The rapid expansion of LEO satellite constellations has enabled an integrated terrestrial network and non-terrestrial network (TN-NTN), connecting diverse users such as aircraft, ships, and remote communities. These networks increasingly need a scalable and efficient multicast protocol for critical applications like emergency alerts, large-scale software updates, and real-time broadcasting. However, traditional multicast protocols, such as IP-based multicast and software-defined multicast approaches, introduce significant control overhead and struggle to adapt to the dynamic and mobile nature of satellite topologies. This paper presents BIER-Star, a stateless multicast protocol designed for the integrated TN-NTN. BIER-Star uses a two-layer geospatial gridding scheme (i.e., H3) to encode destinations as Earth- and space-cell identifiers rather than per-terminal addresses. This cell-based abstraction shortens the header bitstring, simplifies forwarding, and eliminates per-flow state and complex signaling. Our simulations indicate that BIER-Star reduces header size versus BIER and avoids geographic path-finding failures seen in greedy methods.

Low-Earth-Orbit (LEO) satellite networks are expanding broadband access, but major Real-Time Communication (RTC) platforms, including Google Meet, Zoom, and Microsoft Teams, still rely on service-point selection mechanisms that appear to be largely shaped by terrestrial-network assumptions. As a result, they may not consistently account for LEO-specific dynamics such as satellite topology and Point-of-Presence (PoP) changes, especially during in-flight connectivity. To characterize RTC service-point selection over Starlink, we measure RTC performance using a Canada-wide testbed and more than 24 hours of in-flight experiments. Our results show clear platform differences. Google Meet tends to align more closely with the serving PoP, consistent with broader observed edge coverage. Zoom follows a regional service-point selection pattern, while Microsoft Teams often remains tied to fixed service points. Moreover, we show that in-flight PoP handovers can shift traffic egress points mid-session, and that RTC platforms respond differently to these changes. In some cases, this interaction leads to suboptimal service-point selections and RTT inflation. Overall, our findings suggest that LEO connectivity can expose mismatches between Starlink egress dynamics and RTC service-point selection, motivating further measurement and LEO-optimized designs for service placement and selection.

Low Earth Orbit (LEO) satellite ISPs promise universal Internet connectivity, yet their interaction with content delivery remains poorly understood. We present the first comprehensive measurement study decomposing Starlink's web content delivery performance decomposed across Point of Presence (PoP), DNS, and CDN layers. Through two years of measurements combining 225K Cloudflare AIM tests, M-Lab data, and active probing from 99 RIPE Atlas and controlled Starlink probes, we collect 6.1M traceroutes and 10.8M DNS queries to quantify how satellite architecture disrupts terrestrial CDN assumptions. We identify three distinct performance regimes based on infrastructure density. Regions with local content-rich PoPs achieve near-terrestrial latencies with the satellite segment dominating 80-90% of RTT. Infrastructure-sparse regions suffer cascading penalties: remote PoPs force distant resolver selection, which triggers CDN mis-localization, pushing latencies beyond 200 ms. Dense-infrastructure regions show minimal sensitivity to PoP changes. Leveraging Starlink's infrastructure expansion in early 2025 as a natural experiment, we demonstrate that relocating PoPs closer to user location reduces median page-fetch times by 60%. Our findings reveal that infrastructure proximity, not satellite coverage, influences web performance, requiring fundamental changes to CDN mapping and DNS resolution for satellite ISPs.

Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.

Two-path transmission with the involvement of LEO satellites is an increasingly common scenario. LEO networks may offer higher bandwidth compared with some terrestrial networks, such as cellular, but often come with increased variability in latency. Effective traffic allocation to maximize bandwidth utilization in such dynamic environments is challenging. This paper addresses two-path scheduling problem under a high dynamic environment by proposing the FTRL-WRR algorithm, which combines a lightweight, learning-based optimization algorithm with a weighted round-robin scheduler. We model traffic allocation as a 1-dimensional optimization problem and demonstrate the algorithm's effectiveness through evaluations in emulated Starlink-cellular scenarios. Results show that FTRL-WRR improves bandwidth utilization and reduces median completion time by up to 27.71%.

Integrating terrestrial networks together with non-terrestrial networks, such as Low Earth Orbit (LEO) satellites, can significantly enhance network reliability and reduce latency in unicast and multicast protocols. To use such a capability and reduce end-to-end latency, many transport-layer unicast protocols, such as Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), have adopted multi-connectivity (MC) by transmitting over multiple interfaces at the sender host. However, traditional and modern multicast protocols suppose that the sender host accesses the core network using a single path, leading to high end-to-end latency at destinations. In this regard, we first demonstrate in a real-world testbed that MC significantly improves network latency in multicasting compared to single-connectivity approaches. We then introduce a Bit Indexed Explicit Replication (BIER) Multi-Connectivity (BIER-MC) method designed to reduce end-to-end latency within the BIER protocol, as a modern multicast protocol. Our comparison indicates that BIER-MC outperforms traditional BIER implementations using multicast trees in terms of latency up to 5×, bandwidth usage up to 2.5×, and edge betweenness centrality up to 15×.

Low Earth Orbit (LEO) satellite constellations offer great promise for distributed sensing and intelligence in future networks. However, their potential is hindered by communication inefficiencies and heterogeneous data distributions, which lead to excessive transmission costs, slow convergence, and degraded model performance. Existing federated learning (FL) methods alleviate data privacy and communication load, but typically rely on centralized coordination and lack robustness to Non-IID data, especially in dynamic satellite environments. To address these limitations, we propose the GOOD (Gossip Learning with OOD Enhancement) framework, a fully decentralized, gossip-based FL system enhanced with adaptive out-of-distribution (OOD) detection. GOOD enables satellites to evaluate the distributional compatibility of incoming model updates using lightweight OOD scores, thereby reducing negative transfer and eliminating ineffective transmissions. Additionally, it introduces an adaptive grouping mechanism that dynamically clusters satellites based on distributional similarity, optimizing inter-satellite communication. Extensive experiments demonstrate that GOOD consistently outperforms prior decentralized FL approaches in terms of convergence accuracy and communication efficiency under both statistical and semantic Non-IID conditions, making it a compelling solution for autonomous on-orbit learning.

Topological attacks, including Sybil and wormhole attacks, modify a network topology by adding fake nodes and links to bypass typical routing paths, significantly degrading the Internet of Things (IoT) network performance. The presence of fake nodes and links introduces inconsistencies between the modified topology and the actual channel states of the nodes. Therefore, a fast and precise method for detecting topological attacks is crucial to identify and count these inconsistencies, which reveal the presence of an attack and affected nodes. In this paper, we propose a straightforward numerical metric called the Number of Channel Footprint Inconsistencies (NCFI) to count inconsistencies between nodes' transmission, reception, and idle states on the channel and the network topology. In the proposed method, each node reports its channel states to a central node, which uses a Constraint Satisfaction Problem (CSP)-based approach in graph coloring to calculate the NCFI. Additionally, each pair of IoT nodes can use the NCFI in a distributed manner to detect the presence of fake links between them. Our simulation results indicate that the proposed method can accurately detect the presence of attackers in the network compared to the state-of-the-art methods.

Real-time communication (RTC) systems, such as video conferencing and cloud gaming, depend on effective congestion control (CC) algorithms to manage diverse network conditions and access technologies like Wi-Fi, LTE/5G, and satellite networks. While tools like AlphaRTC and Pandia have significantly advanced CC algorithm development for WebRTC, there is an absence of a unified framework for systematic benchmarking and cross-platform evaluation.

The growing popularity of Low-Earth Orbit (LEO) satellites, which are increasingly cheaper to manufacture and launch, has revolutionized the Internet market. Given the impact of packet loss on the quality of service (QoS) and optimization strategies of communication systems, it is of great significance to model packet loss in LEO satellite networks. The existing packet loss models considered in the literature have different assumptions and characteristics, but it remains unclear whether they are suitable for LEO satellite networks. This paper aims to evaluate several packet loss models. Through the evaluation of several metrics, the advantages and disadvantages of each model are discussed. We then introduce the Markovian Arrival Process (MAP) as a choice for packet loss modeling, and the experimental results show that the performance is better than that of existing models.

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines.

Video streaming performance may degrade substantially in a mobile environment due to fast-changing wireless links. On the other hand, to provide ubiquitous services, heterogeneous static and mobile access and backbone networks will be integrated in the sixth-generation (6G) systems, so mobile users can take advantage of multiple access options for better services. Multi-path transport-layer protocols like Multi-Path QUIC (MPQUIC) show promise in utilizing multiple access links to address the impact of mobility. However, the optimal link selection that aims to provide statistical QoS guarantee for video streaming in a mobile environment with both user mobility and network mobility remains an open issue. In this paper, based on a lightweight Multi-Armed Bandit (MAB) technique, we develop aQoS-drivenContextualMAB(QC-MAB) framework for MPQUIC, which makes an intelligent access network selection and adaptively enables FEC coding to trade off delay, reliability and goodput. Extensive simulation results with ns-3 show that the proposed QC-MAB framework can outperform the state-of-the-art solutions. It achieves up to ten times lower video interruption ratio and three times higher goodput in highly dynamic mobile environments.

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink’s network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines.

Low-earth-orbit (LEO) satellite networks have become very popular in recent years, exemplified by Starlink, OneWeb, Kuiper and others, due to the dramatically reduced launch cost and increased demand for connectivity anytime, anywhere. After an exploration of Starlink access, core and backbone networks, in this paper we focus on the satellite access network (SAN) of Starlink around the world. Particularly, we measure the access performance in terms of one-way delay and round-trip time from user terminal (UT) to ground station (GS) and point-of-presence (PoP), both inside-out and outside-in, and even on inactive dishes. It reveals the unique characteristics of Starlink SAN in terms of satellite-GS scheduling, media access control and user contention, and sheds light on the challenges and opportunities for network protocols and applications. The paper will be complemented by public dataset release and conference on-site demo for the research and industry community.

In light of Starlink's recent rapid growth in constructing a global low-Earth-orbit satellite constellation and offering high-speed, low-latency Internet services, the implications of utilizing Starlink for low-latency live video streaming, particularly in the context of its fluctuating latency and regular satellite handovers events, remain insufficiently explored. In this paper, we conducted a thorough measurement study on the Starlink access network, examining its performance across different protocol layers and at multiple geographical installations, including locations where laser intersatellite links are utilized in practice. We performed a comprehensive latency target-based analysis of low-latency live video streaming with three state-of-the-art adaptive bitrate (ABR) algorithms in dash.js over Starlink. We presented a novel ABR algorithm designed for low-latency live video streaming over Starlink networks which leverages satellite handover patterns observed from measurements to dynamically adjust video bitrate and playback speed. The performance evaluation of the proposed algorithm was conducted using both a purpose-built network emulator and actual Starlink networks. The results demonstrate that the proposed algorithm effectively delivers a better quality of experience for low-latency live video streaming over Starlink networks, characterized by low live latency, high average bitrate, minimal rebuffering events and reduced visual quality fluctuation.

Low-Earth-Orbit (LEO) satellite constellations are narrowing the performance gap between satellite networks and the terrestrial Internet. Low-latency satellite Internet offered by Starlink enables functionalities that are otherwise unachievable with the traditional geosynchronous equatorial orbit (GEO) satellite networks, including low-latency live video streaming, cloud gaming and real-time video conferencing. The absence of a comprehensive and long-term network measurement dataset with a global perspective poses significant challenges for researchers to evaluate the application performance over Starlink networks. In this paper, we introduce LENS, which is a LEO satellite network measurement dataset, collected from 13 Starlink dishes, associated with 7 Point-of-Presence (PoP) locations across 3 continents. The dataset currently consists of network latency traces from Starlink dishes with different hardware revisions, various service subscriptions and distinct sky obstruction ratios. We provide a high-level overview and analysis of the latency performance using the dataset and discuss various use cases. This dataset is useful for researchers who wish to understand the long-term network performance of Starlink and to evaluate and optimize the performance of multimedia applications over satellite networks.

Nowadays, vehicles can provide many valuable data (such as the videos recorded by dashcams) for analytical model building. Integrating vehicular ad hoc networks with the Internet of Things (IoT), the Internet of Vehicles (IoV) has a promising future. In IoV, vehicles maintain their own communication, computing, and learning capabilities. Thus, instead of sending the data to a central server for model training, which leads to a high communication overhead, vehicles can train the data locally. However, it is still a challenge to preserve the privacy while keeping both the communication and computation overheads of vehicles acceptable. In this article, we present a distributed machine learning framework with a two-layered architecture. The architecture uniquely involves vehicle clusters, roadside units, and a central server, which provides a basic guarantee to the vehicle privacy and also limits the overhead. By carefully adopting cryptographic tools and techniques, the framework has the following properties: 1) it preserves the privacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the application layer; 4) the evaluation shows that it is lightweight for vehicles. Compared with other existing works, the proposed framework is more suitable for IoV.

With the explosive growth of Internet of Things (IoT) devices, the IPv6 routing protocol for low-power and lossy networks (RPL) has been widely studied and applied. However, due to lack of complete security mechanisms, it faces many threats, one of which is wormhole attack. Wormhole attack could mislead network traffic flow, cause network congestion and increase packet delivery latency. Besides, it can also be combined with the other types of attacks to become more threatening. In this article, we propose two schemes, called routing loop detection for wormhole and routing loop detection for wormhole combined with greyhole, to detect wormhole nodes for different attack modes in RPL-based networks. Our methods could not only detect but also localize the adversaries more effectively in comparison with the state-of-the-art scheme, LiDL. We have completely implemented the proposed two schemes, as well as LiDL, in the Contiki IoT operating system and made an extensive comparison among them based on the Sky mote. The obtained results demonstrate the feasibility and superiority of our schemes in terms of detection speed, accuracy and network performance improvement.

In the Internet of Connected Vehicles, caching content frequently requested by users on edge devices can reduce access latency. Particularly in high-traffic density areas, Unmanned Aerial Vehicles (UAVs) can integrate into future cellular networks to enhance the network capacity and meet increased requests. Therefore, we formulate a joint optimization problem of cooperative caching of Base Station (BS) and UAVs and UAV trajectory planning to minimize network latency while considering the limited energy and storage capacity and dynamic vehicles. First, we propose a Temporal-evolving Bipartite Graph Neural Networks (TBGN) model for traveling areas prediction of vehicles. Then, regarding the coupling of optimization variables, we propose an Energy-aware Monte-Carlo Tree Search algorithm to optimize the UAV’s service trajectory by predicted spatio-temporal vehicle density. Finally, the optimization problem degenerates into a monotonic submodular function to optimize caching decisions. We utilize real vehicle trajectories for simulations. The results show that the TBGN outperforms other advanced models in terms of mobility prediction accuracy by 7.4%, and the proposed scheme reduces average latency by 16% compared to other schemes.

The Integrated Terrestrial and LEO Satellite Network (ITSN) has a high bandwidth-delay product (BDP) and high-speed movement, which makes congestion control difficult. We develop aMobility-AwareCOngestion control (MACO) algorithm for multipath QUIC (MPQUIC) in ITSN. MACO models the dynamic interactions between MPQUIC subflows and LEO networks, including handovers and outages triggered by satellite movement, and changes in network topology and link conditions. With the knowledge of network dynamics influenced by mobility, MACO can estimate changes in path BDP without solely relying on lengthy network probing. It employs a quick start (QS) and an effective congestion avoidance (CA) mechanism based on a multipath fluid model. The QS sets an appropriate initial cwnd to shorten the slow start duration. The CA applies a square root function to quickly increase the cwnd to the equilibrium and conservatively increase when approaching the BDP. We conduct a series of experiments to evaluate MACO using network simulator 3 (ns-3) based on collected data traces on Starlink. Simulation results demonstrate that MACO can achieve upto three times higher throughput and improve the convergence performance by 70.67% against benchmark algorithms.

Privacy-preserving localization plays a crucial role in enabling various applications on the Internet of Things (IoT). Existing work applies random zero-sum noise to develop privacy-preserving localization, which achieves efficiency and accuracy by adding random noise to preserve private information and cancelling the effect of the noise with the zero-sum characteristic, respectively. However, in practice, some nodes in IoT scenarios may misbehave, not following a pre-defined protocol but adding false noise or tampering with information, which leads to the trust issue for privacy-preserving localization. In this paper, we integrate blockchains with zero-sum noise to achieve trusted privacy-preserving localization against misbehaving nodes. Specifically, a three-layer framework is designed by combining private blockchains with a zero-sum noise-adding mechanism. In the sensing layer, nodes are divided into groups to perform the first noise-adding process to preserve their private location information during location aggregation inside the group. In the blockchain layer, each group constructs the private blockchains to achieve trustworthiness without increasing the risk of privacy leakage and performs the second noise-adding process to protect intermediate information. In the application layer, the target node aggregates the intermediate information from each group to estimate its location. Then, under the framework, we propose an Enhanced Privacy-Preserving Localization (EPPL) algorithm to securely calculate the location of the target node against misbehaving nodes. The correctness, accuracy, privacy, trustworthiness, and efficiency of EPPL are analyzed. The performance of EPPL is evaluated by using simulations. Compared with existing random noise-based methods, it is shown that EPPL can effectively enhance privacy-preserving localization.

In this paper, we study a multi-agent bandit problem in an unknown general-sum game repeated for a number of rounds (i.e., learning in a black-box game with bandit feedback), where a set of agents have no information about the underlying game structure and cannot observe each other’s actions and rewards. In each round, each agent needs to play an arm (i.e., action) from a (possibly different) arm set (i.e., action set), andonlyreceives the reward of theplayedarm that is affected by other agents’ actions. The objective of each agent is to minimize her own cumulative swap regret, where the swap regret is a generic performance measure for online learning algorithms. Many network optimization problems can be cast with the framework of this multi-agent bandit problem, such as wireless medium access control and end-to-end congestion control. We propose an online-mirror-descent-based algorithm and provide near-optimal high-probability swap-regret upper bounds based on refined martingale analyses, which can further bound the expected swap regret instead of the pseudo-regret studied in the literature. Moreover, the high-probability bounds guarantee that correlated equilibria can be achieved in a polynomial number of rounds if the algorithms are played by all agents. To assess the performance of the studied algorithm, we conducted numerical experiments in the context of wireless medium access control, and we performed emulation experiments by implementing the studied algorithms through the Linux Kernel for the end-to-end congestion control.

Multi-homing technologies are promising to support seamless handoff and non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths, which is detrimental to users’ quality of experience (QoE). Wireless link characteristics undergo a fast change over time in mobile environments, thus aggravating the OFO issue. In this paper, we present a novel mobility-aware multipath QUIC (MMQUIC) framework in which interactions between link and transport layers are introduced so that the scheduler at a mobile sender is aware of uplink variations, and a new ACK packet structure is designed to inform the scheduler of downlink variations when the receiver is mobile. Based on MMQUIC, a Mobility-Aware Multipath Scheduler (MAMS) is developed, which forecasts the path conditions in successive time slots based on historical and current end-to-end (E2E) path conditions, along with wireless uplink/downlink conditions, and pre-allocates packets on multiple paths accordingly. We conduct a series of experiments to evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that MAMS effectively leverages the information related to mobility, achieving substantial performance gains w.r.t. the goodput and packet delay distribution under different mobility patterns.

