In this paper, we look at making backscatter practical for ultra-low power on-body sensors by leveraging radios on existing smartphones and wearables (e.g. WiFi and Bluetooth). The difficulty lies in the fact that in order to extract the weak backscattered signal, the system needs to deal with self interference from the wireless carrier (WiFi or Bluetooth) without relying on built-in capability to cancel or reject the carrier interference.Frequency-shifted backscatter (or FS-Backscatter) is based on a novel idea -the backscatter tag shifts the carrier signal to an adjacent non-overlapping frequency band (i.e. adjacent WiFi or Bluetooth band) and isolates the spectrum of the backscattered signal from the spectrum of the primary signal to enable more robust decoding. We show that this enables communication of up to 4.8 meters using commercial WiFi and Bluetooth radios as the carrier generator and receiver. We also show that we can support a range of bitrates using packet-level and bit-level decoding methods. We build on this idea and show that we can also leverage multiple radios typically present on mobile and wearable devices to construct multi-carrier or multi-receiver scenarios to improve robustness. Finally, we also address the problem of designing an ultra-low power tag that can frequency shift by 20MHz while consuming tens of micro-watts. Our results show that FS-Backscatter is practical in typical mobile and static on-body sensing scenarios while only using commodity radios and antennas.

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Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14.7% more data with minimal impact on existing traffic, and outpeforms heuristic schedulers by more than 2x. Our work is a valuable step towards designing autonomous, "self-driving" networks that learn to manage themselves from past data.

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One of the central challenges in backscatter is how to enable concurrent transmissions. Most backscatter protocols operate in a sequential TDMA-like manner due to the fact that most nodes cannot overhear each other's transmissions, which is detrimental for throughout and energy consumption. Recent efforts to separate concurrent signals by inverting a system of linear equations is also problematic due to varying channel coefficients caused by system and environmental dynamics. In this paper, we introduce BST, a novel physical layer for backscatter communication that enables concurrent transmission by leveraging vintra-bit multiplexing of OOK signals from multiple tags. The key idea underlying BST is that the reader can sample at considerably higher rates than the tags, hence it can extract time-domain signal edges that result from interleaved transmissions of several tags. Our preliminary experiment results show that BST can achieve 5x the throughput of Buzz and 10x the throughput of TDMA-based solutions, such as EPC Gen 2.

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Virtual synchronous generators (VSGs) present attractive technical advantages and contribute to enhanced system operation and reduced oscillation damping in dynamic systems. Traditional VSGs often lack an interworking during power oscillation. In this paper, a coordinated control strategy for multiple VSGs is proposed for mitigating power oscillation. Based on a theoretical analysis of the parameter impact of VSGs, a coordinated approach considering uncertainty is presented by utilizing polytopic linear differential inclusion (PLDI) and a D-stable model to enhance the small-signal stability of system. Subsequently, the inertia and damping of multiple VSGs are jointly exploited to reduce oscillation periods and overshoots during transient response. Simulation, utilizing a two-area four-machine system and a typical microgrid test system, demonstrates the benefits of the proposed strategy in enhancing operation stability and the anti-disturbing ability of multiple VSGs. The results conclusively confirm the validity and applicability of the method.

In order to solve the problem of vulnerability assessment of complex power systems facing complex structures and large sizes, a novel data driven method based on random matrix theory is proposed in this paper. Firstly, with the use of phasor measurement units (PMUs) big data, evaluation matrices are constructed to extract statistical characteristics of power systems operation. Then, with the combination of random matrix theory and entropy theory, vulnerability evaluation index are constructed considering the degree of influence of some faults in power systems. With full use of big data, the model-free method is more accurate and comprehensive. Simulation results in IEEE 39-bus test system and a real-world power grid in China verify the effectiveness of the method.