Large computer networks are an essential part of modern technology, and quite often information needs to be broadcast to all the computers in the network. If all computers work perfectly all the time, this is simple. Suppose, however, that some of the computers fail occasionally. What is the fastest way to ensure that with high probability all working computers get the information? In this paper, we analyze three algorithms to do so. All algorithms terminate in logarithmic time, assuming computers fail with probability @math independently of each other. We prove that the third algorithm, which runs in time @math , is asymptotically optimal.
The topic of broadcasting information to all nodes of a network has been extensively studied for many different models. For a survey of the different models, see Pelc @cite_4 . Gasieniec and Pelc @cite_2 gave an algorithm working in @math (under slightly different assumptions then ours), Diks and Pelc @cite_6 improved this to @math (see also @cite_15 ). Results for some variation on the model can be found in @cite_8 @cite_9 @cite_1 @cite_12 @cite_16 . In all of these the analysis is up to a constant, whereas we determine the optimal running time up to @math . For examples of real world systems using such broadcasting algorithms, see @cite_7 @cite_11 . Frieze and Grimmett @cite_3 studied the running time of the naive algorithm when there are no faults ( @math ). Their result was further refined by Pittel @cite_5 . Our Theorem generalizes these results to all @math . Doerr, Huber and Levavi @cite_13 analyzed the naive algorithm in the case of faulty links, instead of nodes, getting the same running time as in our Thoerem .
@cite_4 @cite_7 @cite_8 @cite_9 @cite_1 @cite_6 @cite_3 @cite_2 @cite_5 @cite_15 @cite_16 @cite_13 @cite_12 @cite_11
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We consider broadcasting in radio networks, modeled as undirected graphs, whose nodes know only their own label and labels of their neighbors. In every step every node acts either as a transmitter or as a receiver. A node acting as a transmitter sends a message which can potentially reach all of its neighbors. A node acting as a receiver in a given step gets a message if and only if exactly one of its neighbors transmits in this step. Bar-Yehuda, Goldreich, and Itai [J. Comput. System Sci., 45 (1992), pp. 104--126] considered broadcasting in this model. They claimed a linear lower bound on the time of deterministic broadcasting in such radio networks of diameter 3. This claim turns out to be incorrect in this model (although it is valid in a more pessimistic model [R. Bar-Yehuda, O. Goldreich, and A. Itai, Errata Regarding "On the time complexity of broadcast in radio networks: An exponential gap between determinism and randomization," http: www.wisdom.weizmann.ac.il mathusers oded p .html, 2002]). We construct an algorithm that broadcasts in logarithmic time on all graphs from the Bar-Yehuda, Goldreich, and Itai paper (BGI). Moreover, we show how to broadcast in sublinear time on all n-node graphs of diameter @math . On the other hand, we construct a class of graphs of diameter 4, such that every broadcasting algorithm requires time @math on these graphs. In view of the randomized algorithm from BGI, running in expected time @math on all @math -node graphs of diameter D (cf. also a recent @math -time algorithm from [D. Kowalski and A. Pelc, Proceedings of the 22nd Annual ACM Symposium on Principles of Distributed Computing, Boston, 2003, pp. 73--82; A. Czumaj and W. Rytter, Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science, Cambridge, MA, 2003, pp. 492--501]), our lower bound gives the first correct proof of an exponential gap between determinism and randomization in the time of radio broadcasting, under the considered model of radio communication. This paper concerns the communication primitives of broadcasting (one-to-all communication) and gossiping (all-to-all communication) in radio networks with known topology, i.e., where for each primitive the schedule of transmissions is precomputed based on full knowledge about the size and the topology of the network.The first part of the paper examines the two communication primitives in general graphs. In particular, it proposes a new (efficiently computable) deterministic schedule that uses O(D+Δ log n) time units to complete the gossiping task in any radio network with size n, diameter D and max-degree Δ. Our new schedule improves and simplifies the currently best known gossiping schedule, requiring time O(D+√[i+2]DΔ logi+1 n), for any network with the diameter D=Ω(logi+4n), where i is an arbitrary integer constant i ≥ 0, see [17]. For the broadcast task we deliver two new results: a deterministic efficient algorithm for computing a radio schedule of length D+O(log3 n), and a randomized algorithm for computing a radio schedule of length D+O(log2 n). These results improve on the best currently known D+O(log4 n) time schedule due to Elkin and Kortsarz [12].The second part of the paper focuses on radio communication in planar graphs, devising a new broadcasting schedule using fewer than 3D time slots. This result improves, for small values of D, on currently best known D+O(log3n) time schedule proposed by Elkin and Kortsarz in [12]. Our new algorithm should be also seen as the separation result between the planar and the general graphs with a small diameter due to the polylogarithmic inapproximability result in general graphs due to Elkin and Kortsarz, see [11]. In a seminal paper, Bar- (1992) considered broadcasting in radio networks whose nodes know only their own label and labels of their neighbors. They claimed a linear lower bound on the time of deterministic broadcasting in such radio networks, by constructing a class of graphs of diameter 3, with the property that every broadcasting algorithm requires linear time on one of these graphs. Due to a subtle error in the argument, this result is incorrect. We construct an algorithm that broadcasts in logarithmic time on all graphs from the work of Bar- Moreover, we show how to broadcast in sublinear time on all n-node graphs of diameter o(log log n). On the other hand, we construct a class of graphs of diameter 4, such that every broadcasting algorithm requires time spl Omega (4 spl radic n) on one of these graphs. In view of the randomized algorithm, running in expected time O(D log n + log sup 2 n) on all n-node graphs of diameter D, our lower bound gives the first correct proof of an exponential gap between determinism and randomization in the time of radio broadcasting. The paper studies broadcasting in radio networks whose stations are represented by points in the Euclidean plane. In any given time step, a station can either receive or transmit. A message transmitted from station (v) is delivered to every station (u) at distance at most (1) from (v), but (u) successfully hears the message if and only if (v) is the only station at distance at most (1) from (u) that transmitted in this time step. A designated source station has a message that should be disseminated throughout the network. All stations other than the source are initially idle and wake up upon the first time they hear the source message. It is shown in [11] that the time complexity of broadcasting depends on two parameters of the network, namely, its diameter (in hops) (D) and a lower bound (d) on the Euclidean distance between any two stations. The inverse of (d) is called the granularity of the network, denoted by (g). Specifically, the authors of [11] present a broadcasting algorithm that works in time (O (D g)) and prove that every broadcasting algorithm requires Ω (D √g) time. In this paper, we distinguish between the arbitrary deployment setting, originally studied in [11], in which stations can be placed everywhere in the plane, and the new grid deployment setting, in which stations are only allowed to be placed on a (d)-spaced grid. Does the latter (more restricted) setting provides any speedup in broadcasting time complexity? Although the (O (D g)) broadcasting algorithm of [11] works under the (original) arbitrary deployment setting, it turns out that the Ω (D √g) lower bound remains valid under the grid deployment setting. Still, the above question is left unanswered. The current paper answers this question affirmatively by presenting a provable separation between the two deployment settings. We establish a tight lower bound on the time complexity of broadcasting under the arbitrary deployment setting proving that broadcasting cannot be completed in less than Ω ( D √g) time. For the grid deployment setting, we develop a broadcasting algorithm that runs in time O ( D g5 6 log g), thus breaking the linear dependency on (g).