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Register a new userApproximate Aggregate Utility Maximization in Multi-Hop Wireless Networks using Distributed Greedy Scheduling
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Added by
Albert Sunny
Publication date
2011
fields
Informatics Engineering
and research's language is
English
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In this paper, we study the performance of greedy scheduling in multihop wireless networks, where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. The dual can be solved optimally, only with the knowledge of the maximal independent sets in the network. But computation of maximal independent sets is known to be NP-hard. Motivated by this, we propose a distributed greedy heuristic to address the problem of link scheduling. We evaluate the effect of the distributed greedy heuristic on aggregate utility maximization in detail, for the case of an arbitrary graph. We provide some insights into the factors affecting aggregate utility maximization in a network, by providing bounds on the same. We give simulation results for the approximate aggregate utility maximization achieved under distributed implementation of the greedy heuristic and find them close to the maximum aggregate utility obtained using optimal scheduling.
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In this paper, we propose a new Quality Link Metric (QLM), ``Inverse Expected Transmission Count (InvETX) in Optimized Link State Routing (OLSR) protocol. Then we compare performance of three existing QLMs which are based on loss probability measurements; Expected Transmission Count (ETX), Minimum Delay (MD), Minimum Loss (ML) in Static Wireless Multi-hop Networks (SWMhNs). A novel contribution of this paper is enhancement in conventional OLSR to achieve high efficiency in terms of optimized routing load and routing latency. For this purpose, first we present a mathematical framework, and then to validate this frame work, we select three performance parameters to simulate default and enhanc
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Control of multihop Wireless networks in a distributed manner while providing end-to-end delay requirements for different flows, is a challenging problem. Using the notions of Draining Time and Discrete Review from the theory of fluid limits of queues, an algorithm that meets delay requirements to various flows in a network is constructed. The algorithm involves an optimization which is implemented in a cyclic distributed manner across nodes by using the technique of iterative gradient ascent, with minimal information exchange between nodes. The algorithm uses time varying weights to give priority to flows. The performance of the algorithm is studied in a network with interference modelled by independent sets.
Unlike theoretical distributed learning (DL), DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks (e.g., using mmW interfaces). This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and then design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.
Recent advances in antenna technology have made the design of multi-beam antennas (MBA) feasible. Compared to an omni-directional or a single beam directional antenna, an MBA equipped node can achieve a throughput of up to m times, by simultaneously communicating on its m non-interfering beams. As a result, a few multi-beam directional medium access control (MAC) schemes have been proposed in the literature recently, which are implemented mostly on the in-house simulation setups in Matlab or C/C++. These implementations make many assumptions to simplify their design, without a thorough implementation of other network layers. However, the implementation of a multi-beam MAC scheme on the well-known discrete event network simulator platforms (such as the Riverbed Modeler, NS3, QualNet) is challenging as it requires extensive changes and additions to various source code modules. In fact, the network protocols in these simulator packages have been mainly designed for omni-directional communication, and very few implementations of directional MAC and other network protocols exist in the literature. This paper presents a framework to implement a multi-beam directional MAC scheme in multi-hop wireless networks, by using the Wireless Suite of Riverbed Modeler. The detailed implementation procedures are described for multi-beam antenna module, multi-beam node model, concurrent packet transmission and reception, scheduling, collision avoidance, retransmission, and local node synchronization. These MAC modules and methodology can be very helpful to the researchers and developers for implementing the single-beam as well as multi-beam directional MAC and routing protocols in Riverbed Modeler.
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