Saptarshi Debroy

MyPULSE: Mobile Yellow Pages with user interest and Location Sensing Ensemble

In this paper we introduce the mobile yellow pages with user interest and location sensing ensemble (MyPULSE) system, an easy to use, platform-independent mobile application which enables a user to see image and video-based advertisements, get directions and obtain other important information about products and services offered by local businesses, such as restaurants, hotels, shops, taxicabs, etc. near the current location of the user, whether she is stationary or mobile. MyPULSE also empowers the user to make a single click phone call (referred to as dasiaClick to Callpsila) to the selected business. MyPULSE runs on any java-enabled mobile device with built-in GPS receiver and uses the GPS coordinates (obtained by using the GPS receiver) of the userpsilas current location for finding nearby businesses of interest. This paper describes the client server architecture of MyPULSE, provides performance numbers, and compares it with other systems in the space of context-aware advertising. This paper also discusses ongoing work to upgrade MyPULSE system to support push-based mobile advertising as well.

Spectrum Map and Its Application in Resource Management in Cognitive Radio Networks

Measurements on radio spectrum usage have revealed an abundance of under-utilized bands of spectrum that belong to primary (licensed) networks. Prior knowledge about the occupancy of such bands and the expected achievable performance on those bands can help secondary (unlicensed) networks to devise effective strategies to improve utilization. Such prior spatio-temporal spectrum usage statistics can either be obtained from a database that is maintained by the primary networks or could be measured by customized sensors deployed by the secondary networks. In this paper, we use Shepards interpolation technique to estimate a spatial distribution of spectrum usage over a region of interest, which we call the spectrum map. The interpolation is achieved by intelligently fusing the data shared by the the secondary nodes considering their mutual distances and spatial orientation with each other. The obtained map is a two-dimensional (2-D) interpolation function that is continuously differentiable and passes through all the spectrum usage values recorded at arbitrary locations; thus providing a reference for primary occupancy in that region. For determining the optimal locations for sensing primary activity, we use an iterative clustering technique that uses tree structured vector quantization. We use the spectrum map to estimate different radio and network performance metrics like channel capacity, network throughput, and spectral efficiency. As a comprehensive case study, we demonstrate how the spectrum map can be used for efficient resource allocation in TV white space. In particular, we consider an IEEE 802.22-based WRAN and show how the rendezvous probability can be improved for better radio resource allocation, thereby increasing the secondary spectrum utilization.

Intra-Cell Channel Allocation Scheme in IEEE 802.22 Networks

Cognitive radio based IEEE 802.22 wireless regional area networks have been proposed to harness the highly underutilized sub 900 MHz TV bands. In such networks, both base stations and the consumer premise equipments (CPEs) continuously perform spectrum sensing and transmit only on those channels which are not being used by the primary incumbents. In this paper, we propose a heuristic for an efficient channel allocation by a base station to the CPEs in that cell. Due to the lack of dedicated control channels, the BS and CPEs within a cell go through a process of exchanging control messages on free channels. The benefit of such sharing of their mutual spectrum usage helps the base station make informed decisions on the allocation of uplink and downlink channels to CPEs. This, in turn, guarantees no interference to and from the primary licensed incumbents. For validation of the proposed allocation scheme, we conducted simulation experiments. The results show how the proposed scheme ensures allocation to almost all the CPEs in a cell and how the nature of allocation is dependent on the total number of channels scanned, probability of getting a free channel and number of CPEs in the cell.

Utilizing misleading information for cooperative spectrum sensing in cognitive radio networks

In cognitive radio networks, the radios continuously scan the radio spectrum and create a spectrum usage report. Due to channel uncertainty, there are inaccuracies in these reports. Oftentimes, the radios share and fuse the observed data in order to increase the accuracy of the spectrum usage. However, malicious nodes tend to send false information (i.e., attack) in order to mislead the construction of the spectrum usage report. In this paper, we use a trust model to evaluate the trustworthiness of every node and use the trust values to effectively fuse the information from all nodes. A node compares the information sent by a neighboring node with the predicted information. Based on the ratio of matches (or mismatches), the neighboring node is assigned a trust value. Then, we propose a log-weighted metric utilizing trust values to distinguish malicious nodes from others. Subsequently, we propose threshold based Selective Inversion (SI) fusion and Complete Inversion (CI) fusion to effectively combine not only the information sent by honest nodes but also utilize misleading information sent by malicious nodes. We also propose a combination of the two inversion schemes. We compare the performance of the inversion based fusion schemes with blind and trust-based fusions. Results reveal better performance for inversion based fusion schemes for various intensities of attack. We also conduct simulations to evaluate the optimal thresholds that are used for invoking the inversion based fusion schemes.

PCA-based network-wide correlated anomaly event detection and diagnosis

High-performance computing environments supporting large-scale distributed computing applications need multi-domain network performance measurements from open frameworks such as perfSONAR. Network-wide correlated anomaly events that can potentially impact data throughput performance need to be quickly and accurately notified for smooth computing environment operations. Since network topology is not always available along with the measurements data, it is challenging to identify and locate network-wide correlated anomaly events that impact data throughput performance. In this paper, we present a novel PCA-based correlated anomaly event detection scheme that can fuse multiple time-series of measurements and transform them using principal component analysis. We demonstrate using actual perfSONAR one-way delay measurement datasets that our scheme can: (a) effectively distinguish between correlated and uncorrelated anomalies, (b) leverage a source-side vantage point to diagnose whether a correlated anomaly event location is local or in an external domain, (c) act as a “black-box” correlation analysis tool for key insights in eventual root-cause identification.

