GNSS spoofing is a growing threat to UAV operations, especially in decentralized drone swarms. In this paper, we present both a practical spoofing attack and a lightweight detection algorithm based on Wi-Fi RSSI and GNSS coordinates. The attack selectively compromises drones by transmitting a subset of satellite signals, causing drones to calculate wrong positions. Our detection method leverages geometric consistency checks to validate GNSS-based positions without requiring any infrastructure or additional sensors. Simulation results show that the proposed attack successfully misleads victim drones, and that the detection method reliably helps drones identify such attacks.

Significant efforts have been made to analyze the political stance or bias in news articles, especially as political polarization intensifies over the years. Recent advancements in machine learning have enabled researchers to develop various bias prediction models, which typically learn features not only from the text of the news articles but also from external knowledge. However, when training these models, the political bias label assigned to a news article is often based solely on the news source which published it. This approach can be problematic, as a news outlet with a particular political stance might publish an article that reflects a different political perspective. To address this issue, we first identify distinct text patterns associated with specific news sources or publishers, that are minimally relevant to predicting the political bias of a news article. We then conduct comprehensive experiments to investigate (i) whether existing models trained to predict political bias can also accurately predict the source, and (ii) whether these models change their predictions when a distinct pattern from a source with a different political stance is incorporated into a news article. Our experimental results reveal that all existing models tend to predict the source, even when trained solely to predict bias. Based on these findings, we propose a new deep learning model for political bias prediction that avoids learning source-indicative patterns specific to a given news source.

In online data-processing services, a user typically hands over personal data to a remote server beyond the user's control. In such environments, the user cannot be assured that the data is protected from potential leaks. We introduce Pave, a new framework to guarantee data privacy while being processed remotely. Pave provides an arbitrary data-processing program with a sandboxed execution environment. The runtime monitor, PaveBox, intercepts all data flows into and out of the sandbox, allowing them only if they do not compromise user data. At the same time, it guarantees that the benign flows will not be hampered to preserve the program's functionality. As the PaveBox is built on top of Intel SGX, a user can verify the integrity and confidentiality of the PaveBox by remote attestation. We provide a formal model of Pave and prove its security and carry out the quantitative analysis with prototype-based experiments.

Domain verification (DV) using DNS TXT records has become an essential mechanism for proving domain ownership across various online services. However, this practice inadvertently discloses sensitive business relations, increases DNS response sizes, and burdens DNS resolver infrastructures. Our measurement of over 2.5 million DNS TXT records highlights the prevalence of bloated TXT records. This work serves as a preliminary problem statement motivating privacy-preserving and efficient DV mechanisms.

Despite the widespread adoption of TLS to secure many protocols such as the web, DNS, and email, downgrade attacks remain a significant vulnerability—particularly when clients opportunistically fall back to unencrypted communication. To address this, we propose leveraging Certificate Transparency (CT) as a verifiable source of truth regarding a server's security capabilities. Specifically, we introduce a custom X.509 certificate extension that explicitly declares a server's supported protocols, ports, and TLS versions. This information enables clients to detect downgrade attacks. To assess the feasibility of our approach, we conducted a measurement study of DNS-over-TLS, DNS-over-HTTPS, and SMTP servers. Our results show that the vast majority of certificates are already logged in CT logs. Building on this, we propose a CT oracle that aggregates data from all CT logs to provide a reliable and comprehensive view of certificates.

Transport Layer Security (TLS) is expected to transition to Post-Quantum Cryptography (PQC) to mitigate the threats posed by quantum computing. While PQC ensures long-term security, it significantly increases the TLS handshake latency due to the larger key and signature sizes. Various solutions have been proposed to address this issue; however, they suffer from limitations, such as the necessity of pre-fetching certificates or dependency on prior connections. In this paper, we propose PQTLS-AD, a novel method that leverages the Domain Name System (DNS) to efficiently distribute PQC certificates. By offloading certificate transmission to DNS, PQTLS-AD reduces handshake data overhead, effectively lowering handshake latency. We have developed a PQTLS-AD prototype and conducted extensive experiments. Our results show that PQTLS-AD significantly reduces handshake latency, cutting it by more than one Round-Trip Time (RTT) compared to standard PQTLS.

Along with the advent of a high-end SoC with multiple CPU clusters and many GPUs, the automotive industry has a strong motivation to consolidate multi-domain applications on such a single SoC for wiring harness reduction and space/weight saving. For this, it is essential to bound their mutual interferences among CPU+GPU clusters on the system memory since advanced automotive applications use huge size code/data like autonomous driving and multi-screen infotainment. In order to guarantee memory bandwidth to each cluster, this paper proposes a cluster-level memory access regulation that aggregates the total memory access amount by each cluster (by all CPU cores and GPUs within the cluster) and throttles the cluster if it exceeds the given threshold. Then, we propose a few-shot measurement based optimal memory bandwidth allocation that can find a near optimal solution with only a few measurements, which is practically essential to save the system development cost. Our extensive experiments on a real SoC board say that our proposed techniques successfully regulate each cluster's memory bandwidth usage within ± 10 % margin of the allocated bandwidth. Also, our optimization can achieve a near-optimal utility with less than 0.6 % loss on average compared to the real optimal, with only a few measurements (mostly three measurements) instead of 25 measurements needed for the real optimal.

Distributed Denial-of-Service (DDoS) attacks have remained a significant threat to the Internet for years. One strategy for mitigating these attacks involves requiring clients to solve cryptographic puzzles to control the rate of incoming traffic to a target server. For such a puzzle-based DDoS defense mechanism to be effective, it necessitates robust methods for both distributing puzzles to clients and adjusting puzzle difficulty. In this paper, we introduce a puzzle-based DDoS defense mechanism, DDD, which utilizes the Domain Name System (DNS) for distributing puzzles to clients. The target server disseminates its puzzles by publishing them as a DNS record through its authoritative name server, distributing puzzles to clients via their DNS resolvers. Our design incorporates a monitoring server that continuously monitors incoming traffic to the target and dynamically adjusts puzzle difficulty based on the traffic originating afrom each Autonomous System (AS). This enables AS-specific puzzle difficulty customization, and consequently, traffic control. We have implemented our design into the Linux kernel and showcased its effectiveness in traffic control through prototype-based and controlled experiments.

Establishing secure connections fast to end-users is crucial to online services. However, when a client sets up a TLS session with a server, the TLS handshake needs one round trip time (RTT) to negotiate a session key. Additionally, establishing a TLS session also requires a DNS lookup (e.g., the A record lookup to fetch the IP address of the server) and a TCP handshake. In this paper, we propose ZTLS to eliminate the 1-RTT latency for the TLS handshake by leveraging the DNS. In ZTLS, a server distributes TLS handshake-related data (i.e., Diffie-Hellman elements), dubbed Z-data, as DNS records. A ZTLS client can fetch Z-data by DNS lookups and derive a session key. With the session key, the client can send encrypted data along with its ClientHello, achieving 0-RTT. ZTLS supports incremental deployability on the current TLS-based infrastructure. Our prototype-based experiments show that ZTLS is 1-RTT faster than TLS in terms of the first response time.

