In collaborative networked microgrids (NMs), distributed energy resources (DERs) utilize intelligent Internet of Things (IoT)-based controllers to coordinately support various smart community functions. Meanwhile, privacy and security issues occur when IoT-based controllers interact with each other. This article presents a cryptography-based, programmable control (crypto-control) scheme to provably preserve the privacy of DERs while ensuring fast, flexible distributed control in NMs. Specifically, it makes the following contributions: 1) a programmable crypto-control-based NMs (PCNMs) architecture, where crypto-controllers are fully virtualized, is devised; 2) a novel dynamic encrypted weight addition (DEWA) approach, which integrates an enhanced partial homomorphic encryption and a secret sharing scheme, is devised to ensure privacy preserving of distributed controls; 3) the DEWA privacy-preserving property is mathematically analyzed; and 4) a real-time DEWA-based PCNMs testbed is deployed by incorporating DEWA, real software-defined networking switches, and IoT devices. The deployable crypto-control scheme is interfaced with and thoroughly verified in a Real-Time Digital Simulator environment, and the experimental results validate the effectiveness, benefits, and superiority of DEWA-based PCNMs. The inherent resilience of the DEWA-based PCNMs is validated by small signal stability analysis.

Neighbor discovery on duty-cycled mobile devices in group settings arises in many applications. In such scenarios, it is sufficient for an arbitrary node in a group to discover a new node. While pairwise neighbor discovery schemes can be directly applied to group settings, their performance can be severely limited as they are not designed to coordinate the efforts of group members. Explicit coordination among the group members, however, can incur large overhead in mobile networks, where the group membership changes dynamically over time. In this paper, we focus on schemes that require no explicit communication among the group members, and nodes follow deterministic schedules that can be succinctly represented. We first define the notion ofideal duty cyclefor a group, and then develop two deterministic neighbor discovery schemes for group settings, and show that both of them achieve effective duty cycle close to the ideal duty cycle. In addition, we show that the schemes are lightweight and easy to implement using experiments in a testbed. Last, we use a case study to demonstrate the usage of our proposed schemes and show that a simple enhancement leveraging the deterministic nature of the schemes leads to significant performance improvement, at the cost of only slight extra overhead.

Over-the-top video (OTT) streaming accounts for the majority of traffic on cellular networks, and also places a heavy demand on users' limited monthly cellular data budgets. In contrast to much of traditional research that focuses on improving the quality, we explore a different direction---using data budget information to better manage the data usage of mobile video streaming, while minimizing the impact on users' quality of experience (QoE). Specifically, we propose a novel framework for quality-aware Adaptive Bitrate (ABR) streaming involving a per-session data budget constraint. Under the framework, we develop two planning based strategies, one for the case where fine-grained perceptual quality information is known to the planning scheme, and another for the case where such information is not available. Evaluations for a wide range of network conditions, using different videos covering a variety of content types and encodings, demonstrate that both these strategies use much less data compared to state-of-the-art ABR schemes, while still providing comparable QoE. Our proposed approach is designed to work in conjunction with existing ABR streaming workflows, enabling ease of adoption.

Adaptive Bitrate (ABR) streaming is widely used in commercial video services. In this paper, we profile energy consumption of ABR streaming on mobile devices. This profiling is important, since the insights can help developing more energy-efficient ABR streaming pipelines and techniques. We first develop component power models that provide online estimation of the power draw for each component involved in ABR streaming. Using these models, we then quantify the power breakdown in ABR streaming for both regular videos and the emerging 360° panoramic videos. Our measurements validate the accuracy of the power models and provide a number of insights. We discuss use cases of the developed power models, and explore two energy reduction strategies for ABR streaming. Evaluation demonstrates that these simple strategies can provide up to 30% energy savings, with little degradation in viewing quality.

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40 percent) and stalls (reduction up to 85 percent), while incurring very small runtime overhead. We further design PIA-E (PIA Enhanced), which improves the performance of PIA in the important initial playback phase.

