Congestion control in wireless multi-hop networks is challenging and complicated because of two reasons. First, interference is ubiquitous and causes loss in the shared medium. Second, wireless multihop networks are characterized by the use of diverse and dynamically changing routing paths. Traditional end point based congestion control protocols are ineffective in such a setting resulting in unfairness and starvation. This paper adapts the optimal theoretical work of Tassiulas and Ephremedes on cross-layer optimization of wireless networks involving congestion control, routing and scheduling, for practical solutions to congestion control in multi-hop wireless networks. This work is the first that implements in real off-shelf radios, a differential backlog based MAC scheduling and router-assisted backpressure congestion control for multi-hop wireless networks. Our adaptation, called DiffQ, is implemented between transport and IP and supports legacy TCP and UDP applications. In a network of 46 IEEE 802.11 wireless nodes, we demonstrate that DiffQ far outperforms many previously proposed "practical" solutions for congestion control.

This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(\delta ) time and message complexity where \delta is the maximum size of a two-hop neighborhood in a wireless network while message complexity remains O(\delta ), assuming that message delays can be bounded by an unknown constant. DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move, such as wireless mesh networks and wireless sensor networks. We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling. Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced. We further evaluate the effect of the time-varying nature of wireless links on the conflict-free property of DRAND-assigned time slots. This experiment is conducted on a 55-node testbed consisting of the more recent MicaZ sensor nodes.

Existing wireless MACs are known to have fairness and QoS problems1. In response, the wireless community have come up with numerous ldquopoint solutionsrdquo, with one-to-one comparison of their solution with existing MACs for the specific fairness model they target. However, with some tradeoff of flexibility and efficiency, many of these MACs can be made relatively free of a a specific fairness model. This decoupling of the MAC from the fairness model is desirable because it allows objective testing of the MAC under different fairness models and also allows independent evolution of fairness policies and research into medium access issues. However, there is no cross-model evaluation of such MACs especially in the context of real multihop wireless testbeds. In this paper we address this issue by testing three MACs - 802.11e, EY-NPMA and DWOP, (which represent different ways of enabling fairness in the MAC) across a number of fairness and QoS models - temporal/rate and proportional fairness, and EDF and static priority scheduling. In the process we also extend EY-NPMA significantly (which we call Siren) for multihop wireless environments. We perform these comparisons on a 30 node multihop wireless sensor testbed comprising of MicaZ devices equipped with the CC2420 radio.

In this demo, we showcase DiffQ - a congestion control protocol inspired by theoretical cross-layer optimization approaches. DiffQ can support congestion control for network flows that use either single-path or opportunistic multi-path routing. Our demo will focus on the performance in single-path routing environments, where contemporary end-point congestion control algorithms like TCP face severe unfairness or even starvation. This is primarily due to the interaction of such protocols with MAC layer unfairness. We demonstrate micro (5 flows) as well as macro-evaluations (60 flows) of such cases. Our demo is conducted on WiSeNet - a 70-node wireless mesh test-bed hosted in the computer science building at NCSU. Distributed over a 100,000 sq ft building, this is one of the largest test-bed installations both in terms of number of nodes and coverage area, hence an ideal testing ground for such scenarios. Experimental results like throughput, MAC-layer statistics, delay, routing path flaps and network buffer overflows are recorded and displayed in real-time and enable a bird's eye-view of the entire network status and allow us to point out various phenomenon as they happen.

Limited resources and time-varying nature of wireless ad hoc networks demand optimized use of resources across layers. Cross-layer optimization (CLO) for wireless networks, an approach that coordinates protocol behaviors at different layers with a goal to maximize a utility function, has received considerable attention lately. However, most existing work remains as theory and no practical CLO based on utility optimization exists today. The main difficulties in implementing theoretical CLO designs arise often from impractical assumptions about the characteristics of the wireless medium and also from computational and communication overhead of proposed solutions to achieve or approximate the optimality. In contrast, many existing practical approaches for CLO (not necessarily utility optimization) are rather ad hoc in nature and developed mostly based on intuitions. Thus, a clear gap between theory and practice in CLO exists. This paper addresses this dichotomy to close the gap by taking an optimal solution from utility-based CLO and applying practical approximation to enable a practical implementation in a wireless mesh network where nodes are statically positioned in an ad hoc fashion. We focus on the utility of maximizing throughput. We identify the impractical or computationally-intensive components of a theoretically-derived optimal throughput-maximizing solution and then propose, in most cases, practical approximation with O(1) complexity for MAC, scheduling, routing and congestion control. The result is a practical CLO solution that approximates the theoretically-derived optimal solution, but achieves much improved performance over existing practical CLO implementations.

This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(δ) time and message complexity where δ is the maximum size of a two-hop neighborhood in a wire-less network while message complexity remains O(δ), assuming that message delays can be bounded by an unknown constant.DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move,such as wireless mesh networks and wireless sensor networks.We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling.Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced.

This paper presents the design, implementation and performance evaluation of a hybrid MAC protocol, called Z-MAC, for wireless sensor networks that combines the strengths of TDMA and CSMA while offsetting their weaknesses. Like CSMA, Z-MAC achieves high channel utilization and low-latency under low contention and like TDMA, achieves high channel utilization under high contention and reduces collision among two-hop neighbors at a low cost. A distinctive feature of Z-MAC is that its performance is robust to synchronization errors, slot assignment failures and time-varying channel conditions; in the worst case, its performance always falls back to that of CSMA. Z-MAC is implemented in TinyOS.