Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services
We propose a data-driven and context-aware approach to bootstrap trustworthiness of homogeneous Internet of Things (IoT) services in Mobile Edge Computing (MEC) based industrial IoT (IIoT) systems. The proposed approach addresses key limitations in adapting existing trust bootstrapping approaches into MEC-based IIoT systems. These key limitations include, the lack of opportunity for a service consumer to interact with a lesser-known service over a prolonged period of time to get a robust measure of its trustworthiness, inability of service consumers to consistently interact with their peers to receive reliable recommendations of the trustworthiness of a lesser-known service as well as the impact of uneven context parameters in different MEC environments causing uneven trust environments for trust evaluation. In addition, the proposed approach also tackles the problem of data sparsity via enabling knowledge sharing among different MEC environments within a given MEC topology. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on two real-world datasets suitably adjusted to exhibit the context-dependent trust information accumulated in MEC environments within a given MEC topology. The experimental results affirmed the effectiveness of our approach and its suitability to bootstrap trustworthiness of services in MEC-based IIoT systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping
Decentralized federated learning (DFL) facilitates collaborative model training across multiple connected clients without a central coordination server thereby avoiding the single point of failure in traditional cent…
ClusteringFederated LearningTrust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
Reinforcement learning (RL) has become indispensable for pushing Vision-Language-Action Models (VLAs) beyond static imitation learning. However, existing RL methods typically require external environmental feedback, rely…
Reinforcement LearningBootstrapping NLP tools across low-resourced African languages: an overview and prospects
Computing and Internet access are substantially growing markets in Southern Africa, which brings with it increasing demands for local content and tools in indigenous African languages. Since most of those languages are l…
DiversityText GenerationEntropy-Aware Task Offloading in Mobile Edge Computing
Mobile Edge Computing (MEC) technology has been introduced to enable could computing at the edge of the network in order to help resource limited mobile devices with time sensitive data processing tasks. In this paradigm…
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
Program-guided reasoning has shown promise in complex claim fact-checking by decomposing claims into function calls and executing reasoning programs. However, prior work primarily relies on few-shot in-context learning (…
Claim VerificationDiversityFact CheckingIn-Context Learning