This paper proposes a novel routing protocol, DM+, to achieve distributed spatial multiplexing gain in wireless mesh networks. It lets multiple nodes simultaneously send and receive different streams over each hop. To realize this goal, we propose an optimization framework that jointly optimizes spatial multiplexing, routing, and rate limits while taking into account wireless interference. We further design and implement a practical routing protocol that (i) enforces the optimized multiplexed routes, (ii) synchronizes transmissions from different senders, (iii) encodes and decodes analog signals to support simultaneous transmissions, and (iv) compensates for the frequency offset incurred over a multihop path. Using QualNet simulation and USRP implementation, we show it significantly out-performs state-of-the-art shortest path routing and opportunistic routing protocols. To our knowledge, this is the first routing protocol and prototype that achieves distributed spatial multiplexing in a real multihop network.

Over the past decade, white space networking has garnered significant effort and attention from media, industry, regulators, and academics world-wide. Together, these entities have helped push white space in to a unique position of potentially changing how we access the spectrum (more efficiently) forever. Now, building white space networks with full-featured and fully certified equipment is critical in moving forward and retaining the spectrum and position that we have put ourselves in.

In this paper, we present network-on-chip (NoC) design and contrast it to traditional network design, highlighting similarities and differences between the two. As an initial case study, we examine network congestion in bufferless NoCs. We show that congestion manifests itself differently in a NoC than in traditional networks. Network congestion reduces system throughput in congested workloads for smaller NoCs (16 and 64 nodes), and limits the scalability of larger bufferless NoCs (256 to 4096 nodes) even when traffic has locality (e.g., when an application's required data is mapped nearby to its core in the network). We propose a new source throttling-based congestion control mechanism with application-level awareness that reduces network congestion to improve system performance. Our mechanism improves system performance by up to 28% (15% on average in congested workloads) in smaller NoCs, achieves linear throughput scaling in NoCs up to 4096 cores (attaining similar performance scalability to a NoC with large buffers), and reduces power consumption by up to 20%. Thus, we show an effective application of a network-level concept, congestion control, to a class of networks -- bufferless on-chip networks -- that has not been studied before by the networking community.

TV white spaces offer an exciting opportunity for increasing spectrum availability, but white space devices (WSDs) cannot interfere with primary users, including TV channels and wireless microphones (mics). Mics are particularly challenging because their use is dynamic and it is hard to avoid interference since mic receivers are receive-only devices. For this reason the FCC and other regulatory agencies have made very conservatives rules that require WSDs to vacate any TV channel that is used by a mic. However, our measurements show that mics typically require only 5% of a channel, wasting as much as 95% of the spectrum.

In this paper, we present network-on-chip (NoC) design and contrast it to traditional network design, highlighting core differences between NoCs and traditional networks. As an initial case study, we examine network congestion in bufferless NoCs. We show that congestion manifests itself differently in a NoC than in a traditional network, and with application-level awareness in the network to make proper throttling decisions we improve system performance by up to 28%. It is our hope that the unique and interesting challenges of on-chip network design can be met by novel and effective solutions from the networking community.

Entropy-based approaches for anomaly detection are appealing since they provide more fine-grained insights than traditional traffic volume analysis. While previous work has demonstrated the benefits of entropy-based anomaly detection, there has been little effort to comprehensively understand the detection power of using entropy-based analysis of multiple traffic distributions in conjunction with each other. We consider two classes of distributions: flow-header features (IP addresses, ports, and flow-sizes), and behavioral features (degree distributions measuring the number of distinct destination/source IPs that each host communicates with). We observe that the timeseries of entropy values of the address and port distributions are strongly correlated with each other and provide very similar anomaly detection capabilities. The behavioral and flow size distributions are less correlated and detect incidents that do not show up as anomalies in the port and address distributions. Further analysis using synthetically generated anomalies also suggests that the port and address distributions have limited utility in detecting scan and bandwidth flood anomalies. Based on our analysis, we discuss important implications for entropy-based anomaly detection.