Heterogeneous deep learning clusters commonly host a variety of distributed learning jobs. In such scenarios, the training efficiency of learning models is negatively affected by the slowest worker. To accelerate the training process, multiple learning jobs may compete for limited computational resources, posing significant challenges to multi-job placement among heterogeneous workers. This paper presents a heterogeneity-aware scheduler to solve the multi-job placement problem while taking into account job sizing and load balancing, minimizing the average Job Completion Time (JCT) of deep learning jobs. A novel scheme based on proportional training workload assignment, feasible solution categorization, and matching markets is proposed with theoretical guarantees. To further reduce the computational complexity for low latency decision-making and improve scheduling fairness, we propose to construct the sparsification of feasible solution categories through sampling, which has negligible performance loss in JCT. We evaluate the performance of our design with real-world deep neural network benchmarks on heterogeneous computing clusters. Experimental results show that, compared to existing solutions, the proposed sampling-based scheme can achieve 1) results within 2.04% of the optimal JCT with orders-of-magnitude improvements in algorithm running time, and 2) high scheduling fairness among learning jobs.

The rapid growth of data processing demands in large-scale data centers (DCs) has led to increased brown energy (BE) consumption, which has negative environmental impacts. Since most DCs are now powered by both BE and renewable energy (RE), migrating workloads from DCs with insufficient RE to DCs with sufficient RE can decrease the total BE consumption in the network. However, selecting a destination DC is challenging due to the uncertainty of the network and the additional cost associated with using network devices for the migration. This paper proposes to optimize the DC selection and the efficient virtual machine transfer between DCs, minimizing the costs of BE consumption, optical network devices, and migration. Specifically, we formulate the DC selection as a multi-armed bandit problem and estimate the lowest migration cost at each round using the lower confidence bound. We adopt the optical grooming technique to reduce the cost of optical devices used during the migration. We compare our algorithm with the KUBE and -Greedy algorithms on the NSFNET and show that it reduces the total cost by 4.6% and 12.8%, respectively, while having lower regret. We demonstrated the effectiveness of optical grooming by achieving a 12 % reduction in network costs.

Multipath transport protocols including multipath TCP (MPTCP) and multipath QUIC (MPQUIC) are designed to utilize multiple network paths for simultaneous data transfer. These protocols try to improve network performance and offer better resilience in dynamic network environments. Nonethe-less, the actual performance improvement is heavily reliant on the effectiveness of the multipath scheduling algorithms. In specific scenarios such as adaptive video streaming, most existing solutions feature two separate and independent control loops for multipath scheduling and video bitrate adaptation, while multipath scheduling algorithms are usually transparent to the video bitrate adaptation process. Lacking the context of inter-path differences and intra-path fluctuations for both network throughput and latency may potentially result in a suboptimal quality of experience (QoE) for video streaming. Such circumstances may lead to a reduced video bitrate, increased latency, and a greater number of rebuffering events. In this paper, we present a QoE-driven joint decision-making framework based on contextual multi-armed bandit (CMAB) algorithms to efficiently address multipath adaptive video streaming problems. This approach merges application-layer (playback buffer ratio) and network-layer (throughput and latency) metrics to create a context-aware online learning model, which can adaptively select the ideal network path and bitrate for multipath adaptive video streaming. Both network emulation and real-world experiments demonstrate that the proposed algorithm delivers better QoE, including higher average video bitrate and fewer rebuffering events when compared to independent decision-making algorithms.

In this paper, we study the open problems raised by Karp et al. in FOCS 2000, where the authors formulated the end-to-end congestion control as a repeated game between a flow and an adversary. They mentioned several open problems including finding equilibria in a more realistic game model for the situation where the available bandwidth is a result of competition among multiple flows instead of being chosen by an adversary, and designing the randomized algorithm to deal with the dynamic change of network bandwidth. Although there have been many game-theoretic works for congestion control, to the best of our knowledge, the above two problems still remain unsolved over the past decades. We take a step further to address the above two problems by first modeling the end-to-end congestion control as a repeated unknown general-sum game among multiple flows with bandit feedback. Each flow is a player in this unknown game, making decisions on how many packets to send. The throughput for each flow depends on all the flows' rates and the network capacity. The unknown setting and bandit feedback capture the essence of end-to-end congestion control: each flow has no information about others (e.g., the number, actions, and packet loss of other flows), and only receives limited information for its chosen action. Then, we propose a randomized no-regret learning algorithm for each flow called LUC based on a swap-regret-minimizing technique. We prove that LUC can guarantee a polynomial-time convergence rate to correlated equilibria in the multi-player setting. Finally, we have implemented LUC through the Linux kernel, and conducted extensive fairness-related experiments in Mininet and trace-driven experiments with Pantheon to show that each flow with LUC can fairly share the bandwidth in homogeneous scenarios, and be competitive but TCP-friendly in heterogeneous scenarios.

Starlink and alike have attracted a lot of attention recently, however, the inner working of these low-earth-orbit (LEO) satellite networks is still largely unknown. This paper presents an ongoing measurement campaign focusing on Starlink, including its satellite access networks, gateway and point-of-presence structures, and backbone and Internet connections, revealing insights applicable to other LEO satellite providers. It also highlights the challenges and research opportunities of the integrated space-air-ground-aqua network envisioned by 6G mobile communication systems, and calls for a concerted community effort from practical and experimentation aspects.

In this paper, we study a multi-agent bandit problem in an unknown general-sum game repeated for a number of rounds (i.e., learning in a black-box game with bandit feedback), where a set of agents have no information about the underlying game structure and cannot observe each other’s actions and rewards. In each round, each agent needs to play an arm (i.e., action) from a (possibly different) arm set (i.e., action set), and only receives the reward of the played arm that is affected by other agents’ actions. The objective of each agent is to minimize her own cumulative swap regret, where the swap regret is a generic performance measure for online learning algorithms. We are the first to give a near-optimal high-probability swap-regret upper bound based on a refined martingale analysis for the exponential-weighting-based algorithms with the implicit exploration technique, which can further bound the expected swap regret instead of the pseudo-regret studied in the literature. It is also guaranteed that correlated equilibria can be achieved in a polynomial number of rounds if the algorithm is played by all agents. Furthermore, we conduct numerical experiments to verify the performance of the studied algorithm.

The deployment of multipath transport protocols in the mobile environment can enhance the performance of delay-sensitive applications by enabling the simultaneous use of several network paths, resulting in faster data transmission. However, due to the heterogeneity of network paths, packets may not arrive on time or in order, affecting the performance of delay-sensitive applications. Therefore, a well-designed multipath scheduler is important to distribute data packets efficiently to guarantee the per-packet delay requirement. In this paper, we propose Meta-DAMS, a delay-aware learning-based multipath scheduler, aiming to ensure that end-to-end delay is below a predefined threshold for delay-sensitive applications. We introduce a hybrid meta reinforcement learning (meta-RL) architecture for Meta-DAMS in which offline meta-RL and online meta-RL are used to learn the optimal scheduling policy quickly and accurately in response to highly dynamic network conditions. Based on trace-driven emulation experiments, we demonstrate that Meta-DAMS surpasses state-of-the-art MP schedulers, ensuring a delay of 50 ms or less for 98% of packets after sufficient operational episodes, compared to the 83% achieved by existing MP schedulers. Even in initial operational episodes, Meta-DAMS maintains its superiority, guaranteeing 94% of packets with a delay of 50 ms or less, while the performance of the DQN-based MPQUIC scheduler drops to 72%. Meta-DAMS exhibits nearly triple the efficiency in terms of runtime compared to the DQN-based MPQUIC scheduler across varying episode numbers.

In this article, we present a drone-relayed vehicular networking architecture, which aims to improve the achievable data rate of cell-edge vehicles in rural highway scenarios. Specifically, we first incorporate the decode-and-forward (DF) relay protocol with the nonorthogonal multiple access (NOMA) and maximum ratio combining (MRC) techniques, based on which an NOMA- and MRC-Enabled framework is proposed. Next, to fully exploit the advantages of the proposed framework, we separately formulate the total achievable data rate maximization and energy consumption minimization problems by jointly considering the height and 2-D trajectory optimization of relaying drone. The formulated energy consumption minimization problem is transformed into a trajectory optimization problem with obstacle avoidance constraints. Then, for the total achievable data rate maximization problem, we utilize the golden section method to design a height optimization scheme with polynomial complexity. Afterward, we improve the particle swarm optimization (PSO) algorithm, and present an effective 2-D optimization scheme. In addition, the performance superiority of the proposed NOMA- and MRC-Enabled framework is analyzed theoretically. Finally, simulation results verify the efficacy of the proposed height and trajectory optimization schemes. For instance, by using the NOMA and MRC techniques, the total achievable data rate can be improved by 24.4%. Moreover, within the same running time, a shorter trajectory can be obtained by adopting our presented trajectory optimization scheme in comparison with the current works.

By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high-reliability requirements of IoV users for multimedia services. However, the edge node’s coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) how to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users and 2) how to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this article, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle’s driving trajectory and travel preference. The scheme learns the vehicle’s travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility data sets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle’s request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware in-order caching schemes.

Erasure codes have been widely used to enhance data resiliency with low storage overheads. However, in geo-distributed cloud storage systems, erasure codes may incur high service latency as they require end users to access remote storage nodes to retrieve data. An elegant solution to achieving low latency is to deploy caching services at the edge servers close to end users. In this paper, we propose adaptive and scalable caching schemes to achieve low latency in the cloud-edge storage system. Based on the measured data popularity and network latencies in real time, an adaptive content replacement scheme is proposed to update caching decisions upon the arrival of requests. Theoretical analysis shows that the reduced data access latency of the replacement scheme is at least 50% of the maximum reducible latency. With the low computation complexity of our design, nearly no extra overheads will be introduced when handling intensive data flows. For further performance improvements without sacrificing its efficiency, an adaptive content adjustment scheme is presented to replace the subset of cached contents that incur the aforementioned performance loss. Driven by real-world data traces, extensive experiments based on Amazon Simple Storage Service demonstrate the effectiveness and efficiency of our design.

Caching has been considered as a promising solution to achieve low latency in distributed erasure coded storage systems. The previous research work categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal solution by applying the market clearing price on each cache partition. While enjoying the ultimate performance of low data access latency, the optimal scheme suffers from high computation overheads when applied to large-scale storage systems. This paper presents SampleX, which constructs the sparsification of cache partitions through sampling to approximate the optimal caching scheme with substantially reduced computation complexity. Theoretical analysis guarantees the performance of SampleX. Furthermore, SampleX is implemented in a streaming fashion, capturing the characteristics of recent traffic for online cache content replacement. Trace-driven experimental results show that online SampleX is up to 95× faster than the state-of-the-art online scheme while only incurring a performance loss of 0.81%.

Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.

Multi-homing technologies are promising to support seamless user mobility, as a mobile device can use multiple access links and paths for non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths. Mobility poses new challenges due to time-varying link quality and capacity. In this paper, we present a novel Mobility-aware Multipath Quick UDP Internet Connections (MMQUIC) framework which enables collaboration between the link and transport layer. Based on MMQUIC, we develop a Mobility-Aware Multipath Scheduler (MAMS) for goodput enhancement, in which the impacts of mobility such as link outage errors and capacity variations are considered. Finally, we evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that our design has substantial performance gains with respect to the goodput and packet delay distribution in dynamic wireless systems.

Edge computing pushes computation and storage resources to the network edge, which is close to end users, and thus, is critical for latency-sensitive applications, e.g., intelligent vehicularad hocnetworks (VANETs). To enable these services, a set of edge servers needs to be deployed to the roadsides. Such deployment should offer low-latency services to end users, while keeping a low deployment or maintenance cost, which is a nontrivial task. In this article, we study the edge server placement problem in a metropolitan area. This problem is composed of two parts to determine: 1) the locations of the servers and 2) the coverage of each server, with multiple optimization objectives. First, we study the Shanghai Taxi Trace to gain insights into the traffic pattern of taxis, especially how vehicles move between different locations. Second, we build multiobjective optimization models to characterize the tradeoff among three critical performance metrics, namely, the initial deployment cost, the runtime cost (i.e., number of hand-offs between different servers), and the average delay of tasks. Due to the intractability of these NP-hard problems, we propose a heuristic multiobjective optimization method to decompose the global problem into a set of local problems with tractable scale. Numerical results verify that our heuristic strategy achieves a desirable balance among the three performance metrics, e.g., a 5% compromise of the delay can reduce up to 50% of the hand-offs for small local areas, and 10%+ for the entire global area, compared with the best existing algorithms.

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of$Q$-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

Erasure codes have been widely considered as a promising solution to enhance data reliability at low storage costs. However, in modern geo-distributed storage systems, erasure codes may incur high data access latency as they require data retrieval from multiple remote storage nodes. This hinders the extensive application of erasure codes to data-intensive applications. This paper proposes novel caching schemes to achieve low latency in distributed coded storage systems. Assuming that future data popularity and network latency information are available, an offline caching scheme is proposed to explore the optimal caching solution for low latency. The proposed scheme categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal caching decision through market clearing price for each cache partition. Furthermore, guided by the optimal scheme, an online caching scheme is proposed according to the measured data popularity and network latency information in real time, without the need to completely override the existing caching decisions. Both theoretical analysis and experiment results demonstrate that the online scheme can approximate the offline optimal scheme well with dramatically reduced computation complexity.

Datacenter networks (DCNs) play a key role in providing cloud services. The energy consumption and cost of a DCN are growing sharply with the extensions of network bandwidth and network size. The energy consumption, complexity and cost of a DCN depend on some design factors such as the topology structure, addressing scheme and routing mechanism. A novel addressing and routing architecture for cloud-service DCNs with regular topologies is proposed in this paper. First of all, we propose a port-based source-routing addressing (PSRA) scheme, which makes the table-lookup operation unnecessary and decreases the switch complexity. Next, leveraging the characteristics of PSRA and the regularity of DCN topologies, an extremely simple routing mechanism is designed, without switch involvement, control message interaction and topology information storage. Lastly, a high-efficiency fault-tolerance mechanism is proposed for the addressing and routing architecture. The analysis, implementation and simulation results indicate that the proposed architecture not only decreases the energy consumption and thus the cost of a DCN, but also enhances the routing performance and solves the fault-tolerance problem in a very efficient way.

In this paper, we study a distributed stochastic multi-armed bandit problem that can address many real-world problems such as task assignment for multiple crowdsourcing platforms, traffic scheduling in wireless networks with multiple access points and caching at cellular network edge. We propose an efficient algorithm called multi-agent combinatorial upper confidence bound (MACUCB) with provable performance guarantees and low communication overhead. Furthermore, we perform extensive experiments to show the effectiveness of the proposed algorithm.

In a mesh network, to ensure high reliability and low latency, we can explore path diversity. In other words, a packet can be transmitted using all active links in a network to reach the destination. Here, a critical, difficult issue is to calculate the end-to-end reliability of a mesh network, given the reliability of each active link. In this paper, we derive the mesh network reliability with a new approach, which is of lower computational cost and more scalable than the state-of-the-art. Based on a Markov model, the closed-form network reliability as a polynomial expression of link reliability is obtained using the Hop-State Algorithm (HSA). Furthermore, we propose two metrics to assist in selecting the links in a network for routing to ensure performance while reducing link cost. From the analysis and simulation evaluations, exploring path diversity can effectively support Ultra-Reliable Low-Latency (URLL) services.

The Integrated Terrestrial and LEO Satellite Network (ITSN) is promising for providing ubiquitous communication services, which attracts attention but also brings new challenges. In this regard, a new transport layer protocol, Multipath QUIC (MPQUIC) appears salient advantages in tackling with the challenging environment (e.g., large propagation delays, high-speed mobility, etc.). However, the standard congestion control algorithm of MPQUIC, Opportunistic Linked Increases Algorithm (OLIA), still encounters great challenges such as congestion window (cwnd) overshooting whenever handoff, which motivates our proposal, a Mobility-aware Multipath QUIC (MM-QUIC) congestion control algorithm. MM-QUIC leverages the periodical changes of path capacity and good similarity among disjoint subflows to quickly start a new round of transmission, and employs a multipath-based fluid model to determine the cwnd adjustment in the congestion avoidance phase. Finally, simulation results on NS-3 demonstrate that MM-QUIC can offer up to 50% throughput improvement compared to OLIA in ITSN.

The electrocardiogram sensor network (ECG-SN) is a medical monitoring system based on IoT technology, which can detect heart bioelectric signals in real time. But the ECG signal is vulnerable to human mobility and sensitive to the Age of Information (AoI). In this article, we first analyze the impact of human mobility on channel fading based on a real-world activity trace data set and design a two-state ECG work model based on the tradeoff between the ECG signal quality and energy consumption. Furthermore, an AoI model is proposed to evaluate the data timeliness. Then, an online transmission optimization algorithm is proposed to maximize the system utility by optimizing the sampling rate, transmission power, and data dropping rate. Furthermore, performance analysis presents the bounds for data buffer, battery capacity, and AoI. Numerical results show the dynamics of the system and the impact of the parameters on system performance, which verify that the proposed design has a larger utility and a smaller AoI in comparison with two benchmark schemes.

Infrastructure-as-a-Service clouds in geo-distributed data centers offer various pricing options, including on-demand and reserved instances, which provide an elastic and cost-effective infrastructure to support High Performance Computing (HPC) applications. In this paper, we propose an instance reservation based cloud service framework, modeling the cost-minimizing reservation decision issue as an NP-hard integer programming problem for distributed data centers. To ease its computation complexity, two algorithms are proposed to minimize the HPC service cost with the worst-case performance guarantees: an offline heuristic-greedy algorithm, and a rolling-horizon based online algorithm when only short-term demand prediction is available. Facing fluctuating demands, instance reservation in a single data center may incur the highly underutilized capacity. To address this issue for further cost reduction, we extend the scheme with a novel cloud broker federation based resource sharing mechanism, reallocating already reserved but unused instances to computation-intensive and short-lived tasks for continuous execution without interruption. Extensive evaluations driven by large-scale trace-based datasets demonstrate that the proposed mechanism can effectively handle large volumes of service requests, saving considerable service costs with higher reservation resource utilization.

The burgeoning network traffic imposes a huge burden on the network backbone. Caching popular files at the wireless network edge is promising to address the problem. In practice, file popularity is very unlikely to know in advance. Online learning algorithms are effective to learn this uncertainty in a sequential way. In each slot, the learning agent generates a caching policy (i.e., the to-be-cached files) and can observe users' feedback about the caching policy within the same slot. This method implicitly requires that all of the users are able to provide feedback promptly. However, in practice, the availability of each individual user is affected by many factors, e.g., users are moving out of the service area temporarily, or they may still consume files in the previous slots, which may result in the feedback delay. In this paper, we propose a delay-tolerant wireless caching system that takes both the feedback delay and users' availability into consideration. We frame the content caching problem as a stochastic combinatorial multi-armed bandit problem with delayed feedback and forced-to-sleep arms, and devise an intelligent caching algorithm called CFAUD to solve the problem. Also, we show that CFAUD is effective and efficient both theoretically and practically. Finally, experiments are conducted to compare the performance of the proposed algorithm with other well-known algorithms.

