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  1. This paper studies a remote sensing system where multiple wireless sensors generate possibly noisy information updates of various surveillance fields and delivering these updates to a control center over a wireless network. The control center needs a sufficient number of recently generated information updates to have an accurate estimate of the current system status, which is critical for the control center to make appropriate control decisions. The goal of this work is then to design the optimal policy for scheduling the transmissions of information updates. Through Brownian approximation, we demonstrate that the control center’s ability to make accurate real-time estimates depends on the averages and temporal variances of the delivery processes. We then formulate a constrained optimization problem to find the optimal means and variances. We also develop a simple online scheduling policy that employs the optimal means and variances to achieve the optimal system-wide performance. Simulation results show that our scheduling policy enjoys fast convergence speed and better performance when compared to other state-of-the-art policies.
  2. This paper presents a Brownian-approximation framework to optimize the quality of experience (QoE) for real-time video streaming in wireless networks. In real-time video streaming, one major challenge is to tackle the natural tension between the two most critical QoE metrics: playback latency and video interruption. To study this trade-off, we first propose an analytical model that precisely captures all aspects of the playback process of a real-time video stream, including playback latency, video interruptions, and packet dropping. Built on this model, we show that the playback process of a real-time video can be approximated by a two-sided reflected Brownian motion. Through such Brownian approximation, we are able to study the fundamental limits of the two QoE metrics and characterize a necessary and sufficient condition for a set of QoE performance requirements to be feasible. We propose a scheduling policy that satisfies any feasible set of QoE performance requirements and then obtain simple rules on the trade-off between playback latency and the video interrupt rates, in both heavy-traffic and under-loaded regimes. Finally, simulation results verify the accuracy of the proposed approximation and show that the proposed policy outperforms other popular baseline policies.
  3. Recently, considerable research attention has been paid to graph embedding, a popular approach to construct representations of vertices in latent space. Due to the curse of dimensionality and sparsity in graphical datasets, this approach has become indispensable for machine learning tasks over large networks. The majority of the existing literature has considered this technique under the assumption that the network is static. However, networks in many applications, including social networks, collaboration networks, and recommender systems, nodes, and edges accrue to a growing network as streaming. A small number of very recent results have addressed the problem of embedding for dynamic networks. However, they either rely on knowledge of vertex attributes, su er high-time complexity or need to be re-trained without closed-form expression. Thus the approach of adapting the existing methods designed for static networks or dynamic networks to the streaming environment faces non-trivial technical challenges. These challenges motivate developing new approaches to the problems of streaming graph embedding. In this paper, we propose a new framework that is able to generate latent representations for new vertices with high e ciency and low complexity under speci ed iteration rounds. We formulate a constrained optimiza- tion problem for the modi cation ofmore »the representation resulting from a stream arrival. We show this problem has no closed-form solution and instead develop an online approximation solution. Our solution follows three steps: (1) identify vertices a ected by newly arrived ones, (2) generating latent features for new vertices, and (3) updating the latent features of the most a ected vertices. The new representations are guaranteed to be feasible in the original constrained optimization problem. Meanwhile, the solution only brings about a small change to existing representations and only slightly changes the value of the objective function. Multi-class clas- si cation and clustering on ve real-world networks demonstrate that our model can e ciently update vertex representations and simultaneously achieve comparable or even better performance compared with model retraining.« less
  4. This paper proposes a feasibility-optimal decentralized algorithm for real-time wireless ad hoc networks, where a strict deadline is imposed for each packet. While centralized scheduling algorithms provide provably optimal theoretical guarantees, they may not be practical in many settings, such as industrial networked control systems. Therefore, it is of great importance to design an algorithm that achieves feasibility optimality in a decentralized manner. To design a decentralized algorithm, we leverage two widely-used functions of wireless devices: carrier sensing and backoff timers. Different from the conventional approach, the proposed algorithm utilizes a collision-free backoff scheme to enforce the transmission priority of different links. This design obviates the capacity loss due to collision with quantifiably small backoff overhead. The algorithm is fully decentralized in the sense that every link only needs to know its own priority, and links contend for priorities only through carrier sensing. We prove that the proposed algorithm is feasibility-optimal. NS-3 simulation results show that the proposed algorithm indeed performs as well as the feasibility-optimal centralized algorithm. Moreover, the results also show that the proposed algorithm converges to optimality very fast.
  5. The emerging connected-vehicle technology provides a new dimension for developing more intelligent traffic control algorithms for signalized intersections. An important challenge for scheduling in networked transportation systems is the switchover delay caused by the guard time before any traffic signal change. The switch-over delay can result in significant loss of system capacity and hence needs to be accommodated in the scheduling design. To tackle this challenge, we propose a distributed online scheduling policy that extends the wellknown Max-Pressure policy to address switch-over delay by introducing a bias factor favoring the current schedule. We prove that the proposed policy is throughput-optimal with switch-over delay. Furthermore, the proposed policy remains optimal when there are both connected signalized intersections and conventional fixed-time ones in the system. With connected-vehicle technology, the proposed policy can be easily incorporated into the current transportation systems without additional infrastructure. Through extensive simulation in VISSIM, we show that our policy indeed outperforms the existing popular policies.