Effective public transit operations is one of the fundamental requirements for a modern community. Recently, a number of transit agencies have started integrating automated vehicle locators in their fleet, which provides a real-time estimate of the time of arrival. In this paper, we use the data collected over several months from one such transit system and show how this data can be potentially used to learn long term patterns of travel time. More specifically, we study the effect of weather and other factors such as traffic on the transit system delay. These models can later be used to understand the seasonal variations and to design an adaptive and transient transit schedule. Towards this goal we also propose an online architecture called DelayRadar. The novelty of DelayRadar lies in three aspects: (1) a data store that collects and integrates real-time and static data from multiple data sources, (2) a predictive statistical model that analyzes the data to make predictions on transit travel time, and (3) a decision making framework to develop an optimal transit schedule based on variable forecasts related to traffic, weather, and other impactful factors. This paper focuses on identifying the model with the best predictive accuracy to be used in DelayRadar. According to the preliminary study results, we are able to explain more than 70\% of the variance in the bus travel time and we can make future travel predictions with an out-of-sample error of 4.8 minutes with information on bus schedule, traffic, and weather.

Smart emergency response systems, smart transportation systems, smart parking spaces are some examples of multi-domain smart city systems that require large-scale, open platforms for integration and execution. These platforms illustrate high degree of heterogeneity. In this paper, we focus on software heterogeneity arising from different types of applications. The source of variability among applications stems from (a) timing requirements, (b) rate and volume of data they interact with, and (c) behavior depending on whether they are stateful or stateless. These variations result in applications with different computation models. However, a smart city system can comprise multi-domain applications with different types and therefore computation models. As such, a key challenge that arises is that of integration; we require some mechanism to facilitate integration and interaction between applications that use different computation models. In this paper, we first identify computation models based on different application types. Second, we present a generic computation model and explain how it can map to previously identified computation models. Finally, we briefly describe how the generic computation model fits in our overall smart city platform architecture.

The emerging trends of volatile distributed energy resources and micro-grids are putting pressure on electrical power system infrastructure. This pressure is motivating the integration of digital technology and advanced power-industry practices to improve the management of distributed electricity generation, transmission, and distribution, thereby creating a web of systems. Unlike legacy power system infrastructure, however, this emerging next-generation smart grid should be context-aware and adaptive to enable the creation of applications needed to enhance grid robustness and efficiency. This paper describes key factors that are driving the architecture of smart grids and describes orchestration middleware needed to make the infrastructure resilient. We use an example of adaptive protection logic in smart grid substations as a use case to motivate the need for context-awareness and adaptivity.

Public bus transit plays an important role in city transportation infrastructure. However, public bus transit is often difficult to use because of lack of real-time information about bus locations and delay time, which in the presence of operational delays and service alerts makes it difficult for riders to predict when buses will arrive and plan trips. Precisely tracking vehicle and informing riders of estimated times of arrival is challenging due to a number of factors, such as traffic congestion, operational delays, varying times taken to load passengers at each stop. In this paper, we introduce a public transportation decision support system for both short-term as well as long-term prediction of arrival bus times. The system uses streaming real-time bus position data, which is updated once every minute, and historical arrival and departure data - available for select stops to predict bus arrival times. Our approach combines clustering analysis and Kalman filters with a shared route segment model in order to produce more accurate arrival time predictions. Experiments show that compared to the basic arrival time prediction model that is currently being used by the city, our system reduces arrival time prediction errors by 25% on average when predicting the arrival delay an hour ahead and 47% when predicting within a 15 minute future time window.