Over the years, the Internet has become a field in which a small number of large Internet companies dominate most of the Internet services. As users get used to using their services, the users’ generated content and the data about their online behaviors are concentrated in such companies. This phenomenon, called “data consolidation”, has become a serious problem, which makes the Internet society seek to decentralize the current Internet. The decentralized Internet aims to (i) prevent the concentration of user data in a few giant companies like Google and Facebook, and (ii) give users full ownership and control of their data. Various technical solutions that address the data consolidation problem have been proposed; however, those solutions focus on somewhat different scopes of the problem often from their limited viewpoints. The main contributions in this paper are the following. First, we survey the solutions relevant to Internet decentralization based on the following criteria: data consolidation, data ownership, and the privacy of user data. Second, we suggest a holistic reference framework from a functional viewpoint, while the prior proposals in the literature handle a limited set of requirements. Last, we seek to identify remaining research issues, considering additional requirements that have not been addressed in the existing solutions.

To provide secure content delivery, Transport Layer Security (TLS) has become a de facto standard over a couple of decades. However, TLS has a long history of security weaknesses and drawbacks. Thus, the security of TLS has been enhanced by addressing security problems through continuous version upgrades. Meanwhile, to provide fast content delivery globally, websites (or origin web servers) need to deploy and administer many machines in globally distributed environments. They often delegate the management of machines to web hosting services or content delivery networks (CDNs), where the security configurations of distributed servers may vary spatially depending on the managing entities or locations. Based on these spatial differences in TLS security, we find that the security level of TLS connections (and their web services) can be lowered. After collecting the information of (web) domains that exhibit different TLS versions and cryptographic options depending on clients' locations, we show that it is possible to redirect TLS handshake messages to weak TLS servers, which both the origin server and the client may not be aware of. We investigate 7M domains with these spatial differences of security levels in the wild and conduct the analyses to better understand the root causes of this phenomenon. We also measure redirection delays at various locations in the world to see whether there are noticeable delays in redirections.

We introduce 5G K-SimNet, a network simulator for evaluating end-to-end performance of the fifth generation (5G) cellular system.5G K-SimNet provides the features of 5G new radio (NR), 5G core, multi-radio access technology (RAT) protocol, traffic management on multi-connectivity, and software-defined network/network function virtualization (SDN/NFV). In this paper, we present the features of 5G K-SimNet, scenarios that can be simulated by using the simulator, and the simulation results.

Over the past decade, crowdsourcing has been actively studied for indoor localization since surveying sites (e.g. wardriving) is a costly process. However, the existing localization systems based on crowdsourcing usually achieve lower location accuracy than the site survey based systems. We note that the magnetic field is robust to environmental changes like pedestrian activities and door/window movements, particularly compared with radio signals such as WiFi. To overcome the low performance of the crowdsourcing based approaches, we design an indoor positioning system using the crowdsourced data of the magnetic field. We substantiate a novel HMM-based learning model to construct a database of magnetic field fingerprints from smartphone users. Experiments in an indoor space consisting of aisles show that the proposed system achieves the learning accuracy of 96.47% and median positioning accuracy of 0.25m.

Understanding socio-economic systems in MMORPGs can provide an important implication on how people participate in the economy and how people interact with each other. In this paper, we model the socio-economic system of an Aion, a popular MMORPG, as a multi-layer graph. Using the dataset consisting of 94,870 users and their activity records spanning three months, we examine how economic activities are associated with social interactions, and find that social interactions like participating in a party or exchanging messages are highly correlated with the trade activities. We also find that virtual economy in Aion is heavily inclined to a small number of upper-class userswho play a crucial role in virtual economy. Our analysis on the upper-class users reveals that a significant portion of them reach at the max-level and tend to either (i) have many social interactions with others or (ii) play extremely much time with no social activity. We also reveal that there are some low-level upper-class users who gain much money but hardly socialize with others. Lastly, we show how upper-class users who are at low-levels, play the game extremely much more than others, or rarely interact with other users, are associated with the Real Money Trade (RMT), which may be an illegal behavior that gathers in-game money for exchanging into real-world money. We reveal that more than half of total money exchanged through the trade are associated with the upper-class users who involve in the RMT.

Fog computing is an emerging architecture that aims to run applications on multiple devices that lie on a continuum from cloud servers to personal user smartphones. These architectures allow applications to optimize over the information stored at and functionalities run on each device, based on individual device capabilities. We demonstrate the benefits of this approach for mobile video streaming. Existing HAS (HTTP adaptive streaming) techniques often suffer from problems like unstable video quality and suboptimal resource utilization. We find that a lack of coordination prevents both clientand network-side HAS techniques from solving them. However, our fog approach can exploit existing telecommunication APIs, which expose network capabilities to applications, in order to coordinate between clients and the network. Our coordinated HAS solution, FLARE, optimizes the total utility of all clients in a cell while maintaining stable video quality and supporting user- and device-specific needs. We implement FLARE on a commodity LTE femtocell and use the implementation to conduct the first comparison of HAS players on an LTE femtocell. By conducting extensive experiments using the ns-3 simulator, we also demonstrate that FLARE (i) enhances the average video bitrate, (ii) achieves stable video quality, and (iii) balances the throughput of simultaneous video and data flows, compared to other representative HAS solutions.

Event-based social networks (EBSNs) are increasingly popular since they provide platforms on which online and offline activities are combined. Despite the increasing interest in EBSNs, little research has paid attention to the privacy issues coming from the unique features of EBSNs; the on-site information of users is highly relevant to real lives. In this paper, we try to investigate privacy leakages in Meetup, one of the most popular EBSN service. More specifically, we answer what private information can be inferred from the site's publicly available data. To this end, we conduct a measurement study by crawling webpages from Meetup containing 240K groups, 8.9M users, 27M group affiliations and 78M topical interests. By analyzing the dataset, we find that LGBT status of users, which is one of the most sensitive privacy information, can be predicted with 93% accuracy. Finally we discuss the cause of the privacy leakage on EBSNs and its possible ensuing damages.

To cope with recent exponential increases in demand for mobile data, wireless Internet service providers (ISPs) are increasingly changing their pricing plans and deploying Wi-Fi hotspots to offload their mobile traffic. However, these ISP-centric approaches for traffic management do not always match the interests of mobile users. Users face a complex, multi-dimensional tradeoff between cost, throughput, and delay in making their offloading decisions: while they may save money and receive a higher throughput by waiting for Wi-Fi access, they may not wait for Wi-Fi if they are sensitive to delay. To navigate this tradeoff, we develop Adaptive bandwidth Management through USer-Empowerment (AMUSE), a functional prototype of a practical, cost-aware Wi-Fi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and Wi-Fi availability, AMUSE decides which applications to offload to what times of the day. Since nearly all traffic flows from mobile devices are TCP flows, we introduce a new receiver-side bandwidth allocation mechanism to practically enforce the assigned rate of each TCP application. Thus, AMUSE users can optimize their bandwidth rates according to their own cost-throughput-delay tradeoff without relying on support from different apps’ content servers. Through a measurement study of 20 smartphone users’ traffic usage traces, we observe that though users already offload a large amount of some application types, our framework can offload a significant additional portion of users’ cellular traffic. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and Wi-Fi usage data obtained from a trial with 37 mobile users. Our results show that AMUSE improves user utility; when compared with AMUSE, other offloading algorithms yield 14 and 27 percent lower user utilities for light and heavy users, respectively. Intelligently managing users’ competing interests for cost, throughput, and delay can therefore improve their offloading decisions.

