Networks in the 5G and beyond era can use software-defined networks (SDN) to achieve network slicing (NS), so as to meet the extremely diverse service requirements of diverse applications in the Internet of Vehicles (IoV). However, the flow fluctuations in the highly dynamic IoV make it difficult to provide reliable, flexible, and scalable services for the IoV by the SDN control plane. Careful SDN controller placement can be a feasible solution to achieve its robustness and flexibility to deal with the changes in network status. Thus, this paper studies a dynamic controller placement problem to improve the performance of IoV services. To be specific, a hierarchical SDN control plane for the IoV is considered with the SDN controllers placed at the edge of networks. Under this architecture, we model the dynamic controller placement by Markov Decision Process (MDP). To efficiently solve the formulated NP-hard problems, we develop an algorithm based on Deep Deterministic Policy Gradient (DDPG) because of its advantages in solving the problem with multi-dimensional action and large solution space. Further, we incorporate a random process into the action selection strategy of DDPG to prevent it from getting trapped in local optimum. Simulation results show that the proposed DDPG-based controller placement approach can adapt to a highly dynamic IoV environment with outstanding performance.

Mobile Edge Computing (MEC) can accelerate computation-intensive applications and emerge as a promising technology for enabling Internet of Things (IoT). MEC improves the processing performance of tasks by assigning them to the edge nodes. However, with massive terminals contending for computation and communication resources simultaneously, how to develop a flexible computational offloading mechanism becomes the fundamental issue of MEC-enabled IoT systems. This paper aims to develop an effective computational offloading decision scheme by jointly considering the computational resource and diverse user demands with two goals, i.e., minimizing both the latency and the energy consumption. Specifically, we develop a two-stage computational offloading mechanism, where the computational resources and offloading decisions can be allocated and coordinated with the variation of computation requirements. To achieve the two goals, this work introduces an edge node recommendation model within the cloud-edge-end architecture to reduce the offloading optimization search space. Furthermore, we propose a new computational offloading (CROCA) algorithm based on Chemical Reaction Optimization (CRO) for optimizing offloading utility, which thoroughly considers the competition between mobile device requests and computational resources. Extensive evaluation results demonstrate that the proposed CROCA scheme can effectively improve the computational offloading performance.

With the advent of the sixth-generation (6G) wireless communications, transmission speeds are projected to exceed tenfold those of 5G, reaching theoretical peak download speeds of up to 1 Tbps. Data transmission capacity and speed will be significantly enhanced, enabling emerging applications, such as mixed reality, federated learning, and digital twins, driving exponential data traffic growth. To address this, the space-air–ground integrated network (SAGIN) combines satellite, aerial, and ground communication technologies, offering seamless global coverage and high-speed connectivity. In this article, we proposes an SAGIN framework integrated with mobile edge computing (MEC) to jointly optimize system energy consumption and delay costs. Specifically, we decompose the optimization problem into three subproblems: 1) uncrewed aerial vehicle (UAV) computational resource allocation; 2) satellite computational resource allocation; and 3) task offloading and channel allocation. The subproblems are then transformed and addressed using Newton’s interior point method and the deep reinforcement learning DQN algorithm to derive optimal allocation strategies for UAV and satellite computing resources, along with task offloading and channel resources, that our proposed algorithm effectively reduces system energy consumption and delay costs compared to other algorithms.

Online Large Language Models (LLMs) are widely employed across various tasks, including privacy-sensitive ones like financial advice or paragraph rewriting. Presently, users directly submit prompts to online LLM servers, inadvertently revealing sensitive keywords and facilitating server tracking to build user profiles. In this paper, we propose a local privacy-preserving prompt assistant (LPPA) that provides users with a usable method to balance privacy in the prompts and the utility of the LLM output. The LPPA will analyze the users' prompts, suggest modifying the prompts to protect the sensitive keywords, and provide an inference of the potential utility impact of the online LLM output. Specifically, we first propose a privacy module to identify the sensitive keywords in the prompt and adopt four privacy techniques, including remove, mask, replace, and rewrite to hide the keywords. While these techniques affect the utility of online LLM output, we measure such impact using the LLM output of the original prompt and modified prompts and discuss the cases with high, median, and low impact. In addition, we propose a utility inference model to infer the utility impact locally without disclosing the prompts to the online LLM. We evaluated LPPA on the real-world users' prompts and showed that the remove technique achieves the best performance, and it empowers users with meaningful ways to adjust their prompts to safeguard their privacy while still maintaining a satisfactory level of utility in online LLM usage.

