How do Wireless Chains Behave? The Impact of MAC Interactions

In a Multi-hop Wireless Networks (MHWN), packets are routed between source and destination using a chain of intermediate nodes; chains are a fundamental communication structure in MHWNs whose behavior must be understood to enable building effective protocols. The behavior of chains is determined by a number of complex and interdependent processes that arise as the sources of different chain hops compete to transmit their packets on the shared medium. In this paper, we show that MAC level interactions play the primary role in determining the behavior of chains. We evaluate the types of chains that occur based on the MAC interactions between different links using realistic propagation and packet forwarding models. We discover that the presence of destructive interactions, due to different forms of hidden terminals, does not impact the throughput of an isolated chain significantly. However, due to the increased number of retransmissions required, the amount of bandwidth consumed is significantly higher in chains exhibiting destructive interactions, substantially influencing the overall network performance. These results are validated by testbed experiments. We finally study how different types of chains interfere with each other and discover that well behaved chains in terms of self-interference are more resilient to interference from other chains

OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput

Inferring Room Semantics Using Acoustic Monitoring

Having knowledge of the environmental context of the user i.e. the knowledge of the users' indoor location and the semantics of their environment, can facilitate the development of many of location-aware applications. In this paper, we propose an acoustic monitoring technique that infers semantic knowledge about an indoor space \emph{over time,} using audio recordings from it. Our technique uses the impulse response of these spaces as well as the ambient sounds produced in them in order to determine a semantic label for them. As we process more recordings, we update our \emph{confidence} in the assigned label. We evaluate our technique on a dataset of single-speaker human speech recordings obtained in different types of rooms at three university buildings. In our evaluation, the confidence\emph{ }for the true label generally outstripped the confidence for all other labels and in some cases converged to 100\% with less than 30 samples.Comment: 2017 IEEE International Workshop on Machine Learning for Signal Processing, Sept.\ 25--28, 2017, Tokyo, Japa

Unconventional TV Detection using Mobile Devices

Recent studies show that the TV viewing experience is changing giving the rise of trends like "multi-screen viewing" and "connected viewers". These trends describe TV viewers that use mobile devices (e.g. tablets and smart phones) while watching TV. In this paper, we exploit the context information available from the ubiquitous mobile devices to detect the presence of TVs and track the media being viewed. Our approach leverages the array of sensors available in modern mobile devices, e.g. cameras and microphones, to detect the location of TV sets, their state (ON or OFF), and the channels they are currently tuned to. We present the feasibility of the proposed sensing technique using our implementation on Android phones with different realistic scenarios. Our results show that in a controlled environment a detection accuracy of 0.978 F-measure could be achieved.Comment: 4 pages, 14 figure

