Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.

Operating a quantum network incurs high capital and operational expenditures, which are expected to be compensated by the high value of enabled quantum applications. However, existing mechanisms mainly focus on maximizing the entanglement distribution rate and neglect the cost incurred on users. This paper aims to address how to utilize quantum network resources in a cost-efficient manner while sustaining high-quantity and high-quality entanglement distribution. We first consider how to establish a steady stream of entanglements between remote nodes with the minimum cost. Utilizing a recent flow-based abstraction and a novel graph representation, we design an optimal algorithm for min-cost remote entanglement distribution. Next, we consider distributing entanglements with the highest fidelity subject to a cost bound and prove its NP-hardness. To explore the cost-fidelity trade-off due to swapping and purification, we propose an approximation scheme for maximizing fidelity while satisfying an arbitrary cost bound. Our algorithms provide rigorous tools for supporting high-performance quantum network applications with financial consideration and offer strong theoretical guarantees. Extensive simulation results validate the advantageous performance in cost efficiency and/or fidelity compared to existing solutions and heuristics.

Traffic Engineering (TE) in large-scale networks like cloud Wide Area Networks (WANs) and Low Earth Orbit (LEO) satellite constellations is a critical challenge. Although learning-based approaches have been proposed to address the scalability of traditional TE algorithms, their practical application is often hindered by a lack of generalization, high training overhead, and a failure to respect link capacities. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale network scenarios, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of “predicting the optimal TE solution” into “predicting the optimal TE algorithm”, which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact underlying network topology or traffic patterns, and is able to very efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. We train and evaluate TELGEN with random and real-world topologies, with networks of up to 5000 nodes and 3.6×106links in testing. TELGEN shows less than 3% optimality gap while ensuring feasibility in all testing scenarios, even when the test network has 2-20× more nodes than the largest training network. It also saves up to 84% TE solving time than traditional interior-point method, and reduces up to 79.6% training time per epoch than the state-of-the-art learning-based algorithm.

Verifying the safety of latency-aware cyber-physical systems is both critical and challenging due to the interaction between continuous physical dynamics and discrete computational constraints. This paper introduces SOTERIA, a formal framework that integrates digital twins for ensuring safety in these systems. SOTERIA models both the physical dynamics and computational behavior, enabling integrated verification within a specific operating environment. This approach goes beyond conventional methods that either treat physical and computational aspects separately or rely on overly conservative worst-case analyses. By modeling hybrid dynamics alongside computational models and operating environments, SOTERIA verifies both functional and timing correctness. Leveraging established verification tools, SOTERIA determines whether end-to-end latencies meet formal specifications, bridging the gap between computational and physical requirements. We first introduce a simple example of a 1D adaptive cruise control system to illustrate its effectiveness. We then present findings from a case study using the F1Tenth racing car platform and the UPPAAL tool to demonstrate SOTERIA's effectiveness in realistic scenarios, enabling safety verification that was previously infeasible with conventional schedulability analyses. This work underscores the importance of an integrated verification approach for enhancing safety and reliability in autonomous systems.

Low Earth Orbit Satellite Networks (LSNs), as the new generation of backbone networks, can provide low-latency network connectivity anywhere on Earth. However, their dynamic topology and unpredictable global usage patterns hinder reliable communication, limiting their application in supporting real-time applications that require predictable performance. Specifically, the highly dynamic LSN may experience congestion and energy depletion due to uneven user demands and the periodic movement of satellites. In this paper, we design a Congestion and Energy-Aware pricing and resource Reservation algorithm, CEAR, which enables a LSN to reserve network resources for online arriving real-time communication requests, ensuring reliable communication to support performance-critical applications such as disaster monitoring and remote teleconferencing. To maintain the long-term performance of the network, the LSN operator sets resource prices for link bandwidth and satellite energy consumption across the network. The resource prices act as a proxy between the resource reservation decisions for each communication request and the operator’s objective to maximize throughput and network utility and/or to balance network-wide resource depletion. CEAR is guided by online competitive algorithm design and achieves a competitive social welfare. Extensive simulations using real-world LSN topology show that CEAR achieves high social welfare while maintaining low network-wide congestion and energy deficit.

Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. The rapid evolution of language models (LMs) has revolutionized the way we process textual data, which indicates a strong potential to replace shallow text embedding generally used in Graph Neural Networks (GNNs). However, we find that existing LM approaches that exploit text information in graphs suffer from inferior computation and data efficiency. In this study, we introduce LEADING, a novel and efficient approach for end-to-end fine-tuning of language models on TAGs. To enhance data efficiency, LEADING efficiently transfers rich knowledge from LMs to downstream graph learning tasks with limited labeled data by employing end-to-end training of LMs and GNNs in a semi-supervised learning setting. To address associated computation efficiency issues, it introduces two techniques: neighbor decoupling targeting LMs and implicit graph modeling targeting GNNs, respectively. Our proposed approach demonstrates superior performance, achieving state-of-the-art (SOTA) results on the ogbn-arxiv leaderboard, while maintaining computation cost and memory overhead comparable to graph-less fine-tuning of LMs. Through comprehensive experiments, we showcase its superior computation and data efficiency, presenting a promising solution for various LMs and graph learning tasks on TAGs.