Improvements in mobile networking combined with the ubiquitous availability and adoption of low-cost development boards have enabled the vision of mobile platforms of Cyber-Physical Systems (CPS), such as fractionated spacecraft and UAV swarms. Computation and communication resources, sensors, and actuators that are shared among different applications characterize these systems. The cyber-physical nature of these systems means that physical environments can affect both the resource availability and software applications that depend on resource availability. While many application development and management challenges associated with such systems have been described in existing literature, resilient operation and execution have received less attention. This paper describes our work on improving runtime support for resilience in mobile CPS, with a special focus on our runtime infrastructure that provides autonomous resilience via self-reconfiguration. We also describe the interplay between this runtime infrastructure and our design-time tools, as the later is used to statically determine the resilience properties of the former. Finally, we present a use case study to demonstrate and evaluate our design-time resilience analysis and runtime self-reconfiguration infrastructure.

Wireless Mesh Networks (WMNs) serve as a key enabling technology for various smart initiatives, such as Smart Power Grids, by virtue of providing a self-organized wireless communication superhighway that is capable of monitoring the health and performance of system assets as well as enabling efficient trouble shooting notifications. Despite this promise, the current routing protocols in WMNs are fairly limited, particularly in the context of smart initiatives. Additionally, managing and upgrading these protocols is a difficult and error-prone task since the configuration must be enforced individually at each router. Software-Defined Networking (SDN) shows promise in this regard since it enables creating a customizable and programmable network data plane. However, SDN research to date has focused predominantly on wired networks, e.g., in cloud computing, but seldom on wireless communications and specifically WMNs. This paper addresses the limitations in SDN for WMNs by allowing the refactoring of the wireless protocol stack so as to provide modular and flexible routing decisions as well as fine-grained flow control. To that end, we describe an intelligent network architecture comprising a three-stage routing approach suitable for WMNs in uses cases, such as Smart Grids, that provides an efficient and affordable coverage as well as scalable high bandwidth capacity. Experimental results evaluating our approach for various QoS metrics like latency and bandwidth utilization show that our solution is suitable for the requirements of mission-critical WMNs.

Multi-module Cyber-Physical Systems (CPS), such as satellite clusters, swarms of Unmanned Aerial Vehicles (UAV), and fleets of Unmanned Underwater Vehicles (UUV) provide a CPS cluster-as-a-service for CPS applications. The distributed and remote nature of these systems often necessitates the use of Deployment and Configuration (D&C) services to manage the lifecycle of these applications. Fluctuating resources, volatile cluster membership and changing environmental conditions necessitate resilience. Thus, the D&C infrastructure does not only have to undertake basic management actions, such as activation of new applications and deactivation of existing applications, but also has to autonomously reconfigure existing applications to mitigate failures including D&C infrastructure failures. This paper describes the design and architectural considerations to realize such a D&C infrastructure for component-based distributed systems. Experimental results demonstrating the autonomous resilience capabilities are presented.

In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Triggered (TT) architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC) to TT networked control systems. The contributions of the paper include (1) the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2) an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems.

Improvements in mobile networking combined with the ubiquitous availability and adoption of low-cost development boards have enabled the vision of mobile platforms of Cyber-Physical Systems (CPS), such as fractionated spacecraft and UAV swarms. Computation and communication resources, sensors, and actuators that are shared among different applications characterize these systems. The cyber-physical nature of these systems means that physical environments can affect both the resource availability and software applications that depend on resource availability. While many application development and management challenges associated with such systems have been described in existing literature, resilient operation and execution have received less attention. This paper describes our work on improving runtime support for resilience in mobile CPS, with a special focus on our runtime infrastructure that provides autonomous resilience via self-reconfiguration. We also describe the interplay between this runtime infrastructure and our design-time tools, as the later is used to statically determine the resilience properties of the former. Finally, we present a use case study to demonstrate and evaluate our design-time resilience analysis and runtime self-reconfiguration infrastructure.

