3-D-SIS A 3-D-Social Identifier Structure for Collaborative Edge Computing Based Social IoT
ABSTARCT :
The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem called social and spatial physical trade-off. This problem is also referred to as the mismatch problem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure (3-D-SIS) model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the (3-D-SIS) model, we use the real traces of social networks (INFOCOM06). The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.
EXISTING SYSTEM :
? The smart parking can be enabled via existing surveillance cameras and image detection systems.
? The passenger in a moving car might lose its connectivity with the existing RSU and moved to a new RSU range during the content downloading phase.
? Therefore, to commercially deploy the edge computing infrastructure, the existing service providers will have to adopt new pricing models to generate more revenue while maintaining a better QoS.
? However, resource management of resources between coexisting slices is challenging, thus requiring novel resource management schemes.
DISADVANTAGE :
? Despite the efforts done to make users understand the importance of reducing energy consumption in public spaces, work environments, or at home, this problem continues and it is necessary to find a solution to it.
? One of the main problems to be solved is the optimization of a distributed model that encourages as many users as possible to participate in Demand Response programs.
? In their proposal, they address the problems associated with managing a large number of connected devices and the massive volume of data generated by them, as well as the problems related to the transmission, processing and storage of data.
? They solve this problem with a smart gateway between the edge and the cloud that manages the demand side dynamically.
PROPOSED SYSTEM :
• The authors proposed evaluation criteria, such as heterogeneity management, scalability, mobility, federation, and interoperability.
• A comprehensive survey proposed in discussed fog computing architecture, applications, key technologies, and research challenges.
• In addition, the proposed architecture provides the benefits of efficient data sharing, ubiquitous mobile interaction, and reliable resource cooperation.
• To enable resource intensive and latency sensitive web-based AR applications, web-based AR assisted by edge computing is proposed by the authors.
• We derived that novel and lightweight security mechanisms must be proposed for AR-based industrial applications.
ADVANTAGE :
? Edge Computing allows for improving the performance of computer systems by lowering latency, reducing the cost of resources and increasing responsiveness, scalability, reliability, security or privacy.
? To overcome those challenges, they propose to process data at the edge, improving performance and reducing the volume of transmitted data.
? More specifically, this case study examines how the performance of the CAFCLA framework improves through the addition of an Edge Layer, following the Global Edge Computing Architecture.
? In this new work, the independent performance of the CAFCLA framework is assessed.
? Then, the performance of CAFCLA is measured when it operates under the design rules and features of the Global Edge Computing Architecture.
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