The increasing mobile traffic is becoming a serious concern for mobile network providers. To address the traffic explosion problem, there have been a lot of efforts to offload the traffic from cellular networks to other networks, such as WiFi hotspots and femtocells. In this paper, we explore the potential benefits of vehicular networks for data offloading and propose a Data Offloading framework through Vehicular nEtworks (DOVE), which reduces the cellular traffic for in-vehicle data services in a cost effective way. DOVE exploits vehicle trajectories for offloading purposes so that content files requested by vehicles can be delivered via vehicular networks rather than via cellular networks for economical purposes. We formulate the problem of selecting offloading positions as a spatio-temporal set-covering problem, and propose a time-prediction based set-covering algorithm using vehicle trajectories. Simulation results show that our DOVE framework can significantly reduce 57% of cellular link usage by performing data offloading through vehicular networks.

Software-defined networking (SDN) renovates traditional networking systems by replacing a distributed, per-switch control plane with a (logically) centralized one. To design a scalable, highly available SDN control plane, it is inevitable to disseminate the network state to multiple instances horizontally by using measures like replication and partitioning. However, some recent studies reported that it is not sufficient to cover a large scale network in a purely horizontal manner. In this paper, we propose FRACTAL, a framework for recursive abstraction of SDN control-plane, to address this problem. In FRACTAL, a large network is divided into multiple small networks, each of which is abstracted as a single virtual switch. This “divide-and-abstract” process is recursively iterated until a divided network can be handled by a single controller. A virtual switch is controlled by the higher level controller over OpenFlow, so that FRACTAL can coexist with other SDN mechanisms.

Online games have been played mainly in desktop computers over wired networks because of high speed and intensive computation requirements. The advances in mobile devices and ever increasing wireless link bandwidth motivate us to study whether players can enjoy online gaming over broadband wireless networks such as mobile Worldwide Inter-operability for Microwave Access WiMAX. In this paper, we carry out comprehensive measurements of the World of Warcraft WoW over the mobile WiMAX in Seoul, Korea, and analyze the network performance focusing on two aspects: 1 network layer dynamics such as round trip time, jitter, and packet loss and 2 WiMAX link layer statistics such as the radio signal strength, handovers, and piggyback mechanism. From the empirical data, we set up performance models and evaluate the performance of WoW over WiMAX. We also discuss how to improve the service quality of online gaming over WiMAX.Copyright © 2013 John Wiley & Sons, Ltd.

Named Data Networking (NDN) uses content names as routing entries, and thus the scalability of NDN routing is of primary concern. NDN allows in-network caching as a built-in functionality; however, if network nodes make caching decisions individually, duplicate copies of the same content may exist among nearby nodes. To address these problems, we propose Coordinated Routing and Caching (CoRC) that mitigates routing scalability and enhances the efficiency of the in-network storage. CoRC aligns the routing and caching mechanisms to manage the same content namespace for better performance. We evaluate CoRC (and its variants) with Vanilla NDN in terms of the cache hit ratio, hop count, and traffic load by running software routers on Amazon EC2. To demonstrate the feasibility of CoRC, we also implement and test the processing time of CoRC forwarding in Linux machines.

While the current Internet has a host-based TCP/IP architecture, the vast majority of Internet usage is attributed to the content retrieval and distribution. This mismatch has proliferated content delivery network (CDN) technologies and P2P file-sharing systems (e.g., BitTorrent); however, the inefficiency of content delivery is not fundamentally solved, and there are business/operation and content copyright/availability issues, respectively. Also, in the research community, there have been efforts to redesign the current architecture from a content-centric perspective by introducing the route-by-name paradigm and in-network caching, which however entails many issues like the routing scalability and processing overhead. We propose a lookup-based communication framework for content-oriented networking (LOCON), whose main components are locator lookup, coordinated caching, and parallel transmissions. LOCON aims to accommodate incremental deployment, support legacy end-hosts, and provide business incentives between network operators and content publishers. To carry out the experiments, we build a networking testbed on top of Amazon data centers around the world. LOCON performs better than other lookup-by-name networking technologies and CCN (i.e., a route-by-name scheme) in terms of content delivery metrics.

The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each user's access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.

As smartphones are increasingly popular, location-based services (LBSs) have become one of the crucial applications in daily lives. While outdoor localization is relatively easy leveraging GPS signals, localization in indoor environments is difficult due to the lack of GPS. Thus, due to the pervasive deployment of WiFi access points, there have been numerous studies on WiFi based indoor positioning. However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization. Moreover, WiFi scanning period, about 3~4 seconds in general smartphone, may provide poor quality of services in the context of refreshment interval. Motivated by these limitations, we study the usability of tw o other sources that are currently available in smartphones: magnetic field strength and cellular signal strength, for indoor positioning purposes. Our preliminary measurements show that these two sources satisfy three properties needed for localization: time-in-variance, location representativeness and universality. Because these three sensors have their own characteristics, some problems in single sensory data based localization could be complementarily overcome. We will show how to combine the three smartphone sensory data for indoor positioning based on each module's characteristics, and how much accuracy is achieved by the hybrid localization.

Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.

Understanding the group characteristics in MMORPGs is important in user behavior studies since people tend to gather together and form groups due to their inherent nature. In this paper, we analyze the group activities of users in Aion, one of the largest MMORPGs, based on the records of the activities of 94,497 users. In particular, we focus on (i) how social interactions within a group differ from the ones across groups, (ii) what makes a group rise, sustain, or fall, (iii) how group members join and leave a group, and (iv) what makes a group end. We first find that structural patterns of social interactions within a group are more likely to be close-knit and reciprocative than the ones across groups. We also observe that members in a rising group (i.e., the number of members increases) are more cohesive, and communicate with more evenly within the group than the ones in other groups. Our analysis further reveals that if a group is not cohesive, not actively communicating, or not evenly communicating among members, members of the group tend to leave.

While accurate and complete modeling of the Internet topology at the Autonomous System (AS) level is critical for future protocol design, performance evaluation, simulation and analysis, still it remains a challenge to construct its accurate representation. In this paper, we collect BGP route announcements of ASes from Looking glass (LG) servers. By querying LG servers, we build an AS topology estimate of around 116 K AS links, from which we discover 11 K new AS links and 686 new ASes. We conclude that collecting BGP traces from LG servers can help enhance the current view of the AS topology from the BGP collector projects (e.g., RouteViews).

Mobile users face a tradeoff between cost, throughput, and delay in making their offloading decisions. To navigate this tradeoff, we propose AMUSE (Adaptive bandwidth Management through USer-Empowerment), a practical, costaware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. To practically enforce the assigned rate of each TCP application, we introduce a receiver-side TCP bandwidth control algorithm that adjusts the rate by controlling the TCP advertisement window from the user side. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 25 mobile users. Our results show that AMUSE improves user utility.