In the digital divide regions, the edge computing can improve the performance of application services for the Internet of Things (IoT) devices. However, the lagging of information and communication technology (ICT) results in congested access spectrum and imbalanced computational load. Moreover, the mobility of IoT devices further exacerbates the fluctuating quality of communication links and the frequent changing of access positions. So, how to realize the reliable service requirements of devices in a heterogeneous environment with multiscale constraints should be considered appropriately and comprehensively. In this article, we model a relay-assisted multiaccess edge computing (MEC) framework, employing multihop transmission to enable the cross-domain service coverage. Under this framework, we formulate a quantitative model to characterize communication and computation processes within task migration, and derive analytical results for service latency. To improve the access resource efficiency, we adopt a joint nonorthogonal multiple access (NOMA) scheme to extend the transmission dimension, and employ proportional fairness to dynamically allocate resources. Besides, we propose a multiagent deep reinforcement learning (DRL) for optimizing the long-term task offloading scheduling, address the optimization problem of maximizing the system throughput efficiency. And we improve the action exploration and output dimensions of DRL to achieve convergence and performance enhancement. Simulation and analytical results show that our proposed algorithm outperforms the comparison algorithms in the key performance indicators.

Compared with cloud computing, edge computing is capable of effectively solving the high latency problem in cloud gaming. However, there are still several challenges to address for optimizing system performance. On the one hand, the unpredictable bursts of game requests can cause server overload and network congestion. On the other hand, the mobility of players makes the system highly dynamic. Although existing research has studied game fairness and latency separately to improve the Quality of Experience (QoE), a tradeoff between fairness and latency has been largely ignored. Furthermore, how to balance network and computing load is identified as another constraint during optimization. Focusing on latency, fairness, and load balance simultaneously, we propose an adaptive resource allocation strategy through deep reinforcement learning (DRL) for a dynamic gaming system. The experimental results have demonstrated that the proposed algorithm outperforms the traditional optimization methods and classical reinforcement learning algorithms in solving complex multimodal reward problems.

The vision of heterogeneous vehicle networks (HetVNETs) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating mobile-edge computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multivehicle scramble for communication and computational resources challenging. This article intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: 1) high-reliability transmission (HRT) and 2) computational delay minimization (CDM). Specifically, we develop a hybrid MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and nonconvex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the nonsmooth HRT problem. To solve the CDM problem, we transform it into two subproblems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.

Voice Assistant System (VAS) provides a convenient way for users to interact with smart-home devices via a voice interface. However, it raises unique security issues, including voice replay and injection attacks, where attackers remotely and maliciously control the smart-home devices via a voice interface. In this paper, we consider a typical smart-home scenario in which a VAS device and a compromised speaker device are placed in close physical proximity. The attacker can remotely play malicious voice commands through the speaker device to manipulate the VAS device for malicious purposes. We propose a defense system on the VAS device to secure the VAS device against both voice replay and injection attacks, without any additional devices and without any extra user effort. Specifically, our system aims to collect voice data and wireless data continuously from the VAS device and then extracts the Mel-Cepstral Frequency Coefficients (MFCC) features from voice and wireless data. We consider that both voice and wireless data are affected by the same present users' physical activities, and the correlation can be used to detect the attacks. Finally, our system applies a deep learning model that learns from previous time-series data and analyzes real-time data to infer whether the real-time voice command is generated from a user or the speaker device. We have tested our system in certain real-world smart-home scenarios. Our experiments showed that the proposed system has a probability between 76.4% to 89.1% to successfully detect the voice replay and injection attacks in the considered scenarios.

mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, high noise, and multi-path induced shadow reflections and artifacts. In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data. The architecture consists of two back-to-back conditional GAN deep neural networks: the first generator network generates 2D depth images based on raw radar intensity data, and the second generator network outputs 3D point clouds based on the results of the first generator. The architecture exploits both convolutional neural network’s convolutional operation (that extracts local structure neighborhood information) and the efficiency and detailed geometry capture capability of point clouds (other than costly voxelization of 3D space or distance fields). Our experiments have demonstrated 3DRIMR’s effectiveness in reconstructing 3D objects, and its performance improvement over standard techniques.

Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we investigate lesion detection on DR fundus images with CNN-based object detection methods. Lesion detection on fundus images faces two unique challenges. The first one is that our dataset is not fully labeled, i.e., only a subset of all lesion instances are marked. Not only will these unlabeled lesion instances not contribute to the training of the model, but also they will be mistakenly counted as false negatives, leading the model move to the opposite direction. The second challenge is that the lesion instances are usually very small, making them difficult to be found by normal object detectors. To address the first challenge, we introduce an iterative training algorithm for the semi-supervised method of pseudo-labeling, in which a considerable number of unlabeled lesion instances can be discovered to boost the performance of the lesion detector. For the small size targets problem, we extend both the input size and the depth of feature pyramid network (FPN) to produce a large CNN feature map, which can preserve the detail of small lesions and thus enhance the effectiveness of the lesion detector. The experimental results show that our proposed methods significantly outperform the baselines.

