Generating video highlights in the form of animated graphics interchange formats (GIFs) has significantly simplified the process of video browsing. Animated GIFs have paved the way for applications concerning streaming platforms and emerging technologies. Existing studies have led to large computational complexity without considering user personalization. This paper proposes lightweight method to attract users and increase views of videos through personalized artistic media, i.e., static thumbnails and animated GIF generation. The proposed method analyzes lightweight thumbnail containers (LTC) using the computational resources of the client device to recognize personalized events from feature-length sports videos. Next, the thumbnails are then ranked through the frame rank pooling method for their selection. Subsequently, the proposed method processes small video segments rather than considering the whole video for generating artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data; thus, the proposed method complies with sustainable development goals. Furthermore, the proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data. Experiments reveal that the computational complexity of our method is 3.73 times lower than that of the state-of-the-art method.

The increasing demand for ubiquitous wireless connectivity has led to the widespread of Wi-Fi networks. However, due to the limited coverage of Wi-Fi networks, mobile stations (STAs) need to frequently search for neighboring Wi-Fi access points (APs). Inevitable inefficiency occurs during the Wi-Fi scanning since the STA typically does not have any prior knowledge of the neighboring APs, thus leading to unnecessary waste of time and energy. In this paper, we propose BlueScan, a scheme to boost Wi-Fi scanning using collocated Bluetooth radio. BlueScan enhances Wi-Fi scanning with low power Bluetooth radio by identifying the operating channels and target beacon transmission times (TBTTs) of neighboring APs, thus changing the Wi-Fi scanning from a blind search to an intelligent search. We implement a prototype of BlueScan using Ubertooth platform, and evaluate its performance through real experiments. Our results demonstrate that BlueScan reduces the scanning delay up to 77% compared to legacy Wi-Fi scanning.

Wi-Fi channel scanning-the task of searching for available channels at a given location-is a fundamental feature to maintain always-available and high-quality wireless connectivity in today's mobile devices. However, it is a challenging task to design an intelligent scanning algorithm that can discover available access points (APs) in a short time period, because the scanning station has no prior knowledge on the APs in its vicinity. Therefore, traditional scanning algorithms seek to discover available APs by scanning the full set of channels, including channels where no APs exist. This often induces unnecessary scanning latency and/or energy consumption. In this paper, we present a novel scheme, called C-SCAN, that exploits a low-power wireless personal area network interface, such as Bluetooth or ZigBee integrated into the device to offload Wi-Fi scanning overhead by suppressing unnecessary scanning of AP-free Wi-Fi channels. To this end, C-SCAN inspects channel information with a lowpower Bluetooth radio and identifies which Wi-Fi channels are in use, prior to the actual channel scanning with a Wi-Fi interface. By excluding the channels determined to be empty, the Wi-Fi scanning manager can perform scanning only on available Wi-Fi channels. Thereby, a significant performance gain in terms of delay and energy is obtained. We implement a prototype of C-SCAN using a Bluetooth-compliant wireless transceiver and demonstrate its efficiency. Experimental results show that CSCAN achieves high detection accuracy with low latency, even in dense Wi-Fi environments.

As the quantity and complexity of network threats grow, Intrusion Detection Systems (IDSs) have become critical for securing networks. Achieving computer network intrusion detection with these IDSs requires high-level information technology and security expertise because malicious traffic has to be rigorously analyzed and the appropriate IDS rules written to effectively detect vulnerabilities that may potentially be exploited. However, incorrect IDS rules may produce numerous false positives, thereby degrading the performance of the IDS, and even worse, paralyzing the network. In this paper, we present a novel approach that exploits the Latent Dirichle Allocation (LDA) algorithm to generate IDS rules. Our proposed method, called LDA-based Automatic Rule Generation (LARGen), automatically performs an analysis of the malicious traffic and extracts the appropriate attack signatures that will be used for IDS rules. LARGen first extracts multiple signature strings embedded in network flows. Then, the flows are classified based on the extracted signature strings, and key content strings for malicious traffic are identified through the LDA inferential topic model. Those key content strings are the core of an IDS rule that can detect malicious traffic. We study the effectiveness of LDA in the context of network attack signature generation via extensive experiments with real network trace data, consisting of both benign and malicious traffic. Experimental results confirm that threat rules generated from LARGen accurately detect every cyber attack with high accuracy.