Video on demand services have been increasingly proliferated in the Internet. One popular way to disseminate video files among numerous users is to leverage peer-to-peer (P2P) systems (e.g., BitTorrent). However, BitTorrent is not designed with video streaming requirements and hence suffers from long setup delay. In this paper, the drawbacks of existing P2P-based streaming solutions are analyzed in terms of sequential delivery. Then we propose Waterfall that splits the whole swarm into multiple swarms, which are then cascaded by the scene sequence. In this way, peers in a swarm download the chunks of the same video scene from the peers in the same swarm as well as the ones in the preceding swarm that already moved on to the next scene. The average setup delay and maximum playback rate of Waterfall are analyzed. Experiments from a wide area network testbed reveal that Waterfall achieves two to three times higher playback rate and significantly low setup delay than the prior BitTorrent-based streaming solutions.

The problem of developing distributed scheduling algorithms for high throughput in multihop wireless networks has been extensively studied in recent years. The design of a distributed low-complexity scheduling algorithm becomes even more challenging when taking into account a physical interference model, which requires the SINR at a receiver to be checked when making scheduling decisions. To do so, we need to check whether a transmission failure is caused by interference due to simultaneous transmissions from distant nodes. In this paper, we propose a scheduling algorithm under a physical interference model, which is amenable to distributed implementation with 802.11 CSMA technologies. The proposed scheduling algorithm is shown to achieve throughput optimality. We present two variations of the algorithm to enhance the delay performance and to reduce the control overhead, respectively, while retaining throughput optimality.

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.

The The ever increasing traffic demand is a serious concern of mobile network operators, and the conventional pull-based (or request-based) communication model may not be able to handle this data explosion problem. To reduce the traffic load on cellular links for disseminating content, we propose to push the content to a subset of subscribers via cellular links, and to allow the subscribers to share the content via opportunistic local connectivity (i.e. Wi-Fi ad-hoc mode). We theoretically model and analyze how the content can be disseminated by both pushing via cellular links and sharing via Wi-Fi links, where handovers are modeled based on the multi-compartment model. We also formulate a mathematical framework to optimize the content dissemination, by which the trade-off between the dissemination delay and the energy cost is explored.

We conduct comprehensive measurements on the current practice of content bundling to understand the structural patterns of torrents and the participant behaviors of swarms on one of the largest BitTorrent portals: The Pirate Bay. From the datasets of the 120K torrents and 14.8M peers, we investigate what constitutes torrents and how users participate in swarms from the perspective of bundling, across different content categories: Movie, TV, Porn, Music, Application, Game and E-book. In particular, we focus on: (1) how prevalent content bundling is, (2) how and what files are bundled into torrents, (3) what motivates publishers to bundle files, and (4) how peers access the bundled files. We find that over 72% of BitTorrent torrents contain multiple files, which indicates that bundling is widely used for file sharing. We reveal that profit-driven BitTorrent publishers who promote their own web sites for financial gains like advertising tend to prefer to use the bundling. We also observe that most files (94%) in a bundle torrent are selected by users and the bundle torrents are more popular than the single (or non-bundle) ones on average. Overall, there are notable differences in the structural patterns of torrents and swarm characteristics (i) across different content categories and (ii) between single and bundle torrents.

ABSTRACT In wireless network convergence, each mobile host is expected to have multiple kinds of wireless interfaces. Multicast‐based applications are expected to be widely deployed. In this paper, a new network selection scheme is proposed for a mobile host to select the most appropriate wireless access network to maximize user satisfaction and ISP's profit, simultaneously. We have devised a metric to measure a user's satisfaction and we also developed a ‘normalized network resource’ metric for system profit measure. We have compared our scheme with three other reference schemes, through simulations. Depending on the network deployment situations, our scheme exhibits one‐fifth service disruption time of other reference schemes, while the resource consumption of our scheme is comparable to that of the minimum resource scheme. Overall, the gain of our scheme becomes higher as users move faster and/or the population density increases. Copyright © 2010 John Wiley & Sons, Ltd.

BitTorrent has achieved a great success in the field of peer-to-peer (P2P) file sharing. Although BitTorrent allows peers to share files efficiently and scalably, it shows inefficiency when a client participates in multiple torrents where each of them concurrently competing for the limited link bandwidth. In this paper, we propose a new bandwidth allocation algorithm, which greedily increases the bandwidth consumption for downloading, to reduce file transfer time considering the current download/upload status. To compensate overall performance degradation resulting from our greedy allocation, we suggest modifying the choking algorithm of BitTorrent to consider the ratio of seeders and leechers in each torrent. Through comprehensive experiments, we validate the performance gain of the proposed scheme over original BitTorrent in a mix of WiFi and Ethernet testbed and large scale public torrents.

Since Internet routers are not aware of the contents being forwarded, the same content file is often delivered multiple times inefficiently. Similarly, users cannot exploit a nearby copy of the content of interest unless the content file is serviced by costly content delivery networks. Prior studies on the content-aware routing for efficient content delivery suffer from the scalability problem due to a large number of contents. We propose a scalable content routing, dubbed SCAN, which can exploit nearby and multiple content copies for the efficient delivery. SCAN exchanges the information of the cached contents using Bloom filter. Compared with IP routing, SCAN can offer reduced delivery latency, reduced traffic volume, and load balancing among links.

Surveillance systems are developed to enhance security and safety by constantly observing locations of interest. Although those systems can observe scenes from each camera separately, it is difficult to keep track of any moving target across different cameras. This paper firstly proposes Video Diary Service (VDS) to solve this problem. VDS is an automatic diary service, which makes it possible to keep track of users' lives. In addition, VDS can identify social networking relationships among the users while each camera is watching multiple users. By exploiting these properties of VDS, we extend VDS into a new surveillance system called S-VDS. We also illustrate a few application scenarios where the proposed system can enhance security and safety.

We analyze multi-relational social interaction networks in a large-scale commercial Massively Multiplayer Online Role-Playing Game(MMORPG). Our work is based on data from AION, currently the world's second most-played MMORPG with 3.4 million subscribers as of mid 2010, created and serviced by NCSoft, Inc. We construct and characterize six distinct interactivity networks (Friend, Private Messaging, Party invitation, Trade, Mail, and Shop), each representing diverse player interaction types.

Despite the increasing interest in content bundling in BitTorrent systems, there are still few empirical studies on the bundling practice in real BitTorrent communities. In this paper, we conduct comprehensive measurements on one of the largest BitTorrent portals: The Pirate Bay. From the torrents data set collected for 38 days from April to May, 2010, we study how prevalent bundling is and how many files are bundled in a torrent, across different types of contents shared: Movie, Porn, TV, Music, Application, E-book, and Game.

Over the last decade, mobile agent (MA) systems for surveillance applications in wireless sensor networks (WSNs) has gained much attention. However, a conventional MA-based WSN may have the issues of energy efficiency and task duration as the scale of the network is increased. In order to overcome the drawbacks of using a single MA, dispatching two or more MAs for data collection simultaneously is a promising alternative in a WSN. The authors first discuss the itinerary planning issues for multiple MAs: deciding the number of MAs to be dispatched, grouping of source nodes for each MA, routing of each MA for its assigned source nodes. The authors then survey the existing algorithms for these issues, and evaluate their performance by OPNET.

