This work introduces a novel route reservation architecture to manage road traffic within an urban area. The developed routing architecture decomposes the road infrastructure into slots in the spatial and temporal domains and for every vehicle, it makes the appropriate route reservations to avoid traffic congestion while minimizing the traveling time. Under this architecture, any road segment is admissible to be traversed only during time-slots when the accumulated reservations do not exceed its critical density. A roadside unit keeps track of all reservations which are subsequently used to solve the routing problem for each vehicle. Through this routing mechanism, vehicles can either be delayed at their origin or are routed through longer but non-congested routes such that their traveling time is minimized. In this work, the proposed architecture is presented and the resulting route reservation problem is mathematically formulated. Through a complexity analysis of the routing problem, it is shown that for certain cases, the problem reduces to an NP-complete problem. A heuristic solution to the problem is also proposed and is used to conduct realistic simulations across a particular region of the San Francisco area, demonstrating the promising gains of the proposed solution to alleviate traffic congestion.

Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned event-triggering architecture for the IoT domain. It then classifies and reviews the various different event-triggering approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

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Drone technology that has the potential to disrupt and augment our quality of life is swiftly evolving. Drones are rapidly growing in popularity and are used in various applications such as agriculture, emergency response, border control, asset inspection, intelligent transportation, and many more. This is primarily to the rapid advances in mobile embedded computing that allow for various sensors and controllers to be integrated into drone platforms enabling them to sense and understand both their internal state and the external environment. We showcase relevant drone technologies, explore research opportunities, and demonstrate through three use-cases how research can drive forward these disruptive systems within our social, economic and scientific activities.

Dedicated to the memory of Elias Kyriakides.

Critical infrastructure systems (CISs) are large scale and complex systems, across which many interdependencies exist. As a result, several modeling and simulation approaches are being employed to study the concurrent operation of multiple CISs and their interdependencies. Complementary to existing literature, this work develops and implements a modeling and simulation framework based on open hybrid automata to analyze CISs interdependencies. With the proposed approach, it is possible to develop accurate models of infrastructure components, and interlink them together based on their dependencies; in effect creating larger and more complex models that incorporate interdependencies. By implementing specific setups using varying operating conditions, one can study the cascading effects of interdependencies, perform a detailed vulnerability assessment and conduct an extensive planning exercise. To demonstrate the applicability of the proposed framework, a setup with three different types of CISs (i.e., power, telecom and water) components is investigated. Extensive simulation results are used to provide insights on the cascading effects, vulnerabilities and maintenance planning strategies.

In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed. Specifically, we study the problem of cooperative searching and tracking of multiple moving targets by a group of autonomous mobile agents with limited sensing capabilities. We assume that the actual number of targets present is not known a priori and that target births/deaths can occur anywhere inside the surveillance region thus efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we recursively compute and propagate in time the searchingand-tracking (SAT) density. Using the SAT-density, we then develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.

This document is the author's final accepted version of the journal article. There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it. Abstract-In this paper store-carry and forward (SCF) decision policies for relaying within the cell are developed. The key motivation of SCF relaying stems from the fact that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. More specifically, we show how the actual mobility of relay nodes can be incorporated as an additional resource in the system to achieve savings in the required communication energy levels. To this end, we provide a mathematical programming formulation on the aforementioned problem and find optimal routing and scheduling policies to achieve maximum energy savings. By investigating structural properties of the proposed mathematical program we show that optimal solutions can be computed efficiently in time. The trade-offs between energy and delay in the system are meticulously studied and Pareto efficient curves are derived. Numerical investigations show that the achievable energy gains by judiciously storing and carrying information from mobile relays can grow well above 70% for the macrocell scenario when compared to a baseline multihop wireless relaying scheme that uses shortest path routes to the base station.