Neighbor discovery is a fundamental problem in wireless networks. In this paper, we study asynchronous neighbor discovery on duty-cycled mobile devices. Most existing studies develop integer schedules where time proceeds in discrete slots and a node is awake or asleep for an entire slot duration. We show that integer schedules can lead to significant waste of resources, and develop a generalized non-integer model, where time is continuous and a node may become awake or asleep at any point of time (subject to a few constraints) so that the resultant schedules can be significantly more efficient than integer schedules. In addition, we provide a reduction that transforms any schedule in the integer model to a corresponding schedule in the generalized non-integer model while reducing the discovery latency by up to a factor of two. Applying this reduction, an optimal schedule in the integer model becomes an optimal schedule in the non-integer model. We further demonstrate the practicality of non-integer schedules in a testbed, and compare the worst-case discovery latency of several existing schemes under both integer and non-integer models. Last, we establish a family of lower bounds for the best achievable latency guarantee. These lower bounds are applicable to both integer and non-integer models, covering both symmetric and asymmetric settings, and encompassing the existing lower bounds that are only for a subset of settings as special cases.

Adaptive bitrate (ABR) streaming is the predominant approach for video streaming over the Internet. When the audio and video tracks are stored separately (i.e., in demuxed mode), the client needs to dynamically determine which audio and which video track to select for each chunk/playback position. Somewhat surprisingly, there is very little literature on how to best mesh together audio and video adaptation in ABR streaming. In this paper, we first examine the state of the art in the handling of demuxed audio and video tracks in predominant ABR protocols (DASH and HLS), as well as in real ABR client implementations in three popular players covering both browsers and mobile platforms. Combining experimental insights with code analysis, we shed light on a number of limitations in existing practices both in the protocols and the player implementations, which can cause undesirable behaviors such as stalls, selection of potentially undesirable combinations such as very low quality video with very high quality audio, etc. Based on our gained insights, we identify the underlying root causes of these issues, and propose a number of practical design best practices and principles whose collective adoption will help avoid these issues and lead to better QoE.

Streaming videos over cellular networks is highly challenging. Since cellular data is a relatively scarce resource, many video and network providers offer options for users to exercise control over the amount of data consumed by video streaming. Our study shows that existing data saving practices for Adaptive Bitrate (ABR) videos are suboptimal: they often lead to highly variable video quality and do not make the most effective use of the network bandwidth. We identify underlying causes for this and propose two novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. We implement and integrate our solutions into two video player platforms (dash.js and ExoPlayer), and conduct thorough evaluations over emulated/commercial cellular networks using real videos. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

Adaptive Bitrate (ABR) video streaming is widely used for over-the-top (OTT) video delivery. Recently, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, spurred by the potential of improving user QoE (Quality of Experience) and reducing network bandwidth requirements compared to Constant Bitrate (CBR) encodings. However VBR introduces new challenges for ABR streaming, whose nature and implications are little understood. We explore these challenges across diverse video genres, encoding technologies, and platforms. We identify distinguishing characteristics of VBR encodings that impact user QoE and should be factored in any ABR adaptation decision. Traditional ABR adaptation strategies designed for the CBR case are not adequate for VBR. We develop novel best practice design principles to guide ABR rate adaptation for VBR encodings. As a proof of concept, we design a novel and practical control-theoretic rate adaptation scheme, CAVA (Control-theoretic Adaption for VBR-based ABR streaming), incorporating these concepts. Extensive evaluations show that CAVA substantially outperforms existing state-of-the-art adaptation techniques, validating the importance of these design principles.

