The supervisory control theory of discrete event dynamic systems was proposed in the framework of automata and formal languages. The concept of decentralized supervisory control was developed for the local supervisor Si whose concurrent operation results in the closed-loop language L (Si/G) equal to that of global supervisor, L (S/G). In this letter we extend this concept by considering the problem of optinal combination of decentralized with centralized control in case pure decentralized control happens to be inadequate. We introduce the concept of locally controllable complementary tuple and present an analytical framework for nonhomogeneous decentralized supervisory control systems.
Voting is a general way of achieving mutual exclusion and synchronization in distributed systems with replicated data. In centralized voting protocols, a requesting node, which works as a central controller, exchanges messages in order to collect votes from other nodes. This paper proposes decentralized voting protocols, in which all nodes execute the same protocol and reach the same result in a decentralized and autonomous way. When a decentalized voting protocol is implemented by using one-round message exchange, it requires n(n1) messages, where n is the number of nodes. The number of messages can be reduced by using multiple-round message exchange. The paper describes the computation in each node in the form of the finite state automaton, and gives communication structures for it. It is shown that kn(n1/k1) messages are enough when messages are exchanged in k rounds.
Distributed algorithms that entail successive rounds of message exchange are called decentralized consensus protocols. Several consensus protocols use a finite projective plane as a communication structure and require 4nn messages in two rounds, where n is the number of nodes. This paper presents an efficient communication structure that uses a finite projective plane with a duality of indices. The communication structure requires 2nn messages in two rounds, and can therefore halve the number of messages. It is shown that a finite projective plane with a duality can be constructed from a difference set, and that the presented communication structure has two kinds of symmetry.
Makoto TAKANO Motoji KANBE Naoki MATSUO
This paper discusses a way of identifying the network configuration of ATM-LANs, which are composed of a number of ATM hubs. In general, a Network Management System (NMS) sets and gets the necessary data to and from the network elements. In managing an ATM-LAN, the ATM connection between the NMS and each network element, namely the ATM hub, must be established in order to get and set the necessary data. This forms a remarkable contrast with conventional LANs such as the IEEE802.3 LAN, which is a shared media network and enables broadcast communication without setting up any connection. This paper proposes a new protocol and a procedure that establishes the ATM connection between the NMS and each ATM hub, while identifying the overall network configuration. First, this paper makes clear the peculiarity of the ATM-LAN in terms of automatically identifying the network configuration. Next, the identification protocol that achieves the required properties is precisely explained. Then, the proposed identification protocol is evaluated in terms of required bandwidth and identification time.
The optimal coding strategy for signal detection in the correlated gaussian noise is established for the distributed sensors system with essentially zero transmission rate constraint. Specifically, we are able to obtain the same performance as in the situation of no restriction on rate from each sensor terminal to the fusion center. This simple result contrasts with the previous ad hoc studies containing many unnatural assumptions such as the independence of noises contaminating received signal at each sensor. For the design of optimal coder, we can use the classical Levinson-Wiggins-Robinson fast algorithm for block Toeplitz matrix to evaluate the necessary weight vector for the maximum-likelihood detection.