In 2015, the Korean government launched a major software education initiative. The urgency to rally the nation around the 4th Industrial Revolution is the driving force. It consists of two major components. One is to enhance software (computer science) education in universities, and another is to make coding education mandatory in elementary and middle schools.

With the increasing move towards wide band operation in recent Wi-Fi networks, the frequency diversity awareness has become critical for throughput optimization. To exploit frequency diversity in Wi-Fi channels, the access point should measure the channel quality and coordinate the channel contention for all the stations. However, there is a tradeoff between achieving the frequency diversity gain and sustaining protocol efficiency because the channel estimation and coordination consume time and frequency resource that ideally should be used for data transfer. In this paper, we present Diversity-aware Wi-Fi (D-Fi), a novel PHY/MAC protocol, that efficiently exploits frequency diversity. In particular, D-Fi leverages an OFDM-based Bloom filter that synergistically integrates two operations: (i) the channel quality estimation and (ii) the contention based channel allocation. D-Fi also employs a machine learning (ML) method to resolve the false-positive ambiguity caused by the Bloom filter. Furthermore, we develop a decentralized algorithm, called Kε-greedy, based on the Multi-Armed Bandit (MAB) framework, so that it achieves sub-optimal performance by studying the gain for exploring new channel quality information. We implement the prototype of D-Fi on the USRP/GNURadio to validate the feasibility of our work. The experiments and trace-driven simulations show that D-Fi provides up to 3 x throughput improvement compared to the existing solutions.

Super Wi-Fi refers to Wi-Fi-like Internet access via spectrum white spaces (WS), which is expected to enhance today's Wi-Fi thanks to the superior propagation characteristics of the WS compared to ISM/UNII bands. A Super Wi-Fi wireless service provider (WSP) dynamically leases and opportunistically utilizes a licensed band while it is temporarily unoccupied by its (licensed) primary users (PUs). The PUs' spectrum usage pattern presents time-varying spectrum availability, thus necessitating eviction of in-service customers upon return of PUs where the evicted customers are compensated with partial reimbursement of their service charge. This paper investigates the dynamics of a duopoly Super Wi-Fi market where two co-located WSPs compete for leasing better quality channels and for setting competitive service price to entice more customers. The channel quality is measured by the PUs' utilization factor (smaller the better). Since higher quality channels possess more WS incurring larger channel leasing cost, a WSP should strike a balance between channel quality and service tariff in maximizing its profit. The market competition is modeled as hierarchical noncooperative price- and quality-games, and their Nash Equilibria (NE) are derived. In addition, we investigate the impact of differentiated reimbursement rates and limited channel availability. Finally, we demonstrate the tradeoffs among leasing cost, customer arrival rate, and channel characteristics via numerical analyses.

The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order to increase their revenue and improve spectrum utilization. In DSM, determining the optimal spectrum leasing price is an important yet challenging problem that requires a comprehensive understanding of market participants' interests and interactions. In this paper, we study spectrum pricing competition in a duopoly DSM, where two wireless service providers (WSPs) lease spectrum access rights, and secondary users (SUs) purchase the spectrum use to maximize their utility. We identify two essential, but previously overlooked, properties of DSM: 1) heterogeneous spectrum resources at WSPs and 2) spectrum sharing among SUs. We demonstrate the impact of spectrum heterogeneity via an in-depth measurement study using a software-defined radio (SDR) testbed. We then study the impacts of spectrum heterogeneity on WSPs' optimal pricing and SUs' WSP selection strategies using a systematic three-step approach. First, we study how spectrum sharing among SUs subscribed to the same WSP affects the SUs' achievable utility. Then, we derive the SUs' optimal WSP selection strategy that maximizes their payoff, given the heterogeneous spectrum propagation characteristics and prices. We analyze how individual SU preferences affect market evolution and prove the market convergence to a mean-field limit, even though SUs make local decisions. Finally, given the market evolution, we formulate the WSPs' pricing strategies in a duopoly DSM as a noncooperative game and identify its Nash equilibrium points. We find that the equilibrium price and its uniqueness depend on the SUs' geographical density and spectrum propagation characteristics. Our analytical framework reveals the impact of spectrum heterogeneity in a real-world DSM, and can be used as a guideline for the WSPs' pricing strategies.