Routing is a fundamental problem and has been extensively studied in various networks. However, in highly dynamic networks (e.g., wireless ad hoc networks), nodes have limited transmission opportunities due to high mobility, noise and interference, where traditional routing is often not the best approach.Opportunistic routing (OR), on the other hand, can effectively minimize the routing cost (e.g., the number of hops) and improve the success of routing by utilizing link metrics. However, the link metrics are usually unknown in advance and changing. In this paper, we design an adaptive algorithm calledThompson sampling-based opportunistic routing (TSOR)motivated by the distributed Bellman-Ford algorithms. TSOR is able to learn the link metrics and route packets simultaneously to reduce the overall cost. Theoretically, we show a lower bound and an upper bound of the cumulative regret (i.e., performance gap) between TSOR and the optimal routing algorithm that knows all link metrics in advance. The regret increases sublinearly with respect to the number of packets, and has a lower order in terms of the network size than the best-known results. Furthermore, we compare TSOR with the state-of-the-art algorithms, and the evaluation results show that TSOR has a lower regret and a faster convergence rate to the optimal policy than the state-of-the-art algorithms.

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.

Air pollution has become an important health concern. The recent developments of vehicular networks and crowdsensing systems make it possible to monitor fine-grained air quality with vehicles and road-side units. On account of the different precisions of onboard sensors and malicious behaviors of participants, sensory data usually vary in quality. Thus, truth discovery has been a crucial task which targets at better utilizing the data. However, in urban cities, there is a significant difference in traffic volumes of streets or blocks, which leads to a data sparsity problem for truth discovery. To tackle the challenge, we present a truth discovery algorithm incorporating spatial and temporal correlations. Besides, to protect the privacy of participating vehicles, we develop the algorithm into a privacy-preserving truth discovery framework by adopting the technique of masking. The proposed framework is lightweight than the existing cryptography-based methods. Simulations are conducted to show that the proposed framework has a good performance. Although the framework is presented for air quality monitoring, we fully discuss the possible applications and extensions.

With tens of thousands Low Earth Orbit (LEO) satellites covering Earth, LEO satellite networks can provide coverage and services that are otherwise not possible using terrestrial communication systems. The regular and dense LEO satellite constellation also provides new opportunities and challenges for network architecture and protocol design. In this paper, we propose a new routing strategy named Directed Percolation Routing (DPR), aiming to provide Ultra-Reliable and Low-Latency Communication (URLLC) services over long distances. Given the long propagation delay and uncertainty of LEO communication links, using DPR, each satellite routes a packet over several Inter-Satellite-Links (ISLs) towards the destination, without relying on link-layer retransmissions. Considering the link redundancy overhead and delay/reliability tradeoff, DPR can control the size of percolation. Using the Starlink as an example, we demonstrate that with the proposed DPR, the inter-continent propagation delay can be reduced by about 4 to 21 ms, while the reliability can be several orders higher than single-path optimal routing.

Mobile edge computing (MEC) has been considered as a promising technology to handle computation-intensive and delay-sensitive tasks in the Internet of Things (IoT) ecosystem, such as smart city and smart tourism. However, due to user mobility, edge servers with fixed deployment are not flexible enough to handle time-varying user tasks in hot-spot areas. In this article, a novel online unmanned aerial vehicle (UAV)-mounted edge server dispatching scheme is proposed to provide flexible mobile-to-MEC services. UAVs are dispatched to the appropriate hover locations by geographically merging tasks into several hot-spot areas. Theoretical analysis guarantees the worst case performance bound. Extensive evaluation driven by real-world mobile requests shows that while maintaining a good latency fairness, the mobile server dispatching scheme can serve more user equipments (UEs) as well as achieve a high resource utilization. Moreover, the hybrid scheme can satisfy even more user demands while dispatching fewer UAVs with a higher server utilization.

In recent years, with the rapid development and popularization of e-commerce, the applications of e-coupons have become a market trend. As a typical bar code technique, QR codes can be well adopted in e-coupon-based payment services. However, there are many security threats to QR codes, including the QR code tempering, forgery, privacy information leakage and so on. To address these security problems for real situations, in this paper, we introduce a novel fragment coding-based approach for QR codes using the idea of visual cryptography. Then, we propose a QR code scheme with high security by combining the fragment coding with the commitment technique. Finally, an enhanced QR code-based secure e-coupon transaction framework is presented, which has a triple-verification feature and supports both online and offline scenarios. The following properties are provided: high information confidentiality, difficult to tamper with and forge, and the ability to resist against collusion attacks. Furthermore, the performance evaluation of computing and communication overhead is given to show the efficiency of the proposed framework.

Reliable and scalable wireless transmissions for Internet of Vehicles (IoV) are technically challenging. Each vehicle, from driver-assisted to automated one, will generate a flood of information, up to thousands of times of that by a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints while infotainment applications may tolerate a certain degree of latency. On a congested road, thousands of vehicles need to exchange information badly, only to find that service is limited due to the scarcity of wireless spectrum. Considering the service requirements of heterogeneous IoV applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management leveraging the mobility of vehicles, which are addressed in this article. For single-hop transmissions, we study and compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (cellular vehicle-to-everything) protocols, and the enhancement strategies using distributed congestion control. For messages propagated in IoV using multihop V2V relay transmissions, the fundamental network connectivity property of 1-D and 2-D roads is given. To have a message delivered farther away in a sparse, disconnected V2V network, vehicles can carry and forward the message, with the help of infrastructure if possible. The optimal locations to deploy different types of roadside infrastructures, including storage-only devices and roadside units with Internet connections, are analyzed.

Many third-party services and applications have integrated the login services of popular Online Social Networks, such as Facebook and Google+, and acquired user information to enrich their services by requesting user's permission. Although users can control the information disclosed to the third parties in a certain granularity, there are still serious privacy risks due to the inference attack. Even if users conceal their sensitive information, attackers can infer their secrets by exploiting the correlations among private and public information with background knowledge. To defend against such attacks, we formulate the social network data sharing problem through an optimization-based approach, which maximizes the users' self-disclosure utility while preserving their privacy. We propose two privacy-preserving social network data sharing methods to counter the inference attack. One is the efficiency-based privacy-preserving disclosure algorithm (EPPD) targeting the high utility, and the other is to convert the original problem into a multi-dimensional knapsack problem (d-KP) using greedy heuristics with a low computational complexity. We use real-world social network datasets to evaluate the performance. From the results, the proposed methods achieve a better performance when compared with the existing ones.

Nowadays, with the development of micro-electro-mechanical technologies, sweep coverage with mobile sensors is more and more popular in wireless sensor networks, which is also applied widely in other scenarios, such as message ferrying and data routing in ad-hoc networks. In order to reduce the sweep cycle and the number of mobile sensors, we propose the Distance-Sensitive-Route-Scheduling (DSRS) problem, which is to consider the effect of sensing range. We prove that DSRS is NP-hard, and consider three different scenarios: the single sensing-point case, the general case, and the extended case. In the single sensing-point case, we propose an approximation algorithm ROSE to schedule the routes of the mobile sensors efficiently. For the general case and the extended case, we present two other approximation algorithms G-ROSE and E-ROSE based on ROSE. We further characterize the non-locality property and design a distributed algorithm D-ROSE, coordinating sensors to meet the sweep requirements with best effort. Our algorithms are scalable to different sweep coverage problems, and according to the simulation results, they greatly outperform other existing algorithms up to 45 percent especially with a large sensing range.

In geo-distributed cloud storage systems, data replication has been widely used to serve the ever more users around the world for high data reliability and availability. How to optimize the data replica placement has become one of the fundamental problems to reduce the inter-node traffic and the system overhead of accessing associated data items. In the big data era, traditional solutions may face the challenges of long running time and large overheads to handle the increasing scale of data items with time-varying user requests. Therefore, novel offline community discovery and online community adjustment schemes are proposed to solve the replica placement problem in a scalable and adaptive way. The offline scheme can find a replica placement solution based on the average read/write rates for a certain period of time. The scalability can be achieved as 1) the computation complexity is linear to the amount of data items and 2) the data-node communities can evolve in parallel for a distributed replica placement. Furthermore, the online scheme is adaptive to handle the bursty data requests, without the need to completely override the existing replica placement. Driven by real-world data traces, extensive performance evaluations demonstrate the effectiveness of our design to handle large-scale datasets.

Data-intensive applications need to address the problem of properly placing the set of data items in geo-distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those used in Hadoop and Cassandra, but are not efficient for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Some recent papers proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose a general hypergraph-based data placement framework, which considers both the performance metrics related to the co-location of associated data and those related to the exact location of fulfilling each requested data item. In the framework, we present the methods to convert the optimization objectives into hypergraph models and employ a hypergraph partitioning to efficiently partition the set of data items and place them in distributed nodes. Further, we extend the scheme into replica placement where we need to find multiple locations to place the replicas of the same data item. Through extensive experiments based on trace-based datasets, we evaluate the performance of the proposed framework and demonstrate its effectiveness.

Edge caching is promising to alleviate the backhaul pressure and provide low latency delivery for delay sensitive applications. However, it encounters great challenges to make adaptive content placement decisions according to the scattered explicit feedback with spatial and temporal dynamics. We propose a hybrid learning framework to obtain a more accurate prediction of users' preference by combining historical data from the central cloud and real-time data in edge networks. Two hybrid-learning algorithms, i.e., Hybrid Learning based on Alternating Least Squares (HLALS) and Hybrid Learning based on Conjugate Gradient Descent (HLCGD) are designed to achieve efficient caching decisions, where HLCGD is more efficient than HLALS at the expense of complexity. Simulation results show that, compared to the popular stochastic gradient descent strategy, the proposed algorithms can achieve superior performance thanks to more accurate prediction of users preference.

Superimposed signals are anticipated to improve wireless spectrum efficiency to support the ever-growing IoT applications. Implementing the superimposed signal demands on ideally aligned signals in both the time and frequency domains. Prior work applied an average carrier-frequency offset compensation to the superimposed signal under the assumptions of homogeneous devices and static environments. However, this will cause a significant signal distortion in practice when heterogeneous IoT devices are involved in a dynamic environment. This paper presents PhyCode, which exploits the nature of varying offsets across devices, and designs a dynamic decoding scheme which can react to the exact offsets from different signal sources simultaneously. We implement PhyCode via a software-defined radio platform and demonstrate that PhyCode achieves a lower raw BER compared with the existing state-of-the-art method.

In recent years, with the rapid development and popularization of QR code-based services, the research of QR codes has become a hot topic. Because QR codes are easy to use, they are well adopted in e-coupon-based payment services. However, there are many security threats to QR codes, including QR code forgery, privacy information leakage and so on. To address these security problems, in this paper, we first propose a novel fragment coding-based approach for QR codes using the idea of visual cryptography. Second, we propose a QR code scheme with a high security by combining the fragment coding with commitment technique. Then, an enhanced QR code-based secure e-coupon transaction framework is presented, which has a triple verification feature. This framework can provide at least the following properties: high information confidentiality, difficult to tamper with and forge, and the ability to resist collusion attacks. Finally, security analysis and performance evaluation are presented to show the security and efficiency of the proposed framework.

Localization has drawn much attention in the Internet of Things (IoT) era. Under traditional multilateration techniques, existing solutions usually need multiple anchor nodes to perform localization, which introduces more system complexity and cost. In this paper, the single anchor localization (SAL) is first modeled, where a multi-antenna anchor node is able to estimate the location of the target node using both angle and distance information. Then, according to SAL, we propose an accurate and distributed localization (ADL) algorithm, which can not only estimate the location of the target node with fewer anchor nodes but also be more accurate than the traditional multilateration method. Furthermore, we prove that the location estimate under ADL can converge towards the real location of the target node with probability 1. The lower and upper bounds of ADL are also derived under a bounded noise model. Extensive simulations are conducted to demonstrate the performance of ADL and the correctness of the theoretical results.

A burgeoning number of wireless devices connecting to the Internet tend to impose a heavy traffic load on the network backbone. Caching the most popular content at the heterogeneous wireless network edge is a promising way to alleviate the network overload. However, to cache the diverse content effectively, a file popularity profile that may not be known in advance to network operators has to be utilized. To tackle the challenge caused by this uncertainty, online learning techniques can be considered. Additionally, in practice, dense small-cell networks are often deployed to maximize spectral efficiency, which will naturally bring overlapping coverage areas among individual small cells. In this paper, we propose to address the content caching problem in a scenario of overlapping coverage areas among small cells while further allowing users distributed in the overlapping area to stochastically choose to connect to the small-cell base station they can reach. We propose two effective and efficient online learning algorithms to address the aforementioned problem and also provide theoretical guarantees. Finally, experiments are conducted to verify the performance of the proposed algorithms practically.

The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the Internet of Things (IoT) technology. In BSN, prolonging the service cycle of the network is a major challenge due to the limited battery capacity and energy supply for sensors. To this end, improving energy efficiency and harvesting energy are the keys for the network to maintain sustainability. In this paper, we propose a transmission scheduling and energy harvesting strategy to manage energy supply and consumption, and build several dynamic models to capture the stochastic processes in BSN. Besides, a system utility maximization problem is formulated. Since this problem is a multiobjective mixed-integer optimization problem (MMOP) which is difficult to solve directly, we provide a solution framework where MMOP is decomposed into several subproblems by the Lyapunov optimization method. Based on this framework, we propose an online energy sustainability optimization algorithm to solve these subproblems, such as the matching problem and convex optimization problem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the appropriate sizes of the data buffer and battery capacity are derived, which can give a guidance to determine the sizes of these components. Simulation results show the impact of the system parameter on the utility and data and energy queues, and verify that the proposed strategy and methods can maintain the sustainable operation of BSN effectively.

Privacy-preserving data aggregation (DA) in network systems, e.g., Internet of Things (IoT), is a challenging problem, considering the dynamic network topology, limited computing capacity, energy supply of IoT devices, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness of the DA process against dishonest nodes remains an open issue. Different from the widely investigated cryptographic approaches, in this paper, we address this challenging problem by exploiting the distributed consensus technique. To mitigate the pollution from dishonest nodes, we propose an enhanced secure consensus-based DA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishonest nodes. We prove the convergence of the E-SCDA and show that the algorithm can preserve the privacy associated to nodes' initial states. Extensive simulations have shown that the proposed algorithm has a high convergence accuracy and low complexity, even when there exist dishonest nodes in the network.

Internet of Vehicles (IoV) is a promising Internet of Things application, where roadside unit (RSU) plays an important role for network service provisioning. How to select the number and locations of RSUs to deploy and allocate the traffic load to them is a critical and practical open problem. Most of the existing work focused on 1-D scenarios assuming unlimited RSU capacity, while a more practical 2-D case with limited RSU capacity has not been fully considered yet. In this paper, we investigate an RSU deployment problem for 2-D IoV networks considering the expected delivery delay requirements and task assignment. We formulate a novel utility-based maximization problem to solve the RSU deployment problem, where the utility function indicates the total benefit from the RSU deployment. We observe that each RSU has an irregular service area, which makes the problem much more difficult than the traditional facility location problem. Then, we design a utility-based RSU deployment algorithm (URDA), a linear programming-based clustering algorithm, to solve the problem. The gap between URDA and the optimal solution has been analyzed, which proved that the proposed URDA is near optimal if the deployment cost is low. Extensive simulations have been conducted to demonstrate the effectiveness and superiority of the proposed solution for IoV network service guarantee over other approaches.

Due to the vast demand for monitoring a structure or area in linear topology, linear sensor networks (LSNs) have recently attracted plenty of attention. Since sensor nodes are usually battery-powered, duty-cycling techniques have been widely studied to improve energy efficiency, which, however, introduces a significant issue known as sleep latency. Thereafter pipelined forwarding has been proposed in the literature as a promising way to alleviate this issue. This paper focuses on interference analysis for data collection services in a multihop LSN running a duty-cycling and pipelined-forwarding protocol, where multiple concurrent transmissions along a data collection path can severely interfere with each other. We first obtain the nodal distance distributions associated with all concurrent transmissions. Based on the obtained distance distributions and the path-loss model in an interference-limited environment, we analyze the distributions of signal-to-interference-plus-noise ratio (SINR) and link capacity. The obtained SINR distribution indicates the link outage probability at a given SINR threshold. By investigating the transmission which receives the strongest cumulative interference, our model can provide useful guidelines for duty cycle setting to achieve a desired network performance.

Location region estimation (LRE) is a key issue for many location-based applications in the Internet of Things era. This paper explores the problem of accurate LRE (ALRE) with distance distribution methods. First, in order to capture the uncertainties during the distance ranging process, a disk error model is introduced by modeling the target as a random node inside a disk region. Then, a disk error-based ranging (DEBR) approach is designed and analyzed by proving that the parameter estimation of DEBR is unbiased. Furthermore, an ALRE algorithm is developed through taking into account both DEBR and the classical multilateration method. It is proved that the estimated region obtained by ALRE is tighter than that obtained by the traditional estimation method. In addition, extensive simulations are conducted to verify the unbiased estimation of DEBR and evaluate the performance of ALRE.

We study the computation resource management problem in mobile edge-cloud computing networks. Mobile edge servers shall first satisfy the computation requirements of mobile users and Internet of Things (IoT) devices, and then wholesale redundant computation resources to the cloud networks to maximize their profit. Due to the coarse time granularity of wholesales, computation resource buyback may happen occasionally to deal with traffic bursts. Thus, the mobile edge servers need to make a tradeoff between the wholesale profit and the buyback cost. In this paper, the computation resource management problem is modeled as profit maximization. To solve this problem, we first analyze the relationship among the reserved computation resources, the computation tasks of mobile users and IoT devices, and the buyback cost. Then, we design an efficient wholesale scheme to determine the amount of the wholesaled computation resources, by which the total expected profit of the mobile edge server can be maximized. Given the reserved computation resources, we also propose a fast-convergent realtime buyback scheme for mobile edge servers to minimize the buyback cost. Finally, the simulation results show that our proposed efficient wholesale and buyback scheme can increase the total profit while guaranteeing the computation delay of all the computation tasks, especially when the computation workloads are time-varying.

Mobile edge computing (MEC) offloads computation-intensive applications and overcomes the long latency by pushing data traffic towards the network edges. With base stations (BSs) densely deployed in a hot-spot area to improve user experience, mobile user equipments (UEs) have multiple choices to offload tasks to edge servers by jointly considering both the channel condition and the computing capacity. However, precise full system information is hard to be synchronized between BSs and UEs for mobility management decision making. In this paper, a Q-Iearning based mobility management scheme is proposed to handle the system information uncertainties. Each UE observes the task delay as an experience and automatically learns the optimal mobility management strategy through trial and error. Simulations show that the proposed scheme manifests the superiority in dealing with the uncertainties. Compared with the traditional received signal strength-based handover scheme, the proposed scheme reduces the task delay by about 30%.

