101 research outputs found

    Simulating mobile ad hoc networks: a quantitative evaluation of common MANET simulation models

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    Because it is difficult and costly to conduct real-world mobile ad hoc network experiments, researchers commonly rely on computer simulation to evaluate their routing protocols. However, simulation is far from perfect. A growing number of studies indicate that simulated results can be dramatically affected by several sensitive simulation parameters. It is also commonly noted that most simulation models make simplifying assumptions about radio behavior. This situation casts doubt on the reliability and applicability of many ad hoc network simulation results. In this study, we begin with a large outdoor routing experiment testing the performance of four popular ad hoc algorithms (AODV, APRL, ODMRP, and STARA). We present a detailed comparative analysis of these four implementations. Then, using the outdoor results as a baseline of reality, we disprove a set of common assumptions used in simulation design, and quantify the impact of these assumptions on simulated results. We also more specifically validate a group of popular radio models with our real-world data, and explore the sensitivity of various simulation parameters in predicting accurate results. We close with a series of specific recommendations for simulation and ad hoc routing protocol designers

    The Capacity of Smartphone Peer-To-Peer Networks

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    We study three capacity problems in the mobile telephone model, a network abstraction that models the peer-to-peer communication capabilities implemented in most commodity smartphone operating systems. The capacity of a network expresses how much sustained throughput can be maintained for a set of communication demands, and is therefore a fundamental bound on the usefulness of a network. Because of this importance, wireless network capacity has been active area of research for the last two decades. The three capacity problems that we study differ in the structure of the communication demands. The first problem is pairwise capacity, where the demands are (source, destination) pairs. Pairwise capacity is one of the most classical definitions, as it was analyzed in the seminal paper of Gupta and Kumar on wireless network capacity. The second problem we study is broadcast capacity, in which a single source must deliver packets to all other nodes in the network. Finally, we turn our attention to all-to-all capacity, in which all nodes must deliver packets to all other nodes. In all three of these problems we characterize the optimal achievable throughput for any given network, and design algorithms which asymptotically match this performance. We also study these problems in networks generated randomly by a process introduced by Gupta and Kumar, and fully characterize their achievable throughput. Interestingly, the techniques that we develop for all-to-all capacity also allow us to design a one-shot gossip algorithm that runs within a polylogarithmic factor of optimal in every graph. This largely resolves an open question from previous work on the one-shot gossip problem in this model

    On Bioelectric Algorithms

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    Cellular bioelectricity describes the biological phenomenon in which cells in living tissue generate and maintain patterns of voltage gradients across their membranes induced by differing concentrations of charged ions. A growing body of research suggests that bioelectric patterns represent an ancient system that plays a key role in guiding many important developmental processes including tissue regeneration, tumor suppression, and embryogenesis. This paper applies techniques from distributed algorithm theory to help better understand how cells work together to form these patterns. To do so, we present the cellular bioelectric model (CBM), a new computational model that captures the primary capabilities and constraints of bioelectric interactions between cells and their environment. We use this model to investigate several important topics from the relevant biology research literature. We begin with symmetry breaking, analyzing a simple cell definition that when combined in single hop or multihop topologies, efficiently solves leader election and the maximal independent set problem, respectively - indicating that these classical symmetry breaking tasks are well-matched to bioelectric mechanisms. We then turn our attention to the information processing ability of bioelectric cells, exploring upper and lower bounds for approximate solutions to threshold and majority detection, and then proving that these systems are in fact Turing complete - resolving an open question about the computational power of bioelectric interactions

    Trade-offs between Selection Complexity and Performance when Searching the Plane without Communication

