On combining shortest-path and back-pressure routing over multihop wireless networks. In: INFOCOM 2009, IEEE

Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
IEEE/ACM Transactions on Networking (Impact Factor: 1.81). 07/2011; 19(3):841 - 854. DOI: 10.1109/TNET.2010.2094204
Source: DBLP


Back-pressure-type algorithms based on the algorithm by Tassiulas and Ephremides have recently received much attention for jointly routing and scheduling over multihop wireless networks. However, this approach has a significant weakness in routing because the traditional back-pressure algorithm explores and exploits all feasible paths between each source and destination. While this extensive exploration is essential in order to maintain stability when the network is heavily loaded, under light or moderate loads, packets may be sent over unnecessarily long routes, and the algorithm could be very inefficient in terms of end-to-end delay and routing convergence times. This paper proposes a new routing/scheduling back-pressure algorithm that not only guarantees network stability (throughput optimality), but also adaptively selects a set of optimal routes based on shortest-path information in order to minimize average path lengths between each source and destination pair. Our results indicate that under the traditional back-pressure algorithm, the end-to-end packet delay first decreases and then increases as a function of the network load (arrival rate). This surprising low-load behavior is explained due to the fact that the traditional back-pressure algorithm exploits all paths (including very long ones) even when the traffic load is light. On the other-hand, the proposed algorithm adaptively selects a set of routes according to the traffic load so that long paths are used only when necessary, thus resulting in much smaller end-to-end packet delays as compared to the traditional back-pressure algorithm .

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    • "The shadow queue concept in [15] reduces the queue complexity of the original backpressure framework by maintaining a counter per destination instead of a queue per flow. Authors from [16] tackle latency problems associated with the original backpressure algorithm by increasing its number of queues. Their scheme proposes per-hop queues in each node. "
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    ABSTRACT: The increase of demand for mobile data services requires a massive network densification. A cost-effective solution to this problem is to reduce cell size by deploying a low-cost all-wireless Network of Small Cells (NoS). These hyper-dense deployments create a wireless mesh backhaul among Small Cells (SCs) to transport control and data plane traffic. The semi-planned nature of SCs can often lead to dynamic wireless mesh backhaul topologies. This paper presents a self-organized backpressure routing scheme for dynamic SC deployments (BS) that combines queue backlog and geographic information to route traffic in dynamic NoS deployments. BS aims at relieving network congestion, while having a low routing stretch (i.e., the ratio of the hop count of the selected paths to that of the shortest path). Evaluation results show that, under uncongested conditions, BS shows similar performance to that of an Idealized Shortest PAth routing protocol (ISPA), while outperforming Greedy Perimeter Stateless Routing (GPSR), a state of the art geographic routing scheme. Under more severe traffic conditions, BS outperforms both GPSR and ISPA in terms of average latency by up to a 85% and 70%, respectively. We conducted ns-3 simulations in a wide range of sparse NoS deployments and workloads to support these performance claims.
    Ad Hoc Networks 02/2015; 25(A):130-140. DOI:10.1016/j.adhoc.2014.10.002 · 1.53 Impact Factor
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    • "and low end-to-end delay (e.g., [2], [26], [25], [6]). However, these QoS metrics do not fully characterize the Quality-of- Experience (QoE) of users in real-time applications in wireless networks. "
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    ABSTRACT: We consider the design of throughput-optimal scheduling policies in multihop wireless networks that also possess good mean delay performance and provide regular service for all links—critical metrics for real-time applications. To that end, we study a parametric class of maximum-weight-type scheduling policies, called Regular Service Guarantee (RSG) Algorithm, where each link weight consists of its own queue length and a counter that tracks the time since the last service, namely Time-Since-Last-Service (TSLS). The RSG Algorithm not only is throughput-optimal, but also achieves a tradeoff between the service regularity performance and the mean delay, i.e., the service regularity performance of the RSG Algorithm improves at the cost of increasing mean delay. This motivates us to investigate whether satisfactory service regularity and low mean-delay can be simultaneously achieved by the RSG Algorithm by carefully selecting its design parameter. To that end, we perform a novel Lyapunov-drift-based analysis of the steady-state behavior of the stochastic network. Our analysis reveals that the RSG Algorithm can minimize the total mean queue length to establish mean delay optimality under heavily loaded conditions as long as the design parameter weighting for the TSLS scales no faster than the order of $ {{1}over {root{5}of {epsilon }}}$, where $epsilon $ measures the closeness of the network load to the boundary of the capacity region. To the best of our knowledge, this is the first work that provides regular service to all links while also achieving heavy-traffic optimality in mean queue lengths.
    IEEE/ACM Transactions on Networking 01/2015; DOI:10.1109/TNET.2015.2432119 · 1.81 Impact Factor
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    • "In [24], Bui et al. propose a novel architecture and algorithm to improve the delay performance of the back-pressure algorithm. In [25], Ying et al. propose a hop-count based queueing structure to adaptively select a set of optimal routes based on shortest-path information. resulting in much smaller end-to-end packet delays as compared to the traditional back-pressure algorithm. "
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    ABSTRACT: The delay guarantee is a challenge in wireless sensor networks (WSNs), where energy constraints must be considered. The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner for WSNs to remain function for a potentially infinite lifetime. In this paper, we address cross-layer control to guarantee worse case delay for Heterogeneous Powered (HP) WSNs. We design a novel virtual delay queue structure, and apply the Lyapunov optimization technique to develop cross-layer control algorithm only requiring knowledge of the instantaneous system state, which provides efficient throughput-utility, and guarantees bounded worst-case delay. We analyze the performance of the proposed algorithm and verify the theoretic claims through the simulation results. Compared to the existing work, the algorithm presented in this paper achieves much higher optimal objective value with ultralow data drop due to the proposed novel virtual queue structure.
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