We introduce LIDAUS (Localization of IoT Device via Anchor UAV SLAM), an infrastructure-free, multi-stage SLAM system that utilizes an Unmanned Aerial Vehicle (UAV) to accurately localize IoT devices in a 3D indoor space where GPS signals are unavailable or weak, e.g., manufacturing factories, disaster sites, or smart buildings. The lack of GPS signals and infrastructure support makes most of the existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. To address these challenges, the UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on RSSIs, the most widely available measurement of the signals of almost all commodity IoT devices. The main novelties of the system include a weighted entropy-based clustering algorithm to select high quality RSSI observation locations, a 3D U-SLAM algorithm that is enhanced by deploying anchor beacons along the UAV's path, and the path planning based on Eulerian cycles on multi-layer grid graphs that model the space in exploring stage and Steiner tree paths in searching stages. Our simulations and experiments of Bluetooth IoT devices have demonstrated that the system can achieve high localization accuracy based only on RSSIs of commodity IoT devices.

As data-parallel applications process more complex data, the dependencies between computation jobs in a multi-stage job also become more complicated. However, most of the existing scheduling solutions primarily rely on total bytes sent (job size) to differentiate jobs where jobs with fewer? bytes sent are prioritized over the larger ones. This approach overlooks the fact that jobs may consist of multiple computation stages, and that the completion of a computation job stage depends on the completion of other jobs' stage. In this paper, we present a coflow scheduler of multi-stage jobs that minimizes the average job completion time. Our solution prioritizes jobs based on the multi-faceted characteristics of multi-stage job structure per stage, instead of total bytes sent. Our experiments show that our approach provides twice the performance of existing solutions on average and by four times in bursty traffic scenario.

The following topics are dealt with: learning (artificial intelligence); mobile computing; wireless sensor networks; feature extraction; Internet of Things; optimisation; graph theory; social networking (online); telecommunication network topology; pattern clustering.

We investigate a mobile edge computing environment where edge computing nodes provide their computation capacities to process the computation intensive tasks submitted by end users. We introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to a user into a cluster to collaboratively work on the user's tasks. The system enables a user to minimize his/her total cost which is a weighted combination of latency (i.e., the task's completion time), and the costs incurred in working on the task. The total cost captures the tradeoff that the user would like to make between latency and computing related costs. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user's optimization goal, due to the time-varying available capacities and the mobility of edge nodes. To address the challenge, we model the system as a discrete time system in which each edge node's capacity and cost vary over different time slots, and the system assigns parts of the task to the edge nodes in the cluster over time. We introduce a prediction-based dynamic task assignment algorithm, referred to PA-OPT, that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Then we apply our system design to a video data analysis application, and conduct extensive simulations driven by a Google cloud data trace. We have demonstrated that our proposed algorithm/system achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution.

Edge Computing is emerging as a promising solution to meet the ultra-low latency requirement of data processing at the edge of the Internet, and it is close to end users and the smart devices of Internet of Things. We propose an Edge Computing task scheduling model which utilizes the existing resources to achieve low latency by cooperative computing through multiple edge servers and close- range communication at the edge of the Internet. We treat the latency minimization design as an optimization problem. We formulate the latency minimization as an integer programming problem and solve it efficiently via dynamic programming, then we propose an optimal scheduling algorithm based on dynamic programming (OSA-DP). Considering the limited heterogeneous resources shared among tasks, we further propose a cooperative taskserver matchmaking scheduling heuristic (CTMS), which jointly optimizes the computation and communication cost. Extensive simulations demonstrate that ultra- low latency service provision can be achieved by the cooperative design of computation and communication in Edge Computing.

In stream processing applications, a data stream is a continuous stream of data items that are generated from multiple sources distributed at various geographic locations. A common method of streaming processing is to transfer raw data streams to a data center for unified processing. However, the method does not scale well when a huge amount of data for stream processing is generated at the edge of the Internet, with the development of smartphones, Internet of things, 5G and other technologies in recent years. For stream processing applications, processing data at the edge can significantly reduce the response latency of the applications. However, the mobility of edge nodes in a mobile edge environment poses a significant challenge to scheduling stream processing tasks efficiently to achieve high system throughputs. In this paper, we introduce a scheduling algorithm, referred to as Mobile Resource Aware (MRA) stream processing scheduling, for mobile edge environment. Compared with other existing scheduling algorithms, our MRA algorithm can optimally schedule resources for stream processing tasks through adapting to the mobile edge environment with limited node resources. We implement MRA scheduling algorithm in Storm through a custom scheduler and we evaluate the performance of MRA in an emulation mobile edge environment. Our experimental results have demonstrated that our MRA algorithm can achieve significantly higher system performance than the other two existing scheduling algorithms.