Mobile WiFi hotspots have become increasingly popular as a new innovative wireless Internet access technology. Although it has received little attention, adaptive, dynamic (re)assignment of channels in a mobile hotspot router - typically, a smartphone or a laptop or a tablet equipped with heterogeneous network interfaces like 3G/4G, WiFi, Bluetooth or ZigBee - is key to mobile hotspot performance. To fill this important gap, we present a novel scheme, called Ex2R, that finds the best WiFi service-channel by harnessing secondary low-power ZigBee radios accompanied in mobile hotspot systems, enabling intelligent and seamless dynamic channel reconfiguration. Ex2R exploits the RSSI values and clear channel assessment (CCA) outputs from the ZigBee radio sampling interface to measure idle-time fractions on candidate WiFi channels, and determines the channel that can provide the highest hotspot capacity. Ex2R thus provides a mobile hotspot router accurate information necessary for the selection of the best channel. We have implemented and evaluated Ex2R, demonstrating its effectiveness; Ex2R accurately ranks WiFi channels for a mobile hotspot router to dynamically select and switch to the best channel available, thus improving hotspot performance significantly.

The whitespaces (WS) in the legacy spectrum provide new opportunities for the future Wi-Fi-like Internet access, often called Wi-Fi 2.0, since service quality can be greatly enhanced thanks to the better propagation characteristics of the WS than the ISM bands. In the Wi-Fi 2.0 networks, each wireless service provider (WSP) temporarily leases a licensed spectrum band from the licensees and opportunistically utilizes it during the absence of the legacy users. The WSPs in Wi-Fi 2.0 thus face unique challenges since spectrum availability of the leased channel is time-varying due to the ON/OFF spectrum usage patterns of the legacy users, which necessitates the eviction control of in-service customers at the return of legacy users. As a result, to maximize its profit, a WSP should consider both channel leasing and eviction costs to optimally determine a spectrum band to lease and a service tariff. In this paper, we consider a duopoly Wi-Fi 2.0 market where two co-located WSPs compete for the spectrum and customers. The competition between the WSPs is analyzed using game theory to derive the Nash Equilibria (NE) of the price (of the service tariffs) and the quality (of the leased channel, in terms of channel utilization) competitions. The NE existence condition and market entry barriers are also derived. Via an extensive numerical analysis, we show the tradeoffs between leasing/eviction cost, customer arrivals, and channel usage patterns by the legacy users.

Detecting misbehaving users in wireless networks is an important problem that has been drawing considerable attention. Even though there is a plethora of work on 802.11 wireless local area networks (WLANs), most existing schemes employ behavior-based anomaly detection, assuming that the backoff-time information of each transmitting node is available to the monitoring node. Unfortunately, it is practically infeasible to obtain the accurate backoff value chosen by other transmitting nodes because this MAC-layer information is not readily available. In this paper, we propose a practical way of pinpointing the misbehaving nodes without requiring access of hardware-level (e.g., backoff time) information in 802.11 WLANs. In contrast to most prior work, our scheme exploits the sequence of successfully received packets, which are readily observable at the access point. The distinct features of our scheme are that it 1) promptly detects a misbehaving node using a sequential hypothesis test, 2) performs well in realistic erroneous channel conditions due to its ability to accurately capture link heterogeneity, and 3) incurs negligible memory and computation overheads as it makes detection decisions based on runtime observations. The effectiveness of the proposed scheme is evaluated via extensive simulation as well as implementation, demonstrating its capability of accurately detecting nodess' selfish behavior in realistic 802.11 WLAN environments.

Recent works in empirical 802.11 wireless LAN performance evaluation have shown that cross-layer interactions in WLANs can be subtle, sometimes leading to unexpected results. Two such instances are: (i) significant throughput degradation resulting from automatic rate fallback (ARF) having difficulty distinguishing collision from channel noise, and (ii) scalable TCP over DCF performance that is able to mitigate the negative performance effect of ARF by curbing multiple access contention even when the number of stations is large. In this paper, we present a framework for analyzing complex cross-layer interactions in 802.11 WLANs, with the aim of providing effective tools for understanding and improving WLAN performance. We focus on cross-layer interactions between ARF, DCF, and TCP, where ARF adjusts coding at the physical layer, DCF mediates link layer multiple access control, and TCP performs end-to-end transport. We advance station-centric Markov chain models of ARF, ARF-DCF with and without RTS/CTS, and TCP over DCF that may be viewed as multi-protocol extensions of Bianchi's IEEE 802.11 model. We show that despite significant increase in complexity the analysis framework leads to tractable and accurate performance predictions. Our results complement empirical and simulation-based findings, demonstrating the versatility and efficacy of station-centric Markov chain analysis for capturing cross-layer WLAN dynamics.