Quantum networks are essential to establishing long-distance entanglements for many advanced quantum applications. Recent breakthroughs have opened up the possibility of creating a satellite-assisted global-scale quantum network. This paper proposes LACE, a Loss-Aware Constellation Design framework for a new type of satellite-assisted passive-optical quantum networks. The goal of LACE is to ensure lowest possible worst-case loss for ground-to-ground entanglement distribution with a fixed number of satellites. Considering high photon loss and beam propagation, we first develop a detailed loss model, incorporating factors such as beam propagation and diffraction, atmospheric turbulence, and beam truncation during end-to-end entanglement distribution. We then design an algorithm to estimate the end-to-end loss given a specific network constellation design, and propose a constellation design framework to find a suitable constellation design with as low end-to-end loss as possible. Using LACE, we explore diverse satellite constellations under practical constraints, which reveals critical insights into how network parameters and link connectivity affect end-to-end entanglement loss. Notably, we find that a constellation with 25 orbits and 32 satellites per orbit with an altitude 550 km can establish a channel with approximately 30 dB loss, corresponding to only 150 km of ground fiber distance, between ground stations separated by nearly 20,000 km. These insights provide concrete guidance for future constellation design, paving the way toward global-scale entanglement distribution.

The rapid growth in low-Earth-orbit satellites enables providing Earth observation applications to public users via a shared platform. However, the limited satellite-ground communication resources present a major challenge in downloading and fully utilizing satellite-captured Earth observation data on the ground. As a new edge computing paradigm, orbital edge computing allows satellites to host deep learning models with on-board computing resources for in-orbit data analysis, reducing downlink data volume and response time. However, the limited generalizability of in-orbit models and data distribution shifts across geographical locations severely impact the accuracy of in-orbit analytics. In this work, we design a framework, AdaOrb, which dynamically schedules online model retraining for location-specific Earth observation tasks. Scheduling decisions are made with a model predictive control-based algorithm that allocates limited satellite downlink capacity among onboard tasks to download model retraining data. By developing and using a hardware-in-the-loop orbital edge computing testbed, we show that our method achieves superior overall accuracy of in-orbit analytics tasks compared to alternative methods.

Autonomous systems are increasingly used in safety-critical domains, including industrial automation, autonomous vehicles, and the industrial Internet of Things. Verifying both the functional and temporal correctness of these systems is essential to ensure safety before deployment. However, end-to-end verification is challenging due to the interaction of continuous-time physical processes with discrete-time computational systems. Existing formal methods often assume simplified or static computational models, while traditional real-time systems focus on meeting timing constraints without explicitly linking them to physical safety. We address this gap by proposing a physics-informed mixed-criticality (MC) verification framework for cyber-physical systems, which allows the integration of computational and physical models for dynamic, fine-grained safety assurance. Our framework incorporates feedback from the local environment to guide criticality-based mode switching, ensuring adaptive responses to real-time physical states rather than relying on global worst-case assumptions. We demonstrate the feasibility of our approach with a prototype implementation on an autonomous F1 Tenth vehicle using preemptive EDF scheduling on ROS 2. Verification is conducted using UPPAAL to validate system behavior, mode transitions, and physical safety constraints. Results show that our framework effectively manages MC requirements, enhancing responsiveness and safety in dynamic environments.

The rapid increase in networked systems and data transmission requires advanced data compression solutions to optimize bandwidth utilization and enhance network performance. This study introduces a novel byte-level predictive model using Transformer architecture, capable of handling the redundancy and diversity of data types in network traffic as byte sequences. Unlike traditional methods that require separate compressors for different data types, this unified approach sets new benchmarks and simplifies predictive modeling across various data modalities such as video, audio, images, and text, by processing them at the byte level. This is achieved by predicting subsequent byte probability distributions, encoding them into a sparse rank sequence using lossless entropy coding, and significantly reducing both data size and entropy. Experimental results1show that our model achieves compression ratios below 50%, while offering models of various sizes tailored for different communication devices. Additionally, we successfully deploy these models on a range of edge devices and servers, demonstrating their practical applicability and effectiveness in real-world network scenarios. This approach significantly enhances data throughput and reduces bandwidth demands, making it particularly valuable in resource-constrained environments like the Internet of Things sensor networks.