We investigate the design of a Mobile Edge Computing (MEC) system in which self-interested users minimize their own costs and a MEC service provider attempts to maximize its revenue. We introduce a third-party platform that works as an intermediary to facilitate the service transaction between the users and the provider. The platform collects MEC server information and discloses that to users; users rely on their user agent apps to place their application jobs on edge computing servers during their stay in the system. We propose a dynamic programming algorithm for a user to minimize his/her own cost and an efficient heuristic algorithm for the platform to minimize the cost of all users by optimally scheduling the admission of users' jobs and still allowing users to make independent optimal decisions. We have demonstrated the effectiveness of these algorithms via extensive simulations based on an empirical Google cloud dataset and a Web file dataset. Furthermore we model the interaction between users and a provider as a game, referred to as User-Provider Game. We find that when the provider always attempts to maximize its own revenue by adjusting the prices of edge servers, the interaction will lead to an unstable system with severe oscillation and degraded performance. To address the issue, we propose a better response algorithm for the provider which stabilizes the system and results in high performance. This paper sheds light on this important area of MEC, and points a promising direction to further investigate and design an effective MEC system of independent self-optimizing mobile users.

We introduce a game-theoretic framework to explore revenue sharing in an Edge-Cloud computing system, in which computing service providers at the edge of the Internet (edge providers) and computing service providers at the cloud (cloud providers) collectively provide computing resources to clients (e.g., end users or applications) at the edge. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. To achieve high system-level efficiency, the manager of the system adopts a task distribution mechanism to maximize the total revenue received from clients and also adopts a revenue sharing mechanism to split the received revenue among computing servers (and hence service providers). Under those system-level mechanisms, service providers attempt to game with the system in order to maximize their own utilities, by strategically allocating their resources (e.g., computing servers). Our framework models the competition among the providers in an Edge-Cloud system as a non-cooperative game. We have shown the existence of Nash equilibrium in the game both theoretically and practically through simulations and experiments on an emulation system that we have developed. We find that revenue sharing mechanisms have a significant impact on the system-level efficiency at Nash equilibria, and surprisingly the revenue sharing mechanism based directly on actual contributions can result in significantly worse system performance than Shapley value sharing mechanism and Ortmann proportional sharing mechanism. Our framework provides an effective economics approach to the understanding and designing of efficient Edge-Cloud computing systems.

Frequent itemset mining (FIM) is an important topic in data mining, which extracts knowledge of the relationships among items in a transaction dataset. Apriori algorithm and its variants, apriori-like algorithms, are widely used FIM algorithms. However, in a big data environment, these algorithms are inefficient. Due to the iterative calculation and modification of intermediate results, if an apriori-like algorithm is applied on a high-dimension or large-scale dataset, the memory requirement is unacceptable for a single machine. Although parallel and distributed programming could be a solution to deal with big data problems, apriori-like algorithms are not quite suitable for parallel computing because they need extra time overhead of communication to update intermediate results iteratively in cluster memories. To solve this problem, we propose a novel FIM algorithm, Distributed Apriori Based on Itemset-Encoding (DABIE). Different from existing methods, DABIE has two main advantages. Firstly, it stores intermediate results encoded in the form of 0 and 1 to reduce memory usage. Secondly, generating frequent itemsets is based on logical operation of encoding to reduce modification of data in cluster memories. These two advantages make DABIE more friendly to cluster computing. We apply DABIE on datasets with different scales. Compared with other distributed apriori-like algorithms, the results of our experiments show that DABIE can efficiently improve the multi-iterative FIM in big data environment.

Advances in smartphone technologies enable mobile data subscribers to resell their data allowance to other users, creating a secondary data market. The trading environment of this secondary data market is dynamic and ad-hoc: buyers and sellers join and leave the market at all times, changing the trading landscape constantly. The amount of data demanded and offered at any point in time also vary. These conditions make determining a fair transaction price, and matching buyers to sellers difficult in practice. Prior schemes utilize global description of the network and market forces to achieve good performance, but the implementation requires a high overhead cost. In this paper, we present DataMart, a data pricing and user matching platform for trading in this dynamic, ad-hoc and heterogeneous market that works in distributed manner without needing global information. Using insights from real world traces, we demonstrate via simulation that our pricing scheme is converging and consistent with the law of demand and supply. Further, our user matching scheme achieves comparable performance to the optimal solution. We implement a prototype on Android platform, and the experiment results confirm the effectiveness of DataMart.