With the advent of various multimedia streaming applications requiring reliability and high bandwidth, multicasting in wireless LANs has been gaining more attentions as the supported bit rate increases. However, according to the specification of the IEEE 802.11 standard, broadcast/multicast frames are transmitted at a fixed and low bit rate due to the absence of a feedback mechanism such as ACK. This simple broadcasting technique with no feedback signal raises some issues; reliability, efficiency and fairness. In this paper, hence, we propose a framework for multicasting termed Leader-Based Multicast Service (LBMS) to alleviate those limitations. LBMS consists of a leader-based transmission and feedback mechanism for multicasting by extending the IEEE 802.11v standard, which is the next-generation standard for network management. Also, we try to support legacy 802.11 stations can still participate in multicasting. Simulation exhibits that the gain of the proposed multicasting scheme increases as (i) more stations compete for the channel, and (ii) the wireless channel condition becomes poorer.

This paper proposes a service differentiation architecture using virtualization of mobile femtocell. Network-prefix division multiple access (NDMA) is proposed to enable mobile devices inside a moving space to access the Internet with different service demands. With the NDMA scheme, our architecture provides multiple virtual mobile networks for diverse services which are optimized under different network conditions. We experimented on the operation of the proposed architecture with testbed implementation. The result shows the ability of multiple degrees of QoS over each virtual mobile network with additional benefits of personalization, mobility management aggregation, and so on.

Recent research on Internet traffic classification has yield a number of data mining techniques for distinguishing types of traffic, but no systematic analysis on "Why" some algorithms achieve high accuracies. In pursuit of empirically grounded answers to the "Why" question, which is critical in understanding and establishing a scientific ground for traffic classification research, this paper reveals the three sources of the discriminative power in classifying the Internet application traffic: (i) ports, (ii) the sizes of the first one-two (for UDP flows) or four-five (for TCP flows) packets, and (iii) discretization of those features. We find that C4.5 performs the best under any circumstances, as well as the reason why; because the algorithm discretizes input features during classification operations. We also find that the entropy-based Minimum Description Length discretization on ports and packet size features substantially improve the classification accuracy of every machine learning algorithm tested (by as much as 59.8%!) and make all of them achieve >93% accuracy on average without any algorithm-specific tuning processes. Our results indicate that dealing with the ports and packet size features as discrete nominal intervals, not as continuous numbers, is the essential basis for accurate traffic classification (i.e., the features should be discretized first), regardless of classification algorithms to use.

Peer-to-peer (P2P) live streaming services are getting more popular as the average link capacity in the Internet becomes greater for end hosts. In order to provide a high quality P2P live streaming service, it is essential to handle user dynamics effectively to mitigate streaming disruptions caused by user churn. We collect massive user traces from the largest P2P live streaming site in Korea, namely, Afreeca. Based on the trace results obtained, we analyze user dynamics in the service. In particular, we analyze the user behaviors in terms of three criteria: long-stay users, short-stay users, and user churn. (1) The results show that a certain amount of long-stay users exist in a live streaming session, and it is challenging to recognize the long-stay users online, which will help to provide more resilient live streaming service. (2) Around 20% of the users stay in a session shorter than 1% of the session length. Short-stay users incur high control overhead and we discuss how to efficiently handle the short-stay users. (3) By intentionally disrupting a video streaming, we closely observe the users' leaving behavior and suggest the acceptable recovery time to keep the users remaining the session. We believe our findings can be useful not only to P2P live streaming systems, but also to every live streaming systems.

The problem of developing high-performance distributed scheduling algorithms for multi-hop wireless networks has seen enormous interest in recent years. The problem is especially challenging when studied under a physical interference model, which requires the SINR at the receiver to be above a certain threshold for decoding success. Under such an SINR model, transmission failure may be caused by interference due to simultaneous transmissions from far away nodes, which exacerbates the difficulty in developing a distributed algorithm. In this paper, we propose a scheduling algorithm that exploits carrier sensing and show that the algorithm is not only amenable to distributed implementation, but also results in throughput optimality. Our algorithm has a feature called the dual-state approach, which separates the transmission schedules from the system state and can be shown to improve delay performance.

As mobile Internet environments are becoming dominant, how to revamp P2P operations for mobile hosts is gaining more and more attention. In this paper, we carry out empirical traffic measurement of BitTorrent service in various settings (static, bus and subway) in commercial WiMAX networks. To this end, we analyze the connectivity among peers, the download throughput/stability, and the signaling overhead of mobile WiMAX hosts in comparison to a wired (Ethernet) host. We find out the drawbacks of BitTorrent operations in mobile Internet are characterized by lower connection ratio, unstable connections amongst peers, and higher control message overhead.

Flooding, that is to deliver a packet from one node to all other nodes in the network, is an integral part of many wireless protocols. Flooding is often implemented by a series of broadcasts of each node and this causes some problems such as the broadcast storm and low reliability, by being engaged with the effects of radio signal propagation, e.g., multipath fading and interference. Many researchers have studied these problems over the years, however, most of these studies have been carried out assuming that all nodes in the network are equipped with a single radio interface and utilize only a single channel. This implies that most of the existing mechanisms that enhance the performance of flooding in wireless networks will not work in multi-radio multi-channel wireless mesh networks (MR-MC WMNs). Motivated by this, in this paper, we propose a flooding mechanism that works well in MR-MC WMNs. Our flooding mechanism, which can operate with an arbitrary number of radio interfaces and channels, increases the reliability of flooding while alleviates the broadcast storm problem, using only local information. Through a detailed simulation study, we demonstrate that our flooding mechanism improves both the reliability and the efficiency of flooding in MR-MC WMNs.

Compared to conventional wireless sensor networks (WSNs) that are operated based on the client-server computing model, mobile agent (MA) systems provide new capabilities for energy-efficient data dissemination by flexibly planning its itinerary for facilitating agent based data collection and aggregation. It has been known that finding the optimal itinerary is NP-hard and is still an open area of research. In this paper, we consider the impact of both data aggregation and energy- efficiency in sensor networks itinerary selection, We propose an itinerary energy minimum for first-source-selection (IEMF) algorithm, as well as the itinerary energy minimum algorithm (IEMA), the iterative version of IEMF. Our simulation experiments show that IEMF provides higher energy efficiency and lower delay compared to existing solutions, and IEMA outperforms IEMF with some moderate increase in computation complexity.

While the Internet was designed with host-oriented networking applications, recent Internet statistics show that content-oriented traffic has become more and more dominant. Even though content-oriented networking, which tries to resolve this discordance, has received increasing attention, there have been few comprehensive and quantitative studies on how to realize a content-oriented networking architecture. In this paper, we focus on the design alternatives of the content-oriented networking architecture and evaluate their performance: (i) how to locate contents, (ii) how to cache contents, and (iii) how to deliver contents. There are two major infrastructure alternatives in substantiating these mechanisms: a tree and a distributed hash table (DHT).We carry out comprehensive simulation experiments to compare these alternatives in terms of content transfer latency, cache effectiveness, and failure resilience.