Payment Channel Networks (PCNs) offer an efficient off-chain alternative to the blockchain for transactions. Router nodes in PCNs facilitate transactions between non-adjacent nodes in exchange for a fee. PCN topology tends to be centralized, with a select number of routers known as hubs dominating all payment services. The fee-setting choices of hubs in order to maximize their revenue present fertile grounds for the study of PCN communications and economics. In this paper, we conduct a comprehensive analysis of the Hub Price-Setting (HPS) game. In particular, we define approximate Best Response strategies (ϵ-BR) as well as approximate Nash equilibria (ϵ-NE). We prove that for any ϵ > 0, an ϵ-BR always exists, and can be computed in polynomial time. We also prove that for some ϵ > 0, an ϵ-NE may not exist. We furthermore introduce the notion of conservative estimate and present a max-min approach to the HPS game. Extensive evaluation results demonstrate the power of our proposed approach.

Low Earth Orbit (LEO) Earth Observation (EO) satellites have changed the way we monitor Earth. Acting like moving cameras, EO satellites are formed in constellations with different missions and priorities, and capture vast data that needs to be transmitted to the ground for processing. However, EO satellites have very limited downlink communication capability, limited by transmission bandwidth, number and location of ground stations, and small transmission windows due to highvelocity satellite movement. To optimize resource utilization, EO constellations are expected to share communication spectrum and ground stations for maximum communication efficiency. In this paper, we investigate a new attack surface exposed by resource competition in$\mathbf{E O}$constellations, targeting the delay or drop of Earth monitoring data using legitimate EO services. Specifically, an attacker can inject high-priority requests to temporarily preempt low-priority data transmission windows. Furthermore, we show that by utilizing predictable satellite dynamics, an attacker can intelligently target critical data from low-priority satellites, either delaying its delivery or irreversibly dropping the data. We formulate two attacks, the data delay attack and the data overflow attack, design algorithms to assist attackers in devising attack strategies, and analyze their feasibility or optimality in typical scenarios. We then conduct trace-driven simulations using real-world satellite images and orbit data to evaluate the success probability of launching these attacks under realistic satellite communication settings. We also discuss possible defenses against these attacks.

Edge computing gained popularity for its promises of low latency and high-quality computing services to users. However, it has also introduced the challenge of mutual untrust between user and edge devices for service level agreement (SLA) compliance. This obstacle hampers wide adoption of edge computing, especially in pervasive edge computing (PEC) where edge devices can freely enter or exit the market, which makes verifying and enforcing SLAs significantly more challenging. In this paper, we propose a framework for verifying and enforcing SLAs in PEC, allowing a user to assess SLA compliance of an edge service and ensure correctness of the service results. Our solution, called VeriEdge, employs a verifiable delayed sampling approach to sample a small number of computation steps, and relies on randomly selected verifiers to verify correctness of the computation results. To make sure the verification process is non-manipulable, we employ verifiable random functions to post-select the verifier(s). A dispute protocol is designed to resolve disputes for potential misbehavior. Rigorous security analysis demonstrates that VeriEdge achieves a high probability of detecting SLA violation with a minimal overhead. Experimental results indicate that VeriEdge is lightweight, practical, and efficient.

Payment Channel Networks (PCNs) have been proposed as a second-layer solution to the scalability issue of blockchain-based cryptocurrencies, most developed systems still lack effective strategies for further scalability solutions. Virtual payment channel (VPC) has been proposed as an off-chain technique that avoids the involvement of intermediaries for payments in a PCN. However, there is no research on how to efficiently construct VPCs while considering the characteristics of the underlying PCN. To fill this void, this paper focuses on the VPC construction in a PCN. More specifically, we propose a metric, Capacity to the Number of Intermediaries Ratio (CNIR), to consider both the capacity of the constructed VPC and the collateral locked by the involved users. We first study the VPC construction problem for a single pair of users and design an efficient algorithm that achieves the optimal CNIR. Based on this, we propose Thor, a protocol that constructs a virtual payment channel network (VPCN) for multiple pairs. Evaluation results show that Thor can efficiently construct a VPCN and outperform baseline algorithms in terms of the CNIR.

Autonomous systems are heavily used in many safety-critical systems, such as industrial automation, autonomous cars, Industrial Internet of Things (I-IoT), etc. Verification of the functional and temporal correctness of such systems is necessary before deployment to ensure their safety. However, due to the presence of physical systems in the continuous-time domain and computational models in the discrete-time domain, end-to-end verification of these systems is highly challenging. Existing formal methods focus on verifying physical models assuming static or simplified computation models. In contrast, existing real-time systems focus on satisfying strict timing bounds but do not care how those bounds are obtained and how they relate to physical safety. Our approach bridges these two domains, and constitutes an end-to-end verification framework for arbitrary physical models and computational models incorporated within a cyber-physical automated system. By allowing the interaction between the computational and physical models, our verification framework enables a fine-grained scheme that verifies against the local environment instead of verifying against global worst-case assumptions. Moreover, to support locally varying worst-case scenarios, a mixed-criticality system is proposed where the system supports several critical models and switches among the modes based on environmental uncertainty. Finally, a proof-of-concept evaluation of the proposed framework is reported.