Many complex networks in practice can be described by weighted networks. Currently, most existing weighted network models only consider the node strength in evolving conditions, but neglect the influence of node attraction on network evolution. In this paper, we propose an accurate and practical weighted evolving network model based on node fitness dynamic evolution, which takes both node strength and node attraction into consideration. Our theoretical analysis and numerical simulations have demonstrated the scale-free property of the network model, which has been widely observed in many real-world networks. Additionally, the phenomenon that very few nodes possess greater fitness is observed via numerical simulations of our network model, which can be referred to as the fitness property of network. Our network model's dual assessment of node strength and node attraction leads to fewer node clustering and stronger robustness of the whole network than other existing network growth models.

We investigate emerging proximity-based Mobile Crowd Service or pMCS systems, in which services are provided and consumed by users carrying smart mobile devices (e.g., smartphones) and in proximity of each other (e.g., within Bluetooth range). Due to limited resources on smartphones, it is crucial to provide a mechanism to incentivize users' participation and ensure fair trading in a pMCS system. In this paper, we design a multi-market dynamic double auction mechanism for a pMCS system, referred to as MobiAuc, and we show that it is truthful, feasible, individual-rational, no-deficit, and computationally efficient. The novelty and significance of MobiAuc is that it addresses and solves the fair trading problem in a multi-market dynamic double auction setting which naturally occurs in a mobile wireless environment. We demonstrate its efficiency via simulations based on generated user patterns (stochastic arrivals and random market clustering of users) and real-world traces. Our preliminary implementation of MobiAuc and experiments on Android platform have demonstrated the feasibility of MobiAuc mechanism in practice.

Classification of imbalanced datasets has become one of the most challenging problems in big data mining. Because the number of positive samples is far less than the negative samples, low accuracy and poor generalization performance and some other defects always go with learning process of traditional algorithms. Ensemble construction algorithm is an important method to handle this problem. Especially, the ensemble construction algorithm based on random under-sampling or clustering can effectively improve the performance of classification. However, the former causes information loss easily and the latter increases complexity. In this paper, we propose ACUS, an improved ensemble algorithm based on automatic clustering and under-sampling. ACUS conducts clustering first according to the weight of samples, and then it constructs balanced-distributed dataset which consists of a certain percentage of the majority class and all of the minority class from each cluster. With Adaboost algorithm construction, these datasets are used to get an ensemble classifier. Experimental results demonstrate the advantages of our proposed algorithm in terms of accuracy, simplicity and high stability.

There is a fast growing number of public spaces offering Wi-Fi access to meet the rising demands for Internet access. It is common for such service to be offered to users at no charge or for a flat fee. Both situations provide very little incentive for Wi-Fi providers to offer better service to the users. Similarly, Wi-Fi providers pay a monthly flat rate to ISP for Internet access and, this too does not incentivize ISP to offer better service to Wi-Fi users. As a result, Wi-Fi users may experience poor connection when network becomes congested during peak hours. In this paper we propose a dynamic pricing scheme for Internet access and a revenue sharing mechanism that provides incentives for both ISP and Wi-Fi providers to offer better service to their users. We build our revenue sharing model based on Shapley value mechanism. Importantly, our proposed revenue sharing mechanism captures the power negotiation between ISP and Wi-Fi providers, and how shifts in power influences revenue division. Specifically, the model assures that the party who contributes more receives a higher portion of the revenue. In addition, our simulation demonstrates that our model captures the bargaining power shifts between Wi-Fi providers and ISP, and shows that the division of revenue asymptotically converges to a percentage value.

There is a fast growing number of public spaces offering Wi-Fi access to meet the rising demands for the Internet. It is common for such service to be offered to users at no charge or for a flat fee. Both situations provide very little incentive for Wi-Fi providers to offer better service to the users. Similarly, Wi-Fi providers pay a monthly flat-rate to ISP for Internet access, which does not incentivize ISP to offer better service to Wi-Fi users. As a result, Wi-Fi users may experience poor Internet connection when network becomes congested during peak hours. In this paper, we propose a dynamic pricing mechanism for both ISP and Wi-Fi providers in order to give mobile Wi-Fi users better service, while providing economic incentive for both ISP and Wi-Fi provider.

