Vincenzo Liberatore

Networked control system co-simulation for co-design

We provide a general framework for networked control systems (NCSs) and review previous theoretical results for NCS co-design. We present experimental studies of control and feedback scheduling of NCSs, consisting of dynamic system simulations for the control agents and environment and packet-level network simulations for the communications. To this end, we have extended the ns-2 release in order to simulate the transmissions of plants and controllers that are modeled by ODEs (solved via a linked package). Our results show the overall control and network performances achieved while modeling the individual control and network components. Major co-design issues, such as scalability and network heterogeneity, are explored.

A co-simulation platform for actuator networks

Actuator networks will enable an unprecedented degree of distributed control of physical environments, and further progress will critically depend on the availability of a simulation platform that can capture both the physical and the communication dynamics.

Co-simulation Tools for Networked Control Systems

In this paper, we argue that simulation of Networked Control Systems (NCSs) needs to be carried out through co-simulation, which requires the joint and simultaneous simulation of both physical and communication networks dynamics. Co-simulation enables construction of synthetic large-scale networks and workloads, replay of collected traces, and obtaining a complete snapshot of both the network behavior and the physical systems states. Therefore, co-simulation provides in-depth understanding of the interaction between communication networks and physical systems dynamics. In this paper, we overview three co-simulation tools that we have developed for NCS co-simulation. The first two tools are extensions to ns-2 called Agent/Plant and NSCSPlant ; the third tool integrates Modelica and ns-2 . For each tool, we present demonstrative case studies that highlight its capabilities.

Decentralized and dynamic bandwidth allocation in networked control systems

In this paper, we propose a bandwidth allocation scheme for networked control systems that have their control loops closed over a geographically distributed network. We first formulate the bandwidth allocation as a convex optimization problem. We then present an allocation scheme that solves this optimization problem in a fully distributed manner. In addition to being fully distributed, the proposed scheme is asynchronous, scalable, dynamic and flexible. We further discuss mechanisms to enhance the performance of the allocation scheme. We present analytical and simulation results

Multicast scheduling for list requests

Advances in wireless and optical communication, as well as in Internet multicast protocols, make broadcast and multicast methods an effective solution to disseminate data. In particular, repetitive server-initiated broadcast is an effective technique in wireless systems and is a scalable solution to relieve Internet hot spots. A critical issue for the performance of multicast data dissemination is the multicast schedule. Previous work focused on a model where each data item is requested by clients with a certain probability that is independent of past accesses. We consider the more complex scenario where a client accesses pages in blocks (e.g., a HTML file and all its embedded images), thereby introducing dependencies in the pattern of accesses to data. We present a sequence of heuristics that exploit page access dependencies. We measured the resulting client-perceived delay on multiple Web server traces, and observed an average speed-up over previous methods ranging from 8% to 91%. We conclude that scheduling for multi-item requests is a critical factor for the performance of repetitive broadcast.

An experiment in Internet-based, human-assisted robotics

Describes an experimental exploration in Internet-based control of robots. The motivation of this work is that Internet communications can be exploited to achieve greater productivity from machines with local intelligence. Local intelligent systems contact human experts to solicit advice when a problem facing the machine is beyond its cognitive capabilities. This topic is explored in the context of a robot performing a sample domestic task (sorting laundry). An experimental system was constructed that has limited autonomous competence, but which proved to be significantly more productive through the use of occasional Internet-based human supervision.

On Broadcast Disk Paging

Broadcast disks are an emerging paradigm for massive data dissemination. In a broadcast disk, data is divided into n equal-sized pages, and pages are broadcast in a round-robin fashion by a server. Broadcast disks are effective because many clients can simultaneously retrieve any transmitted data. Paging is used by the clients to improve performance, much as in virtual memory systems. However, paging on broadcast disks differs from virtual memory paging in at least two fundamental aspects: A page fault in the broadcast disk model has a variable cost that depends on the requested page as well as the current state of the broadcast. Prefetching is both natural and a provably essential mechanism for achieving significantly better competitive ratios in broadcast disk paging. In this paper, we design a deterministic algorithm that uses prefetching to achieve an O(n log k) competitive ratio for the broadcast disk paging problem, where k denotes the size of the clients cache. We also show a matching lower bound of

Integrated Play-Back, Sensing, and Networked Control

\Omega(n\log k)

Evaluation of Algorithms for Local Register Allocation

that applies even when the adversary is not allowed to use prefetching. In contrast, we show that when prefetching is not allowed, no deterministic online algorithm can achieve a competitive ratio better than

Middleware support for multicast-based data dissemination: a working reality

\Omega(nk)