The utilization of Multi-path TCP (MPTCP) has been demonstrated to provide superior transport-layer support for data center networks (DCNs) due to its exceptional resource utilization and load-balancing capabilities. However, the substantial path diversity can make it challenging to utilize network resources to their full potential in DCNs. This paper focuses on studying the resource allocation issue of MPTCP from a resource optimization perspective. Based on theoretical analysis, we propose FMPTCP, which uses a feedback-based congestion control algorithm (FCC) and a feedback-based multi-path routing algorithm (FMP) to jointly achieve high bandwidth utilization and low round-trip time (RTT) in DCNs. The FCC algorithm utilizes probabilistic explicit congestion notification (ECN) to provide feedback on path congestion degree, and uses a gradient descent method to adjust the congestion window for optimal resource utilization and load balancing under a fixed routing topology. On the other hand, the FMP algorithm employs a hop-by-hop feedback mechanism to notify in-network congestion and path delay information, allowing for transparent multi-path routing for MPTCP flows. Our extensive simulations demonstrate that FMPTCP enables effective network resource utilization, which not only enhances overall throughput but also reduces transmission latency for DCNs.

The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements.

A quantum network distributes quantum entanglements between remote nodes, and is key to many applications in secure communication, quantum sensing and distributed quantum computing. This paper explores the fundamental trade-off between the throughput and the quality of entanglement distribution in a multi-hop quantum repeater network. Compared to existing work which aims to heuristically maximize the entanglement distribution rate (EDR) and/or entanglement fidelity, our goal is to characterize the maximum achievable worst-case fidelity, while satisfying a bound on the maximum achievable expected EDR between an arbitrary pair of quantum nodes. This characterization will provide fundamental bounds on the achievable performance region of a quantum network, which can assist with the design of quantum network topology, protocols and applications. However, the task is highly non-trivial and is NP-hard as we shall prove. Our main contribution is a fully polynomial-time approximation scheme to approximate the achievable worst-case fidelity subject to a strict expected EDR bound, combining an optimal fidelity-agnostic EDR-maximizing formulation and a worst-case isotropic noise model. The EDR and fidelity guarantees can be implemented by a post-selection-and-storage protocol with quantum memories. By developing a discrete-time quantum network simulator, we conduct simulations to show the characterized performance region (the approximate Pareto frontier) of a network, and demonstrate that the designed protocol can achieve the performance region while existing protocols exhibit a substantial gap.

Authentication and key agreement are two basic functionalities to guarantee secure network communications, which are naturally integrated as an Authentication and Key Agreement (AKA) protocol. AKAs usually either need a dedicated device to store a cryptographic key or require the user to remember a password. In recent years, AKAs built on biometrics, e.g., human fingerprints, have gained research attention since they avoid these issues. Unlike keys or passwords that can be updated, biometrics are at greater risk that cannot be reused once disclosed. However, existing mechanisms either explicitly expose the biometrics to the server or consume a massive amount of resources. This paper proposes UFinAKA, a privacy-preserving fingerprint-based authentication and key agreement system with updatable blind credentials. UFinAKA explores a fingerprint-based blind credential authentication scheme as a building block such that the server has no access to the fingerprint data hidden within the credential. Furthermore, UFinAKA provides an updatable fingerprint-based credentials AKA protocol, which allows the server to update the blind credentials and guarantees anonymous fingerprint authentication to mitigate further leakage when the server is corrupted. We perform security analysis and experimental evaluation on UFinAKA. The evaluation results show that UFinAKA requires only linear computation overhead for the client, a single round of interaction, and roughly linear computation and storage cost for the server. The running time of UFinAKA is at least 4 times faster than the state-of-the-art solutions, and the storage cost of these solutions is at least 100 times more than UFinAKA.

Scalability is a critical challenge for blockchain-based cryptocurrencies. Payment channel networks (PCNs) have emerged as a promising solution for this challenge. However, channel balance depletion can significantly limit the capacity and usability of a PCN. Specifically, frequent transactions that result in unbalanced payment flows from two ends of a channel can quickly deplete the balance on one end, thus blocking future payments from that direction. In this paper, we propose Fence, an online balance-aware fee setting algorithm to prevent channel depletion and improve PCN sustainability and long-term throughput. In our algorithm, PCN routers set transaction fees based on the current balance and level of congestion on each channel, in order to incentivize payment senders to utilize paths with more balance and less congestion. Our algorithm is guided by online competitive algorithm design, and achieves an asymptotically tight competitive ratio with constant violation in a unidirectional PCN. We further prove that no online algorithm can achieve a finite competitive ratio in a general PCN. Extensive simulations under a real-world PCN topology show that Fence achieves high throughput and keeps network channels balanced, compared to state-of-the-art PCN routing algorithms.