Connected electric vehicles (EVs) are a key component of future intelligent and green transportation systems, and the penetration of EVs depends on convenient and cost-effective charging services. In addition to being charged at home or on parking lots, a charging network is needed for EVs right off the road. This paper first focuses on the optimal charging network design for charging service providers, considering the time-varying and location-dependent demands from vehicles and constraints of power grids. To optimize the charging station locations and the number of chargers in each station, we first model the coverage area of each possible location to estimate the dynamic charging requirements of EVs. Then, we formulate the problem as profit maximization, which is a mixed-integer program. To make the problem tractable, we investigate the features of the problem and obtain a necessary condition to deploy a charging station and derive the upper and lower bounds of the number of chargers in each station. Given the analysis, we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm is further proposed based on the profit of each possible location. Using real vehicle traces, simulation results show that the proposed algorithm can maximize the total profit when fewer charging stations and chargers are initially needed, which is more attractive for charging service providers.

In this paper, we propose a novel real-time sound event detection framework, which combines multi-label learning and edge computing, to classify and localize abnormal sound events for city surveillance. Multiple devices equipped with acoustic sensors are deployed to collect the audio information. A learning-based approach is introduced to address the difficulties of accurately classifying the temporally overlapping acoustic events in a noisy environment. Then, edge computing is adopted to handle the high processing complexity of the learned analytics. Computation-intensive tasks of classification and localization can be offloaded to the nearby edge server for low-latency sound detection. An ensemble-based cooperative decision-making algorithm is also presented to aggregate the information from distributed devices in order to obtain better classification results. Extensive evaluations show the effectiveness of edge computing which helps reduce the time latency as well as the superiority of cooperative post-processing on the edge server to obtain a high accuracy.

A promising solution to alleviate the mobile traffic burden on the Internet is to cache the most popular content at the heterogeneous wireless network edge. However, due to the vast content stored at the remote server, and to cache effectively, it concerns the file popularity profile that may not be known by the network operators in advance. Therefore, online learning techniques are used to tackle the challenges brought by the unknown knowledge. We present an effective and efficient algorithm based on the stochastic combinatorial multi-armed bandits with locked-up slots to address the content caching problem. Our work particularly addresses the scenario where dense small cells with diverse user populations are deployed. Additionally, this network is only given limited external resources such as computational resource to learn the caching policies and wireless backhaul resource to refresh the caches. Our algorithm learns the caching policies online which is to decide which files to be cached sequentially. Despite sharing the limited external resources, the proposed algorithm guarantees the performance of each small cell to approach the optimum. Experiments are conducted to cross-validate the theorem presented in this work.

Low-latency data access is an important challenge for data center networks. Proper placement of the data items can reduce the data travel time in the distributed storage systems, which contributes significantly to the latency reduction. Most existing data placement approaches have often assumed the prior distribution of data requests or discovered so through trace analysis. However, the traditional static model-based solutions are less effective to handle the system uncertainties in a dynamic environment. We present DataBot, a reinforcement learning-based adaptive framework, to learn the optimal data placement policies faced with the dynamic network conditions and time-varying request patterns. DataBot utilizes a neural network, trained with a variant of Q-learning, whose input is the realtime data flow measurements and whose output is a value function estimating the near-future latency. For rapid decision making, DataBot is divided into two decoupled production and training components, ensuring that the convergence time of the training will not introduce more overheads to serve the read/write requests. Evaluation results demonstrate that the average write and read latency of the whole system can be lowered by about 35% and 40%, respectively.

Wi-Fi Mesh Networks (WMNs) as a popular platform can be used for the construction of dynamic backhaul networks especially over small cells, which is an indispensable part of the 5G technology. Finding the optimal single-path flow routing solution over the multi-hop backhaul networks is a classic NP-hard problem and there are non-trivial drawbacks such as packet re-ordering to implement multi-path routing as a practical solution. However, Software Defined Networking (SDN) as an emerging paradigm can provide a great opportunity to implement fast and efficient solutions for routing the network flows over WMNs. In this paper, we propose a randomized single-path flow routing that can be applied to SDN-aware WMNs. The randomized nature of our introduced solution avoids the complexities of implementing a multi-path flow routing and it presents a viable routing scheme that guarantees certain performance bounds. In addition, it considers the key characteristics of wireless networks and it can be employed for multi-channel multi-radio WMNs. Through numerical results, we have shown that our solution follows the theoretical, tighter and more general performance bounds. Moreover, in contrast to most of the prior studies, the performance of the proposed solution is not only evaluated through a real testbed (in terms of the aggregated throughput and protocol overhead) but also compared with some of the most popular WMN routing protocols.

Privacy-preserving localization in crowdsourcing has drawn much attention recently. Under the classical nonadjacent subtraction-based localization (NSL) model, existing solutions based on homomorphic encryption techniques are of high computational and communication overheads. In this paper, an adjacent subtraction-based localization (ASL) model is first proposed. Then, an efficient privacy-preserving localization (EPPL) algorithm is developed under ASL without using any homomorphic encryption technique. In terms of the correctness, privacy, and efficiency, a comprehensive analysis is presented to investigate EPPL's performance. Furthermore, the statistical equivalence between ASL and NSL is proved through the fact that the difference between their average location estimation results converges toward zero. The lower and upper bounds of the localization error are also derived for ASL under a bounded noise model. Extensive simulations are conducted to illustrate the equivalence between ASL and NSL, and the performance of EPPL regarding the correctness, privacy, and efficiency.

Utilizing analytical models to evaluate proposals or provide guidance in high-level architecture decisions is been becoming more and more attractive. A certain number of methods have emerged regarding cache behaviors and quantified insights in the last decade, such as the stack distance theory and the memory level parallelism (MLP) estimations. However, prior research normally oversimplified the factors that need to be considered in out-of-order processors, such as the effects triggered by reordered memory instructions, and multiple dependences among memory instructions, along with the merged accesses in the same MSHR entry. These ignored influences actually result in low and unstable precisions of recent analytical models. By quantifying the aforementioned effects, this article proposes a cache performance evaluation framework equipped with three analytical models, which can more accurately predict cache misses, MLPs, and the average cache miss service time, respectively. Similar to prior studies, these analytical models are all fed with profiled software characteristics in which case the architecture evaluation process can be accelerated significantly when compared with cycle-accurate simulations. We evaluate the accuracy of proposed models compared with gem5 cycle-accurate simulations with 16 benchmarks chosen from Mobybench Suite 2.0, Mibench 1.0, and Mediabench II. The average root mean square errors for predicting cache misses, MLPs, and the average cache miss service time are around 4%, 5%, and 8%, respectively. Meanwhile, the average error of predicting the stall time due to cache misses by our framework is as low as 8%. The whole cache performance estimation can be sped by about 15 times versus gem5 cycle-accurate simulations and 4 times when compared with recent studies. Furthermore, we have shown and studied the insights between different performance metrics and the reorder buffer sizes by using our models. As an application case of the framework, we also demonstrate how to use our framework combined with McPAT to find out Pareto optimal configurations for cache design space explorations.

For vehicular networks, dropboxes are very useful for assisting the data dissemination, as they can greatly increase the contact probabilities between vehicles and reduce the data delivery delay. However, due to the costly deployment of dropboxes, it is impractical to deploy dropboxes in a dense manner. In this paper, we investigate how to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the cost of dropbox deployment. This is a very challenging issue due to the difficulty of accurate delay estimation and the complexity of solving the optimization problem. To address this issue, we first provide a theoretical framework to estimate the delivery delay accurately. Then, based on the idea of dimension enlargement and dynamic programming, we design a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We prove that ODDA has a fast convergence speed, which is less than κ (κ <; n) iterations for convergence. We also prove that the computational complexity of ODDA is O(nkm logm), i.e., ODDA has a polynomial computational complexity for a given m, the number of dropboxes for deployment. Performance evaluation by simulation demonstrates the superior performance of the proposed strategies compared with the benchmark methods.

Currently, one of the main enablers for network evolution is software-defined networking (SDN), where the control plane is decoupled from the data plane. A controller, as a (logically) centralized entity in the control plane, is the Achilles' heel of SDN resilience since its failure would affect the proper functioning of the entire network. The resilience of the control plane is strongly linked to the controller placement problem, which deals with the positioning and assignment of controllers to the forwarding devices (i.e., switches). A resilient controller placement problem needs to assign more than one controller to a switch while it satisfies certain quality of service requirements. In this paper, we propose a solution for such a problem that, unlike most of the former studies, takes both the switch-controller/inter-controller latency requirements and the capacity of the controllers into account to meet the traffic load of switches. The proposed algorithms, one of which has a polynomial-time complexity, adopt a clique-based approach in graph theory to find high-quality solutions heuristically. It is evaluated with real wide area network (WAN) topologies and the corresponding results are extensively analyzed. The resultant studies equip the service providers with helpful insights into the design of a resilient software-defined WAN.

The collection of sensory data is crucial for cyber‐physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on‐demand MDC; that is, MAs collect data based on the real‐time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve‐based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.

Evaluating cache performance is becoming critically important to predict the overall performance of out-of-order processors. Non-blocking caches, which are very common in out-of-order CPUs, can reduce the average cache miss penalty by overlapping multiple outstanding memory requests and merging different cache misses with the same cacheline address into one memory request. Normally, memory-level-parallelism (MLP) has been used as a metric to describe the concurrency of memory access. Unfortunately, due to the extremely dynamic dependences among the program memory references, it is very difficult to quantify MLP without time-consuming simulations. Moreover, the merging of multiple cache misses, which makes the average cache miss service time less than the physical DDR access latency, is seldom considered in the existing researches. In this paper, we propose a cache performance evaluation framework based on program trace analysis and analytical models to fast estimate MLP and the effective cache miss service time without simulations. Comparing with the results by Gem5 simulations of MobyBench 2.0, Mibench 1.0 and Mediabench II, the average accuracy of the modeled MLP and the average cache miss service time is higher than 91% and 92%, respectively. Combined with cache misses calculated by the stack distance theory, the average absolute error of CPU stall time (due to cache misses) is lower than 10%, while the evaluation time can be sped up by 35 times relative to the Gem5 full simulations.

In this paper, the performance of device-to-device (D2D) communications in cellular networks is studied when the downlink communication resources are reused. To guarantee the successful coexistence of multiple D2D pairs reusing the identical radio resource, the Power Emission Density (PED)-based interference modeling method is adopted to explore proper network settings. With the constraint of the Signal-to-Interference Ratio (SIR) requirements for both the macro-cell and D2D communications, an exclusive region-based analytical model is proposed to obtain the guard distances from a D2D user to the transmitting base station, to the receiving cellular user, and to other communicating D2D pairs, respectively. With these guard distances, the bounds of the throughput improvement provided by D2D communications are then derived for different cases. Finally, extensive simulations are conducted to verify our analytical results. The new results obtained in this work can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

To improve energy efficiency for energy-constrained Wireless Sensor Networks (WSNs), duty-cycling techniques have been widely studied and adopted in the design of Media Access Control protocols. On the other hand, to alleviate the well-known sleep latency issue caused by duty-cycling techniques, the study on pipelined forwarding over duty cycling has also attracted plenty of attention from researchers. Noticing that in the current literature for a typical duty-cycled pipelined-forwarding protocol, there is lack of physical interference model which takes into account the effect of cumulative interference, this paper fills the gap by proposing such a model based on nodal distance distributions. Based on the model, the distribution of Signal-to-Interference-plus-Noise Ratio (SINR) achieved at the receiver can be obtained, and the performance metrics that are functions of SINR, such as outage probability and link capacity, can be analyzed. We utilize the proposed model to conduct performance evaluations for a WSN with the pipelined- forwarding feature and investigate the tradeoff among packet delivery latency, energy efficiency, and network capacity by setting an important network parameter, called sleep factor, which determines how long a node can turn its radio off every cycle.

Delay-tolerant networks (DTNs) are wireless mobile networks that exhibit frequent intermittent connectivity and large transmission delay among nodes. Research results have shown that quota-controlled routing protocols can strike a reasonable balance between routing performance and cost, where quota is a value to control the number of message copies. However, the question of how to set the optimal quota dynamically in order to achieve the lower bound of routing cost is still open. In this paper, we model the optimization of quota control as an extremal functional problem and analyze it by a classic mathematical method called Calculus of Variations (CoV) for the first time. The function of time-variant quota with minimal average number of message copies is obtained in closed form, and an optimal quota control algorithm is proposed under practical routing design considerations. Both the numerical and simulation results show that the proposed model and algorithm are effective and efficient.

Channel assignment is a key problem in Cognitive Radio Networks. The channels are leased to the secondary users when the licensed or primary users are OFF, provided that the secondary users will leave those channels as soon as the primary users are reactivated. Therefore, to achieve a high performance of the network, we should lease a channel to a secondary user while both of them achieve the best possible utility. In this paper, we first define two utility functions, one for the secondary user and another for the channel. Then, in the pre-matching stage, we find a trade-off between these two utilities for each secondary user-channel pair by defining a joint utility function. Next, using the maximal secondary user utility and the maximal channel utility corresponding to the trade-off point as a measure of their preference for each other, we match secondary users and channels to each other by the Gale-Shapley stable marriage algorithm. We also obtain the optimal transmission power of the secondary user and the optimal bandwidth of the channel. Finally, we provide numerical results by computer simulation of our scheme.

Protecting location privacy of mobile systems is important for various location-based services in pervasive computing scenarios. How to quickly compute the target user's location without knowing each anchor user's location has drawn much attention. Under the classical nonadjacent subtraction based localization model, existing solutions based on homomophic encryption introduce much computation and communication overheads to achieve privacy-preserving localization. In this paper, an adjacent subtraction based localization model is proposed, which is suitable to efficiently protect users' privacy. Then, under such a model, an efficient privacy-preserving localization algorithm is developed without using homomophic encryption. A closed-form expression of the relationship between the localization error and the measurement noise is derived. Furthermore, a comprehensive analysis, including correctness analysis, privacy analysis, and efficiency analysis, is presented. Some simulations are conducted to show that the proposed model has equivalent accuracy and efficiency with the classical model. Some numerical results are presented to show the efficiency of the proposed privacy-preserving localization algorithm.

In recent years, dramatic growth of mobile data traffic has left the operators no choice but to consider Wi-Fi networks as an economic complementary solution. To achieve this, WLANs require to adopt some of the key features of carrier-grade operators, such as centralized resource management. As an emerging paradigm, Software Defined Networking (SDN) can be used to provide salient centralized network solutions for Wi-Fi infrastructures. In fact, applying SDN to different wireless platforms, e.g., Wi-Fi Mesh Networks (WMNs), brings unprecedented opportunities to improve the network performance by employing more sophisticated algorithms at SDN controllers. Moreover, it should be noted that traffic engineering over WMNs incorporates tightly correlated steps including association control, gateway selection and flow routing which are individually NP-hard problems. In this paper, we present an agile and fine-grained access provisioning solution via bridging the cellular and Wi-Fi technologies that empowers us to address the users demand by steering data flows on different tiers of WMNs. In contrast to the prior work, we present a detailed unified formulation for joint gateway selection and flow routing in Multi-Channel Multi-Radio (MCMR) WMNs that considers the key attributes of wireless networks. The functionality of the presented solution is evaluated through various experiments with extensive numerical results.

The distributed cache system contains a group of servers caching different contents based on consistent hashing. The dynamic provisioning of servers helps to improve the system efficiency, which leads to a reduction of energy cost. We first measure the cache hit rate, request batching effect and cache warm-up time of the system through experiments, considering that they can affect the system performance and efficiency. Then we formulate a stochastic network optimization problem, which aims at achieving objectives on the queue stability, energy cost and cache hit rate simultaneously, through the dynamic control of server activeness and request dispatching. The problem is transformed into a minimization problem in each time slot, which is further addressed through the proposed efficient online algorithm based on dynamic programming. Moreover, we improve the scheme with several practical considerations in the scheme implementation. Finally, the proposed algorithm and the improvements are evaluated through extensive experiments.

For disconnected Vehicular Ad hoc NETworks (VANETs), the carry-and-forward mechanism is promising to ensure the delivery success ratio at the cost of a longer delay, as the vehicle travel speed is much lower than the wireless signal propagation speed. Estimating delay is critical to select the paths with low delay, and is also challenging given the random topology and high mobility, and the difficulty to let the message propagate along the selected path. In this paper, we first propose a simple yet effective propagation strategy considering bidirectional vehicle traffic for two-dimensional VANETs, so the opposite-direction vehicles can be used to accelerate the message propagation and the message can largely follow the selected path. Focusing on the propagation delay, an analytical framework is developed to quantify the expected path delay. Using the analytical model, a source node can apply the shortest-path algorithm to select the path with the lowest expected delay. Performance evaluation by simulation show that, when the vehicle density is uneven but known, the proposed Minimum Delay Routing Algorithm can achieve a substantial reduction in delay compared with the geocast-routing approach, and its performance is close to the flooding-based Epidemic algorithm, while our solution maintains only a single copy of the message.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network performance. However, they may cause severe intra/inter-cell interference that can considerably degrade the performance of cellular users, and vice versa. Therefore, interference analysis has been one of the most important research topics in such a system. Focusing on an uplink resource reusing scenario, this paper presents a framework based on a probabilistic distance and path-loss model to obtain the distributions of signal, interference, and further Signal-to-Interference-plus-Noise Ratio (SINR), based on which, the performance metrics that are functions of SINR can be analyzed, such as outage probability and capacity. Different from the previous work, this proposed framework: 1) obtains interference and SINR distributions for both cellular and D2D communications, through which insights into their performance metrics and mutual influence are provided and 2) has no limitations on cell shapes, except that they are approximated by polygons or circles. The framework can also be applied to a downlink reusing scenario. Our results indicate that the developed framework is helpful for network planners to effectively tune the network parameters, and thus to achieve the optimum system performance for both cellular and D2D communications.

Nowadays, Wireless Mesh Networks (WMNs) have received worldwide acceptance as an attractive paradigm on top of the existing and upcoming wireless technologies. Up to now, a lot of research studies have been conducted on the channel assignment in multi-radio WMNs. To address the destructive impact of interference as an inevitable element of WMNs, it is necessary to reconfigure the wireless radios for switching to the less-congested channels, frequently. Although this problem has been investigated through extensive modeling and simulations, the consequences of channel switching for popular WMN routing schemes have not been assessed sufficiently. In this paper, we present a concise analysis of channel switching latency in multi-hop multi-radio scenarios for OLSR, BATMAN-adv and Open80211s protocols through experimentation testbed. Moreover, we evaluate the functionality of SDN-based WMNs in terms of channel switching delay. The acquired results substantiate that although by using SDN it is feasible to improve the network responsiveness, still the channel switching latency is not negligible.

Research on individuals in online social networks often requires the collection of personal information such as demographics. Due to both user privacy concerns and unformatted textual information, it is quite difficult to build a completely labeled social network directly. However, both social relations and network characteristics can help attribute inference to profile online social network users. In this paper, we propose several attribute inference models based on these two factors and implement them with Naive Bayes, Decision Tree and Logistic Regression. Also, to study network characteristics and evaluate the performance of our proposed models, we use a well-labeled Google employee social network extracted from Google+ to test the proposed models on inferring the social roles of Google employees. The experiment results demonstrate that the proposed models are effective in social role inference with Dyadic Label Model performing best.