Network-Wide Anomaly Event Detection and Diagnosis With perfSONAR

High-performance computing (HPC) environments supporting data-intensive applications need multidomain network performance measurements from open frameworks such as perfSONAR. Detected network-wide correlated anomaly events that impact data throughput performance need to be quickly and accurately notified along with a root-cause analysis for remediation. In this paper, we present a novel network anomaly events detection and diagnosis scheme for network-wide visibility that improves accuracy of root-cause analysis. We address analysis limitations in cases where there is absence of complete network topology information, and when measurement probes are mis-calibrated leading to erroneous diagnosis. Our proposed scheme fuses perfSONAR time-series path measurements data from multiple domains using principal component analysis (PCA) to transform data for accurate correlated and uncorrelated anomaly events detection. We quantify the certainty of such detection using a measurement data sanity checking that involves: 1) measurement data reputation analysis to qualify the measurement samples and 2) filter framework to prune potentially misleading samples. Lastly, using actual perfSONAR one-way delay measurement traces, we show our proposed schemes effectiveness in diagnosing the root-cause of critical network performance anomaly events.

Frequency-minimal moving target defense using software-defined networking

With the increase of cyber attacks such as DoS, there is a need for intelligent counter-strategies to protect critical cloud-hosted applications. The challenge for the defense is to minimize the waste of cloud resources and limit loss of availability, yet have effective proactive and reactive measures that can thwart attackers. In this paper we address the defense needs by leveraging moving target defense protection within Software-Defined Networking-enabled cloud infrastructure. Our novelty is in the frequency minimization and consequent location selection of target movement across heterogeneous virtual machines based on attack probability, which in turn minimizes cloud management overheads. We evaluate effectiveness of our scheme using a large-scale GENI testbed for a just-in-time news feed application setup. Our results show low attack success rate and higher performance of target application in comparison to the existing static moving target defense schemes that assume homogenous virtual machines.

Critical sections in networked games

This work introduces the concept of critical sections for online first person shooter games (FPS). A critical section is a section of game-play which demands higher precision or tighter deadlines. Critical section traffic is more sensitive to network degradations than sections immediately preceding or following it. Critical sections provide game developers and network programmers a notion of relative priority of game traffic, and can identify segments whose preservation can lead to superior user perceived quality of playing FPS games on a network. By analyzing video-recordings of over 5 hours of FPS gameplay by 10 volunteers, we identify sections of FPS game-play whose degradation would cause inconsistent game-state updates resulting in user frustration. We observe that critical sections exhibit a pattern of occurrence and can account for upto 17% of game-play time. We next quantify the expected network induced degradations on critical sections for online FPS games on the Internet. Using traces from a deployment of FPS workloads on 50+ nodes in the Internet, we study network dynamics and their ensuing effect on critical sections. Using traces from this experiment, we derive the lower bound on potentially degraded game-play session on todays Internet. We argue that critical sections of FPS games can be preserved. This can allow a variety of network architectures to better deliver higher perceptual experience when deployed on the Internet. Overall, our results have implications for FPS game-design, network provisioning, and game quality evaluation.

Quantifying Trust for Robust Fusion While Spectrum Sharing in Distributed DSA Networks

In this paper, we quantify the trustworthiness of secondary nodes that share spectrum sensing reports in a distributed dynamic spectrum access network. We propose a spatio-spectral anomaly monitoring technique that effectively captures anomalies in the spectrum sensing reports shared by individual cognitive radio nodes. Based on this, we propose an optimistic trust model for a system with a normal risk attitude and using approximation to the Beta distribution. For a more conservative and risk averse system, we propose a multinomial Dirichlet distribution-based conservative trust framework. Using a machine learning approach, we classify malicious nodes with a high degree of certainty regardless of their aggressiveness of attacks or variations introduced by the wireless environment. Subsequently, we propose two instantaneous fusion models: 1) optimistic trust-based fusion and 2) conservative trust-based fusion, which exclude untrustworthy sensing reports from participating nodes during spectrum data fusion. Our work considers random, deterministic, and preferential (ON–OFF) attack models to demonstrate the utility of our proposed model under varied attack scenarios. Through extensive simulation experiments, we show that the trust values help identify malicious nodes with a high degree of certainty.

Network measurement recommendations for performance bottleneck correlation analysis

Multi-domain network performance monitoring (NPM) federations, such as perfSONAR rely on collaborative measurement intelligence to identify network anomaly events and diagnose performance bottlenecks affecting data-intensive science applications. In this paper, we present a novel measurement recommendation scheme to assist network operators and application users by recommending pertinent samples from a pool of measurement data involving multiple domains to detect and troubleshoot correlated network anomaly events. The recommendations are based on the principles of content-based filtering. Such recommendations are complimented with Bayesian Inference based domain reputation meta-information to strengthen the veracity information of the recommended traces. Using actual long-term and short-term perfSONAR traces, we analyze recommendation results and show: a) how the content-based filter recommends the most pertinent traces based on their attributes, and b) the time-variant characteristics of domain reputation. Finally, using synthetic traces, we show the effectiveness of our proposed measurements recommendation scheme in accurately identifying anomaly events for an exemplar use case, and also show how our content filter based recommendation scheme performs better in terms of false alarms in comparison to: a) recommendations that consider partial trace features for filtering, and b) greedy recommendation approaches based on random trace selection.