Are you working on an edge computing, publish/subscribe, or real-time systems middleware research prototype? Maybe you're exploring robotics and looking to open up the hood on ROS2 to tinker with the underlying communications middleware? Or you're trying to measure distributed communications performance, and you dread the idea of debugging a hand-written distributed application protocol and telemetry code? Well, you might consider a data-centric approach using the open standard Data Distribution Service (DDS). And if you do, please attend this tutorial to learn how to make the most of DDS. RTI is proud to support the academic research community by holding this tutorial as well as providing free software licenses for our industry-leading DDS-based distributed connectivity framework to academic researchers: https://www.rti.com/free-trial/university-program

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.

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.

Are you working on an edge computing, publish/subscribe, or real-time systems middleware research prototype? Maybe you're exploring robotics and looking to open up the hood on ROS2 to tinker with the underlying communications middleware? Or you're trying to measure distributed communications performance, and you dread the idea of debugging a hand-written distributed application protocol and telemetry code? Well, you might consider a data-centric approach using the open standard Data Distribution Service (DDS). And if you do, please attend this tutorial to learn how to make the most of DDS. RTI is proud to support the academic research community by holding this tutorial as well as providing free software licenses for our industry-leading DDS-based distributed connectivity framework to academic researchers: https://www.rti.com/free-trial/university-program Rather than getting bogged down in network programming, cross-platform complexity, and asynchronous handler headaches, come to this tutorial and learn how to employ an open standards-based connectivity framework instead so you can focus more on the research! We'll show you how to quickly define your DDS data models, topics, application data requirements, and detailed protocol QoS configurations using an intuitive web-based interface. You will learn how to auto-generate compilable data type and example code for your platform of choice or dynamically load the XML data type into your favorite scripting language. Topics covered will range from the basics of DDS to briefly touching on advanced topics that will help you configure the middleware and its network protocol according to your unique requirements. Throughout the tutorial, we will relate the content to one of our hottest real-world customer markets - autonomous vehicles. We will also discuss why the open DDS standard is trusted by mission-critical industries such as healthcare, energy, transportation, aerospace, and defense. For those early in their research careers, we will end with a discussion about ongoing efforts at RTI Research and some open research topics relevant to real-time distributed systems e.g. 5G, SDN, software safety certification, real-time WAN technology, etc.

Real-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. 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.

This poster presents our prototype implementation of FireDeX [1], a cross-layer middleware that supports timely delivery of mission-critical messages (i.e. events) over an IoT data exchange service. Emergency scenarios may challenge/congest the network infrastructure. FireDeX addresses these situations by prioritizing event delivery and by dropping some low priority events.

Internet of Things (IoT) deployments rely on data exchange middleware to manage communications between constrained devices and cloud resources that provide analytics, data storage, and serve user applications. In this paper, we propose the Resilient IoT Data Exchange (Ride) middleware that enables resilient operation of IoT applications despite prevalent network failures and congestion. It leverages programmable Software-Defined Networking (SDN)-enabled infrastructure along with both localized edge and cloud services. The two-phase Ride middleware extends existing publish-subscribe-oriented IoT data exchanges according to application-specified resilience requirements and without IoT device client modifications. The first phase, Ride-C, improves IoT data collection by gathering network-awareness via a novel resource-aware adaptive probing mechanism and dynamically redirecting IoT data flows across multiple public and local (edge) cloud data exchange connections. The second phase, Ride-D, uses this information to disseminate time-critical alerts via an intelligent network-aware resilient multicast mechanism. Results from our prototype smart campus testbed implementation, Mininet-based emulated experiments, and larger-scale simulations show that Ride enables network awareness for greater cloud connection up-times, timely fail-over to edge services, and more resilient local alert dissemination.

Safe Community Awareness and Alerting Network (SCALE) is a community government/academic/industry partnership effort that aims to deploy, actuate and evaluate techniques to support multiple heterogeneous IoT technologies in real world communities. SCALE2, an extension of SCALE, engages a multi-tier and multi-network approach to drive data flow from IoT devices to the cloud platforms. While devices are used to gather data, most of the analytics are executed in the cloud. Managing and utilizing these multiple networks, devices and technologies is a big challenge that calls for an integrated management. In this context, we propose to leverage a related effort, MINA (Multi-network INformation Architecture), that aims at integrating operations of multi-networks IoT deployments in a hierarchical manner. This paper discusses the mapping of the SCALE2 heterogeneous platforms in the MINA environment and argues for a hierarchical approach to extending and managing community IoT multi-networks. We discuss resilience methods that can be employed at different tiers in the hierarchical architecture. We illustrate examples of how multiple applications can be supported in this heterogeneous setting; example applications include cooperative seismic event detection, mobile data collections for air quality information and assisted living for elders. Finally, we discuss novel research challenges associated with managing multi-network IoT architecture.

This paper considers the reliable delivery of sensor data from Internet of Things (IoT) devices to distributed cloud service instances in the face of localized failures for event detection, community infrastructure management, and emergency response. We develop and explore the notion of GEOgraphically Correlated Resilient Overlay Networks (GeoCRON) to capture the localized nature of community IoT deployments and their impact/vulnerability in the context of small failures and massive disruptions such as that caused by a large seismic event. As a focus use case, we study the viability and utility of GeoCRON in two different community sensor networks. The first, a large participatory sensing-style network of small inexpensive seismic sensors, reports ground motion in homes and businesses to a cloud service for detecting and characterizing earthquakes. The second, a smaller infrastructure-based network of wireless devices embedded in the water distribution network, reports water pressure changes at pipe junctions that may be indicative of leaks that require human intervention. Using realistic topologies derived from experiences with real world community scale deployments, we study the impact of geo-correlated failures on the combined network infrastructure and evaluate a range of GeoCRON heuristics to improve commnunications resilience in this context. We validate the promise of the proposed GeoCRON-based approach using extensive simulations and an initial prototype system implementation.

