Biblio

This article models the interaction between freeway traffic dynamics and capacity-reducing incidents as a stochastic switched system, and analyzes its long-time properties. Incident events on a multi-cell freeway are modeled by a Poisson-like stochastic process. Randomness in the occurrence and clearance of incidents results in traffic dynamics that switch between a set of incident modes (discrete states). The rates of occurrence and clearance depend on the traffic densities (continuous state). The continuous state evolves in each incident mode according to mode-dependent macroscopic flow dynamics. At steady state, the system state resides in its accessible set, which supports an invariant probability measure. Behavior of the accessible set is studied in terms of inputs (on-ramp inflows) and incident parameters (incident intensity). An over-approximation of accessible set and some useful bounds on the performance metrics (throughput and travel time) are also derived using the limiting states of individual incident modes. These results provide following insights about the steady-state system behavior: (i) Expected loss of throughput increases with the incident intensity for fixed incident rate, but this loss is less sensitive to changes in the occurrence rate for fixed intensity; (ii) Operating an incident-prone freeway close to its capacity significantly increases the expected travel time; (iii) The impact of incidents reduces when certain inputs upstream of incident-prone sections are metered.

Unreliable link capacities cause a significant amount of delay in transportation networks. In this paper, we propose a novel approach to studying the traffic queues due to capacity-reducing events under a class of control policies. First, we propose a Piecewise-Deterministic Queueing (PDQ) model in which the link saturation rates switch between a finite set of values (modes) according to a Markov chain, which captures the occurrence and clearance of capacity-reducing events. Second, we derive results on the stability of PDQ networks, i.e. when the joint distribution of the link queue sizes converges to a unique invariant probability measure. On one hand, a necessary condition for stability is that the average inflow to each link is less than the link's effective capacity. On the other hand, a sufficient condition is that a set of bilinear matrix inequalities involving model parameters and the control policy has a feasible solution. Third, we provide an analytical characterization of the steady-state distribution of bimodal PDQ systems, which enables us to obtain the optimal static/mode-dependent routing policy for bimodal PDQ networks by solving a convex min-cost problem.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.