One of the key challenges in designing a rate adaptation scheme for IEEE 802.11 wireless LANs (WLANs) is to differentiate bit errors from link-layer collisions. Many recent rate adaptation schemes adopt the RTS/CTS mechanism to prevent collision losses from triggering unnecessary rate decrease. However, the RTS/CTS handshake incurs significant overhead and is rarely activated in today's infrastructure WLANs. In this paper we propose a new rate adaptation scheme that mitigates the collision effect on the operation of rate adaptation. In contrast to previous approaches adopting fixed rate-increasing and decreasing thresholds, our scheme varies threshold values based on the measured network status. Using the "retry" information in 802.11 MAC headers as feedback, we enable the transmitter to gauge current network state. The proposed rate adaptation scheme does not require additional probing overhead incurred by RTS/CTS exchanges and can be easily deployed without changes in firmware. We demonstrate the effectiveness of our solution by comparing with existing approaches through extensive simulations.

Abstract — Rate adaptation is one of the basic functionalities in today’s 802.11 wireless LANs (WLANs). Although it is primarily designed to cope with the variability of wireless channels and achieve higher system spectral efficiency, its design needs careful consideration of cross-layer dependencies, in particular, linklayer collisions. Most practical rate adaptations focus on the time-varying characteristics of wireless channels, ignoring the impact of link-layer collisions. As a result, they may lose their effectiveness due to unnecessary rate downshift wrongly triggered by the collisions. Some recently proposed rate adaptations use RTS/CTS to suppress the collision effect by differentiating collisions from channel errors. The RTS/CTS handshake, however, incurs significant overhead and is rarely activated in infrastructure WLANs. In this paper, we introduce a new approach for optimizing the operation of rate adaptations by adjusting the rate-increasing and decreasing parameters based on link-layer measurement. To construct the algorithm, we study the impact of rate-increasing and decreasing thresholds on performance and show that dynamic adjustment of thresholds is an effective way to mitigate the collision effect in multi-user environments. Our method does not require additional probing overhead incurred by RTS/CTS exchanges and may be practically deployed without change in firmware. We demonstrate the effectiveness of our solution, comparing with existing approaches through extensive simulations. I.

Wireless Internet access is facilitated by IEEE 802.11 WLANs that, in addition to realizing a specific form of CSMA/CA-distributed coordination function (DCF)- implement a range of performance enhancement features such as multi-rate adaptation that induce cross-layer protocol coupling. Recent works in empirical WLAN performance evaluation have shown that cross-layer interactions can be subtle, sometimes leading to unexpected outcomes. Two such instances are: significant throughput degradation (a bell-shaped throughput curve) resulting from automatic rate fallback (ARF) having difficulty distinguishing collision from channel noise, and scalable TCP performance over DCF that is able to curtail effective multiple access contention in the presence of many contending stations. The latter also mitigates the negative performance effect of ARF. In this paper, we present station-centric Markov chain models of WLAN cross-layer performance aimed at capturing complex interactions between ARF, DCF, and TCP. Our performance analyses may be viewed as multi-protocol extensions of Bianchi's IEEE 802.11 model that, despite significantly increased complexity, lead to tractable and accurate performance predictions due to modular coupling. Our results complement empirical and simulation-based findings, demonstrating the versatility and efficacy of station-centric Markov chain analysis for capturing cross-layer WLAN dynamics.

The IEEE 802.11 standard for wireless local area networks (WLANs) employs a medium access control (MAC), called distributed coordination function (DCF), which is based on carrier sense multiple access with collision avoidance (CSMA/CA). The collision avoidance mechanism utilizes the random backoff prior to each frame transmission attempt. The random nature of the backoff reduces the collision probability, but cannot completely eliminate collisions. It is known that the throughput performance of the 802.11 WLAN is significantly compromised as the number of stations increases. In this paper, we propose a novel distributed reservation-based MAC protocol, called early backoff announcement (EBA), which is backward compatible with the legacy DCF. Under EBA, a station announces its future backoff information in terms of the number of backoff slots via the MAC header of its frame being transmitted. All the stations receiving the information avoid collisions by excluding the same backoff duration when selecting their future backoff value. Through extensive simulations, EBA is found to achieve a significant increase in the throughput performance as well as a higher degree of fairness compared to the 802.11 DCF.