We present an argument in favor of forming coalitions of peers in a data swarming system consisting of peers with heterogeneous upload capacities. In this paper, a coalition refers to a set of peers that explicitly cooperate with other peers inside the coalition via choking, piece selection, and capacity allocation strategies. Furthermore, each peer in a coalition exchanges data with peers outside its coalition via distinct choking, piece selection, and capacity allocation strategies. We first propose a simple Random Choking strategy for peers inside a coalition and develop an analytical model for studying its performance. Our model accurately predicts a coalition's performance and shows that the proposed strategy helps a coalition achieve near-optimal performance. Furthermore, our model can be easily adapted to model a BitTorrent-like swarm. We show that our Random Choking strategy significantly outperforms Tit-for-Tat and Unchoke-All strategies proposed in prior work. We also introduce a simple piece selection strategy, which significantly improves data availability within a coalition as compared to Rarest-First strategy employed in BitTorrent systems. Using cooperative game theory, we prove the existence of stable coalitions when peer population is fixed and each peer has complete information of other peers' actions and payoffs. When peers are allowed to freely join or leave coalitions, we propose a Cooperation-Aware Better Response strategy that achieves convergence of the dynamic coalition formation process. Finally, using extensive simulations, we demonstrate that forming coalitions results in significant improvements in the overall performance of a data swarm.

Concomitant to the growing popularity of Internet enabled mobile devices such as smartphones, tablets, PDAs, portable media players etc., however, are the concerns about availability of Internet access points for these devices. Mobile users often either overpay for service availability such as (3G or LTE) or suffer incapability of accessing Internet services due to limited hardware resources (3G or LTE) or exhaustion of carrier enforced data plans. In this paper we introduce Collab Assure, an auction based, ad-hoc market model assuring service for users with no Internet access capability. Collab Assure framework provides service assurance through opportunistic ad-hoc networks formed by spatio-temporally co-existing mobile users. The system allows users to "sublet" their surplus data plans to the users without Internet access. We discuss the design and implementation of Collab Assure technology in Android framework. Our simulation results advocate the success of this approach on real world traces, where mobile users need to participate in auctions for achieving on-demand and low-cost data service.

Active Queue Management (AQM) aims to provide high link utilization and low queuing delay in communication networks. However, it is challenging to adapt AQM parameters in response to dynamic network scenarios with varying round-trip time, link capacity and traffic load. In order to maintain a stable queue with desired performance such as high link utilization and low queuing delay, this paper proposes a Statistical Adapting RED (SA-RED) to dynamically tune RED parameters based on the standard deviation of instantaneous queue size, which can be readily measured in practice. The proposed mechanism and corresponding algorithms can be implemented easily because they avoid the problem of measuring network parameters, such as the round-trip time and number of TCP flows. In addition, they are effective in achieving high performance, i.e., maintaining low queuing delay while providing the desired transient and steady-state performance under widely varying network conditions. These advantages are demonstrated by extensive NS-2 simulation experiments.

We investigate whether friend relationship in online social networks (OSNs) can help to improve the performance of Peer-to-Peer (P2P) data swarming systems. Due to the importance and popularity of OSNs and P2P swarming (the two major applications on the Internet), the research community shows increasing interest in leveraging OSNs for data swarming system design. In this paper, we present our initial findings about some of the basic issues in this emerging area, which are largely missing from existing work. Specifically, we conduct a measurement study of a popular online social network - Douban [1], and our analysis of this OSN provides strong empirical evidence of the association between users' content interests and their online social friend relationship. Then we introduce a simple public social streaming scheme that lets peers simultaneously join multiple swarms of the same data content with the help from their online social friends. Our simulation studies demonstrate that this social scheme can lead to significant performance improvement in vanilla P2P streaming systems. Furthermore we explore the impact of various social graphs on the performance improvement brought about by the social scheme. Our findings indicate that our proposed social scheme consistently achieves greater performance improvement on Erdos-Renyi's random graphs than on other graphs such as Barabasi-Albert's scale-free graphs and the empirical Facebook and Douban social graphs. This result points to an important research direction of leveraging OSNs in data swarming system design.

We design and analyze a mechanism for forming coalitions of peers in a data swarming system where peers have heterogeneous upload capacities. A coalition is a set of peers that explicitly cooperate with other peers inside the coalition via choking, data replication, and capacity allocation strategies. Further, each peer interacts with other peers outside its coalition via potentially distinct choking, data replication, and capacity allocation strategies. Following on our preliminary work in [14] that demonstrated significant performance benefits of coalitions, we present here a comprehensive analysis of the choking strategy for coalitions. We first develop an analytical model to understand a simple random choking strategy as a within-coalition strategy and show that it accurately predicts a coalition's performance. Our model shows that the random choking strategy can help a coalition achieve near-optimal performance by optimally choosing the re-choking interval lengths and the number of unchoke slots. Further, our analytical model can be easily adapted to model a BitTorrent-like swarm. Using extensive simulations, we demonstrate improvements in the performance of a swarming system due to coalition formation.

