Title: DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms Show Chinese title

Title: DCMF：一种动态上下文监控和缓存框架用于上下文管理平台

Abstract: The rise of context-aware IoT applications has increased the demand for timely and accurate context information. Context is derived by aggregating and inferring from dynamic IoT data, making it highly volatile and posing challenges in maintaining freshness and real-time accessibility. Caching is a potential solution, but traditional policies struggle with the transient nature of context in IoT (e.g., ensuring real-time access for frequent queries or handling fast-changing data). To address this, we propose the Dynamic Context Monitoring Framework (DCMF) to enhance context caching in Context Management Platforms (CMPs) by dynamically evaluating and managing context. DCMF comprises two core components: the Context Evaluation Engine (CEE) and the Context Management Module (CMM). The CEE calculates the Probability of Access (PoA) using parameters such as Quality of Service (QoS), Quality of Context (QoC), Cost of Context (CoC), timeliness, and Service Level Agreements (SLAs), assigning weights to assess access likelihood. Based on this, the CMM applies a hybrid Dempster-Shafer approach to manage Context Freshness (CF), updating belief levels and confidence scores to determine whether to cache, evict, or refresh context items. We implemented DCMF in a Context-as-a-Service (CoaaS) platform and evaluated it using real-world smart city data, particularly traffic and roadwork scenarios. Results show DCMF achieves a 12.5% higher cache hit rate and reduces cache expiry by up to 60% compared to the m-CAC technique, ensuring timely delivery of relevant context and reduced latency. These results demonstrate DCMF's scalability and suitability for dynamic context-aware IoT environments. Show Chinese abstract

Abstract: 随着上下文感知物联网应用的兴起，对及时且准确的上下文信息的需求增加。 上下文是通过聚合和推断动态物联网数据得出的，使其高度易变，并在保持新鲜度和实时可访问性方面带来挑战。 缓存是一种潜在的解决方案，但传统的策略难以应对物联网中上下文的短暂性（例如，确保频繁查询的实时访问或处理快速变化的数据）。 为了解决这个问题，我们提出了动态上下文监控框架（DCMF），通过动态评估和管理上下文来增强上下文管理平台（CMPs）中的上下文缓存。 DCMF包含两个核心组件：上下文评估引擎（CEE）和上下文管理模块（CMM）。 CEE使用服务质量（QoS）、上下文质量（QoC）、上下文成本（CoC）、及时性和服务等级协议（SLAs）等参数计算访问概率（PoA），分配权重以评估访问可能性。 基于此，CMM采用混合Dempster-Shafer方法来管理上下文新鲜度（CF），更新置信度水平和信心分数，以确定是否缓存、驱逐或刷新上下文项。 我们在一个上下文即服务（CoaaS）平台上实现了DCMF，并使用真实世界的智慧城市数据进行评估，特别是交通和道路施工场景。 结果表明，与m-CAC技术相比，DCMF的缓存命中率提高了12.5%，缓存过期减少了高达60%，确保了相关上下文的及时交付和降低延迟。 这些结果展示了DCMF的可扩展性和在动态上下文感知物联网环境中的适用性。