Interactive applications like web browsing are sensitive to latency. Unfortunately, TCP consumes significant time in its start-up phase and loss recovery. Existing sender-side optimizations use more aggressive start-up strategies to reduce latency, but at the same time they harm safety in the sense that they can damage co-existing flows' performance and potentially the network's overall ability to deliver data. In this paper, we experimentally compare existing solutions' latency performance and more importantly, the trade-off between latency and safety at both the flow level and the application level. We argue that existing solutions are still operating away from the sweet spot on this trade-off plane. Based on the diagnosis of existing solutions, we introduce Halfback, a new short-flow transmission mechanism that operates on a better latency-safety trade-off point: Halfback achieves lower latency than the lowest latency previous solution and at the same time significantly better safety. As Halfback is TCP-friendly and requires only sender-side changes, it is feasible to deploy.

After more than two decades of evolution, TCP and its end host based modifications can still suffer from severely degraded performance under real-world challenging network conditions. The reason, as we observe, is due to TCP family's fundamental architectural deficiency, which hardwires packet-level events to control responses and ignores emprical performance. Jumping out of TCP lineage's architectural deficiency, we propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender controls its sending strategy based on empirically observed performance metrics. We show through preliminary experimental results that PCC achieves consistently high performance under various challenging network conditions.

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

In this paper, we tackle the spectrum allocation problem in cognitive radio (CR) networks with time-frequency flexibility consideration using combinatorial auction. Different from all the previous works using auction mechanisms, we model the spectrum opportunity in a time-frequency division manner. This model caters to much more flexible requirements from secondary users (SUs) and has very clear application meaning. The additional flexibility also brings theoretical and computational difficulties. We model the spectrum allocation as a combinatorial auction and show that under the time-frequency flexible model, reaching the social welfare maximal is NP hard and the upper bound of worst-case approximation ratio is √m, m is the number of time-frequency slots. Therefore, we design an auction mechanism with near-optimal winner determination algorithm, whose worst-case approximation ratio reaches the upper bound √m. Further we devise a truthful payment scheme under the approximation winner determination algorithm to guarantee that all the bids submitted by SUs reflect their true valuation of the spectrum. To further address the issue and reach optimality, we simplify the general model to that only frequency flexibility is allowed, which is still useful, and propose a truthful, optimal and computationally efficient auction mechanism under modified model. Extensive simulation results show that all the proposed algorithms generate high social welfare as well as high spectrum utilization ratio. What's more, the actual approximation ratio of near-optimal algorithm is much higher than the worst-case approximation ratio.

In this paper, we deal with possible data transmission congestion on the sink node in wireless sensor networks (WSNs). We consider a scenario in which all the sensor nodes have a certain amount of storage space and acquire data from the surroundings at heterogeneous speed. Because receiving bandwidth of the sink node is limited, a proper bandwidth allocation mechanism should be implemented to avoid possible congestion or data loss due to the overflow of some sensor nodes. To address this problem, we firstly design a novel bandwidth allocation mechanism, SWM, that can maximize the social utility, an indicator of every sensor node's satisfaction degree and the social fairness. Furthermore, we model the allocation process under the SWM as a noncooperative game and figure out the unique Nash Equilibrium. The uniqueness of the equilibrium demonstrates that this network will actually approach to a fair and stable state.

In this paper, we deal with the scenario that a single secondary user (SU) attempts to opportunistically access the 3G wireless CDMA networks licensed to primary users (PUs). Different to earlier studies in which consider the channel as two state Markov process, here, we expand the channel's availability into a multiple-state Markov process according to the transmission power of the SU. We propose a cyclical, but not periodic sensing and transmission strategy, which can be divided into two steps: 1) the SU acquires the estimation about how many PUs are in this network during a sensing period Ts; 2) Based on the estimation, the SU determines how much transmission power and how long it can transmit. Further, we derive the optimal transmission strategy which consists of different transmission power and time corresponding to different number of PUs in service. Finally, simulation results indicate that our optimal strategy is superior to the empirical strategy such as the maximum power transmission strategy and the maximum time transmission strategy, and achieves the highest throughput of SUs in the long run.