The multicore revolution is having limited impact in safety-critical application domains. A key reason is the "one-out-of-m" problem: when validating real-time constraints on an m-core platform, excessive analysis pessimism can effectively negate the processing capacity of the additional m-1 cores so that only "one core s worth" of capacity is available. Two approaches have been investigated previously to address this problem: mixed-criticality allocation techniques, which provision less-critical software components less pessimistically, and hardware-management techniques, which make the underlying platform itself more predictable. A better way forward may be to combine both approaches, but to show this, fundamentally new criticality-cognizant hardware-management tradeoffs must be explored. Such tradeoffs are investigated herein in the context of a large-scale, overhead-aware schedulability study. This study was guided by extensive trace data obtained by executing benchmark tasks on a new variant of the MC^2 framework that supports configurable criticality-based hardware management. This study shows that the two approaches mentioned above can be much more effective when applied together instead of alone.

Recent work involving a mixed-criticality framework called MC2 has shown that, by combining hardware-management techniques and criticality-aware task provisioning, capacity loss can be significantly reduced when supporting real-time workloads on multicore platforms. However, as in most other prior research on multicore hardware management, tasks were assumed in that work to not share data. Data sharing is problematic in the context of hardware management because it can violate the isolation properties hardware-management techniques seek to ensure. Clearly, for research on such techniques to have any practical impact, data sharing must be permitted. Towards this goal, this paper presents a new version of MC2 that permits tasks to share data within and across criticality levels through shared memory. Several techniques are presented for mitigating capacity loss due to data sharing. The effectiveness of these techniques is demonstrated by means of a large-scale, overhead-aware schedulability study driven by micro-benchmark data.

The ever increasing popularity of model-based system- and software engineering has resulted in more and more systems---and more and more complex systems---being modeled. Hence, the problem of managing the complexity of the models themselves has gained importance. This paper introduces three abstractions that are specifically targeted at improving the scalability of the modeling process and the system models themselves.

In-depth consideration and evaluation of security and resilience is necessary for developing the scientific foundations and technology of Cyber-Physical Systems (CPS). In this demonstration, we present SURE [1], a CPS experimentation and evaluation testbed for security and resilience focusing on transportation networks. The testbed includes (1) a heterogeneous modeling and simulation integration platform, (2) a Web-based tool for modeling CPS in adversarial environments, and (3) a framework for evaluating resilience using attacker-defender games. Users such as CPS designers and operators can interact with the testbed to evaluate monitoring and control schemes that include sensor placement and traffic signal configuration.

Evaluation of the smart grid in the presence of dynamic market-based pricing and complex networks of small and large producers, consumers, and distributers is a very difficult task. It not only involves multiple, interacting, heterogeneous cyber-physical domains, but also requires tight integration of power markets, dynamic pricing and transactions, and price-sensitive consumer behavior, where consumers can also be producers of power. These dynamics introduce a huge challenge of maintaining stability of the power grid. Moreover, evolving business models and regulatory environment, including human factors need to be a key part of the evaluation as they directly affect the demand-response in the grid. Furthermore, as sensors and computations are becoming more distributed on edge devices and as they often employ normal communication channels, cyber security of the critical power grid infrastructure has become ever more important to prevent cyber intrusions and attacks. Current research has largely focused on one or a few of these challenges and the simulation tools developed cater to these individual tasks, such as network simulators, power distribution simulators, or human organization and policy simulators. However, to attain a deeper understanding of the transactive grid behavior, analysis of grid stability, and to optimize resources both from consumers’ and generators’ perspective, a comprehensive platform is needed to facilitate end-to-end evaluation of all of these aspects. This paper describes an open platform that provides a coherent framework for integrated transactive energy simulations of smart grids and is readily customizable and extensible for various simulation tools.

