors is a major challenge these days. In this paper, a service matchmaking algorithm is presented for service discovery utilizing IoT devices and services in a particular geographic area. It helps us to identify services based on a variety of parameters (location, query size and processing time, etc.). Customization of service selection and discovery are also explored. The conceptual framework is provided for the proposed model along with a matchmaking algorithm based on IoT devices virtualization. The simulation results elaborate the increased complexity of processing time with respect to the increasing pool of available services. The average processing time varies as the number of conditions are multiplied. Query size and complexity increases with additional number of filters and conditions which results in the reduction of the number of matching services. Moreover, upon decreasing the radius of geographic search area, the number of candidate services decreases for service matching algorithm. This is based on the assumption that IoT devices and services are evenly distributed in a given geographic area. Similarly, the remaining energy of IoT devices is also assumed to be uniformly distributed and, therefore, if we are interested in IoT devices or services with more residual energy, then a limited number of IoT devices or services will fulfill this criterion.

scholarly journals Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2098 ◽  
Author(s):  
Guang Xing Lye ◽  
Wai Khuen Cheng ◽  
Teik Boon Tan ◽  
Chen Wei Hung ◽  
Yen-Lin Chen
Keyword(s):  
Internet Of Things ◽  
Service Discovery ◽  
Early Stage ◽  
Hybrid Approach ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Heterogeneous Objects ◽  
High Satisfaction ◽  
The Social ◽  
Smart Community

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

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