Software Defined Networking (SDN) is an emerging paradigm for network design and management. By providing network programmability and separation of control and data planes, SDN offers salient features such as simplified and centralized management and control, reduced complexity and accelerated innovation. However, SDN introduces new challenges that should be addressed properly in order to benefit from its unprecedented capabilities. Due to the (logically) centralized control in SDN, the resilience of the control plane has a great impact on the functioning of the whole system. In this case, resilient controller placement problem (how many controllers are needed and where to place them to provide higher reliability) is a hot research topic that affects the reliability and performance of SDN in Wide Area Networks (WANs). Thus, we define a resilient controller placement problem, which satisfies a set of constraints, some of which are missing in the existing solutions. The acquired results on real tier-1 US service provider network topologies demonstrate the effectiveness of the approach. This can give helpful insights to the network operators for designing or modifying their network topologies to enhance the resilience of the control plane in SDN.

Delay-Tolerant Networks (DTNs) are wireless mobile networks, where the nodes are sparse and end-to-end connectivity is rare. Since DTN nodes are mostly energy-limited devices, there is an immediate need to have energy-efficient routing protocols, allowing the network to perform better and function longer. Besides, in the real world, people carrying the nodes form a lot of communities because of similar interests, and they behave with social selfishness. How to improve the energy efficiency in multi-community scenarios has been an important problem. In this paper, we analytically model the performance of epidemic routing protocols in multi-community scenarios with social selfishness considerations using the Ordinary Differential Equations (ODEs). Further, an energy-efficient copy-limit-optimized algorithm based on the Box's complex method for epidemic routing is proposed, which is designed to determine the optimal copy limit in multiple communities, and can improve the energy efficiency effectively. At last, both the numerical and simulation results show that the routing protocol with the proposed algorithm can reduce the energy consumption effectively, and the impact of social selfishness is also analyzed.

With the increasing demand of big data applications, a variety of problems on how to operate the supporting infrastructures more intelligently and efficiently have attracted much attention in the literature. To optimize the data placement among distributed network locations is one of the fundamental problems, which aims at facilitating the data storage and access. However, traditional schemes meet challenges on the running time and the overhead introduced due to the increasing scale of datasets. Therefore, we propose a novel data placement scheme based on sketches to overcome these challenges. We first justify the effectiveness of applying the hypergraph sparsification on the data placement problem, and then present the method of constructing sparsifiers through the sketches of request traffic. Besides, the scheme features on the support of aggregating distributed sketches to make the decision and capturing the pattern of recent traffic through sliding windows. Finally, we obtain numerical results through simulations which confirm that the proposed scheme can place data effectively while reducing the introduced overhead in terms of algorithm running time, space and network traffic.

Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.

Modern wireless broadband networks are crucial for different mission-critical applications and public safety agencies. Various natural disasters and physical attacks would result in the malfunction or failure of wireless and cellular infrastructures. This subsequently affects the correct functioning of the dependent mission- critical applications. Thus, disaster management and response is of great importance. In this paper, we focus on disaster response using D2D communications to extend the coverage of base stations, and on controller-assisted routing to maximize the total end-to-end throughput for all of the current flows from the area without network coverage using the ant colony optimization. The proposed routing scheme outperforms the schemes based on the shortest path routing in terms of the total throughput as well as fairness in allocating the rates to the flows.

Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly weaken the effectiveness of data transmission. Although social-based strategies can be used to deal with the problem, most existing approaches adopt multicopy strategy to forward messages which inevitably add more unnecessary cost. One of the most important issues is the selection of the best intermediate node to forward messages to the destination node. In this paper, we focus on finding a quality metric associated with better relays which is evaluated by Reachable Probability Centrality (RPC) as we proposed. RPC combines the contact matrix and multi-hop forwarding probability based on the weighted social network, thus ensuring an effective relay selection. We also propose a distributed RPC-based routing algorithm, which demonstrates the applicability of our scheme in the decentralized environment of DTNs. Extensive trace-driven simulations show that RPC outperforms other centrality measures and our proposed routing algorithm can significantly reduce the data forwarding cost while having comparable delivery ratio and delay to those of the Epidemic routing.

To collect data from large-scale wireless sensor networks (WSNs) is a challenging issue and there are mainly two approaches to increase the efficiency: 1) by hierarchical routing based on node clustering and 2) by mobile elements (MEs). Since either method has pros and cons, this paper presents a hybrid approach, called node-density-based clustering and mobile collection (NDCMC), to combine the hierarchical routing and ME data collection in WSNs. A number of cluster heads (CHs) gather information from cluster members and then an ME visits these CHs to collect data. First, for a randomly deployed WSN, a new CH selection scheme based on the node density is proposed. The advantage is that the nodes which are surrounded by more deployed nodes are more likely to be CHs. Thus, the efficiency of both intracluster routing and ME data collection is improved. Second, a low-complexity traveling track planning algorithm is designed for an ME to pass by all CHs. The analytical model of NDCMC is also developed and the expectation of the sensor power consumption and network lifetime are derived. In addition, a simple random clustering and mobile collection (RCMC) scheme is introduced by which a number of CHs are selected randomly in a WSN. Although RCMC yields performance degradation, it has much less complexity. Extensive simulations show that the proposed hybrid NDCMC scheme leads to not only remarkable performance improvement but also convenient tradeoff between the network energy saving and the data collection latency.

Minimizing the end-to-end delay for data dissemination in a large-scale VANET with both buses of fixed schedules and taxis of random schedules is a challenging issue, due to the scalability, high-mobility, and network heterogeneity concerns. Particularly, the mix of random taxis and fixed-scheduled buses makes the delay components along a path dependent and hard to estimate. In this paper, to address the scalability and high-mobility issues, we introduce a store-and-forward framework for VANETs with extra storage using “drop boxes”, which function similar to network routers. Next, we propose an optimal link strategy which is independent of the message arrival time and can be executed in a distributed manner. Then, we derive the expected path delay, considering the dependence of the delay components along the path, and propose the optimal routing strategy to minimize the expected path delay. Trace-driven simulations have been used to validate the rigorous analysis, and demonstrate the superior performance of the proposed strategies, which result in a substantial delay reduction and a much higher delivery ratio when compared with the state-of-the-art solutions without drop boxes. The strategies can further improve the delay performance when compared with the over-simplified routing solutions which ignore the dependence of the delay components.

Effective inter-vehicle communication is fundamental to a decentralized traffic information system based on Vehicular Ad Hoc Networks (VANETs). To reflect the uncertainty of the information propagation, most of the existing work was conducted by assuming the inter-vehicle distance follows some specific probability models, e.g., the lognormal or exponential distribution, while reducing the analysis complexity. Aimed at providing more generic results, a recursive modeling framework is proposed for VANETs in this paper when the vehicle spacing can be captured by a general i.i.d. distribution. With the framework, the analytical expressions for a series of commonly discussed metrics are derived respectively, including the mean, variance, probability distribution of the propagation distance, and expectation for the number of vehicles included in a propagation process, when the transmission failures are mainly caused by MAC contentions. Moreover, a discussion is also made for demonstrating the efficiency of the recursive analysis method when the impact of channel fading is also considered. All the analytical results are verified by extensive simulations. We believe that this work is able to potentially reveal a more insightful understanding of information propagation in VANETs by allowing to evaluate the effect of any vehicle headway distributions.

Mobile Elements (MEs) are widely employed for data collection in Wireless Sensor Networks (WSNs) to balance the energy consumption and prolong the network lifetime. However, the limited travel speed of the MEs causes a large data collection latency, which in turn degrades the performance of the data collection task and weakens the applicability of the data collection schemes. Considering the deterministic concentric circle-like trajectories-covered sensing field, we propose a Low-latency Data Sweeping scheme, LDS, which utilizes the communication opportunities among MEs to shorten the data collection latency. The data are swept by the MEs, which traverse along the trajectories, and are relayed to the adjacent MEs towards the sink node. Besides, the transmission range of sensor nodes is limited to the minimum distance to the nearest trajectory for energy-conservation purposes. The performance of the proposed scheme is first analyzed with probabilistic methods. Extensive simulations further show that our scheme outperforms the well-known heuristic algorithms in terms of the data collection latency, energy dissipation, and network lifetime.

Tiered networks have been introduced to mitigate the issues related to poor cellular coverage in dead zones and indoor environments. However, the large-scale deployment of multiple tiers can result in severe intra-tier and inter-tier interference that can considerably degrade the performance of users in all tiers. Thus, the network interference analysis has been an important topic in tiered networks. In this paper, we focus on the uplink resource reusing scenario in a two-tier cellular network consisting of a macro cell and multiple femto cells. Without imposing any limitations on the shape of the macro/femto cells (as long as they are approximated by polygons), for the first time in the literature, we obtain the distance distributions associated with tiered structures. Utilizing these distance distributions and the path-loss model in an interference-limited environment, we obtain the distributions of the received signal and interference for both tiers. Further, we give details on how our approach applies to the downlink resource reusing scenario as well as a network with multiple macro cells. Our performance study provides insights into the Signal-to-Interference Ratio and outage probability for macro/femto-cell base stations.

Data-intensive applications need to address the problem of how to properly place the set of data items to distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those in Hadoop and Cassandra, but they do not work efficiently for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Recent works proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose an associated data placement (ADP) scheme, which improves the co-location of associated data and the localized data serving while ensuring the balance between nodes. In ADP, we employ the hypergraph partitioning technique to efficiently partition the set of data items and place them to the distributed nodes, and we also take replicas and incremental adjustment into considerations. Through extensive experiments with both synthesized and trace-based datasets, we evaluate the performance of ADP and demonstrate its effectiveness.

How to minimize the energy dissipation and extend the lifetime of wireless sensor networks (WSNs) is still an active research topic nowadays. Hierarchical routing based on node clustering is an effective method, while using mobile elements (MEs) to gather data can prevent huge energy consumption of the sensors from long-distance transmission. Considering that both methods have pros and cons, this paper presents a hybrid approach, called Node Density based Clustering and Mobile Collection (NDCM), to combine the hierarchical routing and ME data collection in WSNs. A number of Cluster Heads (CHs) first gather information from the cluster members and then the ME visits these CHs to collect data. A new CH selection scheme based on the node density is proposed. Thus, a node at the center of an area where nodes are densely deployed is more likely to be a CH, which can improve the efficiency of both intra-cluster routing and ME data collection. We also introduce a simple Random Clustering and Mobile Collection (RCM) scheme according to which a number of CHs are selected randomly throughout the network. In addition, the nodes which are covered by the radio range of the ME, called Virtual Heads (VHs), can also send/relay packets directly to the ME. The different mobility schemes are compared through extensive simulations and the results show that the proposed hybrid NDCM scheme leads to remarkable improvement in network lifetime and convenient trade off between the network energy saving and packet latency.

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network throughput, spectrum efficiency, and transmission delay. In this paper, we first introduce the concept of guard distance to explore a proper system model for enabling multiple concurrent D2D pairs in the same cell. Considering the Signal to Interference Ratio (SIR) requirements for both macro-cell and D2D communications, a geometrical method is proposed to obtain the guard distances from a D2D user equipment (DUE) to the base station (BS), to the transmitting cellular user equipment (CUE), and to other communicating D2D pairs, respectively, when the uplink resource is reused. By utilizing the guard distances, we then derive the bounds of the maximum throughput improvement provided by D2D communications in a cell. Extensive simulations are conducted to demonstrate the impact of different parameters on the optimal maximum throughput. We believe that the obtained results can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging (DMC) problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work, we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

Device-to-device (D2D) communications in cellular networks are considered a promising technology for improving network throughput, spectrum efficiency, and transmission delay. In this paper, the Power Emission Density (PED)-based interference modeling method is applied to explore proper network settings for enabling multiple concurrent D2D pairs in a sector-partitioned cell. With the constraint of the Signal-to-Interference Ratio (SIR) requirements for both the macro-cell and D2D communications, an exclusive region-based analytical model is proposed to obtain the guard distances from a D2D user to the base station, to the transmitting cellular user, and to other communicating D2D pairs, respectively, when the uplink resource is reused. With these guard distances, the bounds of the maximum throughput improvement provided by D2D communications are then derived for different sector-based resource allocation schemes. Extensive simulations are conducted to verify our analytical results. The new results obtained in this work can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.

In order to avoid harmful interference to primary users (PUs), secondary users (SUs) in a cognitive radio network need some information about the primary network, such as the location of the active PUs. However, the advent of high-speed primary networks, e.g., LTE, has created a necessity for fast and accurate localization methods. In this paper, we propose a novel generalized method of moments-based localization technique that only requires the knowledge of traffic distribution and power allocation strategy of the primary network. Each SU is capable of efficiently locating any PU inside a certain geometric region using the received signal strength. The proposed method converges in linear time with respect to the number of signal power measurements to localize the PUs with high accuracy, as demonstrated by the simulation results. Moreover, we discuss the effect of the SU and PU locations on the localization performance.

In today's network-centric battlefield, the ad hoc style, self-organizing networks play a more and more important role in the operation of mobile forces that are deployed quickly to meet the tactical demands. However, the unique features of the tactical scenario also pose significant challenges for networking, which makes the existing research results for the general mobile ad hoc networks (MANETs) difficult to be reused directly. In this paper, we focus on the connectivity issue of the mobile tactical networks (MTNs). To better describe the special formation-oriented mobility pattern of MTNs, a more realistic node-following mobility model is used, based on which the neighbor connectivity is obtained from both the geometrical and physical communication point of view for not only 1-D but also 2-D network scenarios. After that, we further evolve our recently proposed decomposition and recursion method to derive the end-to-end connectivity for some formations commonly used in the MTNs. All our analytical results are verified by extensive simulations. We believe that this work is able to shed new lights on the MTNs' performance characteristics, which can be used for guiding the development of the next-generation MTNs.

Information-Centric Networking (ICN) has a great potential to better support the content distribution over the future Internet. Meanwhile, users with mobile devices will also access ICN, introducing a new challenge to ICN providers to handle such mobile content requests. In this paper, a mobility and popularity-based caching strategy is proposed to increase the cache hit rate through WiFi while people are moving from one WiFi hotspot to another. It thus lowers the network traffic from the local ICN to the outside Internet. Specifically, a precise derivation on the content request rate is provided based on the content popularity and user mobility. A novel caching strategy is further developed based on this derivation for both the mobility-oblivious and mobility-aware scenarios. We evaluate different caching strategies with a trace-driven simulation and our new approach can achieve a 33.84% reduction on the network traffic when compared with the caching strategies only considering the content popularity.

To reduce the difficulties in calculating the aggregated interference power at an observed receiver, a Power Emission Density-based analysis method is proposed in this paper. By utilizing the new method, the traditional discrete-style calculation (i.e., obtain each concurrent interferer's impact on the observed receiver individually, and add them together) can be replaced with a concise integration over the entire network area, which could effectively reduce the complexity of interference-related studies. The accuracy of the proposed method is verified by extensive simulations in different network scenarios. The results and analytical methods given in this paper will lead to a series of geometrical-based studies on the interference in random wireless networks, which could be used to guide the design and implementation of large-scale wireless networks.

Recently, much research effort has been devoted to employing mobile chargers for energy replenishment of the robots in robotic sensor networks. Observing the discrepancy between the charging latency of robots and charger travel distance, we propose a novel tree-based charging schedule for the charger, which minimizes its travel distance without causing the robot energy depletion. We analytically evaluate its performance and show its closeness to the optimal solutions. Furthermore, through a queue-based approach, we provide theoretical guidance on the setting of the remaining energy threshold at which the robots request energy replenishment. This guided setting guarantees the feasibility of the tree-based schedule to return a depletion-free charging schedule. The performance of the tree-based charging schedule is evaluated through extensive simulations. The results show that the charger travel distance can be reduced by around 20%, when compared with the schedule that only considers the robot charging latency.

We consider a scheduling problem in a wireless network which consists of one base station, N legitimate users and one (or more) eavesdropper(s). The scheduling problem jointly considers the reliability, security and stability of the system, and is to allocate wireless resources to the legitimate users, stabilize the system and maximize the secure transmission rate. Based on the stochastic network optimization framework, the scheduling problem is decomposed to an online optimization problem. A scheduling algorithm and a low computational complexity algorithm that both do not consider power adaptation are proposed, along with a power adaptive one. Extensive simulations are conducted to show the impact of the information arrival rate and the eavesdropper's channel condition on the system performance. These observations provide important insights and guidelines for the design and resource management of future wireless networks using secure communication technologies.

Geocast, delivering messages to a specific location, has become an important issue with the accelerated development of the location-based services in mobile networks. Geocast in the automotive domain is of particular interest, enabling many promising applications, such as geographic advertising, location-based traffic alerts, etc. Different from the conventional geocast algorithms focusing on the distance-based approaches, in this paper, we propose a mobility-aware geocast algorithm (GeoMob) for urban VANETs from the Delay-Tolerant Network (DTN) perspective to better deal with the high mobility and transient connectivity issues. Different levels and aspects of vehicle mobility information are employed, making GeoMob very simple, scalable and communication and compunction-effective. Practical issues are well considered by introducing real-world trace analysis, trace-driven simulation and efficient buffer management. Extensive performance comparisons with other protocols have been conducted to show the advantages of GeoMob.

Combined Heat and Power (CHP) systems are well known for their high efficiency and relatively low emissions. Existing CHP economic dispatch schemes do not use the energy buffer to minimize the average cost in the long term. Motivated by the queueing analysis and buffer management solutions in data communication systems, in this paper, we investigate how to use a battery pack and a water tank to optimize the average cost for the CHP systems by jointly considering the real-time electricity price, renewable energy generation, energy buffer states, etc. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions are obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. Since the battery pack queue and water tank queue are correlated by the CHP, the capacity relationship between them is further explored. Through the theoretical performance analysis, we also show the tradeoff between the cost saving and the energy buffer capacity. Finally, the effectiveness of the proposed algorithms is evaluated with practical data.

The dynamic provisioning of distributed cache services helps to improve the system efficiency. We model the system as groups of servers caching different and none-overlapping key segments of content objects, and investigate the benefit of cache hit and request batching. A stochastic network optimization problem is formulated, which aims at achieving the system stability, low energy cost and certain cache hit rate simultaneously through the dynamic control of server activeness and request dispatching. The problem is transformed into a minimization problem at each time slot and the online algorithm to solve it is proposed. Also we show that dynamic programming helps to lower the computational complexity. Finally, the proposed algorithm is evaluated through extensive simulations.

The user-aware content retrieval services are always data-intensive and require much resource to satisfy the user demand, which incurs the high cost of implementation. Considering that they could largely exploit the pay-as-you-go paradigm and the almost unlimited resource pool of the cloud, we investigate the design issues of deploying the services to the cloud in a cost-effective way. We formulate the resource allocation and request handling problem which aims at lowering the deployment cost and guaranteeing the service quality simultaneously. Due to the hardness of obtaining an optimal solution, we design two approximate algorithms with different points of emphasis and analyze their approximation ratios as well. In addition, we discuss the implementation issues in applying the proposed algorithms to the practical systems. Finally, the algorithms are evaluated and validated through both trace-based and synthesized simulations where they show a large improvement in terms of the total system cost.