Complex systems, such as modern advanced driver assistance systems (ADAS), consist of many interacting components. The number of options promises considerable flexibility for configuring systems with many cost-performance-value tradeoffs; however the potential unique configurations are exponentially many prohibiting a build-test-fix approach. Instead, engineering analysis tools for rapid design-space navigation and analysis can be applied to find feasible options and evaluate their potential for correct system behavior and performance subject to functional requirements. The OpenMETA toolchain is a component-based, design space creation and analysis tool for rapidly defining and analyzing systems with large variability and cross-domain requirements. The tool supports the creation of compositional, multi-domain components, based on a user-defined ontology, which captures the behavior and structure of components and the allowable interfaces. Design spaces in OpenMETA allow product families to be defined in a single model, with component/subsystem alternatives and parametric variation. Using this system design space, OpenMETA then enables analysis of the system, via composition of the system and environment/scenario models into engineering tools, and executing simulations to compute metrics. System models can be created and executed in many abstractions based on the required accuracy, phenomena, and execution speed. This paper explores use cases from simulations with high fidelity components, to a gamified environment using Unity with a simple model of vehicle physics. This allows for user-in-the-loop analysis of controllers and components. This approach benefits ADAS by allowing for rapid prototyping across an array of candidate designs while evaluating the requirements of the vehicle at the appropriate fidelity level.

Cyber-physical systems (CPS) are systems with a tight integration between the computational (also referred to as software or cyber) and physical (hardware) components. While the reliability evaluation of physical systems is well-understood and well-studied, reliability evaluation of CPS is difficult because software systems do not degrade and follow a well-defined failure model like physical systems. In this paper, we propose a framework for formulating the CPS reliability evaluation as a dependence problem derived from the software component dependences, functional requirements and physical system dependences. We also consider sensor failures, and propose a method for estimating software failures in terms of associated hardware and software inputs. This framework is codified in a domain-specific modeling language, where every system-level function is mapped to a set of required components using functional decomposition and function-component association; this provides details about operational constraints and dependences. We also illustrate how the encoded information can be used to make reconfiguration decisions at runtime. The proposed methodology is demonstrated using a smart parking system, which provides localization and guidance for parking within indoor environments.

Rapid evolution of energy generation technology and increased used of distributed energy resources (DER) is continually pushing utilities to adapt and evolve business models to align with these changes. Today, more consumers are also producing energy using green generation technologies and energy pricing is becoming rather competitive and transactional, needing utilities to increase flexibility of grid operations and incorporate transactive energy systems (TES). However, a huge bottleneck is to ensure stable grid operations while gaining efficiency. A comprehensive platform is therefore needed for grid-scale multi-aspects integrated evaluations. For instance, cyber-attacks in a road traffic controller’s communication network can subtly divert electric vehicles in a particular area, causing surge in the grid loads due to increased EV charging and people activity, which can potentially disrupt, an otherwise robust, grid. To evaluate such a scenario, multiple special-purpose simulators (e.g., SUMO, OMNeT++, GridlabD, etc.) must be run in an integrated manner. To support this, we are developing a cloud-deployed web- and model-based simulation integration platform that enables integrated evaluations of transactive energy systems and is highly extensible and customizable for utility-specific custom simulation tools.

Abstract—As distributed systems become more complex, understanding the underlying algorithms that make these systems work becomes even harder. Traditional learning modalities based on didactic teaching and theoretical proofs alone are no longer sufficient for a holistic understanding of these algorithms. Instead, an environment that promotes an immersive, hands-on learning of distributed system algorithms is needed to complement existing teaching modalities. Such an environment must be flexible to support learning of a variety of algorithms. Moreover, since many of these algorithms share several common traits with each other while differing only in some aspects, the environment should support extensibility and reuse. Finally, it must also allow students to experiment with large-scale deployments in a variety of operating environments. To address these concerns, we use the principles of software product lines (SPLs) and model-driven engineering and adopt the cloud platform to design an immersive learning environment called the Playground of Algorithms for Distributed Systems (PADS). The research contributions in PADS include the underlying feature model, the design of a domainspecific modeling language that supports the feature model, and the generative capabilities that maximally automate the synthesis of experiments on cloud platforms. A prototype implementation of PADS is described to showcase a distributed systems algorithm illustrating a peer to peer file transfer algorithm based on BitTorrent, which shows the benefits of rapid deployment of the distributed systems algorithm.