Detecting And Tracking Attacks in Mobile Edge Computing Platforms

Device-to-device (d2d) communication has emerged as a solution that promises high bit rates, low delay and low energy consumption which represents the key for novel technologies such as Google Glass, S Beam, and LTE-Direct. Such d2d communication has enabled computational offloading among collaborative mobile devices for a multitude of purposes such as reducing the overall energy, ensuring resource balancing across device, reducing the execution time, or simply executing applications whose computing requirements transcend what can be accomplished on a single device. While this novel computation platform has offered convenience and multiple other advantages, it obviously enables new security challenges and mobile network vulnerabilities. We anticipate challenging future security attacks resulting from the adoption of collaborative mobile edge cloud computing platforms, such as MDCs and FemtoClouds. In typical botnet attacks, “vertical communication” between a botmaster and infected bots, enables attacks that originate from outside the network. While intrusion detection systems typically analyze network traffic to detect anomalies, honeypots are used to attract and detect attackers, and firewalls are placed at the network periphery to filter undesired traffic. However, these traditional security measures are not as effective in protecting networks from insider attacks such as MobiBots, a mobile-to-mobile distributed botnet. This shortcoming is due to the mobility of bots and the distributed coordination that takes place in MobiBot attacks. In contrast to classical network attacks, these attacks are difficult to detect because MobiBots adopt “horizontal communication” that leverages frequent contacts amongst entities capable of exchanging data/code. In addition, this architecture does not provide any pre-established command and control channels (C&C) between a botmaster and its bots. Overall, such mobile device infections will circumvent classical security measures, ultimately enabling more sophisticated and dangerous attacks from within the network. We propose HoneyBot, a defense technique that detects, tracks, and isolates malicious device-to-device communication insider attacks. HoneyBots operate in three phases: detection, tracking, and isolation. In the detection phase, the HoneyBot operates in a vulnerable mode in order to detect lower layer and service-based malicious communication. We adopt a data driven approach, using real world indoor mobility traces, to evaluate the impact of the number of HoneyBots deployed and their placement on the detection delay performance. Our results show that utilizing only a few HoneyBot nodes helps detect malicious infection in no more than 15 minutes. Once the HoneyBot detects malicious communication, it initiates the tracking phase which consists of disseminating control messages to help “cure” the infected nodes and trace back the infection paths used by the malicious nodes. We show that HoneyBots are able to accurately track the source(s) of the attack in less than 20 minutes. Once the source(s) of the attack is/are identified, the HoneyBot activates the isolation phase that aims at locating the suspected node. We assume that the suspect node is not a cooperative device that aims at hiding its identity by ignoring all the Honeybot messages. Therefore, the HoneyBot requests wireless fingerprints from all nodes that have encountered this suspect nodes at a given time period. These fingerprints are used to locate these nodes and narrow down the suspect\u27s location. To evaluate our localization accuracy, we first deploy an experimental testbed where we show that HoneyBots accurately localize the suspect node within 4 to 6 m2. HoneyBots can operate efficiently in small numbers, as few as 2 or 3 nodes while improving the detection, tracking, and the isolation by a factor of 2 to 3. We also assess the scalability of HoneyBots using a large scale mobility trace with more than 500 nodes. We consider, in the attached Figure, a scenario of a corporate network consisting of 9 vulnerable devices labeled 1 to 9. Such network is attacked by one or many botmaster nodes using d2d MobiBot communication. We notice that attacks are propagated horizontally, bypassing all Firewall and intrusion detection techniques deployed by the corporate network administrators. In this scenario, we identify 3 main actors; the botmaster (red hexagon), the HoneyBot (green circle), the infected bot (red circle), and the cured or clear node (blue circle). We assume that the 9 nodes shown in the figure only represent the vulnerable d2d nodes in this corporate networks. We propose detection, tracking and isolation technique that aim at accurately and efficiently defend networks from insider d2d malicious communication

Senyapan Dalam Ujaran Isyana Dan Cindercella Pada Video Talkshow “Metal” Di Youtube

Penelitian ini merupakan penelitian terhadap fenomena senyapan dalam produksi ujaran Isyana dan Cindercella dalam talkshow ringan “Metal” di youtube. Metode yang digunakan yaitu metode kualitatif deskriptif dengan tujuan untuk mendeskripsikan temuan dari hasil analisis terhadap fenomena senyapan dalam produksi ujaran Isayana dan Cindercella di dalam video tersebut seperti jenis senyapan, distribusi senyapan, dan penyebab senyapan. Hasilnya ditemukan dua jenis senyapan di dalam video tersebut yaitu senyapan diam dan senyapan terisi yang meliputi senyapan yang terisi dengan bunyi, kata, pengulangan, dan kombinasi pengulangan. Distribusi senyapan semuanya berada di posisi tengah klausa. Ada pun penyebab terjadinya senyapan yaitu: (1) pengambilan napas, (2) pencarian kosakata yang tepat, (3) gugup, (4) lupa pada kosakata tertentu, (5) koreksi dari penutur, (6) keraguan, dan (7) penekanan pada kata tertentu

On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems