Despite having billions of data sensing devices that virtually acquire data about anything, anywhere, not all smart applications are intelligent. Context-awareness being at the core of intelligence, it is a contradiction founded on the impotence of these applications to effectively leverage this plethora of big data and infer context in real-time. Hence, in this paper, we propose a conceptual architecture and instantiate a working prototype of the Distributed Contextual Intelligence System (DCIS) - a ubiquitous, mobile edge-based context management mechanism. Unlike any traditional Context Management System, DCIS is an ultra lightweight, event-driven, dynamically scaling agent-based system that overcomes the barriers to ubiquitous contextfacilitating context anywhere, anytime, and everywhere so that any application can be intelligent. Our prototype - Zoolocity, is based on edge devices that execute light-weight context inference on data that it can acquire and dynamically collaborate with nearby edges in real-time. Thus, unlike any previous works that do not provide any evidence or motivation towards distributed context management, we demonstrate how applications can use DCIS agents to become fully context-aware about any scene it is involved in, unlocking any intelligent feature. This is enabled by two key novel contributions: a method to spontaneously share interoperable context and infer geospatially sparse complex activities in near real-time using generalised entity interaction modelling.

Predicting situations in real-time applications is non-trivial. Fusing and incorporating the plethora of heterogeneous context information from many sources in the ecosystem that a user resides in to derive their situation is an expensive and time-consuming process. Yet context is useful only when a user can effectively make use of it in time and reliably. In this paper, using a proactive cyclist hazard alerting scenario, we propose a mechanism to proactively cache context information, so that cyclists are alerted of impending hazards before they might even occur. Our novel approach, which is capable of caching reliable predictive context information has significantly reduced the time to deliver context by 91% and the cost by 80%. We ensure the reliability of predictive cached context using a cross-verification routine that the false-positive rate tends to zero. The context cache is structured hierarchically such that our novel proactive context caching mechanism is capable of caching all low-level to high-level pieces of context, unlike any previous approaches.

Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.

Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while maintaining adequate Quality of Context and delivering in time has been non-trivial for state-of-the-art centralized Context Management Systems. Further, handling complex situations and entity relations based on local awareness are areas that still need investigation. In this paper, we propose a novel edge-computing based architecture for distributed contextual intelligence systems that could address these research problems. First, we critically evaluate the current state-of-the-art in context-awareness and establish the necessity of a distributed architecture. Then, our proposed architecture is introduced along with the protocols and algorithms accompanied by real-world examples. The paper also highlights the future direction for research work in the area.