Cyclists are frequently exposed to collision risks at traffic intersections. Internet of Things (IoT) offers promising cost-effective solutions to enhance cyclist safety. This paper presents Intersection Safety Alert System (ISAS), an IoT-driven system designed to detect and alert cyclists of potential hazards at intersections. ISAS uses data from IoT edge devices, including image-vision cameras monitoring intersections and the smartphones carried by cyclists, to assess vehicles as potential hazard sources and generate real-time (online) hazard alerts. It is also envisioned to incorporate historical crash data and traffic density information to generate offline alerts, providing cautions about high-risk intersections. Alerts are delivered to cyclists as audio and visual messages through a smartphone application, enabling them to take preventative measures as they approach intersections. The paper also discusses algorithms used in ISAS for generating the online and offline alerts. ISAS is evaluated through real-world trials over two days with 38 participants. The results demonstrate the system’s ability to generate accurate and timely alerts and its scalability to accommodate varying user volumes.

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.

The Internet of Things (IoT) is growing at a rapid pace. Applications using IoT technologies have transformed many activities to be digitized enabling more productivity, economy, and quality of work. Context Management Platforms (CMPs) that unify heterogeneous streams of big IoT data and derive insights of a sensed environment (called context) using inferencing, massively enhance the smartness of IoT-based applications. Handling this massive scale of data and processing in an IoT-ecosystem for context-aware applications are both time and resource consuming, especially consid-ering the bottlenecks in network and processing resources. While traditional data caching is a time-proven technique to enable low-latency delivery of data items for a superior perceived user experience, work in context caching is extremely limited. Context information is different from typically discussed forms of data in many ways. Context-aware traditional data caching techniques have limited applicability to caching context information due to many unique challenges. These challenges can be categorized by features of context, context quality demands, techniques for caching con-text information, and context cache memory technologies. We categorically discuss each challenge in this article supported by real-world scenarios and experimental results. We contend that context caching is distinct from traditional data caching techniques and highlight the importance of con-text caching for time-critical, adaptive, context-aware applications. This article aims to demystify the unique research opportunities and challenges when developing context caching techniques for scale and efficiency objectives of CMPs and provide directions for future work.

Performance metrics-driven context caching has a profound impact on throughput and response time in distributed context management systems for real-time context queries. This paper proposes a reinforcement learning based approach to adaptively cache context with the objective of minimizing the cost incurred by context management systems in responding to context queries. Our novel algorithms enable context queries and sub-queries to reuse and repurpose cached context in an efficient manner. This approach is distinctive to traditional data caching approaches by three main features. First, we make selective context cache admissions using no prior knowledge of the context, or the context query load. Secondly, we develop and incorporate innovative heuristic models to calculate expected performance of caching an item when making the decisions. Thirdly, our strategy defines a time-aware continuous cache action space. We present two reinforcement learning agents, a value function estimating actor-critic agent and a policy search agent using deep deterministic policy gradient method. The paper also proposes adaptive policies such as eviction and cache memory scaling to complement our objective. Our method is evaluated using a synthetically generated load of context sub-queries and a synthetic data set inspired from real world data and query samples. We further investigate optimal adaptive caching configurations under different settings. This paper presents, compares, and discusses our findings that the proposed selective caching methods reach short- and long-term cost- and performance-efficiency. The paper demonstrates that the proposed methods outperform other modes of context management such as redirector mode, and database mode, and cache all policy by up to 60

IoT system interoperability, data fusion, data discovery and access control for providing Context-as-a-Service as well as tools for building context-aware smart city applications are all significant research challenges for IoT-enabled smart cities. These middleware platforms have to cope with potentially big data generated from millions of devices in large cities. The amount of context, metadata, annotations in IoT ecosystems equals and may even exceed the amount of raw data. This paper discusses the challenges of context storage, retrieval and indexing for smart city applications. We analyse, compare and categorise existing approaches, tools and technologies relevant to the identified challenges. The paper proposes a conceptual architecture of a hybrid context storage and indexing mechanism that enables and supports the Context Spaces theory based representation of context for large-scale smart city applications. We illustrate the proposed approach using solid waste management system with adaptive on-demand garbage collection from IoT-enabled garbage bins.