We present an argument in favor of forming coalitions of peers in a data swarming system consisting of peers with different upload capacities. A coalition is a set of peers with the same upload capacity that explicitly cooperate with other peers inside the coalition via choking and capacity allocation strategies. Further, each peer interacts with other peers outside its coalition via potentially distinct choking and capacity allocation strategies. This paper focuses on the efficiency of different choking strategies, assuming that peers do not share data with other peers outside their coalitions. We first develop an analytical model that accurately predicts the performance of a coalition of peers adopting BitTorrent's Tit-for-Tat choking strategy. Our model highlights a number of inefficiencies of Tit-for-Tat strategy. Accordingly, we propose a random choking strategy, and show that it can help a coalition achieve near-optimal performance and it significantly outperforms not only Tit-for-Tat strategy but also unchoke-all strategy. Using cooperative game theory, we prove the existence of stable coalitions, and demonstrate the convergence of the dynamic coalition formation process when peers use our cooperation-aware better response strategy. Using extensive simulations, we demonstrate significant performance benefits due to coalition formation.

We propose two unstructured file sharing games, unilateral and bilateral unstructured file sharing games, to study the interaction among self-interested players (users) of unstructured P2P file sharing applications. In a unilateral unstructured file sharing game, players compete for network resources (link bandwidth) by opening multiple connections to each other on multiple paths so as to maximize their individual benefits. A player always allows other players to connect to itself. Multiple concurrent connections are allowed on any path between a pair of players. Per-connection throughput is determined by the transport protocol implemented by users' computers. In a bilateral unstructured file sharing game, users adopt a Tit-for-Tat strategy, under which an active connection between two players is set up only when they both find it beneficial. Two players can set up at most one connection between themselves and bottlenecks occur only at upstream access links in a star network. For both games, we prove the existence of an equilibrium, quantify the efficiency losses of equilibria, and demonstrate the dynamic stability of equilibria in best-response or better-response dynamic game playing processes.

There is growing interest in designing high speed routers with small buffers that store only tens of packets. Recent studies suggest that TCP NewReno, with the addition of a pacing mechanism, can interact with such routers without sacrificing link utilization. Unfortunately, as we show in this paper, as workload requirements grow and connection bandwidths increase, the interaction between the congestion control protocol and small buffer routers produce link utilizations that tend to zero. This is a simple consequence of the inverse square root dependence of TCP throughput on loss probability. In this paper we present a new congestion controller that avoids this problem by allowing a TCP connection to achieve arbitrarily large bandwidths without demanding the loss probability go to zero. We show that this controller produces stable behavior and, through simulation, we show its performance to be superior to TCP NewReno in a variety of environments. Lastly, because of its advantages in high bandwidth environments, we compare our controller's performance to some of the recently proposed high performance versions of TCP including HSTCP, STCP, and FAST. Simulations illustrate the superior performance of the proposed controller in a small buffer environment.

In this paper, we investigate the problem of highly available, massive-scale file distribution in the Internet. To this end, we conduct a large-scale measurement study of BitTorrent, a popular class of systems that use swarms of actively downloading peers to assist each other in file distribution. The first generation of BitTorrent systems used a central tracker to enable coordination among peers, resulting in low availability due to the tracker's single point of failure. Our study analyzes the prevalence and impact of two recent trends to improve BitTorrent availability: (i) use of multiple trackers, and (ii) use of Distributed Hash Tables (DHTs), both of which also help to balance load better. The study considered more than 1,400 trackers and 24,000 DHT nodes (extracted from about 20,000 torrents) over a period of two months. We find that both trends improve availability, but for different and somewhat unexpected reasons. Our findings include: (i) multiple trackers improve availability, but the improvement largely comes from the choice of a single highly available tracker, (ii) such improvement is reduced by the presence of correlated failures, (iii) multiple trackers can significantly reduce the connectivity of the overlay formed by peers, (iv) the DHT improves information availability, but induces a higher response latency to peer queries.

We study the interaction among users of unstructured file sharing applications, who compete for available network resources (link bandwidth or capacity) by opening multiple connections on multiple paths so as to accelerate data transfer. We model this interaction with an unstructured file sharing game. Users are players and their strategies are the numbers of sessions on available paths. We consider a general bandwidth sharing framework proposed by Kelly [1] and Mo and Walrand [2], with TCP as a special case. Furthermore, we incorporate the Tit-for-Tat strategy (adopted by BitTorrent [3] networks) into the unstructured file sharing game to model the competition in which a connection can be set up only when both users find this connection beneficial. We refer to this as an overlay formation game. We prove the existence of Nash equilibrium in several variants of both games, and quantify the losses of efficiency of Nash equilibria. We find that the loss of efficiency due to selfish behavior is still unbounded even when the Tit-for-Tat strategy is believed to prevent selfish behavior.