Quantum entanglement distribution between remote nodes is key to many promising quantum applications. Existing mechanisms have mainly focused on improving throughput and fidelity via entanglement routing or single-node scheduling. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Two practical scenarios are considered. When requests do not have deadlines, we seek to minimize the average completion time of the communication requests. If deadlines are specified, we seek to maximize the number of requests whose deadlines are met. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI, and develop a probabilistic protocol to implement the optimized solutions in a general buffered quantum network. We develop a discrete-time quantum network simulator for evaluation. Results show the superior performance of ESDI compared to existing solutions.

The wide spread of vehicular cameras has raised broad privacy concerns. Ubiquitous vehicular cameras capture bystanders like people or cars nearby without their awareness. To address privacy concerns, most existing works either blur out direct identifiers such as vehicle license plates and human faces, or obfuscate whole video frames. However, the former solution is vulnerable to re-identification attacks based on general features, and the latter severely impacts utility of the transformed videos. In this paper, we propose an INStance-level PrIvacy-pREserving (INSPIRE) video transformation framework for vehicular camera videos. INSPIRE leverages deep neural network models to detect and replace sensitive object instances in vehicular videos with their non-existent counterparts. We design INSPIRE as a modular framework to enable flexible customization of protected instance categories and their protection modules. An implementation of INSPIRE focused on protecting people and cars is described, which we tested on six re-identification datasets and three real-world vehicular video datasets to evaluate its privacy protection and utility preservation capability. Results show that INSPIRE can thwart 97% of re-identification attacks for people and cars while maintaining a 0.75 object detection mean average precision on transformed instances. We also demonstrate experimentally that INSPIRE is robust against model inversion attacks. Compared to solutions that provide comparable privacy protection, INSPIRE achieves relatively 1.76 times higher counting accuracy and 31.61% higher object detection mean average precision.

Edge computing promises to bring low-latency and high-throughput computing, but the limited edge resources may cause frequent congestion and lead to unstable and unpredictable performance. To ensure performance guarantee, application owners can establish Service-Level Agreements (SLAs) with the edge provider for resource reservation or priority usage. But it is cost-inefficient for application owners to lease long-term SLAs based on peak demands, as demands can fluctuate, and the leased resources may be idle or underutilized at most times. This paper studies market mechanism design for short-term edge SLA leases, focusing on real-time big data applications with throughput and latency goals. Applications submit short-term SLA requests to serve users with guaranteed performance during peak hours. As SLA requests arrive over time, the edge provider dynamically provisions edge resources to fulfill the requests, while charging application owners based on the current demands. We design EA-Market, an online combinatorial auction mechanism that achieves a competitive social welfare, while guaranteeing truthfulness, budget balance, individual rationality, and computational efficiency. Notably, our mechanism enables each application owner to bid without knowledge of the edge infrastructure, and gives edge provider full control over resource provisioning to fulfill the requests. We perform theoretical analysis and simulations to evaluate the efficacy of our mechanism.

Emerging as an alternative to cloud computing, fog computing is expected to provide low-latency, high-throughput, reliable services for ever-growing Internet of Things (IoT) applications, especially real-time applications with strict responsiveness requirements. By offloading time-critical and computation-intensive applications to proximal fog nodes (FNs), both application response time and network congestion can be markedly reduced. However, the FNs commonly suffer from limited resources compared to cloud computing nodes and, hence, may not serve all application users with guaranteed performance. The dynamic and heterogeneous nature of FNs also brings difficulty and overhead to fog computing resource management. These issues are addressed in the present study with the design of a double auction mechanism, namely, truthful auction for the fog system (TAFS), which provides incentives for FNs to satisfy as many application demands as possible with guaranteed performance. TAFS takes into account the latency tolerance of application users during the FN assignment and resource allocation to satisfy real-time requirements. We theoretically prove that TAFS satisfies several desired economic properties, including truthfulness, individual rationality, and budget balance. The performance of TAFS is evaluated through simulation experiments.

Multipath TCP (MPTCP) improves the bandwidth utilization in wireless network scenarios, since it can simultaneously utilize multiple interfaces for data transmission. However, with the fast growth of mobile devices and applications, link interruptions caused by handoffs still lead to drastic performance degradation in such scenarios. Typically, a series of packet losses on part of the links will block the transmission of the entire connection when handoff occurs. This paper proposes an Experience-driven Adaptive Redundant packet scheduler (EdAR) for MPTCP, aiming at achieving seamless handoffs in mobile networks. EdAR enables flexibly scheduling redundant packets with an experience-driven learning-based approach in the face of drastic network environment changes for multipath performance enhancement. To enable accurate learning and prediction, both the network environment and the best course of actions are jointly learned via a Deep Reinforcement Learning (DRL) agent, which we design with a hybrid structure to deal with the complexity of system states. Furthermore, both offline and online learning are utilized to allow the agent to adapt to different and changing network environments. Evaluation results show that EdAR outperforms the state-of-the-art MPTCP schedulers in most network scenarios. Specifically in mobile networks with frequent handoffs, EdAR brings$2\times $improvement in terms of the overall goodput.