To investigate reported emergency incidents and provide better situational awareness during an emergency response effort in a shanty town, we envision the use of volunteers with networked sensing devices employed as Mobile Data Collectors (MDCs). These MDCs are heterogeneous depending upon the type of roads they can access. They gather information about events reported dynamically at random and relay it to central command center. We consider the problem of minimizing the Travel Time of such heterogeneous volunteer MDCs and maximizing the gathering of event data before its expiry time. We model this problem as a Dynamic Vehicle Routing Problem with Time Windows (DVRPTW), which reduces to a Combinatorial Optimization Problem and is NP-Hard to solve. In this paper, we developed two algorithms, Minimum Deviated Walk and Ortho Walk, to dynamically route or reroute the path of these MDCs to capture the data efficiently. We tested the effectiveness of these algorithms with three different classes of MDCs on simulated non- deterministic random sets of events applied to a real road map of Dharavi, a shanty town in Mumbai, India. We show that both these algorithms are capable of capturing 20% more data than a naive algorithm as well as more than 90% of the events generated within a specified time.

We propose the Safe Community Awareness and Alerting Network (SCALE), a cyber-physical system (CPS) leveraging the pervasive Internet of Things (IoT) to extend a smarter, safer home to all residents at a low incremental cost. SCALE uses novel networking technologies, commodity sensor devices, cloud services, and middleware abstractions to sense, analyze, and act on sensed events in a distributed manner. It monitors environmental factors (i.e. smoke, explosive gas) and automatically alerts residents via phone upon discovery of a possible emergency, enabling them to confirm the event and contact emergency dispatchers with minimal effort. This article describes the inception, design, development, and deployment of a prototype system to achieve these goals. We discuss lessons learned and future directions for general CPS/IoT platforms.

This dissertation explores issues of resilience in pervasive Internet of Things (IoT) deployments. It discusses facilitating the classic observe-analyze-adapt loop by addressing problems with devices, networks, and applications. We discuss our experiences with two projects: the Safe Community Awareness and Alerting Network, which leverages pervasive devices to extend a smarter safer home to residents, and the Community Seismic Network, which creates a massive network of low-cost sensors for detecting earthquakes. We propose a middleware framework for improving resilient communications between devices and services, facilitating a scalable resilient multiprotocol data exchange, and supporting more resilient distributed applications.

Information gathering during a disaster management has a crucial role in designing the disaster response mechanism. Several factors such as geography, infrastructure, and population influence the information gathering process. The task is particularly complex when disasters strike a shanty town. A shanty town terrain is characterized by its high population density, considerable level of under-development, and poor infrastructure. Mobile Data Collection agents (MDCs) can be assigned with the task of data collection in the aftermath of a disaster. In this paper, we study the difficulty of data gathering process using two movement models, Path Type Based Movement and Path Memory Based Movement in combination with data hand-off strategies No Hand-off, Superior-Only Hand-off and Superior-Peer Hand-off. We use the metrics such as percentage data collected, percentage way coverage, and the number of inter-MDC meetings for analyzing the performance of MDCs in the data gathering process. The low values of data collected and way coverage show the difficulty in obtaining disaster-data from a complex shanty town terrain.

In this paper, we consider many-to-one communication, in particular Internet-connected sensors and their relation to disaster response. We explore the application of resilient overlay networks to aid these devices, or individuals if we consider participatory sensing, in quickly and effectively routing around geographically correlated failures in the underlying network infrastructure, as would occur during a large-scale natural disaster. We develop a formal model of this system, a heuristic for choosing overlay paths without relying on any knowledge of the underlying network infrastructure, and show its merit through simulations using real Internet topologies.

The Quake-Catcher Network (QCN) uses low-cost sensors connected to volunteer computers across the world to monitor seismic events. The location and density of these sensors' placement can impact the accuracy of the event detection. Because testing different special arrangements of new sensors could disrupt the currently active project, this would best be accomplished in a simulated environment. This paper presents an accurate and efficient framework for simulating the low cost QCN sensors and identifying their most effective locations and densities. Results presented show how our simulations are reliable tools to study diverse scenarios under different geographical and infrastructural constraints.

In this paper, we propose a mechanism for coping with double-link failures in an autonomous and distributed manner. We call it Tie-set Based Fault Tolerance (TBFT) because it utilizes tie-sets, which represent a set of the edges comprising a loop within the graph that represents the network. An autonomous distributed control method based on dividing a network into a set of tie-sets, whose union covers every edge in the network, has been verified to be more effective than traditional tree-based restoration techniques in case of single link failure. The proposed method efficiently and gracefully handle double-link failures and also decrease the communication overhead incurred during network configuration. We demonstrate these results by simulating and comparing TBFT with the traditional approach of using Rapid Spanning Tree Protocol (RSTP).

In this paper, we present a novel distributed control technique to distribute renewable energy resources to consumers in a future large-scale power grid connecting real-time end-use devices to anticipate demand automatically. The proposed technique, which integrates tie-set graph theory with an intelligent agent system, effectively divides the power grid into a set of loops. Autonomous agents constantly navigate the grid to dynamically synchronize state information among tie-sets and completely automate the future power grid. The supply and load of electric power at every instant can be balanced even if the future load is uncertain and renewable generation is highly variable and unpredictable. Simulation results on a one hundred-node network demonstrate the optimal real-time distribution of renewables and thus the effectiveness of the proposed method.