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    We consider the ANTS problem [Feinerman et al.] in which a group of agents collaboratively search for a target in a two-dimensional plane. Because this problem is inspired by the behavior of biological species, we argue that in addition to studying the {\em time complexity} of solutions it is also important to study the {\em selection complexity}, a measure of how likely a given algorithmic strategy is to arise in nature due to selective pressures. In more detail, we propose a new selection complexity metric χ\chi, defined for algorithm A{\cal A} such that χ(A)=b+log\chi({\cal A}) = b + \log \ell, where bb is the number of memory bits used by each agent and \ell bounds the fineness of available probabilities (agents use probabilities of at least 1/21/2^\ell). In this paper, we study the trade-off between the standard performance metric of speed-up, which measures how the expected time to find the target improves with nn, and our new selection metric. In particular, consider nn agents searching for a treasure located at (unknown) distance DD from the origin (where nn is sub-exponential in DD). For this problem, we identify loglogD\log \log D as a crucial threshold for our selection complexity metric. We first prove a new upper bound that achieves a near-optimal speed-up of (D2/n+D)2O()(D^2/n +D) \cdot 2^{O(\ell)} for χ(A)3loglogD+O(1)\chi({\cal A}) \leq 3 \log \log D + O(1). In particular, for O(1)\ell \in O(1), the speed-up is asymptotically optimal. By comparison, the existing results for this problem [Feinerman et al.] that achieve similar speed-up require χ(A)=Ω(logD)\chi({\cal A}) = \Omega(\log D). We then show that this threshold is tight by describing a lower bound showing that if χ(A)<loglogDω(1)\chi({\cal A}) < \log \log D - \omega(1), then with high probability the target is not found within D2o(1)D^{2-o(1)} moves per agent. Hence, there is a sizable gap to the straightforward Ω(D2/n+D)\Omega(D^2/n + D) lower bound in this setting.Comment: appears in PODC 201

    Bounds on Contention Management in Radio Networks

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    The local broadcast problem assumes that processes in a wireless network are provided messages, one by one, that must be delivered to their neighbors. In this paper, we prove tight bounds for this problem in two well-studied wireless network models: the classical model, in which links are reliable and collisions consistent, and the more recent dual graph model, which introduces unreliable edges. Our results prove that the Decay strategy, commonly used for local broadcast in the classical setting, is optimal. They also establish a separation between the two models, proving that the dual graph setting is strictly harder than the classical setting, with respect to this primitive

    Approximate Neighbor Counting in Radio Networks

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    For many distributed algorithms, neighborhood size is an important parameter. In radio networks, however, obtaining this information can be difficult due to ad hoc deployments and communication that occurs on a collision-prone shared channel. This paper conducts a comprehensive survey of the approximate neighbor counting problem, which requires nodes to obtain a constant factor approximation of the size of their network neighborhood. We produce new lower and upper bounds for three main variations of this problem in the radio network model: (a) the network is single-hop and every node must obtain an estimate of its neighborhood size; (b) the network is multi-hop and only a designated node must obtain an estimate of its neighborhood size; and (c) the network is multi-hop and every node must obtain an estimate of its neighborhood size. In studying these problem variations, we consider solutions with and without collision detection, and with both constant and high success probability. Some of our results are extensions of existing strategies, while others require technical innovations. We argue this collection of results provides insight into the nature of this well-motivated problem (including how it differs from related symmetry breaking tasks in radio networks), and provides a useful toolbox for algorithm designers tackling higher level problems that might benefit from neighborhood size estimates

    Fault-Tolerant Consensus with an Abstract MAC Layer

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    In this paper, we study fault-tolerant distributed consensus in wireless systems. In more detail, we produce two new randomized algorithms that solve this problem in the abstract MAC layer model, which captures the basic interface and communication guarantees provided by most wireless MAC layers. Our algorithms work for any number of failures, require no advance knowledge of the network participants or network size, and guarantee termination with high probability after a number of broadcasts that are polynomial in the network size. Our first algorithm satisfies the standard agreement property, while our second trades a faster termination guarantee in exchange for a looser agreement property in which most nodes agree on the same value. These are the first known fault-tolerant consensus algorithms for this model. In addition to our main upper bound results, we explore the gap between the abstract MAC layer and the standard asynchronous message passing model by proving fault-tolerant consensus is impossible in the latter in the absence of information regarding the network participants, even if we assume no faults, allow randomized solutions, and provide the algorithm a constant-factor approximation of the network size
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