Abstract Routing protocols in wireless sensor networks (WSNs) typically employ a transmitter‐oriented approach in which the next hop node is selected based on neighbor or network information. This approach incurs a large overhead when the accurate neighbor information is needed for efficient and reliable routing. In this paper, a novel receiver‐oriented load‐balancing and reliable routing (RLRR) protocol is proposed. In RLRR, an intermediate node solicits next hop candidates, each of which is to respond with its own backoff time dubbed a temporal gradient (TG). In this way, the next hop is selected without any central coordination on a packet‐by‐packet basis. Thus, each node needs not maintain any neighbor information. The remaining energy level used to determine the TG is always accurate and up‐to‐date. Furthermore, neighbor nodes whose hop count is less than the soliciting node participate in the next‐hop selection process with loop‐free operation guarantee. Comprehensive simulations are carried out to show that RLRR achieves relatively longer network lifetime and higher reliability than other existing schemes. Copyright © 2007 John Wiley & Sons, Ltd.

The IEEE 802.11 standards support multiple bit rates at the physical layer so that stations can maximize the system performance by controlling the transmission rate responding to the conditions of underlying time-varying channels. This paper proposes a novel scheme, termed as rate adaptation using rate-adaptive acknowledgment (RARA). The key idea of RARA is to regulate the ACK transmission rate as a means to dictate the sender to adjust the data transmission rate. Compared with previous works, the proposed scheme can react quickly to the prevailing conditions by the receiver-based decision while inducing marginal overhead. Simulation results show that the RARA consistently outperforms other rate adaption schemes.

In this paper, we seek to enhance the poor performance of original TCP in wireless multi-hop environments due to the intra-flow contention between TCP-DATA and TCP-ACK packets. Assuming multi-radio multi-channel networks, where each station is equipped with multiple radios and the same number of orthogonal channels, we propose to use virtually different paths with different channel assignments for TCP-DATA and TCP-ACK transmissions. Simulations show that TCP performance can be improved significantly since TCP-DATA and TCP-ACK paths hardly interfere with each other.

Flooding is a mechanism that distributes packets to every node of the network. The flooding mechanism is frequently used in many operations in wireless multi-hop networks. Since flooding exploits hop-by-hop broadcasting that suffers from unreliable transmission and fading, it is hard to achieve the reliability in flooding. As unreliable flooding may lead to a coverage hole, it will have a negative effect upon upper layer protocols. In this paper, we introduce a Reliability Aware Flooding Algorithm (RAFA), which estimates the expected reliability using two-hop topology knowledge. The estimated reliability is used for deciding whether or not to retransmit a packet. Using NS-2 [19] simulator, we show that RAFA achieves the higher reliability than RBP [1] by adjusting the number of retransmissions considering the network topology, regardless of the network topologies, the node density or the number of bottlenecks.

In simulating wireless networks, modeling of the physical layer behavior is an important yet difficult task. Modeling and estimating wireless interference is receiving great research attention, and is crucial in a wireless network performance study. The implementation of physical layer capture, preamble detection, and carrier sense threshold plays an important role in successful frame reception in the presence of interference. We showed in our previous testbed study that the operations of the frame reception and the capture effect in real IEEE 802.11a systems differ from those of popular research simulators. We present our modifications of the IEEE 802.11a PHY models to the current simulators. The modifications can be summarized as follows. (i) The current simulators' frame reception is based only on the received signal strength. However, the real 802.11 systems can start the frame reception only when the Signal-to-Interference Ratio (SIR) is high enough to detect the preamble. (ii) Different chipset vendors implement the frame reception and capture algorithms differently, resulting in different operations for the same event. We provide different simulation models for several popular chipset vendors and show the performance differences between the models. (iii) The current simulators set the carrier sense threshold equal to the receiver sensitivity. The standard however states that it should be 20 dB higher than the receiver sensitivity. We implement our modifications to the QualNet simulator and conduct a wireless network performance study to evaluate the impact of PHY model implementation.

Multicasting is useful for various applications such as multimedia broadcasting. In current 802.11, multicast frames are sent as broadcast frames at a low transmission rate without any acknowledgement or binary exponential backoff. This naive multicasting mechanism degrades the performance of not only multicast flows but also unicast flows. In this paper, we propose a new multicasting mechanism based on the leader- based approach to improve the legacy multicast transmissions, maintaining coexistence with legacy 802.11 devices. Simulations show that our protocol achieves well-balanced performance in terms of reliability, latency, goodput, and transmission fairness in comprehensive environments.

Finding a reliable and efficient routing path in vehicular ad hoc networks (VANETs) is a challenging issue due to high mobility of vehicles and frequent link breakage. Motivated by this, we propose a robust and efficient routing protocol, called MMR. The contribution of this paper is two-fold: two-level routing and a new routing metric. A routing process of MMR consists of the macro level and the micro level. MMR forwards a packet to an approximate location of the destination at the macro level and then forwards a packet to the exact location of the destination at the micro level. This two-level routing reduces the protocol overhead and improves scalability in terms of the number of nodes. MMR also introduces a new routing metric that reduces the protocol overhead and path breakage by considering velocities of vehicles. Through simulations, we show that MMR improves the routing performance by about 30~40% in highly mobile environments, compared to the existing ad hoc routing protocols such as AODV and GPSR.

The IEEE 802.11e standard supports a direct-link setup (DLS) mechanism optionally to improve the throughput. Using this mechanism, IEEE 802.11e stations in proximity can directly exchange frames with no intervention of an access point. However, extensive simulations reveal the severe unfairness between external and local TCP connections in the IEEE 802.11e DLS mode due to the characteristics of TCP: a window-based flow control mechanism. This paper first analyzes why a fairness problem happens between external and local TCP connections in the IEEE 802.11e DLS mode. Then, we seeks to achieve fairness between them by introducing a novel mechanism dubbed half direct-link setup plus (H-DLS) which differentiates the paths for TCP DATA and ACK packets of local TCP connections. Simulation results reveal that H-DLS achieves the fairness between external and local TCP connections while keeping the aggregate throughput higher than the original IEEE 802.11 infrastructure.

In wireless networks where communications are made over a shared medium, interference and collisions are the primary causes of packet drops. In multi-hop networks such as wireless mesh networks, due to the hidden terminal problem, limiting the effects of collisions and interference is a key in achieving high performance. Typical wireless medium access control protocols perform carrier sensing to avoid collisions. Most research efforts so far has used the binary model when studying carrier sensing and interference; a node is either carrier sensed or not, and a link is either interfered or not. In reality however, there exists a gray zone. Carrier sensing and interference should be represented in continuous values. Using the measurement data from our 802.11a wireless mesh network test-bed, we propose metrics that represent the levels of carrier sensing and interference. Using our metrics, we also propose methods to estimate broadcast throughput and goodput. We evaluate the accuracy of our methods by comparing our models with the measured data. In addition, we investigate the impact of the capture effect on interference.

In this paper, we propose a hybrid query tree protocol that combines a tree based query protocol with a slotted backoff mechanism. The proposed protocol decreases the average identification delay by reducing collisions and idle time. To reduce collisions, we use a 4-ary query tree instead of a binary query tree. To reduce idle time, we introduce a slotted backoff mechanism to reduce the number of unnecessary query commands. For static scenarios of tags, we extended the proposed protocol by adopting two phases. First, inleafqueryphasefor existing tags, the interrogator queriesleaf-nodes directly to reuse query strings in the previous session. Second, inrootqueryphasefor new arriving tags, the interrogator starts the query process from theroof-node. Simulation reveals that the proposed protocol achieves lower identification delay than existing tag collision arbitration protocols regardless of whether tags are mobile or not.