This paper studies how an edge-based federated learning algorithm called FedAegis can be designed to be ro-bust under both heterogeneous data distributions and Byzantine adversaries. The divergence of local data distributions leads to suboptimal results for the training process of federated learning, and the Byzantine adversaries aim to prevent the training process from converging in a distributed learning system. In this paper, we show that an edge-based hierarchical federated learning architecture can help tackle this dilemma by utilizing edge nodes geographically close to clusters of local devices. By combining a distributionally robust global loss function with a local Byzantine-robust aggregation rule, FedAegis can defend against remote Byzantine adversaries who cannot manipulate local devices' connections to edge nodes, meanwhile accounting for global data heterogeneity across benign local devices. Experiments with the MNIST, FMNIST and CIFAR-IO datasets show that our proposed algorithm can achieve convergence and high accuracy under heterogeneous data and various attack scenarios, while state-of-the-art defenses and robustness mechanisms are non-converging or have reduced average and/or worst-case accuracy.

Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.

This paper studies how to provision edge computing and network resources for complex microservice-based applications (MSAs) in face of uncertain and dynamic geo-distributed demands. The complex inter-dependencies between distributed microservice components make load balancing for MSAs extremely challenging, and the dynamic geo-distributed demands exacerbate load imbalance and consequently congestion and performance loss. In this paper, we develop an edge resource provisioning model that accurately captures the inter-dependencies between microservices and their impact on load balancing across both computation and communication resources. We also propose a robust formulation that employs explicit risk estimation and optimization to hedge against potential worst-case load fluctuations, with controlled robustness-resource trade-off. Utilizing a data-driven approach, we provide a solution that provides risk estimation with measurement data of past load geo-distributions. Simulations with real-world datasets have validated that our solution provides the important robustness crucially needed in MSAs, and performs superiorly compared to baselines that neglect either network or inter-dependency constraints.

Payment channel networks (PCNs) are proposed to improve the cryptocurrency scalability by settling off-chain transactions. However, PCN introduces an undesirable assumption that a channel participant must stay online and be synchronized with the blockchain to defend against frauds. To alleviate this issue, watchtowers have been introduced, such that a hiring party can employ a watchtower to monitor the channel for fraud. However, a watchtower might profit from colluding with a cheating counterparty and fail to perform this job. Existing solutions either focus on heavy cryptographic techniques or require a large collateral. In this work, we leverage smart contracts through economic approaches to counter collusions for watchtowers in PCNs. This brings distrust between the watchtower and the counterparty, so that rational parties do not collude or cheat. We provide detailed analyses on the contracts and rigorously prove that the contracts are effective to counter collusions with minimal on-chain operations. In particular, a watchtower only needs to lock a small collateral, which incentivizes participation of watchtowers and users. We also provide an implementation of the contracts in Solidity and execute them on Ethereum to demonstrate the scalability and efficiency of the contracts.

Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and fluctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these different factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several efficiency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.

Electric vehicles can place a significant load on the power grid due to their unscheduled charging events. One way of improving power grid stability is to schedule electric vehicle charging in advance. Before a charging visit, the electric vehicle provides necessary information to request for charging at a charging station, which prepares and reserves the energy before the visit. However, the reported information can cause privacy leakage of the electric vehicle user. Anonymous information reporting can protect user privacy, but also enables attacks on the charging station by unauthorized users. An anonymous authentication system can address these issues, but cannot detect misbehaviors by authenticated users. One remedy to this is revocable anonymity-based authentication, which can revoke the anonymity of malicious users after their misbehaviors. However, we show that such a system is still vulnerable to application-level Denial of Service attacks, where a malicious user requests for large amounts of energy simultaneously from many charging stations, preventing these stations from serving other users. To address this, we improve upon an existing revocable anonymity-based authentication framework. We propose a permit-based mechanism, where each electric vehicle is only issued with one blind signature-based permit at a time. A request is valid only if it contains a valid and unused permit, which protects the system from the application-level Denial of Service attacks. Security analysis and experiments demonstrate that our framework, while ensuring user anonymity and being robust to the aforementioned attack, is also scalable and lightweight.

Recent advances in security and cryptography have enabled new paradigms for secure networking in various scenarios. The payment channel network (PCN) is a notable example, which has emerged from the combination of the traditional credit network in economics and the latest blockchain technology. PCN provides a secure and efficient way for conducting payments, by addressing both the intrinsic financial risk of the credit network and the scalability issue of the blockchain. A crucial challenge in PCN is routing, i.e., to find a set of paths that fulfill a payment request. Due to the fully distributed and dynamic nature of PCN, existing routing algorithms utilize active probing to improve routing success probability. However, while the payment itself is privacy-preserving through existing protocols, the probing process can leak sensitive information including the location of the sender or the recipient. In this paper, we address the privacy of the users in the path probing process, filling in the last piece of the privacy puzzle in PCN. We propose P4PCN, a cryptographic protocol for anonymous active probing without knowing the identities or public keys of the intermediate nodes, while hiding the locations of sender and recipient as well as any path-related information. Our protocol is lightweight and scales with the number of hops a probe explores. We confirm its performance via real-world implementation and simulation experiments.