We study traces taken from UMass DieselNet, a Disruption-Tolerant Network consisting of WiFi nodes attached to buses. As buses travel their routes, they encounter other buses and in some cases are able to establish pair-wise connections and transfer data between them. We analyze the bus-to-bus contact traces to characterize the contact process between buses and its impact on DTN routing performance. We find that the all-bus-pairs aggregated inter-contact times show no discernible pattern. However, the inter-contact times aggregated at a route level exhibit periodic behavior.Based on analysis of the deterministic inter-meeting times for bus pairs running on route pairs, and consideration of the variability in bus movement and the random failures to establish connections, we construct generative route-level models that capture the above behavior. Through trace-driven simulations of epidemic routing, we find that the epidemic performance predicted by traces generated with this finer-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the coarse-grained all-bus-pairs aggregated model. This suggests the importance in choosing the rightlevel of model granularity when modelingmobility-related measures such as inter-contact times in DTNs.

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We present a game-theoretic study of the selfish behavior of TCP users when they are allowed to use multiple concurrent TCP connections so as to maximize their goodputs or other utility functions. We refer to this as the TCP connection game. A central question we ask is whether there is a Nash equilibrium in such a game, and if it exists, whether the network operates efficiently at such a Nash equilibrium. Combined with the well known PFTK TCP model (1998), we study this question for three utility functions that differ in how they capture user behavior. The bad news is that the loss of efficiency or price of anarchy can be arbitrarily large if users have no resource limitations and are not socially responsible. The good news is that, if either of these two factors is considered, efficiency loss is bounded. This may partly explain why there will be no congestion collapse if many users use multiple connections.

In this paper, we study the interaction between overlay routing and traffic engineering (TE) in a single autonomous system (AS). We formulate this interaction as a two-player non-cooperative non-zero sum game, where the overlay tries to minimize the delay of its traffic and the TE's objective is to minimize network cost. We study a Nash routing game with best-reply dynamics, in which the overlay and TE have equal status, and take turns to compute their optimal strategies based on the response of the other player in the previous round. We prove the existence, uniqueness and global stability of Nash equilibrium point (NEP) for a simple network. For general networks, we show that the selfish behavior of an overlay can cause huge cost increases and oscillations to the whole network. Even worse, we have identified cases, both analytically and experimentally, where the overlay's cost increases as the Nash routing game proceeds even though the overlay plays optimally based on TE's routing at each round. Experiments are performed to verify our analysis.

In this paper we introduce an innovation in differentiated services architecture consisting of adaptive two-level coloring at the edge and preferential marking at the core. We identify general properties of these two processes which, when met, guarantee a desirable fixed point for the network; i.e., one where aggregated flow rates meet or exceed given targets in an over-provisioned network. Specific mechanisms realizing the aforementioned properties lead to so-called active rate management controllers for edge coloring, and a preferentially-marking, active queue management controller at the core. We discuss stability of the fixed point for this network, and validate results using ns simulations.

Congestion control in TCP/AQM networks is expected to perform well for a wide-range of conditions, but recent advances in modeling and analysis indicate that present AQM (active queue management) schemes need an extra dose of adaptability to cope. The paper answers the call and proposes a self-tuning structure wherein AQM parameters are automatically tuned in response to on-line estimation of link capacity and traffic load. This approach is applicable to any AQM scheme that is parameterizable in terms of link capacity and TCP load. We describe this self-tuning structure, illustrate its application to PI (proportional-integral) and RED (random early detection) AQMs, provide stability analysis, and conduct ns simulations to compare with both fixed AQM schemes and the recently proposed adaptive RED.

Congestion control in TCP/AQM networks is expected to perform well for a wide-range of conditions, but recent advances in modeling and analysis indicate that present AQM schemes need an extra dose of adaptability to cope. This paper answers the call and proposes a self-tuning structure wherein AQM parameters are automatically tuned in response to on-line estimation of link capacity and traffic load. This approach is applicable to any AQM scheme that is parameterizable in terms of link capacity and TCP load. In this paper, we will describe this self-tuning structure, illustrate its application to PI and RED AQMs, provide stability analysis, and conduct ns simulations to compare with both fixed AQM schemes and the recently proposed adaptive RED.

In this paper we propose a new paradigm for a Differentiated Service (DiffServ) network consisting of two-color marking at the edges of the network using token buckets coupled with differential treatment in the core. Using fluid-flow modelling, we present existence conditions for token-bucket rates and differential marking probabilities at the core that result in all edges receiving at least their minimum guaranteed rates. We then present an integrated DiffServ architecture comprising of an active rate management controller at the marking edge and a two-level active queue management controller at the core. The validity of the fluid flow model and performance of this new scheme are verified using ns simulations.