Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc.) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this article, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

Numerous middleware application programming interfaces (APIs) and protocols were introduced in the literature in order to facilitate the application development of the Internet of Things (IoT). Such applications are built on reliable or even unreliable protocols that may implement different quality-of-service (QoS) delivery modes. The exploitation of these protocols, APIs and QoS modes, can satisfy QoS requirements in critical IoT applications (e.g., emergency response operations). To study QoS in IoT applications, it is essential to leverage a performance analysis methodology. Queueing-network models offer a modeling and analysis framework that can be adopted for the IoT interactions of QoS representation through either analytical or simulation models. In this paper, various types of queueing models are presented that can be used for the representation of various QoS settings of IoT interactions. In particular, we propose queueing models to represent message-drop probabilities, intermittent mobile connectivity, message availability or validity, the prioritization of important information, and the processing or transmission of messages. Our simulation models demonstrate the significant effect on delivery success rates and response times when QoS settings are varied.

This paper presents Hetero-Genius, a middleware architecture that enables construction and mediation in Internet of Things (IoT) systems. IoT systems are deployed across physical spaces such as urban parks, residential areas, and highways. The services provided by such IoT deployments are constrained to specific devices and deployment contexts. While existing interoperability solutions enable the “design time” development and deployment of IoT systems, it is often essential to dynamically compose systems that consist of other “small scale” IoT systems. To achieve this, post-deployment composition is needed, i.e., runtime composition of diverse IoT devices and capabilities. Hetero-Genius supports system and service discoverability, as well as automatic composability. We demonstrate this using a real-world Internet of Vehicles (IoV) scenario. Our experimental evaluation shows that developers can save up to 47% of their time when using Hetero-Genius, as well as improve code correctness by 55% on average.

This paper presents a model-based approach to facilitate the development of IoT applications in Transportation Systems. Existing public transportation services are provided by relying on standard data models such as GTFS. However, such models are limited in representing IoT-based infrastructures and the locations that IoT devices cover (e.g., bus seating areas). We introduce a context-aware publish/subscribe IoT platform that supports synchronous data requests, asynchronous notifications and analytics applications. Data requests are created using the system’s context, which in our case is based on a transport bus system. Both static and dynamic context properties are modeled by extending the NGSI smart data models. We then introduce a GTFS-to-NGSI mapping tool to enable the enhancement of existing GTFS-based transportation systems with IoT capabilities. We develop a prototype of our platform and we demonstrate the applicability of our approach using open data from the Roma Mobilità bus transportation system.

Public transportation is essential for sustainable and economical development of cities. Several transport organizations aim to provide service information to commuters through web and mobile apps. This information includes possible routes between two stations, estimated travel and arrival times, and real-time updates about traffic conditions. However, this information is currently not personalized according to commuter preferences. In this work, we emphasize the need for personalized transit service information to commuters and present a vision of our work in this direction. Our final goal is to develop a fully-functional personalized route recommendation system for public transit commuters. This involves identifying commuter preferences and suitable recommendation techniques, and developing a platform to communicate this information to the commuters. We identify the requirements for the development of this platform, and propose an architecture for our system. As a proof of concept, we present an Android participatory sensing application - MetroCognition, which acquires feedback on convenience experienced by commuters in public transit.