Geometrical distance distribution (GDD) between nodes in wireless communication networks plays a significant role in modeling network performance metrics. Existing work on obtaining GDD assumes the network geometry to be a regular one, such as circle and square. Due to the various complex effects of wireless signals, however, the network geometry usually is quite irregular. Therefore, this paper proposes a novel systematic and unified approach to obtain the GDD between two random nodes associated with arbitrary network geometries. To the best of our knowledge, this is the first work that will fill the gap in the literature of this field.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumptions, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging re- quests sequence of nodes with their sequence on the TSP tours. Experiment and simulation demonstrate ESync can reduce charger travel distance and nodes charging delay by about 30% and 40% respectively.

Opportunistic routing has been extensively studied and utilized in networks with high dynamics and large scales, e.g., city-wide vehicle networks. The extensive use of nodes' local information, i.e., the routing metrics, in such routings can cause severe security and privacy problems. Existing solutions of anonymous routing can introduce undesired overhead and fail to provide the confidentiality of the routing metric. In this paper, we propose an advanced framework for opportunistic routings, providing following properties: the confidentiality of nodes' routing metric, anonymous authentication and an efficient key agreement for pair-wise secret communication. A comprehensive evaluation, including security analysis, efficiency analysis and simulation evaluation, is presented to show the security and feasibility of the proposed framework.

Mobility-assisted data collection in sensor networks creates a new dimension to reduce and balance the energy consumption for sensor nodes. However, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to schedule the movement of mobile elements throughout the field is of ultimate importance. In this paper, the on-demand scenario where data collection requests arrive at the mobile element progressively is investigated, and the data collection process is modelled as an$M/G/1/c$-$NJN$queuing system with an intuitive service discipline of nearest-job-next (NJN). Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. NJN is further extended by considering the possible requests combination (NJNC). The simulation results validate our models and offer more insights when compared with the first-come-first-serve (FCFS) discipline. In contrary to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have better performance than FCFS, in both the average and more importantly the worst cases, which offers the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes, as well as other similar scheduling scenarios.

The connectivity of ad hoc networks has been extensively studied in the literature. Most recently, researchers model ad hoc networks with two-dimensional lattices and apply percolation theory for connectivity study. On the lattice, given a message source and the bond probability to connect any two neighbor vertices, percolation theory tries to determine the critical bond probability above which a giant connected component appears. This paper studies a related but different problem, directed connectivity: what is the exact probability of the connection from the source to any vertex following certain directions? The existing studies in math and physics only provide approximation or numerical results. In this paper, by proposing a recursive decomposition approach, we can obtain a closed-form polynomial expression of the directed connectivity of square lattice networks as a function of the bond probability. Based on the exact expression, we have explored the impacts of the bond probability and lattice size and ratio on the lattice connectivity, and determined the complexity of our algorithm. Further, we have studied a realistic ad hoc network scenario, i.e., an urban VANET, where we show the capability of our approach on both homogeneous and heterogeneous lattices and how related applications can benefit from our results.

Synchronization is a pre-requisite for many sensor network applications. However, it remains challenging in sensor networks due to both the limited resources and the dynamic environments. In this paper, we propose a new two-phase clock synchronization scheme. The first one is the external clock synchronization phase, during which nodes update their clock by exchanging timestamp messages with the reference clock. Different from the conventional solutions, we propose to directly remove the clock skew during the external synchronization to achieve a higher synchronization accuracy and lower computational complexity. The second one is the clock self-calibration phase, as the accumulated clock skew will make the synchronized clock drift away again, we need to compensate the clock skew to maintain the clock synchronization accuracy. However, the compensation is non-trivial as the clock skew may not be constant due to the changing environment. Thus we propose the temperature-assisted clock self-calibration (TACSC) to dynamically compensate the clock skew according to the working temperature. Extensive simulation demonstrates that the proposed synchronization scheme can achieve a much lower root mean square error in the external synchronization phase. Furthermore, during the clock self-calibration phase, the TACSC scheme can improve the synchronization accuracy by more than one order of magnitude, which is verified by both simulation and testbed experimentation.

In dense IEEE 802.11 networks, improving the efficiency of contention-based media access control is an important and challenging issue. Recently, the IEEE802.11ah Task Group has discussed a group-synchronized distributed coordination function (GS-DCF) for densely deployed wireless networks with a large number of stations. By using the restricted access window (RAW) and RAW slots, the GS-DCF is anticipated to improve the throughput substantially, primarily due to relieving the channel contention. However, optimizing the MAC configurations for the RAW, i.e., the number and duration of RAW slots, is still an open issue. In this paper, we first build an analytical model to track the performance of the GS-DCF in saturated 802.11 networks. Then, we study and compare the GS-DCF throughput using both centralized and decentralized grouping schemes. The accuracy of our model has been validated with simulation results. It is observed that the GS-DCF obtains a throughput gain of seven times or more over DCF in a network of 512 or more stations. Moreover, it is demonstrated that the decentralized grouping scheme can be implemented with a small throughput loss when compared with the centralized grouping scheme.

In wireless sensor networks, grid-based clustering and routing schemes have attracted considerable attention due to their simplicity and feasibility. In this paper, we adopt a hexagonal tessellation approach and propose a Fast Hexagonal Clustering (FHC) algorithm for efficient data collection. The traffic load is intentionally concentrated at a small portion of Cluster Heads (CHs) and Convergence Points (CPs). In order to provide a steady and efficient energy supply for the nodes under our clustering scheme, a mobile energy replenishment approach is designed by using the Mobile Elements (MEs) to conduct wireless energy replenishment for the CPs. We construct an optimal route of the MEs for a better energy replenishment efficiency. Through extensive simulations, our scheme is demonstrated to outperform the best known clustering algorithms, in terms of both energy dissipation of data collection and energy replenishment.

This paper presents the design of a duty-cycling MAC, called PRI-MAC (Pipelined-forwarding, Routing-integrated, and effectively-Identifying MAC), for large-scale wireless sensor networks. PRI-MAC divides the whole network into grades around the sink node. The higher grade a node is in, logically the further away it is from the sink which is in the lowest grade. Staggered sleep-wakeup schedules are established between any two adjacent grades such that data can be forwarded in a pipelined fashion, largely reducing the packet delivery latency to meet the real-time transmission requirement. Meanwhile, the routing function is seamlessly integrated into PRI-MAC, which reduces the protocol overhead and increases the network scalability. Furthermore, each node utilizes a randomly-generated integer as its identifier only when it is involved in a data transmission, instead of allocating a unique address for each sensor node. The performance of PRI-MAC is evaluated in comparison with PW-MAC by OPNET, in terms of the packet delivery latency, energy efficiency, and throughput.

In IEEE 802.11 networks, how to improve the efficiency of contention-based media access is an important, challenging issue. Recently, the grouping strategy is introduced in the IEEE 802.11ah standard to alleviate the channel contention. In IEEE 802.11ah networks, stations can be divided into groups and each group is only allowed to access wireless channel during the designated channel access period. By limiting the number of stations participating in the channel contention, it is anticipated that such a grouping strategy could substantially improve the communication efficiency. However, how to allocate the channel among different groups and how to adjust the number and sizes of groups are still open issues. In this paper, we first study the impact of the grouping strategy on the network performance, and then propose an analytical model to track the performance under saturated traffic. The accuracy of our model has been validated by simulation results. Our analytical model and results also provide important guidelines in optimizing grouping parameters.

By leveraging node mobility and exploring a store-carry-and-forward paradigm, delay-tolerant networking enables and assists end-to-end message delivery in many scenarios, e.g., vehicular ad hoc networks and mobile social networks. Most existing work in the literature either focuses on the routing strategies for unicast, or history-based routing for multicast communications. In this paper, we discover the most important and independent social features from the Infocom 06 trace data, and propose a social profile-based multicast routing scheme. Our proposed scheme reduces the delivery cost greatly compared with flooding-based schemes and achieves a similar performance to the history-based schemes, without the cost of maintaining the contact history. The efficiency of the proposed scheme has been confirmed by trace-driven simulation, which also reflects the efficacy of exploring social features in delay-tolerant networks.

Connectivity has been extensively studied in ad hoc networks, most recently with the application of percolation theory in two-dimensional square lattices. Given a message source and the bond probability to connect neighbor vertexes on the lattice, percolation theory tries to determine the critical bond probability above which there exists an infinite connected giant component with high probability. This paper studies a related but different problem: what is the connectivity from the source to any vertex on the square lattice following certain directions? The original directed percolation problem has been studied in statistical physics for more than half a century, with only simulation results available. In this paper, by using a recursive decomposition approach, we have obtained the analytical expressions for directed connectivity. The results can be widely used in wireless and mobile ad hoc networks, including vehicular ad hoc networks.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than the systems that harvested dynamic energy from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present an example on determining the optimal remaining energy level for individual sensor nodes to send out their recharging requests. Through extensive simulation with real-world system settings, we verify our analysis matches the simulation results well and the system designs based on our analysis are effective.

Nowadays, peer-assisted video on-demand (PA-VoD) systems offer high-definition (HD) channels to online users. However, the quality of service in such HD channels is usually not comparable to the standard-definition (SD) ones, as HD channels have to seek more bandwidth support and cache space from peers, which is a challenging task. In this paper, we focus on peer cache and upload bandwidth management at the same time for multi-channel PA-VoD systems with heterogeneous video playback rates, i.e., HD and SD channels coexist with different bandwidth and cache requirements. We first take user viewing behaviors into account and derive the statistical performance bounds on server bandwidth consumption, which lead to the conclusion that such behaviors can easily affect the provisioning for HD channels, even if there is enough upload bandwidth from SD peers. We then formulate bandwidth allocation as a linear programming problem to calculate the tight lower bound at any time instant, with global information available and system-wide coordination possible (e.g., through a tracker). Next, we design heuristic algorithms for peer cache replacement and upload bandwidth allocation to fit with the nature of a P2P structure, and the results are compared with the statistical and instance performance bounds through extensive simulation, which shows the efficacy of the proposed algorithms in dynamic scenarios.

The introduction of mobile elements has created a new dimension to reduce and balance the energy consumption in wireless sensor networks. However, data collection latency may become higher due to the relatively slow travel speed of mobile elements. Thus, the scheduling of mobile elements, i.e., how they traverse through the sensing field and when they collect data from which sensor, is of ultimate importance and has attracted increasing attention from the research community. Formulated as the traveling salesman problem with neighborhoods (TSPN) and due to its NP-hardness, so far only approximation and heuristic algorithms have appeared in the literature, but the former only have theoretical value now due to their large approximation factors. In this paper, following a progressive optimization approach, we first propose a combine-skip-substitute (CSS) scheme, which is shown to be able to obtain solutions within a small range of the lower bound of the optimal solution. We then take the realistic multirate features of wireless communications into account, which have been ignored by most existing work, to further reduce the data collection latency with the multirate CSS (MR-CSS) scheme. Besides the correctness proof and performance analysis of the proposed schemes, we also show their efficiency and potentials for further extensions through extensive simulation.

The non-saturated performance of the IEEE 802.11 broadcast scheme in the two-dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of average packet reception probability, non-saturated throughput, and average queuing delay. Compared with the existing work in the literature, the proposed system model takes into account the effect of arbitrary transmission queue length. Moreover, our analysis considers both the impact of concurrent transmissions caused by identical backoff interval selection and that of hidden nodes caused by limited sensing range on an observed transmitter. All the analysis results are validated by extensive simulations.

Using mobile elements (MEs) as mechanical carriers to collect data brings many opportunities to wireless sensor networks, such as improving the energy efficiency of sensor nodes and prolonging network lifetime. However, the limited travel speed of MEs leads to a higher data transfer latency, which in turn degrades the performance of the data collection task. The optimal use of the limited mobility of MEs is thus critical to the overall performance optimization. Considering the data-rate constraints of wireless communications, and by following a progressive optimization approach, we propose a sweeping tour optimization scheme with active skipping (SAS) in this paper. The performance of the proposed scheme is evaluated by investigating the tour length and the data collection latency. Through extensive simulations, our scheme is shown to outperform the best known heuristic algorithms in terms of the data collection latency.

Taxicabs, as one of the major transportation platforms in metropolises, are of great interest in vehicular communications and networking research. The mobility of taxicabs, determined by driver behaviors and passenger destinations, has attracted a lot of attention in recent years. Among different mobility models, trace-driven taxicab mobility models preserve many details but often bring too much overhead, while simple, random mobility models largely miss the needed social and geographical features. In this paper, we follow a new approach to capture the social behaviors (e.g., transition among regions) and geographical features (e.g., hot spots) of taxicabs in a metropolis, and build a hierarchical taxicab mobility model to strike a better balance between fidelity and tractability. The performance study shows that the synthesized mobility model can well capture the original trace data while being simple and extensible. It also reveals the difference between taxicab and pedestrian mobility, the latter of which is also often used for vehicles in the literature.

Mobility-assisted data collection in wireless sensor networks brings in new opportunities to improve the energy efficiency of sensor nodes. However, it also introduces new challenges such as large data collection latency. The optimal usage of the limited mobility of mobile elements in the network is of great importance to reduce this latency, and a lot of research efforts have been devoted to it. In this paper, focusing on the scenario where a mobile sink is available to carry out the data collection, a simple and efficient Partition-based Nearest Job Next data collection scheme is proposed, which schedules the travel of the mobile sink based on a clustered structure of the network. Corresponding geometrical probability-based analysis is also presented to shed light on the performance of the scheme. The efficiency of the scheme, along with the accuracy of the analysis, is verified through extensive simulation.

In this paper, we propose a modeling framework to capture the major characteristics of peer-assisted video ondemand systems offering standard and high-definition channels. Our framework can be extended to model a variety of caching strategies, including FIFO, passive caching, and active caching. We use the framework to prove that passive caching is sufficiently effective for stationary user behaviors, and generate the optimal caching solutions when the channels in the system demonstrate different popularity evolutions, i.e., with non-stationary behaviors. Simulation results verify the efficacy of our active-caching strategy and provide further insights into such systems that are gaining more popularity over the Internet.

The introduction of mobile elements in wireless sensor networks creates a new dimension to reduce and balance the energy consumption for resource-constrained sensor nodes; however, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to arrange and schedule the movement of mobile elements throughout the sensing field is of ultimate importance. In this paper, the online scenario where data collection requests arrive progressively is investigated, and the data collection process is modeled as an M/G/1/c-NJN queuing system, where NJN stands for nearest-job-next, a simple and intuitive service discipline. Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. The NJN discipline is further extended by considering the possibility of requests combination (NJNC). The simulation results validate our analytical models and give more insights when comparing with the first-come-first-serve (FCFS) discipline. In contrast to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have a better performance than FCFS, in both the average and more importantly the worst cases, which gives the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes for mobile elements in wireless ad hoc sensor networks, as well as many other similar scheduling application scenarios.

Clock synchronization is a fundamental requirement for network systems. It is particularly crucial and challenging in wireless sensor networks (WSNs), because WSN environments are dynamic and unpredictable. To tackle this problem, how to accurately estimate clock skew, the inherent reason causing clock desynchronization, is investigated. According to the measurement results, clock skew is a non-stationary random process highly correlated to temperature, and its measurements contain severe noises. Based on the observation, an additional information aided multi-model Kalman filter (AMKF) algorithm is proposed, which uses temperature measurements to assist clock skew estimation. Using AMKF, an environment-aware clock synchronization (EACS) scheme is proposed to dynamically compensate clock skew. The scheme is simple, scalable, and of low computation and energy cost. Using EACS as an additional component of the conventional synchronization protocols, the clock is updated with local information before the clock re-synchronization process is triggered, so it can substantially prolong the re-synchronization period, which not only reduces the energy consumption but also is essential for the scenarios where frequent synchronization is infeasible. The theoretical lower bound of clock skew estimation error is derived as a benchmark. Extensive simulation and experimental verification results have demonstrated the feasibility and effectiveness of the proposed scheme which can prolong the time resynchronization period by an order of magnitude in dynamic environments.

Node locations and distances are of profound importance for the operation of any communication networks. With the fundamental inter-node distance captured in a random network, one can build probabilistic models for characterizing network performance metrics such as k-th nearest neighbor and traveling distances, as well as transmission power and path loss in wireless networks. For the first time in the literature, a unified approach is developed to obtain the closed-form distributions of inter-node distances associated with hexagons. This approach can be degenerated to elementary geometries such as squares and rectangles. By the formulation of a quadratic product, the proposed approach can characterize general statistical distances when node coordinates are interdependent. Hence, our approach applies to both elementary and complex geometric topologies, and the corresponding probabilistic distance models go beyond the approximations and Monte Carlo simulations. Analytical models based on hexagon distributions are applied to the analysis of the nearest neighbor distribution in a sparse network for improving energy efficiency, and the farthest neighbor distribution in a dense network for minimizing routing overhead. Both the models and simulations demonstrate the high accuracy and promising potentials of this approach, whereas the current best approximations are not applicable in many scenarios. This geometrical probability approach thus provides accurate information essential to the successful network protocol and system design.

The saturation performance of the IEEE 802.11 broadcast scheme in two dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of two broadcast reliability metrics, Distance-based Packet Reception Probability and Average Packet Reception Probability. Compared with the existing work in the literature, the proposed system model accounts for more general 2-D network scenarios, and considers both the impact of concurrent transmissions caused by identical backoff intervals and that of hidden nodes caused by a limited sensing range on a tagged reception. All the analysis results are validated by extensive simulations.

In this paper, we investigate the optimal streaming strategy for dynamic adaptive streaming over HTTP (DASH). Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of experience (QoE) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. We further define a reward parameter in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness. We also use a simple testbed to validate our solution. Experiment results show the feasibility of the proposed solution and its advantage over the existing work.

Cognitive radio users are allowed to utilize the unused portions of the licensed spectrum, which leads to performance enhancement. However, they need to carefully inspect the environment and make intelligent decisions. Secondary Users (SUs) are required to vacate the channel when a Primary User (PU) appears on the same licensed channel. This may cause interruptions in secondary network transmissions. In this paper we propose a distributed channel assignment scheme for cognitive radio networks. We consider a multi-radio node architecture in order to better utilize the multiple available channels. Our RIMCA (Robust Interference Minimizing Channel Assignment) scheme includes a collaborative sensing mechanism as well as channel assignment. We also consider channel reclaim by a primary user. When making decisions, secondary users consider the interference imposed on primary users as well as the total interference in the secondary network. Simulation results show that our RIMCA outperforms the most related channel assignment schemes. Moreover, our channel assignment scheme is robust to PU activities.

User cooperation in wireless networks is inherently a cross-layer optimization problem. We identify a new direction for cooperative communications: i.e., in addition to the point-to-point communication channel between the transmitter and the receiver, the communication configuration should take the network topology into account. In this paper, we first propose a network modulation (NM) design that can transmit bits with different SNR requirements in a single symbol transmission. We then propose an error-correction coding assisted relay (EAR) scheme that is also configured according to the network topology. We study the performance of NM and EAR in both a three-node collinear network and a two-dimensional cellular network. Extensive simulations have been conducted, which demonstrate the substantial performance gain of the proposed schemes, in terms of both a higher network throughput and a lower bit-energy consumption. Comparing between NM and EAR, NM is more beneficial for the downlink performance and EAR is more beneficial for the uplink performance. Combining NM and EAR leads to a more efficient cooperative network. It is concluded that the topology-aware physical layer design will be a promising direction with many open issues for further study.