This paper via analysis and simulation revisits the interaction between MAC contention and TCP congestion control over IEEE 802.11 WLANs, misled in the previous efforts. The results reveal that the effective number of contending wireless stations is not proportional to the number of wireless stations with an upstream TCP flow in IEEE 802.11 wireless LANs. Thus, we propose a new scheme called TCP ACK priority (TAP) in which, by allowing an access point to transmit TCP ACKs at the highest priority, the optimal number of competing stations are allowed to contend for media access to utilize link bandwidth efficiently. We use an ns-2 simulator to evaluate the performance of TAP with the IEEE 802.11 DCF. The results show that there is an improvement in network performance without the loss of fairness between upstream TCP flows. The extensions for IEEE 802.11e/n are also considered.

A crucial issue in deploying wireless sensor networks is to perform a sensing task in an area of interest in an energy-efficient manner since sensor nodes have limited energy power. The most practical solution to solve this problem is to use a node scheduling protocol that some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy. In this paper, we present a distributed node scheduling protocol, which can maintain sensing coverage required by applications and yet increase network lifetime by turning off some redundant nodes. In order to do this, we use the concept of an effective sensing area (ESA). The ESA of a sensor node refers to the sensing area that is not overlapping with other sensor's sensing area. A sensor node determines whether it will be active or not after calculating its own ESA. The proposed protocol allows sensor nodes to sleep opportunistically while satisfying the required sensing coverage. Through extensive simulation experiments, we have observed significant improvement in terms of network lifetime, which we have compared with the existing protocols.

In vehicular ad hoc networks (VANETs), geographic routing protocols appear to be a fascinating option since they have generally low delay and small routing overhead and GPS devices are becoming affordable. However, we reveal that geographic routing protocols suffer from routing holes when nodes are distributed only on lines such as cars on roads, trains on rails, and ships on courses. To tackle this problem, we propose a novel geographic routing protocol, virtual vertex routing (VVR), which uses the information of the lines. Using graph formulation, we introduce a new concept, the proximity of a vertex (or a virtual vertex). An intermediate node in this proximity performs routing toward the destination by Floyd algorithm. For routing holes, we propose two countermeasures: greedy routing (VVR-GR) and face routing (VVR-FR). The latter can guarantee the packet delivery. Extensive simulations are performed to show that VVR outperforms GPSR and AODV.

Abstract — The IEEE 802.11 protocol has become the de facto standard in wireless LANs. However, it reveals the unfairness problem between unicast flows and multicast flows since multicast packets are not subject to binary exponential backoff. To prevent a multicast flow from overwhelming the wireless link bandwidth is a crucial issue. This paper seeks to achieve fairness between unicast and multicast flows by introducing Unicast-Friendly Multicast (UFM). The central idea behind the UFM algorithm is to dynamically change the contention window size for multicast packets to limit the bandwidth share of a multicast flow equal to that of a unicast flow. The proposed UFM algorithm adjusts the multicast contention window size depending on the number of competing stations. We present two versions of UFM: the first one calculates the multicast window size by inferring the average contention window size of unicast flows, while the second one maintains the mapping table between the number of competing stations and the corresponding multicast window size given by related analysis. Simulation reveals that both versions of UFM achieve the fairness by providing almost the fair share of bandwidth to each flow regardless of unicast or multicast under the saturated network conditions. I.

IEEE 802.11 DCF exhibits poor scalability due to the large contention overhead. Therefore, the more the number of stations, the less the aggregate throughput. We propose a two-phase collision avoidance scheme to reduce the collision probability and to enhance the throughput performance. In our proposed scheme, contention among stations is resolved in two phases: SuperSlots and SubSlots. Also, our truncated backoff mechanism increases the throughput by reducing the idle time slots. We analyze the performance of our proposed scheme based on the previous analysis of 802.11 DCF. Both the analysis and simulation results exhibit that our proposed scheme achieves higher throughput than the current IEEE 802.11 backoff mechanism, and this differential increases with the number of stations in the network.

This paper introduces a new split-TCP approach for improving TCP performance over IEEE 802.11-based wireless LANs. TCP over wireless LANs is not aggressive, which is a fundamental reason for poor performance. We propose augmented split-TCP (AS-TCP) to mitigate this problem. Our scheme extends the split-connection approach that divides a connection into two different connections at a split point such as an access point. Using AS-TCP, a mobile host emulates TCP ACK packets using MAC ACK frames, instead of receiving real TCP ACK packets. We compared AS-TCP with both normal TCP and I-TCP (indirect TCP) by simulation. Results show that AS-TCP achieves higher throughput, fairer resource allocation and, in power-saving mode, shorter delays.

IEEE 802.11 MAC protocol in distributed coordination function (DCF) mode is not scalable as the number of contending users increases. In this paper, we propose two new contention resolution schemes to reduce the number of collisions due to a large number of contending users. The first scheme, hybrid DCF (H-DCF), splits the monolithic contention resolution into two phases to enhance scalability. The other scheme, enhanced hybrid DCF (EH-DCF), provides fairness with the current IEEE 802.11 MAC and yet improves the efficiency of contention resolution. The major contribution of our work is to provide inter-operability with the IEEE 802.11 DCF as well as an efficient channel access, unlike the previous works. We conduct comprehensive simulation experiments by using an ns-2 simulator to demonstrate the advantages of these two schemes. The results show that they improve network performance by up to 10~30%, compared to the original IEEE 802.11 DCF. Therefore, H-DCF and EH-DCF can be good candidates for MAC protocols for hotspots where numerous users access wireless LANs intensively

IEEE 802.11 WLAN has been widely accepted throughout the world. However, it has large overhead due to idle backoff slots and frequent collisions depending on the number of nodes. In this paper, we propose a novel medium access control scheme, scheduling-based coordination function (SCF). SCF polls the next station to be serviced by piggybacking. The selection of the next station is based on self-clocked fair queueing (SCFQ) scheduling in a distributed manner. Due to polling, SCF does not suffer from collisions and is efficient owing to the SCFQ scheme. SCF improves the system throughput up to 63.2% compared to IEEE 802.11 DCF. Comprehensive simulation is performed to compare SCF with DCF, PCF, and fast collision resolution (FCR).

