Multiple Message Broadcasting Research Articles

Correction to: Dynamic Multiple-Message Broadcast: Bounding Throughput in the Affectance Model

Correction to: Dynamic Multiple-Message Broadcast: Bounding Throughput in the Affectance Model

Dynamic Multiple-Message Broadcast: Bounding Throughput in the Affectance Model

Dynamic Multiple-Message Broadcast: Bounding Throughput in the Affectance Model

Implementing The Abstract MAC Layer in Dynamic Networks

Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhn <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> <xref ref-type="bibr" rid="ref24" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[24]</xref> . Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

Distributed multiple-message broadcast in wireless ad hoc networks under the SINR model

In a multiple-message broadcast, an arbitrary number of messages originate at arbitrary nodes in the network at arbitrary times. The problem is to disseminate all these messages to the whole network. This paper gives the first randomized distributed multiple-message broadcast algorithm with worst-case performance guarantee in wireless ad hoc networks employing the SINR interference model which takes interferences from all the nodes in the network into account. The network model used in this paper also considers the harsh characteristics of wireless ad hoc networks: there is no prior structure, and nodes cannot perform collision detection and have little knowledge of the network topology. Under all these restrictions, our proposed randomized distributed multiple-message broadcast protocol can deliver any message m to all nodes in the network in O(D+k+log2⁡n) timeslots with high probability, where D is the network diameter, k is the number of messages whose broadcasts overlap with m, and n is the number of nodes in the network. We also study the lower bound for randomized distributed multiple-message broadcast protocols. In particular, we prove that any uniform randomized algorithm needs Ω(D+k+log2⁡nlog⁡log⁡log⁡n) timeslots to disseminate k messages initially stored at k nodes.

Process cooperation in multiple message broadcast

We present an optimal algorithm for broadcasting m messages from one process to n - 1 other processes in a one-port fully connected communication model, where m ⩾ 1 , n > 1 . In this algorithm, the processes are organized into 2 ⌊ log n ⌋ cooperation units, each consisting of one or two processes. Messages are broadcast among the units following a basic schedule. Processes in each two-process unit cooperate to carry out the basic schedule. At any communication round, either process has at most one message that the other has not received. This algorithm completes the broadcast operation in m + ⌈ log n ⌉ - 1 communication rounds, which is theoretically optimal. We consider practical issues for efficient implementation of the algorithm and develop a schedule construction that has both time and space complexity of O ( log n ) . Empirical study shows that this algorithm outperforms other widely used algorithms significantly when the data to broadcast is large.

Optimal multiple message broadcasting in telephone-like communication systems

We consider the problem of broadcasting multiple messages from one processor to many processors in telephone-like communication systems. In such systems, processors communicate in rounds, where in every round, each processor can communicate with exactly one other processor by exchanging messages with it. Finding an optimal solution for this problem was open for over a decade. In this paper, we present an optimal algorithm for this problem when the number of processors is even. For an odd number of processors, we provide an algorithm which is within an additive term of 3 of the optimum. A by-product of our solution is an optimal algorithm for the problem of broadcasting multiple messages for any number of processors in the simultaneous send/receive model. In this latter model, in every round, each processor can send a message to one processor and receive a message from another processor.

An Optimal Algorithm for Broadcasting Multiple Messages in Trees

We consider multiple message broadcasting in tree networks. The source (considered as the root of the tree) has k messages which have to be broadcast to all nodes of the tree. In every time unit each node can send one of its already obtained messages to one of its children. A k-message broadcasting scheme prescribes in which time unit a given node should send a message to which child. It is k-optimal if it achieves the smallest possible time for broadcasting k messages from the source to all nodes. We give an algorithm to construct a k-optimal broadcasting scheme for an arbitrary n-node tree. The time complexity of our algorithm is O(nk), i.e., the best possible.

Multiple message broadcasting in the postal model

Broadcasting is an important operation in many message-passing systems that has been widely investigated. Most existing broadcasting algorithms, however, do not address several emerging trends in distributed-memory parallel computers and high-speed communication networks. These trends include (i) treating the system as being fully connected with all processors equally distant, (ii) packetizing large amounts of data into sequences of messages, and (iii) tolerating communication latencies. In this paper, we explore the broadcasting problem in the postal model that addresses these issues. We provide two efficient algorithms for broadcasting m messages in a fully connected message-passing system with n processors and communication latency λ. A lower bound on the time required for this problem is (m - 1) + fλ(n), where fλ(n) is the optimal time for broadcasting one message. We present two algorithms: The first is Algorithm PARTITION, the running time of which is at most m + n + 2λ when m ≥ n and 3m + fλ(n) + 2λ when m ≤ n. The second is Algorithm D-D-TREES, the running time of which is at most m + 2fλ(n) + O(λ) for any value of m. © 1997 John Wiley & Sons, Inc.

Multiple message broadcasting in the postal model

Multiple message broadcasting in the postal model

Multiple message broadcasting in communication networks

AbstractBroadcasting refers to the process of dissemination of a set of messages originating from one node to all other nodes in a communication network. We assume that, at any given time, a node can transmit a message along at most one incident link and simultaneously receive a message along at most one incident link. We first present an algorithm for determining the amount of time needed to broadcast k messages in an arbitrary tree. Second, we show that, for every n, There exists a graph with n nodes whose k‐message broadcast time matches the trivial lower bound ⌈ log n⌉ + k − 1 by designing a broadcast scheme for complete graphs. We call those graphs minimal broadcast graphs. Finally, we construct an n node minimal broadcast graph with fewer than (⌈log n⌉ + 1)2⌈ log n⌉ −1 edges.