Advances in virtualization technologies and edge computing have inspired a new paradigm for Internet-of-Things (IoT) application development. By breaking a monolithic application into loosely coupled microservices, great gain can be achieved in performance, flexibility and robustness. In this paper, we study the important problem of load balancing across IoT microservice instances. A key difficulty in this problem is the interdependencies among microservices: the load on a successor microservice instance directly depends on the load distributed from its predecessor microservice instances. We propose a graph-based model for describing the load dependencies among microservices. Based on the model, we first propose a basic formulation for load balancing, which can be solved optimally in polynomial time. The basic model neglects the quality-of-service (QoS) of the IoT application. We then propose a QoS-aware load balancing model, based on a novel abstraction that captures a realization of the application's internal logic. The QoS-aware load balancing problem is NP-hard. We propose a fully polynomial-time approximation scheme for the QoS-aware problem. We show through simulation experiments that our proposed algorithm achieves enhanced QoS compared to heuristic solutions.

The Internet-of-Things (IoT) has inspired numerous new applications ever since its invention. Nevertheless, its development and utilization have always been restricted by the limited resources in various application scenarios. In this paper, we study the problem of resource provisioning for real-time IoT applications, i.e., applications that process concurrent data streams from data sources in the network. We investigate joint application placement and data routing to support IoT applications that have both quality-of-service and robustness requirements. We formulate four versions of the provisioning problem, spanning across two important classes of real-time applications (parallelizable and non-parallelizable), and two provisioning scenarios (single application and multiple applications). All versions are proved to be NP-hard. We propose fully polynomial-time approximation schemes for three of the four versions, and a randomized algorithm for the forth. Through simulation experiments, we analyze the impact of parallelizability and robustness on the provisioning performance, and show that our proposed algorithms can greatly improve the quality-of-service of the IoT applications.

The scarcity of spectrum channels resides in the limited bandwidth resource and the exploding demand from spectrum-based services and devices. To help ease this scarcity, the concept of cognitive radio networks (CRNs) is proposed, where licensed spectrum holders (primary users) may lease their channels to unlicensed users (secondary users). Many CRN auctions are thus designed to incentivize primary users (PUs) to share their idle channels with secondary users (SUs). Most of these auctions assume that a transmitting PU does not lease its channel to SUs; if it leases its channel to SUs, it does not transmit itself. To further utilize the resource, researchers have studied the scenario where a transmitting PU is allowed to lease its channels to SUs if the transmissions of the SUs do not undermine the transmission of the PU. However, the study assumes that there is only one PU who owns the licensed channels, whereas in practice, channels may be contributed by multiple PUs. This prevents the result of the study from being directly applied to the multi-PU scenario, as the potential competitions among the PUs are neglected. We extend the scenario to the CRN with multiple PUs and propose TDSA-PS as a Truthful Double Spectrum Auction with transmitting Primary users Sharing. We prove that TDSA-PS is truthful, individually rational, budget-balanced, and computationally efficient.

Although cryptocurrencies have witnessed explosive growth in the past year, they have also raised many concerns, among which a crucial one is the scalability issue of blockchain-based cryptocurrencies. Suffering from the large overhead of global consensus and security assurance, even leading cryptocurrencies can only handle up to tens of transactions per second, which largely limits their applications in real- world scenarios. Among many proposals to improve cryptocurrency scalability, one of the most promising and mature solutions is the payment channel network (PCN), which offers off-chain settlement of transactions with minimal involvement of expensive blockchain operations. In this paper, we investigate the problem of payment routing in PCN. We suggest crucial design goals in PCN routing, and propose a novel distributed dynamic routing mechanism called CoinExpress. Through extensive simulations, we have shown that our proposed mechanism is able to achieve outstanding payment acceptance ratio with low routing overhead.

S-The emergence of the Internet-of-Things (IoT) has inspired numerous new applications. However, due to the limited resources in current IoT infrastructures and the stringent quality-of-service requirements of the applications, providing computing and communication supports for the applications is becoming increasingly difficult. In this paper, we consider IoT applications that receive continuous data streams from multiple sources in the network, and study joint application placement and data routing to support all data streams with both bandwidth and delay guarantees. We formulate the application provisioning problem both for a single application and for multiple applications, with both cases proved to be NP-hard. For the case with a single application, we propose a fully polynomial-time approximation scheme. For the multi-application scenario, if the applications can be parallelized among multiple distributed instances, we propose a fully polynomial-time approximation scheme; for general non-parallelizable applications, we propose a randomized algorithm and analyze its performance. Simulations show that the proposed algorithms greatly improve the quality-of-service of the IoT applications compared to the heuristics.