In wireless sensor networks (WSNs), reducing energy consumption of resource constrained sensor nodes is one of the most important issues. In this paper, we propose a task aware (TA) MAC protocol, which improves energy efficiency and throughput by introducing a channel access scheme depending on traffic load in WSNs. The amount of traffic load of a sensor node can be estimated by its task activity, where a task is an operation that the sensor node performs based on the schedule set by data dissemination procedures in advance. In addition, the sensor node collects neighbor nodes' task activities and determines its channel access probability using the collected information. Consequently, the sensor node can choose a more suitable channel access probability which is adaptive to its traffic load as well as neighbor's traffic load. We carry out performance analysis using a p-persistent MAC protocol. The results reveal that the TA-MAC protocol exhibits less collisions than the normal p-persistent MAC protocol and thus it achieves energy efficient operations. Also, it can been seen that the TA-MAC protocol improves system throughput compared with other protocols

Mobile IP is the basic solution to provide host mobility, whereas network mobility refers to the concept of collective mobility of a set of nodes. In the simplest scenario, a mobile network moves as a single unit with one mobile router (MR) that connects it to the global Internet. Also, multiple mobile networks can be nested in a hierarchical form, e.g., a wireless personal area network (PAN) in a vehicular network. In a nested mobile network, multiple MRs form a tree hierarchy in which the root MR is called the top-level mobile router (TLMR). Nested mobile networks exhibit the pinball routing problem, which becomes worse in proportion to the number of nested levels in the hierarchy. To solve this problem, we propose a routing optimization scheme using a tree information option (ROTIO) that extends the NEMO basic support protocol. In the ROTIO scheme, each MR in the nested mobile network sends two binding updates (BUs): one to its home agent and the other to the TLMR. The former BU contains the TLMR's home address, while the latter contains routing information between the issuing MR and the TLMR. This alleviates the pinball routing problem significantly. Now, a packet from a correspondent node only needs to visit two transit nodes (the home agents of the MR and the TLMR), regardless of the degree of nesting. Moreover, the ROTIO scheme provides location privacy and mobility transparency. We also extend ROTIO to perform routing between two mobile network nodes inside the same nested mobile network more efficiently and to substantially reduce the disruption when a mobile network hands off.

Mobility support in IEEE 802.11 networks is a challenging issue. Recently, a new scheme, called proactive neighbor caching (PNC), was proposed and adopted as an IEEE standard. The PNC scheme introduces a neighbor graph, which dynamically captures the mobility topology of a wireless network for pre-positioning the context of a mobile host (MH). However, the PNC scheme may result in a significant signaling overhead because the MH's context is propagated to all neighbor access points (APs). We propose a selective neighbor caching (SNC) scheme, which propagates an MH's context to only the selected neighbor APs considering handoff frequencies between APs. When the context transfer is needed, neighbor APs with handoff probabilities equal to or higher than a predefined threshold value are selected. We also derive an optimal threshold value when the target cache hit probability is given. Simulation results reveal that the SNC scheme significantly reduces the signaling overhead while guaranteeing a comparable cache hit probability compared to the PNC scheme.

Recently, mobile agents have been proposed for efficient data dissemination in sensor networks. In the traditional client/server-based computing architecture, data at multiple sources is transferred to a destination; whereas, a task-specific executable code traverses the relevant sources to gather data in the mobile-agent based computing paradigm. As described in Hairong Qi, et al. (2003), many inherent advantages (e.g. scalability, extensibility, energy awareness, reliability) of the mobile agent architecture make it more suitable for sensor networks than the client/server architecture. In this paper, a mobile agent is exploited in three levels (e.g. node level, task level, and combined task level) to reduce the information redundancy and communication overhead.

Although hierarchical mobile IPv6 (HMIPv6) can reduce the signaling overhead and the handoff latency, it results in the non-optimal local routing problem when two mobile nodes communicate in the same mobility anchor point domain. To address this problem, we propose an adaptive local route optimization (ALRO) scheme. The ALRO scheme chooses either the global route optimization scheme or the local route optimization scheme depending on the session-to-mobility ratio (SMR). Based on the proposed analytical model, we find the optimal SMR threshold at which the ALRO scheme shows the best performance. Numerical results demonstrate that the ALRO scheme shows a good performance in terms of the total cost, session delivery time, and session disruption time.

In this paper, we propose a mobility-based load control scheme, which consists of two sub-algorithms: (1) a threshold-based admission control algorithm; and (2) a session-to-mobility ratio (SMR) based replacement algorithm. Here the SMR is defined as a ratio of the session arrival rate to the handoff rate. When the number of mobile nodes (MNs) at a mobile anchor point (MAP) reaches to the full capacity, the MAP replaces an existing MN at the MAP, whose SMR is high, with an MN that just requests a binding update. The replaced MN is redirected to its home agent. We analyze the proposed load control scheme using the Markov chain model in terms of the new MN blocking probability and the ongoing MN dropping probability. By combining the threshold-based admission control with the SMR-based replacement, the above probabilities are lowered significantly compared to the threshold-based admission control alone.

Recently, the radio frequency identification (RFID) technology has gained significant attention. One of the important performance issues in RFID systems is to resolve the collision among responses from RFID tags from the viewpoint of wireless media access control. We consider two kinds of smart antenna systems to enhance the RFID tag reading rate, namely the adaptive array antenna and the multiple-input multiple-output (MIMO) antenna. We consider passive tags that are operating without battery. We evaluate how much performance can be improved by employing smart antennas in the cases of the binary tree splitting algorithm and the Slotted-Aloha algorithm.

The standards that are supposed to play a leading role in third-generation mobile telecommunication and personal area networks in the near future are the Universal Mobile Telecommunication System (UMTS) and the Bluetooth (BT) radio technology, respectively. In this paper, we advocate that a hybrid architecture of UMTS and Bluetooth can take advantage of the complementary characteristics of these two technologies and provide a total solution for an indoor communication environment. We envision a cooperating scenario in which small Bluetooth networks (scatternets) offer basic wireless connectivity to several peripheral units scattered over small areas, while indoor UMTS supports communication among scatternets and provides wireless access to the Internet. We focus our analysis on a centralized topology, in which communication occurs only between the peripheral units and the access point. This topology can be used in many different application scenarios and represents an example of cooperation between 3G and PAN technologies. In addition to describing the architecture, we address the issue of fair capacity allocation in such a centralized topology and provide some analytic and simulation results for the topology considered.

There is a growing interest in deploying multimedia services in mobile cellular networks. Call admission control (CAC) is a key factor in quality of service (QoS) provisioning for these services. We propose a CAC which maximizes the revenue by determining the call admission decisions for each state. A semi-Markov decision process is employed to model the cellular system. Also, the QoS requirement such as the handoff dropping probability is taken into consideration. Simulations reveal that our CAC outperforms the upper limit CAC while satisfying the QoS requirements.

Due to scarce radio resources (e.g., wireless link bandwidth), the cell size is likely to become smaller to increase the frequency reuse ratio, especially in next generation mobile cellular networks. In this scenario, the analysis of handoff traffic is crucial for adequate QoS provisioning. With the assumption that incoming handoff traffic into a cell is equivalent to outgoing handoff traffic from the cell, we propose an iterative computation technique to analyze handoff traffic approximately. The iterative relaxation method is employed to calculate the steady state probabilities. The call blocking probability and the handoff dropping probability are accordingly obtained. We conduct the approximate analysis of handoff traffic in the case of trunk reservation call admission policy and compare the results with those of simulation experiments.

Recently there is a growing interest in adaptive multimedia networking where the bandwidth of an ongoing multimedia call is variable. In this paper, we propose a measurement-based call admission control (CAC) which guarantees the upper bound of the call degradation probability defined herein. Our CAC measures the state of the cellular system in a distributed manner and reflects the observed history of the system on making call admission decisions. Also, a bandwidth adaptation algorithm to minimize the call degradation probability is presented. Simulations reveal that our CAC algorithm outperforms the upper limit CAC algorithm while maintaining quality of service.