CCACK: Efficient Network Coding Based Opportunistic Routing Through Cumulative Coded Acknowledgments

The use of random linear network coding (NC) has significantly simplified the design of opportunistic routing (OR) protocols by removing the need of coordination among forwarding nodes for avoiding duplicate transmissions. However, NC-based OR protocols face a new challenge: How many coded packets should each forwarder transmit? To avoid the overhead of feedback exchange, most practical existing NC-based OR protocols compute offline the expected number of transmissions for each forwarder using heuristics based on periodic measurements of the average link loss rates and the ETX metric. Although attractive due to their minimal coordination overhead, these approaches may suffer significant performance degradation in dynamic wireless environments with continuously changing levels of channel gains, interference, and background traffic. In this paper, we propose CCACK, a new efficient NC-based OR protocol. CCACK exploits a novel Cumulative Coded ACKnowledgment scheme that allows nodes to acknowledge network coded traffic to their upstream nodes in a simple way, oblivious to loss rates, and with practically zero overhead. In addition, the cumulative coded acknowledgment scheme in CCACK enables an efficient credit-based, rate control algorithm. Our experiments on a 22-node 802.11 WMN testbed show that compared to MORE, a state-of-the-art NC based OR protocol, CCACK improves both throughput and fairness, by up to 3.2x and 83%, respectively, with average improvements of 11- 36% and 5.7-8.3%, respectively, for different numbers of concurrent flows. Our extensive simulations show that the gains are actually much higher in large networks, with longer routing paths between sources and destinations

App Parameter Energy Profiling: Optimizing App Energy Drain by Finding Tunable App Parameters

In this paper, we observe that modern mobile apps come with a large number of parameters that control the app behavior which indirectly affect the app energy drain, and using incorrect or non-optimal values for such app parameters can lead to app energy drain deficiency or even energy bugs. We argue conventional app energy optimization using an energy profiler which pinpoints energy hotspot code segments in the app source code may be ineffective in detecting such parameter-induced app energy deficiency. We propose app parameter energy profiling which identifies tunable app parameters that can reduce app energy drain without affecting app functions as a potentially more effective solution for debugging such app energy deficiency. We present the design and implementation of Medusa, an app parameter energy profiling framework. Medusa overcomes three key design challenges: how to filter out and narrow down candidate parameters, how to pick alternative parameter values, and how to perform reliable energy drain testing of app versions with mutated parameter values. We demonstrate the effectiveness of Medusa by applying it to a set of Android apps which successfully identifies tunable energy-reducing parameters

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps

Impact of the Inaccuracy of Distance Prediction Algorithms on Internet Applications--an Analytical and Comparative Study

Distance prediction algorithms use O(N) Round Trip Time (RTT) measurements to predict the N2 RTTs among N nodes. Distance prediction can be applied to improve the performance of a wide variety of Internet applications: for instance, to guide the selection of a download server from multiple replicas, or to guide the construction of overlay networks or multicast trees. Although the accuracy of existing prediction algorithms has been extensively compared using the relative prediction error metric, their impact on applications has not been systematically studied. In this paper, we consider distance prediction algorithms from an application\u27s perspective to answer the following questions: (1) Are existing prediction algorithms adequate for the applications? (2) Is there a significant performance difference between the different prediction algorithms, and which is the best from the application perspective? (3) How does the prediction error propagate to affect the user perceived application performance? (4) How can we address the fundamental limitation (i.e., inaccuracy) of distance prediction algorithms? We systematically experiment with three types of representative applications (overlay multicast, server selection, and overlay construction), three distance prediction algorithms (GNP, IDES, and the triangulated heuristic), and three real-world distance datasets (King, PlanetLab, and AMP). We find that, although using prediction can improve the performance of these applications, the achieved performance can be dramatically worse than the optimal case where the real distances are known. We formulate statistical models to explain this performance gap. In addition, we explore various techniques to improve the prediction accuracy and the performance of prediction-based applications. We find that selectively conducting a small number of measurements based on prediction-based screening is most effective

On the Accuracy of Poisson's Formula Based N-Body Algorithms

We study the accuracy-cost tradeoffs of a Poisson's formula based hierarchical N-body method. The parameters that control the degree of approximation of the computational elements and the separateness of interacting elements, govern both the arithmetic complexity and the accuracy of the method. Empirical models for predicting the execution time and the accuracy of the potential and force evaluations for three-dimensional problems are presented. We demonstrate how these models can be used to minimize the execution time for a prescribed error and verify the predictions through simulations on particle systems with up to one million particles. An interesting observation is that for a given error, defining the near-field to consist of only nearest neighbor elements yields a lower computational complexity for a given error than the two-element separation recommended in the literature. We also show that the particle distribution may have a significant impact on the error.Engineering and Applied Science

Securing Bystander Privacy in Mixed Reality While Protecting the User Experience

The modern Mixed Reality devices that make the Metaverse viable can also require vast information about the physical world. These devices can also violate the privacy of unsuspecting or unwilling bystanders in their vicinity. In this article, we explore the problem, existing solutions, and avenues for future research.Comment: 12 pages, 2 Figure

Extracting Source Level Program Similarities from Dynamic Behavior

The vast majority of work on comparing program similarities to detect software piracy either assumes the availability of the program source code (e.g., Moss) or performs a complicated source program transformation to embed carefully designed signatures, or software watermarks, into the binary code. In this paper, we propose a new approach to detecting program similarities that requires neither the availability of the program source nor complicated compile-time watermarking techniques. Furthermore, in contrast to the alternatives, our framework is resistant to standard attacks such as code obfuscation. Our approach exploits the observation that the sequence of system calls performed by a program execution provides a strong signature of the program semantics or functionality, thereby using the inherent properties of a program to identify it. By statistically analyzing sequences of system calls, the relative similarities and differences of program regions can be automatically determined. We have developed a framework that automatically extracts system call sequences, computes the similarities between two binaries via statistical analysis, and maps dynamically similar regions onto textually similar source files. We present several case studies showing the applicability of our framework in pinpointing pirated segments. Our experimental study also shows that directly comparing the binary files of the programs without considering their dynamic behavior is ineffective, and demonstrates strong consistency between the output of our new framework and that of Moss