Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically towards the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance

CapEst: A Measurement-based Approach to Estimating Link Capacity in Wireless Networks

Estimating link capacity in a wireless network is a complex task because the available capacity at a link is a function of not only the current arrival rate at that link, but also of the arrival rate at links which interfere with that link as well as of the nature of interference between these links. Models which accurately characterize this dependence are either too computationally complex to be useful or lack accuracy. Further, they have a high implementation overhead and make restrictive assumptions, which makes them inapplicable to real networks. In this paper, we propose CapEst, a general, simple yet accurate, measurement-based approach to estimating link capacity in a wireless network. To be computationally light, CapEst allows inaccuracy in estimation; however, using measurements, it can correct this inaccuracy in an iterative fashion and converge to the correct estimate. Our evaluation shows that CapEst always converged to within 5% of the correct value in less than 18 iterations. CapEst is model-independent, hence, is applicable to any MAC/PHY layer and works with auto-rate adaptation. Moreover, it has a low implementation overhead, can be used with any application which requires an estimate of residual capacity on a wireless link and can be implemented completely at the network layer without any support from the underlying chipset

CA-AQM: Channel-Aware Active Queue Management for Wireless Networks

In a wireless network, data transmission suffers from varied signal strengths and channel bit error rates. To ensure successful packet reception under different channel conditions, automatic bit rate control schemes are implemented to adjust the transmission bit rates based on the perceived channel conditions. This leads to a wireless network with diverse bit rates. On the other hand, TCP is unaware of such {\em rate diversity} when it performs flow rate control in wireless networks. Experiments show that the throughput of flows in a wireless network are driven by the one with the lowest bit rate, (i.e., the one with the worst channel condition). This does not only lead to low channel utilization, but also fluctuated performance for all flows independent of their individual channel conditions. To address this problem, we conduct an optimization-based analytical study of such behavior of TCP. Based on this optimization framework, we present a joint flow control and active queue management solution. The presented channel-aware active queue management (CA-AQM) provides congestion signals for flow control not only based on the queue length but also the channel condition and the transmission bit rate. Theoretical analysis shows that our solution isolates the performance of individual flows with diverse bit rates. Further, it stabilizes the queue lengths and provides a time-fair channel allocation. Test-bed experiments validate our theoretical claims over a multi-rate wireless network testbed

Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks

This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multi-rate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with time-varying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dual-based algorithm remains stable and optimal when the constraint set is modulated by an irreducible finite-state Markov chain. This paper thus presents a step toward a systematic way to carry out cross-layer design in the framework of “layering as optimization decomposition” for time-varying channel models

Symbiot: Congestion-driven Multi-resource Fairness for Multi-User Sensor Networks

© 2015 IEEE.In this paper, we study the problem of multi-resource fairness in multi-user sensor networks with heterogeneous and time-varying resources. Particularly we focus on data gathering applications run on Wireless Sensor Networks (WSNs) or Internet of Things (IoT) in which users require to run a serious of sensing operations with various resource requirements. We consider both the resource demands of sensing tasks, and data forwarding tasks needed to establish multi-hop relay communications. By exploiting graph theory, queueing theory and the notion of dominant resource shares, we develop Symbiot, a light-weight, distributed algorithm that ensures multi-resource fairness between these users. With Symbiot, nodes can independently schedule its resources while maintaining network-level resource fairness through observing traffic congestion levels. Large-scale simulations based Contiki OS and Cooja network emulator show the effectiveness of Symbiot in adaptively utilizing available resources and reducing average completion times