Consumer electronics such as cellular phones and portable computers with short-range communication capabilities have enabled the large-scale information dissemination through user mobility and contact, without the assistance of communication infrastructures. In this new paradigm, one challenge is to determine when and how to forward a message to the destination, possibly through a series of third-party users. This problem has attracted a lot of attention in the literature lately, with proposals ranging from epidemic to single or multi-copy spray and wait or focus strategies, but most existing work assumed independent and identically distributed mobility. Observing most human mobility and interaction are interest-driven in the real world, in this paper, we evaluate the performance of these schemes with an interest-driven mobility model. We further propose to take the user interest into account when determining routing strategies to further improve the performance of these schemes for mobile social networks. Simulation results have demonstrated the efficacy of the interest-aware routing strategies.

Exploiting mobile elements to conduct data collection in wireless sensor networks offers a new approach to reducing and balancing the energy consumption of sensor nodes; however, the resultant data collection latency may be large due to the limited travel speed. Many research efforts have been made on reducing the data collection latency with the scenario where a single mobile element is available. A potential problem with this scenario is the scalability, and a straightforward solution is to employ multiple mobile elements to collect data collaboratively. In this paper, the network where multiple homogeneous mobile elements are available is modeled as an M/G/c queuing system, and insights on the data collection performance are obtained through theoretically analyzing the measures of the queue. In addition, a heuristic formula to determine the optimal number of mobile elements is proposed based on this model. The accuracy of our modeling and analysis, along with the performance evaluation of the proposed heuristic formula, is verified through extensive simulation.

Performance metrics in cellular systems, such as per-user link capacity and co-channel interference, are dependent on the statistical distances between communicating nodes. An analytical model based on geometric probability in cellular systems is presented here for capacity analysis and interference estimation. We first derive the closed-form distance distribution between cellular base stations and mobile users, giving the explicit probability density functions of the distance from a base station to an arbitrary user in the same hexagonal cell, or to the users in adjacent cells. Different from numerical methods or approximation, and the existing approaches in geometric probability, this unified approach provides explicit distribution functions that can lead to all statistical moments, and is not limited by coordinate distributions, either of base stations or subscribers. Analytical results on per-user link capacity and co-channel interference are derived and validated through simulation, which shows the high accuracy and promising potentials of this approach.

View-upload decoupling (VUD) has become a novel and effective strategy in balancing the supply and demand of bandwidth resources in peer-to-peer (P2P) live streaming systems. In this paper, we investigate the strategy of migrating the existing VUD design from live streaming to P2P video on-demand (VoD) systems. To address the immediate concern of the huge overhead while applying VUD to P2P VoD, we formulate the problem into an optimization problem aiming at minimizing the total overhead induced by VUD, which proves to be a 0-1 integer programming problem. Due to the intractability of this NP-hard problem, we propose a simple yet effective heuristic water-leveling algorithm to balance the supply and demand of bandwidth resources among the system while reducing VUD overhead. Finally, numerical results are presented to demonstrate the efficacy of our overhead-aware VUD design for P2P VoD systems.

Data collection with mobile elements in wireless sensor networks brings in new opportunities to reduce and balance the energy consumption of sensor nodes, however, it may result in high data collection latency. The optimal scheduling of mobile element's limited mobility is of great importance to reduce this latency, and a lot of research efforts have been made on it. Focusing on the on-demand data collection scenario, where data collection requests appear progressively, and based on the fact that several nearby requests can be combined and served from the same collection site, we propose a simplified combine-skip-substitute (CSS) scheme, which is shown to be able to reduce the data collection latency greatly. We also analyze the probability for the combination to happen, and how many requests can be combined, to gain more insights in its performance. The efficacy of the proposed scheme and the accuracy of the analytical results are verified through extensive simulation.

Vehicular ad hoc networks have attracted a lot of attention in recent years, in either vehicle-to-vehicle or vehicle-to-infrastructure scenarios. In this paper, we focus on the latter, particularly for vehicles to upload to roadside units, the so-called drive-thru Internet, in a secure and efficient manner. Due to the ad hoc nature and wireless communications, traditional certificate-based security schemes are either infeasible or inefficient in this scenario. Thus we propose a certificateless approach to secure upload in a drive-thru Internet. We discuss the attack model and the desired security properties, and how to achieve these properties through the proposed certificateless scheme. We implement and evaluate the proposed scheme, and also investigate how to mitigate the security overhead through the separation of security association and data transfer in a drive-thru Internet.

BitTorrent has been a very successful peer-to-peer (P2P) file-sharing application, and several BitTorrent-based P2P video streaming systems have been proposed in the literature. Nowadays, network address translation (NAT) has been widely used since it reduces the usage of IP addresses, but it is also considered as a factor that degrades the performance of P2P systems because NAT limits the direction of connectivity. In order to understand what impact NAT has on the performance of BitTorrent-based P2P video streaming systems, we build an analytical model which can be used to predict the average continuity index, a video streaming performance metric, when a fraction of the participating peers are behind NAT devices. A software simulator is written to validate our analytical model, and the simulation results also give some insights on the fairness issue of P2P video streaming systems in the presence of NAT devices. In this paper, both our analytical model and simulation results are presented and verified.

In wireless networks using hybrid MAC, nodes can reserve time periods inside scheduling cycles, and the time which is not reserved can be used by all the nodes through contention-based access. The hybrid MAC is attractive because it can provide satisfactory QoS by resource reservation and also achieve high channel utilization by multiplexing gain in the contention periods. However, we are still lacking a clear understanding of its performance and the optimization design scheme. In this paper, we propose an analysis framework for hybrid MAC using soft-reservation, where the unused reserved time can be released and accessed by the other nodes through contention. By the mean value analysis approach, the collision probability and average service time of one frame are obtained. The hybrid MAC based on the WiMedia ECMA-368 standard has been simulated to validate the analysis and compared to the conventional contention-based MAC and the hard-reservation hybrid MAC, which shows the soft reservation has much better performance and higher capacity when the network is relatively heavily loaded.

One promise of Vehicular Ad-hoc Networks (VANET) is to considerably increase road safety and travel comfort by enabling inter-vehicle communications. Among a vast array of potential applications, emergency message (EM) dissemination has attracted a lot of attention in the literature. In this paper, we propose a time/location-critical (TLC) framework for EM dissemination and use our scalable modulation and coding (SMC) scheme to achieve the goal. In specific, vehicles near the accident site (or the point-of-interest location) receive guaranteed, detailed messages to take proper reaction immediately (e.g., slow down or change lanes), and vehicles further away have a high probability to be informed and make location-aware decisions accordingly (e.g., detour or reroute), with the assistance of reverse traffic when possible and necessary. The efficacy of the proposed framework is analyzed and validated by extensive numerical and simulation results. The TLC framework and the use of the SMC scheme are shown to be able to disseminate EMs effectively and efficiently by taking both the time and location criticality into account, while simplifying the design of radio transceivers and media access control protocols for VANET.

Ultra-wideband (UWB) is a new wireless technology that transfers data over shorter distances with higher data rates and lower transmission power. WiMedia Alliance's Media Access Control (MAC) protocols for UWB-based wireless personal area networks have become an international standard. The Distributed Reservation Protocol (DRP) is part of this standard that reserves the wireless channel on a slot-by-slot basis for different flows. Knowing that one of the most common use of DRP is to transport multimedia traffic, we propose distributed reservation algorithms for UWB-based home networks according to the video streaming traffic specification and quality-of-service requirement. The proposed algorithms also take the WiMedia UWB MAC policies into account. We analyze and validate the reservation algorithms and policies with a tiered overflow model, and evaluate and compare their performance with Network Simulator (NS-2) and a MPEG-4 video traffic generator. We further discuss the ways of improving video streaming quality and system resource utilization in UWB-based home networks.

Video streaming over wireless links is a challenging issue due to the stringent quality-of-service requirement of video traffic and the contention nature of wireless media with limited channel resources. Various wireless Media Access Control (MAC) protocols have been proposed, and most of them follow either the contention-based or contention-free approach. In this paper, we propose to stream video traffic over both contention-based and contention-free MAC protocols, exampled by WiMedia UWB Prioritized Contention Access (PCA) and Distributed Reservation Protocol (DRP), and then evaluate the performance of such an approach with extensive simulation. The novelty of our approach is to use both PCA and DRP for every single video stream by reserving well below the peak data rate and handling traffic burst through reduced contention. The simulation results further reveal the tradeoff between hard and soft DRP with single or dual buffer on the performance of video streaming.

Random linear network coding has been recently proved as a feasible solution to large-scale, peer-to-peer video dissemination over the Internet. In this paper, we use a simple analytical model to characterize and verify the efficiency of network coding in peer-to-peer video streaming systems. Several system parameters such as block size, server capacity, and peer aggressiveness are investigated with their influence on the system performance under flash crowd scenarios. Both our theoretical analysis and simulation results demonstrate that network coding can perform very close to the idealized scheduling algorithm for peer-to-peer video streaming.

Clock synchronization is a fundamental issue in communication networks and distributed systems, and clock skew is the inherent cause for clock desynchronization. Clock skew estimation is essential to improve the efficiency and reduce the overhead of clock synchronization schemes, and it is especially beneficial for resource-constrained devices such as sensor nodes in dynamic environments. According to the measurement, clock skew is environment sensitive, and no existing clock skew estimation schemes can accurately capture such dynamic behaviors. In this paper, we investigate a general clock synchronization problem with variable clock skews and propose a new skew estimation model based on a hybrid approach to characterizing the dynamic of clock skews. To estimate the time-varying clock state vector, we employ the Interactive Multi-Model (IMM) Kalman filter, which can make soft decisions by combining results from different models. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed adaptive clock skew estimation algorithm, which achieves a better performance with moderate computational complexity.

Hybrid media access control (MAC) protocols use reservation and contention-based approaches simultaneously, so they can provide satisfactory quality-of-service to multimedia applications by resource reservation, and achieve high resource utilization with multiplexing gain during the contention periods. However, reservation can significantly affect the behavior of the contention-based access. How to split channel time between reservation periods and contention periods and how to adjust the contention scheme for hybrid MAC are important, open issues. In this paper, an analytical model for the hybrid MAC with saturated traffic is first proposed and then extended to the unsaturated traffic case. Based on the mean value analysis, the proposed models give the average frame service time and throughput for the contention-based MAC with the presence of reserved channel periods. They are also applicable to online admission control due to their low computational complexity.

Due to limited energy supplies, reducing power consumption is an important goal in wireless sensor networks. Clustering techniques are used to reduce power consumption and prolong network lifetime in many existing research efforts, among which grid-based ones are often used due to their simplicity and scalability. However, most existing work uses average distance as a simplification in calculating distance-related power consumption, which leads to a large underestimation of the actual energy depletion rate. In this paper, we propose an energy optimization model based on probabilistic distance distributions, which captures the distance-induced power consumption with high accuracy. We further analyze the uneven traffic distribution in wireless sensor networks and propose a nonuniform griding scheme to balance the energy depletion in all grids. Through our analysis, we are able to obtain the optimal grid size ratio that minimizes the energy consumption. Analytical results are validated through simulation, which shows the promising potentials of our method and the nonuniform griding technique.

In conventional wireless systems with layered architectures, the physical layer treats all data streams from upper layers equally and apply the same modulation and coding schemes. Newer systems such as Digital Video Broadcast start to introduce hierarchical modulation schemes with SuperPosition preCoding (SPC) and support data streams of different priorities. However, SPC requires specialized hardware and has high complexity beyond most existing handheld devices. We thus propose scalable modulation (s-mod) by reusing the current mainstream modulation schemes with software-based bit-remapping. In this paper, we study how to optimize the configuration of the PHY layer s-mod and coding schemes to maximize the utility of videos with Scalable Video Coding (SVC). Simulation results demonstrate significant performance gains using s-mod and the cross-layer optimization, indicating s-mod and SVC is a good combination for wireless video multicast.

With the proliferation of wireless technologies and the convenience they offer, transporting Quality-of-Service (QoS) demanding traffic such as compressed video over wireless links becomes a trend and a challenging issue. Among many factors, Media Access Control (MAC) protocols play an important role in the network stack to ensure the QoS provisioning for multimedia applications and the efficient utilization of wireless channels. Various contention-based or contention-free MAC protocols have been proposed to solve these problems. In this paper, we model, analyze with an existing framework, and evaluate two reservation algorithms, subframe-fit and isozone-fit, proposed for distributed reservation protocols exampled by WiMedia UWB MAC. The models have been validated by extensive simulations using ns-2 and an MPEG-4 traffic generator. We further improve the system performance by introducing cross-isozone allocation and on-demand compaction to isozone-fit, and discuss how to leverage both contention-based and contention-free MAC protocols.

Minimizing energy consumption in wireless sensor networks has been a challenging issue, and grid-based clustering and routing schemes have attracted a lot of attention due to their simplicity and feasibility. Thus how to determine the optimal grid size in order to minimize energy consumption and prolong network lifetime becomes an important problem during the network planning and dimensioning phase. So far most existing work uses the average distances within a grid and between neighbor grids to calculate the average energy consumption, which we found largely underestimates the real value. In this paper, we propose, analyze and evaluate the energy consumption models in wireless sensor networks with probabilistic distance distributions. These models have been validated by numerical and simulation results, which shows that they can be used to optimize grid size and minimize energy consumption accurately. We also use these models to study variable-size grids, which can further improve the energy efficiency by balancing the relayed traffic in wireless sensor networks.

Experimenting in courses such as operating systems, computer networks, and distributed systems is challenging due to the need to access system kernels and network infrastructures, as well as the distributed nature of these experiments. In this paper, we design and develop a laboratory platform that can be rapidly and economically adopted in any educational institutions to teach introductory computer networks courses. By using commercial, off-the-shelf hardware and open-source software, and together with our technical solutions, the platform gives students an opportunity to experiment in a real, flexible and scalable network, and to interact with the network both as a network user and more importantly as a network operator. These features can help students to better understand the algorithms, protocols and practice of computer networks. This easy-to-use lab platform has been received favorably by students. Also, the low-cost platform is easy to maintain, and can be repeatedly used in multiple, back-to-back lab sections.

Exploring mobility to accomplish the data collection in wireless sensor networks (WSNs) has become the focus of recent studies, which can improve the energy efficiency of sensor nodes by shifting the data forwarding task from them to mobile elements (MEs). However, the data collection latency in this case can be much higher. We consider an on-demand data collection scenario in this paper, in which sensor nodes broadcast service requests when their buffer is about to be full. On receiving such requests, the ME moves toward the sensor nodes to collect data, and uploads the data to the sink when possible. An M/G/1 queue-based analytical model is presented, and analytical results on several important system performance metrics are derived. Furthermore, we propose an improved service scheme, which combines requests whenever they are in proximity. The work is evaluated through extensive simulations, which validate the accuracy of our model. The efficacy of the proposed service scheme to improve the system performance is also verified.

As a major component of Internet routing infrastructure, the Border Gateway Protocol (BGP) is vulnerable to malicious attacks. While Secure BGP (S-BGP) provides a comprehensive framework to secure BGP, its high computational cost and low incremental deployment benefits seriously impede its wide usage in practice. Using a lightweight symmetric signature scheme, SPV is much faster than S-BGP. However, the speed boost comes at the price of prohibitively large signatures. Aggregated path authentication reduces the overhead of securing BGP in terms of both time and space, but the speed improvement is still limited by public key computation. In this paper, we propose a keychain-based signature scheme called KC-x. It has low CPU and memory overheads and provides strong incentive for incremental deployment on the Internet. As a generic framework, KC-x has the flexibility of using different signature algorithms, which can even co-exist in a hybrid deployment. We investigate two implementations of KC-x: KC-RSA based on RSA and KC-MT based on Merkle hash tree. Using real BGP workloads, our experimental results show that KC-RSA is as efficient as SAS-V (the most efficient software approach for aggregated path authentication), and KC-MT is even three times faster than SPV with 40% smaller signatures. Through the hybrid deployment of KC-MT and KC-RSA, KC-x can achieve both small signature and high processing rate for BGP speakers.

To reserve or not for bursty video traffic over wireless access networks has been a long-debated issue. For uplink transmissions in infrastructure-based wireless networks and peer-to-peer transmissions in mesh or ad-hoc networks, reservation can ensure the Quality-of-Service (QoS) provisioning at the cost of a lower degree of resource utilization. Contention-based Medium Access Control (MAC) protocols are more flexible and efficient in sharing resources by bursty traffic to achieve a higher multiplexing gain, but the performance may degrade severely when the network is congested and collisions occur frequently. More and more wireless standards adopt a hybrid approach, which allows the coexistence of resource reservation and contention-based MAC protocols. However, how to cost-effectively support video traffic using hybrid MAC protocols is still an open issue. In this paper, we first propose how to use hybrid MAC protocols to support video streaming over wireless networks. Then, we quantify the performance of video traffic over wireless networks with contention-only, reservation-only, and hybrid MAC protocols, respectively. Admission regions for video streams with these three approaches are obtained. Using the standard WiMedia MAC protocols as an example, extensive simulations with a commonly-used network simulator (NS-2) and real video traces are conducted to verify the analysis. The analytical and simulation results reveal the tradeoff between reservation and contention-based medium access strategies, and demonstrate the effectiveness of the hybrid approach.

IEEE 802.11 WLAN is preferred for IPTV in-home distribution, but the achievable throughput and coverage are still limited due to the high attenuation and interference in a household environment. Through our measurement study with a WDS-based multi-hop wireless testbed, we have found that it is possible for multi-hop wireless networks to increase the coverage and improve the video streaming performance at the same time. To analyze the throughput of IEEE 802.11 multihop wireless networks, we propose an extended two-dimensional Markov-chain model in this paper. Different from existing work, our model takes the retry limit and post-backoff stage into account to better capture the behavior of IEEE 802.11 MAC protocols in a non-ideal channel and with non-persistent traffic. The throughput analysis is validated by network simulation with extended lower and upper-layer simulation modules. The achievable throughput gives an upper bound of the video streaming performance, which is further validated by our H.264-based video streaming simulation with application-layer performance metrics. The results correspond to the observation we had on the multi-hop testbed. Further, this paper also provides some guidance on how to achieve the optimal balance for a given scenario, which is important when deploying video streaming services with end-to-end quality-of-service provisioning.

BitTorrent nowadays is one of the most popular peer-to-peer (P2P) applications on the Internet; on the other hand, network address translation (NAT) has become pervasive in almost all networking scenarios. Despite the effort of NAT traversal, it is still very likely that P2P applications cannot receive incoming connection requests properly if they are behind NAT. Although this phenomenon has been widely observed, so far there is no quantitative study in the literature examining the impact of NAT on P2P applications. In this paper, we build analytical models to capture the performance of BitTorrent-like P2P systems with the presence of homogeneous and heterogeneous NAT peers. We further propose biased optimistic unchoke strategies in order to improve the overall system performance considerably. The analytical models have been validated by simulation results, which also reveal some interesting facts about the coexistence of NAT and public peers in P2P systems.

