Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in AI and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than 30 studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject.

In Smart City applications, urban mobility involves complex interactions between traffic infrastructure, diverse road users, and physical environment. This paper addresses the limitations of conventional scene modeling methods that often fail to capture the varied and volatile nature of urban road scenes, particularly in representing the dynamic situations that unfold within them. Inaccurate situation representation hinders precise depiction of scene evolution, limiting our ability to understand and respond effectively to complex urban situations. This paper addresses these challenges by presenting the novel concept of the Context-Aware Scene Graph (CSG) for representing situations used in reasoning applications for enhancing safety and efficiency in urban environments, particularly for bicycle riders. CSG integrates multi-modal data, including ontological knowledge, sensor data, and images, to provide a comprehensive representation of urban road situations, enabling informed decision-making. This paper also validates the effectiveness of the proposed approach using real- world IoT datasets and camera images, with a focus on the bicycle dooring use case. The results outperform existing scene modeling methods by accurately representing situations, including those previously overlooked. The approach also ensures consistent representation, completeness, and captures transitions between situations, including causal relations. These findings highlight our approach's effectiveness in improving road safety, efficiency, and urban life quality through enhanced scene understanding.

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.

The success of advanced Background Subtraction (BGS) algorithms for dynamic backgrounds is mostly in land scenes such as those in CDNet benchmarks; few handle underwater scenes, since existing underwater video datasets are either in low resolution or with only static backgrounds. Consequently, the lack of reliable BGS support makes supervised Moving-Objects Segmentation (MOS) algorithms much harder to adapt to unknown underwater scenes because of the diversities of the aquatic environments. For example, those trained by the latest underwater image dataset, SUIM, are ineffective in the underwater videos of our experiments.The underwater waving objects (e.g., plants) often render existing BGS algorithms inaccurate due to three types of errors: (a) incompletely identified MOs (Moving Objects), (b) missing MOs, and (c) falsely identified MOs. In this paper, we propose a novel Clustering-Based Multi-State Background Representation (CBMSBR) model to learn and represent the repeatable patterns of waving movements in k background states (i.e., color ranges) per pixel, and thus accurately subtract the background waving objects to reduce these errors. In addition, we further develop a CBMSBR+ model to remove the more challenging background objects in unusually large magnitudes of wavings. Both models come from a basic observation: the video pixels in the waving zones repeatedly switch among multiple background states; e.g., a pixel switches among water state, plant 1 state, and plant 2 state. To test our proposed models, we create experiments using three types of challenging scenarios that each often covers at least two error types, i.e., the scattered MOs scenario covering (b) and (c), the crowded MOs scenario covering (a) - (c), and the slow MOs scenario covering (a) and (c). Experiments on these scenarios demonstrate the accuracy, effectiveness, and efficiency of our models and their applications in MOS improvements.