Accurate cellular link bandwidth prediction can benefit upper-layer protocols significantly. In this paper, we investigate how to predict cellular link bandwidth in LTE networks. We first conduct an extensive measurement study in two major commercial LTE networks in the US, and identify five types of lower-layer information that are correlated with cellular link bandwidth. We then develop a machine learning based prediction framework, LinkForecast, that identifies the most important features (from both upper and lower layers) and uses these features to predict link bandwidth in realtime. Our evaluation shows that LinkForecast is lightweight and the prediction is highly accurate: At the time granularity of one second, the average prediction error is in the range of 3.9 to 17.0 percent for all the scenarios we explore. We further investigate the prediction performance when using lower-layer features obtained through standard APIs provided by the operating system, instead of specialized tools. Our results show that, while the features thus obtained have lower fidelity compared to those from specialized tools, they lead to similar prediction accuracy, indicating that our approach can be easily used over commercial off-the-shelf mobile devices.

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a feedback control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40%) and stalls (reduction up to 85%), while incurring very small runtime overhead.

This paper introduces the notion of one-way communication schemes with partial noisy feedback. To support this communication, the schemes suppose that Alice and Bob wish to communicate: Alice sends a sequence of alphabets over a channel to Bob, while Alice receives feedback bits from Bob for δ fraction of the transmissions. An adversary is allowed to tamper up to a constant fraction of these transmissions for both forward rounds and feedback rounds separately. This paper intends to determine the Maximum Error Rate (MER), as a function of δ (0 ≤ δ ≤ 1), under the MER rate, so that Alice can successfully communicate the messages to Bob via some protocols with δ fraction of noisy feedback. To provide a reasonable solution for the above problem, we need to explore a new kind of coding scheme for the interactive communication. In this paper, we use the notion of “non-malleable codes” (NMC) which relaxes the notions of error-correction and error-detection to some extent in communication. Informally, a code is non-malleable if the message contained in a modified codeword is either the original message or a completely unrelated value. This property largely enforces the way to detect the transmission errors. Based on the above knowledge, we provide an alphabet-based encoding scheme, including a pair of (Enc, Dec). Suppose the message needing to be transmitted is m; if m is corrupted unintentionally, then the encoding scheme Dec(Enc(m)) outputs a symbol ‘⊥’ to denote that some potential corruptions happened during transmission. In this work, based on the previous results, we show that for any δ ∈ (0; 1), there exists a deterministic communication scheme with noiseless full feedback(δ = 1), such that the maximal tolerable error fraction γ (on Alice's transmissions) can be up to 1/2, theoretically. Moreover, we show that for any δ ∈ (0; 1), there exists a communication scheme with noisy feedback, denoting the forward and backward rounds noised with error fractions of γ0and γ1respectively, such that the maximal tolerable error fraction γ0(on forward rounds) can be up to 1/2, as well as the γ1(on feedback rounds) up to 1.

Neighbor discovery is a fundamental problem in wireless networks. In this paper, we study asynchronous neighbor discovery between duty-cycled mobile devices. Each node is duty-cycled, i.e., its radio may only be active for a small fraction of the time. The duty cycles of the nodes can be the same or different, leading to symmetric or asymmetric cases of the neighbor discovery problem. In addition, the setting is asynchronous, i.e., clocks of different nodes may not be synchronized. Most existing studies assume an integer model (where time proceeds in discrete steps); two recent studies break away from this assumption, which allows them to develop significantly more efficient schemes. Our study improves the state-of-the-art in three main fronts. Firstly, we develop a generalized non-integer model (where time is continuous) that permits unified treatment of the assumptions in existing studies. We also provide a reduction that transforms any schedule in the basic integer model to a corresponding schedule in the generalized non-integer model while improving the performance by a factor of two. Applying this reduction, an optimal schedule in the integer model becomes an optimal schedule in the non-integer model. Thirdly, we establish a new family of lower bounds for the best achievable latency guarantee in the non-integer model. They are applicable to both symmetric and asymmetric settings, and encompass the lower bounds for the integer model as special cases. Finally, we develop a novel optimal construction based on Sidon sets for the symmetric setting. Our approach differs from the approaches taken by all existing studies, and provides a new direction for constructing neighbor discovery schedules.