Video streaming has become the application that drives the Internet to a new height. In this paper, we analyze and evaluate the performance of H.264-based video streaming over multi-hop wireless local area networks (WLANs). Contrary to common believes that adding relays in the same wireless channel may increase coverage but has to reduce throughput, our analysis and simulation results show a wide spectrum of coverage-capacity tradeoff in generic scenarios and confirm previous measurement observation in specific cases. In addition, the paper provides guidance on how to achieve the optimal balance for a given scenario, which is important when deploying end-to-end video streaming services with quality of service guarantee.

The use of directional antennae in ad hoc networks has received growing attention in recent years. However, most existing directional MAC protocols assume interchangeable directional and omnidirectional modes of operation. Such operation reduces the spatial gain and introduces the asymmetry-in-gain problem. In this paper, we propose a directional-to-directional (DtD) MAC protocol for ad-hoc networks that operates in the directional mode exclusively. The protocol is fully distributed, does not require any synchronization, eliminates the asymmetry- in-gain problem, and alleviates the deafness problem. To study the performance of the proposed DtD MAC, we develop an analytical model that estimates the saturation throughput as a function of the number of antenna sectors, packet size and number of contending nodes. The analytical results are validated by extensive simulations with the QualNet simulator. We show that the DtD MAC protocol is practical to take the advantage of directional antennae to improve network throughput and achieve better fairness in ad-hoc networks.

Emerging broadband entertainment applications such as IPTV (Internet Protocol Television) and whole-house PVR (Personal Video Recorder) bring new challenges to existing home networks. Gigabit Ethernet is an obvious choice, but consumers are still reluctant due to the need for rewiring in most dwellings. Several “no-new-wires” technologies have been proposed in recent years, but there is little work on how to distribute IPTV, VoIP (Voice over IP) and data traffic together effectively and efficiently in a household environment. In this paper, we propose a wireless/wired-hybrid, multi-link structure for broadband home networks, and investigate its feasibility and performance through a multimedia over multi-link testbed. Our measurement study shows that the proposed multi-link structure can improve the performance, reliability and availability of home networks considerably, indicating that it is an attractive approach to multimedia in-home distribution. In addition, the paper also discusses the challenges and approaches in further improving the performance of heterogeneous, multi-link home networks.

The high cost of rewiring existing houses with Ethernet to support broadband home networks has motivated consumers and service providers for "no-new-wires" and "no- wires" technologies. IEEE 802.11 wireless LAN is preferred due to its flexibility and affordability, but the achievable coverage and data rate are still very limited in a household environment due to high attenuation and interference. In this paper, we evaluate the performance of MPEG-4 AVC video streaming over IEEE 802.11 Wireless Distribution System (WDS), which is often used to increase wireless coverage for data applications. Our results show that by properly adjusting wireless transmission power and rate, it is possible to increase the coverage and performance at the same time, which allows broadband, multimedia applications such as IPTV to be delivered in multiple hops across house.

As the major component of Internet routing infrastructure, the Border Gateway Protocol (BGP) is vulnerable to malicious attacks. While Secure BGP (S-BGP) provides a comprehensive framework to secure BGP, its high computational cost and low incremental deployment benefits seriously impede its wide usage in practice. Using a lightweight symmetric signature scheme, SPV is much faster than S-BGP. However, the speed boost comes at the price of prohibitively large signatures. Aggregated path authentication reduces the overhead of securing BGP in terms of both time and space, but the speed improvement is still limited by public key computation. In this paper, we propose a simple key chain-based signature scheme called KC-x, which has low CPU and memory overheads and provides strong incentive for incremental deployment over the Internet. As a generic framework, KC-x has the flexibility of using different signature algorithms. We implement two realizations of KC-x. One is based on RSA called KC-RSA, and the other is based on Merkle hash tree called KC-MT. After characterizing the overheads of KC-RSA and KC-MT, we evaluate their performance with real BGP workloads. Our experimental results show that KC-RSA is as efficient as SAS-V, and KC-MT is even 3-fold faster than SPV with a 40% smaller signature. Through the hybrid deployment of KC-MT and KC-RSA, KC-x can achieve both small signature and high processing rate for BGP speakers.

In wireless sensor networks, sensors should have some mechanisms to learn their locations since sensed data without associated location information may be meaningless. While many sensor localization algorithms have been proposed, security issues in sensor localization are usually not addressed in their original design. Secure sensor localization is very challenging due to limited computation and energy resources in sensors. It is highly desirable that a localization scheme is robust and is able to detect malicious attacks without using complex cryptographic operations. In this paper, we present and analyze detection methods purely based on geometric constraints in sensor networks. Our detection methods can strengthen the localization algorithm to be resilient to malicious attacks by detecting and eliminating the negative impact of fake information.

Mobility management is a key issue in mobile hotspots which enable ubiquitous Internet services while onboard a vehicle. In this paper, we compare two representative mobility management schemes for mobile hotspots: the network mobility (NEMO) basic support protocol at the network layer and the session initiation protocol (SIP)-based network mobility support protocol at the application layer. We evaluate their salient features and quantify their handoff latency over a wireless fading channel. It is shown that the SIP-based network mobility support protocol can be easily deployed and can reduce the tunneling overhead incurred in the NEMO basic support protocol. However, it can increase the handoff latency due to longer message length.

Fast and accurate generation of worm signatures is essential to contain zero-day worms at the Internet scale. Recent work has shown that signature generation can be automated by analyzing the repetition of worm substrings (that is, fingerprints) and their address dispersion. However, at the early stage of a worm outbreak, individual edge networks are often short of enough worm exploits for generating accurate signatures. This paper presents both theoretical and experimental results on a collaborative worm signature generation system (WormShield) that employs distributed fingerprint filtering and aggregation over multiple edge networks. By analyzing real-life Internet traces, we discovered that fingerprints in background traffic exhibit a Zipf-like distribution. Due to this property, a distributed fingerprint filtering reduces the amount of aggregation traffic significantly. WormShield monitors utilize a new distributed aggregation tree (DAT) to compute global fingerprint statistics in a scalable and load-balanced fashion. We simulated a spectrum of scanning worms including CodeRed and Slammer by using realistic Internet configurations of about 100,000 edge networks. On average, 256 collaborative monitors generate the signature of CodeRedl-v2 135 times faster than using the same number of isolated monitors. In addition to speed gains, we observed less than 100 false signatures out of 18.7-Gbyte Internet traces, yielding a very low false-positive rate. Each monitor only generates about 0.6 kilobit per second of aggregation traffic, which is 0.003 percent of the 18 megabits per second link traffic sniffed. These results demonstrate that the WormShield system offers distinct advantages in speed gains, signature accuracy, and scalability for large-scale worm containment.

By introducing a mobility anchor point (MAP), Hierarchical Mobile IPv6 (HMIP6) reduces the signaling overhead and handoff latency associated with Mobile IPv6. However, if a mobile node (MN)'s session activity is high and its mobility is relatively low, HMIPv6 may degrade end-to-end data throughput due to the additional packet tunneling at the MAP. In this paper, we propose an adaptive route optimization (ARO) scheme to improve the throughput performance in HMIPv6 networks. Depending on the measured session-to-mobility ratio (SMR), ARO chooses one of the two different route optimization algorithms adaptively. Specifically, an MN informs a correspondent node (CN) of its on-link care-of address (LCoA) if the CN's SMR is greater than a predefined threshold. If the SMR is equal to or lower than the threshold, the CN is informed with the MN's regional CoA (RCoA). We analyze the performance of ARO in terms of balancing the signaling overhead reduction and the data throughput improvement. We also derive the optimal SMR threshold explicitly to achieve such a balance. Analytical and simulation results demonstrate that ARO is a viable scheme for deployment in HMIPv6 networks.

In this paper, we develop a novel analytical framework for modeling and quantifying the performance of window-controlled multimedia flows in a hybrid wireless/wired network. The framework captures the traffic characteristics of window-controlled flows and is applicable to various wireless links and packet transmission schemes. We show analytically the relationship between the sender window size, the wireless link throughput distribution, and the delay distribution. We then substantiate the analysis by demonstrating how to statistically bound the end-to-end delay of flows controlled by a TCP-like datagram congestion control protocol (DCCP) over an M-state Markovian wireless link. Simulation results validate the analysis and demonstrate the effectiveness and efficiency of the proposed delay control scheme. The scheme can also be applied to other window-based transport layer protocols

Wireless sensor networks are becoming increasingly important in recent years due to their ability to detect and convey real-time, in-situ information for many civilian and military applications. A fundamental challenge for such networks lies in energy constraint, which poses a performance limit on the achievable network lifetime. We consider a two-tier wireless sensor network and address the network lifetime problem for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions proposed for maximizing network lifetime require AFNs to split flows to different paths during transmission, which we call multisession flow routing solutions. If an AFN is equipped with a single transmitter/receiver pair, a multisession flow routing solution requires a packet-level power control at the AFN so as to conserve energy, which calls for considerable overhead in synchronization among the AFNs. In this paper, we show that it is possible to achieve the same optimal network lifetime by power control on a much larger timescale with the so-called single-session flow routing solutions, under which the packet-level power control and, thus, strict requirement on synchronization are not necessary. We also show how to perform optimal single-session flow routing when the bit-rate of composite flows generated by AFNs is time-varying, as long as the average bit-rate can be estimated

We propose a TCP-friendly Additive Increase and Multiplicative Decrease (AIMD) based Datagram Congestion Control Protocol (DCCP) protocol for supporting multimedia traffic in hybrid wireless/wired networks. We further demonstrate how to select the protocol parameters to fairly and efficiently utilize network resources with the consideration of quality of service (QoS) requirements. Since the parameter selection procedure requires only the exchange of parameters among the application, the transport layer protocol, and the link layer protocol, our approach preserves the end-to-end semantics of the transport layer protocol and the layered structure of the Internet. Extensive simulations are performed to evaluate the proposed protocol. It is shown that the AIMD protocol can appropriately regulate multimedia traffic to efficiently utilize the wireless link and fairly share the network resources with coexisting TCP flows, and it can provide satisfactory QoS for delay-sensitive multimedia applications. In addition, AIMD protocol can outperform the non-responsive User Datagram Protocol (UDP) when transporting multimedia traffic over hybrid wireless/wired networks. With satisfactory QoS provisioning, end-systems have more incentives to voluntarily regulate multimedia traffic with an AIMD-based congestion controller, which is vital for network stability, integrity, and future proliferation.

Distributed denial-of-service attacks pose a serious threat to today's Internet. To counter these attacks, many IP traceback schemes have been proposed; among them, distance-indexed probabilistic packet marking and its variants are attractive due to their stateless, low-overhead and incrementally-deployable design. However, some schemes may become vulnerable in practice, and the implication is yet to be quantified. In this paper, we first reveal these vulnerabilities. Sustained by efficacy analysis and numerical results, we then design several exploits that allow attackers to take full advantage of these vulnerabilities. We also examine the causes of these vulnerabilities as well as possible remedies, and discuss the distance-related buffer overflow in the context of network protocols.

Directional antenna has great potential to reduce power consumption in energy constrained wireless sensor networks. We consider a two-tier wireless sensor network where directional antenna is employed for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions for maximizing network lifetime require each AFN to transmit multiple flows to different nodes at the same time, which we call multi-beam flow routing solution. In this paper, we show that it is possible to develop single-beam flow routing solution for nodes with directional antenna. More important, we show that the single-beam flow routing solution developed here is provably optimal in terms of network lifetime performance. This result is based on a novel technique to transform the optimal multi-beam flow routing into an equivalent single-beam flow routing solution. Numerical example illustrating how to obtain an optimal single-beam flow routing solution is also given.

A TCP-friendly additive increase and multiplicative decrease (AIMD) protocol is proposed to support time-sensitive applications in hybrid wired/wireless networks. By analyzing the performance of AIMD-controlled flows in hybrid networks, we propose a cross-layer procedure to select the AIMD protocol parameters with consideration of wireless link characteristics and application QoS requirements, in terms of delay, loss, and throughput. Since the cross-layer interaction only exchanges parameters among the application, the transport layer protocol, and the link layer protocol, our approach preserves the end-to-end semantics of the transport protocol and the layered structure of the Internet, and it is applicable to supporting various multimedia applications over heterogeneous wireless links. With appropriate parameters, AIMD-controlled flows can fairly share network resources with TCP flows, efficiently utilize wireless resources, and statistically guarantee end-to-end delay for time-sensitive applications. Extensive simulations are performed to validate the analytical results, evaluate the protocol performance, and demonstrate that the AIMD protocol can outperform the unresponsive UDP protocol when transporting multimedia traffic in hybrid networks. With satisfactory QoS, end systems have more incentives to voluntarily regulate multimedia traffic with an AIMD-based congestion controller, which is vital for network stability, integrity, and future proliferation.

We consider generic two-tiered wireless sensor networks (WSNs) consisting of sensor clusters deployed around strategic locations, and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. This paper focuses on the topology control process for ANs and BSs, which constitute the upper tier of two-tiered WSNs. Since heterogeneous ANs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. By proposing algorithmic approaches to locate BSs optimally, we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for ANs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different lifetime definitions according to the mission criticality of WSNs. By studying intrinsic properties of WSNs, we establish the upper and lower bounds of maximal topological lifetime, which enable a quick assessment of energy provisioning feasibility and topology control necessity. Numerical results are given to demonstrate the efficacy and optimality of the proposed topology control approaches designed for maximizing network lifetime of WSNs.

In this paper, the performance of TCP-friendly generic AIMD (Additive Increase and Multiplicative Decrease) algorithms for Web-based playback and multirate multimedia applications is investigated. The necessary and sufficient TCP-friendly condition is derived, and the effectiveness and responsiveness of AIMD are studied. Due to practical implications, a Dynamic TCP-friendly AIMD (DTAIMD) algorithm is proposed. Extensive simulation results are given to verify the derived necessary and sufficient condition, and to demonstrate the performance of the proposed DTAIMD algorithm.

Under the weak consistency paradigm, time-to-live (TTL)-based hierarchical caching systems are proposed to support Web content delivery. Within such systems, due to strictly hierarchical caching structure and linear TTL countdown function, a single user request may encounter consecutive cache miss events at cache servers of different levels in the hierarchy. This behavior, referred to as miss synchronization, is the main cause of a sudden increase in user-perceived response time. In this paper, we examine this undesirable behavior to gain a better understanding of its properties and characteristics. To mitigate this problem, we propose a family of nonlinear TTL countdown functions using a novel concept called extended lifetime. Performance analysis indicates that the proposed approach can effectively avoid miss synchronization in hierarchical caching systems. Further, a carefully-designed nonlinear countdown function can reduce cache miss ratio and user response time without any significant increase in outlived objects.

Identifying elephant flows is very important in developing effective and efficient traffic engineering schemes. In addition, obtaining the statistics of these flows is also very useful for network operation and management. On the other hand, with the rapid growth of link speed in recent years, packet sampling has become a very attractive and scalable means to measure flow statistics; however, it also makes identifying elephant flows become much more difficult. Based on Bayes' theorem, this paper develops techniques and schemes to identify elephant flows in periodically sampled packets. We show that our basic framework is very flexible in making appropriate trade-offs between false positives (misidentified flows) and false negatives (missed elephant flows) with regard to a given sampling frequency. We further validate and evaluate our approach by using some publicly available traces. Our schemes are generic and require no per-packet processing; hence, they allow a very cost-effective implementation for being deployed in large-scale high-speed networks.

Caching is an important means to scale up the growth of the Internet. Weak consistency is a major approach used in Web caching and has been deployed in various forms. The paper investigates some fundamental properties and performance issues associated with an expiration-based caching system. We focus on a hierarchical caching system based on the time-to-live expiration mechanism and present a basic model for such system. By analyzing the intrinsic timing behavior of the basic model, we derive important performance metrics from the perspectives of the caching system and end users, respectively. Based on the results for the basic model, we introduce threshold-based and randomization-based techniques to enhance and generalize the basic model further. Our results offer some important insights into a hierarchical caching system based on the weak consistency paradigm.

Adopting time-to-live (TTL) based hierarchical caching systems is considered to be a viable approach to support Web content delivery under the weak consistency paradigm. However, with a strictly hierarchical structure in these systems, a single user request may trigger multiple consecutive miss events at cache servers of different levels. This undesirable miss synchronization can cause a sudden degradation of user-perceived performance in terms of response time. We offer a comprehensive examination of this undesirable behavior and propose a randomized request redirection approach to circumvent the structural restrictions in TTL-based hierarchical caching systems. Performance analysis and evaluation indicate that the proposed approach can effectively rebalance server overhead and network delay, which in turn reduces end user response time.

Hierarchical caching is deployed to scale up the explosive Web growth, and the expiration-based mechanism is adopted as an economic means to support the weak consistency in this context. However, given a hierarchy, the user perceived performance heavily depends on its position. Normally, a user near the hierarchy leaf suffers higher miss rate and longer response time. Such an intrinsic property can discourage users from participating in any hierarchical caching systems. In this paper, we analyze the performance of a proposed approach, i.e., freshness and retrieval threshold based cache prefetching, to mitigate the bias against leaf users. We also use ns-2 to further substantiate our analysis. By adopting this approach with the appropriate parameters, the fairness among users within a caching hierarchy can be considerably improved.

We consider a two-tiered Wireless Sensor Network (WSN) consisting of sensor clusters deployed around strategic locations and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode and transmit relevant information from the designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. In practice, both SN and AN are battery-powered and energy-constrained, and their node lifetimes directly affect the network lifetime of WSNs. In this paper, we focus on the topology control process for ANs and BSs, which constitute the upper tier of a two-tiered WSN. We propose approaches to maximize the topological network lifetime of the WSN, by arranging BS location and inter-AN relaying optimally. Based on an algorithm in Computational Geometry, we derive the optimal BS locations under three topological lifetime definitions according to mission criticality. In addition, by studying the intrinsic properties of WSNs, we establish the upper and lower bounds of their maximal topological lifetime. When inter-AN relaying becomes feasible and favorable, we continue to develop an optimal parallel relay allocation to further prolong the topological lifetime of the WSN. An equivalent serialized relay schedule is also obtained, so that each AN only needs to have one relay destination at any time throughout the mission. The experimental performance evaluation demonstrates the efficacy of topology control as a vital process to maximize the network lifetime of WSNs.

Caching is an important means to scale up the growth of the Internet. Weak consistency is a major approach used in Web caching and has been deployed in various forms. The paper investigates some properties and performance issues of an expiration-based caching system. We focus on a hierarchical caching system based on the time-to-live (TTL) expiration mechanism and present a basic model for such a system. By analyzing the intrinsic TTL timing behavior in the basic model, we derive several important performance metrics from the perspective of the caching system and end users, respectively. Our results offer some basic understanding of a hierarchical caching system based on the weak consistency paradigm.

TCP performs reasonably well over the Internet where packet losses are mainly due to network congestion. However, TCP suffers significant throughput degradation over hybrid wireless/IP networks where packet losses are also due to transmission errors in wireless segments and during end-host handovers. In this paper, the micro-scale behavior and packet-level performance of four popular TCP variants over wireless links are assessed, and a modified TCP sender which incorporates a heuristic segment-in-flight estimation algorithm to improve the TCP throughput is proposed. Extensive simulations show that the modified TCP achieves better end-to-end performance, and still keeps the fairness and the compatibility with ordinary TCP variants.