We consider the problem of solving large-scale instances of the Max-Cut semidefinite program (SDP), i.e., optimizing a linear function over $n\times n$ positive semidefinite (PSD) matrices with unit diagonal. When the cost matrix is PSD, we show how to exactly reformulate the problem as maximizing a smooth concave function over PSD matrices with unit trace. By applying the Frank-Wolfe method, we obtain a simple algorithm that is compatible with recent sampling-based techniques to solve SDPs using low memory. We demonstrate the practical performance of our method on $10^6\times 10^6$ instances of the max-cut SDP with costs having up to $5 \times 10^6$ non-zero entries. Theoretically, we show that our method solves problems with diagonally dominant costs to relative error $\epsilon$ in $O(n\epsilon^{-1})$ calls to a randomized approximate largest eigenvalue subroutine, each of which succeeds with high probability after $O(\log(n)\epsilon^{-1/2})$ matrix-vector multiplications with the cost matrix.

We consider the design of distributed algorithms that govern the manner in which agents contribute to a social sensing platform. Specifically, we are interested in situations, where fairness among the agents contributing to the platform is needed. A notable example is the platforms operated by public bodies, where fairness is a legal requirement. The design of such distributed systems is challenging due to the fact that we wish to simultaneously realize an efficient social sensing platform but also deliver a predefined quality of service to the agents (for example, a fair opportunity to contribute to the platform). In this article, we introduce iterated function systems (IFSs) as a tool for the design and analysis of systems of this kind. We show how the IFS framework can be used to realize systems that deliver a predictable quality of service to agents, can be used to underpin contracts governing the interaction of agents with the social sensing platform, and which are efficient. To illustrate our design via a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative center, this network proceeds to search for moving missing entities of interest using RFID-based techniques. We regulate which vehicles are actively searching for the moving entity of interest at any point in time. In doing so, we seek to equalize vehicular energy consumption across the network. This is illustrated through simulations of a search for a missing Alzheimer’s patient in Melbourne, Australia. The experimental results are presented to illustrate the efficacy of our system and the predictability of access of agents to the platform independent of initial conditions.

In this paper, we demonstrate a system for locating missing entities using radio-frequency identification (RFID)based techniques. A key feature of our system is that we utilize the large, high-density networks of parked vehicles incident to urban areas for the detection and reporting process. RFID readers and antennas are placed within the vehicles, while RFID passive tags are carried on the entity of interest via some means, e.g., a wrist band. If an entity is reported as missing, then the application on board each of the parked vehicles is awoken by an administrative center. The technology on board the vehicles enables those participating in the service to attempt to locate the missing entity, sending useful information back to the administration center, which could be tied to an organization like the police. We demonstrate our system via a use case of a missing Alzheimer's patient in inner-city Dublin, Ireland. One of the key challenges in validating our system is being able to replicate a large-scale, realworld setting. Our technique for obtaining an early evaluation of our system thus employs the use of the microscopic traffic simulation package Simulation of Urban MObility (SUMO). SUMO permits multiple emulations of hundreds or thousands of parked vehicles participating in the service to be carried out, while simulated pedestrians walk random routes. Our results show that a simulated wandering person in need can be detected within a 30-min time frame, in the heart of Dublin city center, during a typical weekday, up to approximately 98% of the time, depending on how various parameters of the system are set.

Two new distributed speed advisory systems (SASs) are introduced in this paper. The systems implement consensus algorithms that guide a set of vehicles toward a common driving speed. A major innovation is that consensus is achieved over a multi-layer network, in which parallel network topologies of connected vehicles are superimposed. The reason for the use of these parallel networks is that, in this way, the state obfuscation is possible, with the benefit that common driving speed is attained with no vehicle knowing the exact state of other vehicles. Convergence of the SASs is formally proved and two new results for the consensus of multi-layer networks modeled via stochastic differential equations are introduced. The SASs are also validated via simulation and via a hardware-in-the-loop setup, in which a real vehicle interacts with simulated entities.

Parking spaces are resources that can be pooled together and shared, particularly when there exist complementary daytime and nighttime users. We provide solutions to two design questions. First, given a quality of service requirement, how many spaces should be set aside as contingency during the day for nighttime users? Next, how can we replace the first-come-first-served access method by one that aims for optimal efficiency while keeping user preferences private?

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale connected vehicle scenario. Various applications of the platform are presented.

Ever increasing expectations regarding the penetration level of electric vehicles (EVs) are driving several areas of research related to EV charging. One topic of interest treats EVs not only as controllable loads but also as storage systems, which can be used to mitigate the load on the grid during peak times by offering power. This is known as vehicle to grid (V2G). Since returning energy to the grid affects mobility patterns, V2G has an associated environmental cost. In this paper, to investigate this issue, we formulate the problem of returning electrical load to the grid as an optimization whose goal is to return the desired energy in a fashion that minimizes the cost on the environment. We show that this optimization is highly complex, and in some circumstances, the cost of V2G can be prohibitive.