With the proliferation of smart mobile devices (smart phone, tablet, and wearable), mobile crowdsensing becomes a powerful sensing and computation paradigm. It has been put into application in many fields, such as spectrum sensing, environmental monitoring, healthcare, and so on. Driven by promising incentives, the power of the crowd grants crowdsensing an advantage in mobilizing users who perform sensing tasks with the embedded sensors on the smart devices. Auction is one of the commonly adopted crowdsensing incentive mechanisms to incentivize users for participation. However, it does not consider the incentive for user solicitation, where in crowdsensing, such incentive would ease the tension when there is a lack of crowdsensing users. To deal with this issue, we aim to design an auction-based incentive tree to offer rewards to users for both participation and solicitation. Meanwhile, we want the incentive mechanism to be robust against dishonest behavior such as untruthful bidding and sybil attacks, to eliminate malicious price manipulations. We design RIT as a Robust Incentive Tree mechanism for mobile crowdsensing which combines the advantages of auctions and incentive trees. We prove that RIT is truthful and sybil-proof with probability at least H, for any given H ∈ (0, 1). We also prove that RIT satisfies individual rationality, computational efficiency, and solicitation incentive. Simulation results of RIT further confirm our analysis.

Cloud computing has emerged as a powerful and elastic platform for internet service hosting, yet it also draws concerns of the unpredictable performance of cloud-based services due to network congestion. To offer predictable performance, the virtual cluster abstraction of cloud services has been proposed, which enables allocation and performance isolation regarding both computing resources and network bandwidth in a simplified virtual network model. One issue arisen in virtual cluster allocation is the survivability of tenant services against physical failures. Existing works have studied virtual cluster backup provisioning with fixed primary embeddings, but have not considered the impact of primary embeddings on backup resource consumption. To address this issue, in this paper we study how to embed virtual clusters survivably in the cloud data center, by jointly optimizing primary and backup embeddings of the virtual clusters. We formally define the survivable virtual cluster embedding problem. We then propose a novel algorithm, which computes the most resource-efficient embedding given a tenant request. Since the optimal algorithm has high time complexity, we further propose a faster heuristic algorithm, which is several orders faster than the optimal solution, yet able to achieve similar performance. Besides theoretical analysis, we evaluate our algorithms via extensive simulations.

The ever-increasing mobile traffic has inspired deployment of capacity and performance enhancing network services within mobile networks. Owing to recent advances in network function virtualization, such network services can be flexibly and cost-efficiently deployed in the mobile network as software components, avoiding the need for costly hardware deployment. Nevertheless, this complicates network planning by bringing the need for service function chaining. In this paper, we study mobile network planning through a software-defined approach, considering both quality-of-service and reliability of different classes of traffic. We define and formulate the traffic steering problem for service function chaining in mobile networks, which turns out to be NP-hard. We then develop a fast approximation scheme for the problem, and evaluate its performance via extensive simulation experiments. The results show that our algorithm is near-optimal, and achieves much better performance compared with baseline algorithms.

The proliferation of crowdsourcing brings both opportunities and challenges in various fields, such as environmental monitoring, healthcare, and so on. Often, the collaborative efforts from a large crowd of users are needed in order to complete crowdsourcing jobs. In recent years, the design of crowdsourcing incentive mechanisms has drawn much interest from the research community, where auction is one of the commonly adopted mechanisms. However, few of these auctions consider the robustness against false-name attacks (a.k.a. sybil attacks), where dishonest users generate fake identities to increase their utilities without devoting more efforts. To provide countermeasures against such attacks, we have designed a Truthful Auction with countermeasures against False-name Attacks (TAFA) as an auction-based incentive mechanism for crowdsourcing. We prove that TAFA is truthful, individually rational, budget-balanced, and computationally efficient. We also prove that TAFA provides countermeasures against false-name attacks, such that each user is better off not generating any false name. Extensive performance evaluations are conducted and the results further confirm our theoretical analysis.

We propose a new network abstraction, termed critical network flow, which models the bandwidth requirement of modern Internet applications and services. A critical network flow defines a conventional flow in a network with explicit requirement on its aggregate bandwidth, or the flow value as commonly termed. Unlike common bandwidth-guaranteed connections whose bandwidth is only guaranteed during normal operations, a critical network flow demands strictly enforced bandwidth guarantee during various transient network states, such as network reconfiguration or network failures. Such a demand is called the bandwidth criticality of a critical network flow, which is characterized both by its flow value and capability to satisfy bandwidth guarantee in the transient states.We study algorithmic solutions to the accommodation of critical network flows with different bandwidth criticalities, including the basic case with no transient network state considered, the case with network reconfiguration, and the case with survivability against link failures. We present a polynomial-time optimal algorithm for each case. For the survivable case, we further present a faster heuristic algorithm. We have conducted extensive experiments to evaluate our model and validate our algorithms.

Recent development in Cloud and networking technologies have stimulated unification of network and Cloud service provisioning, in which service composition plays a crucial role. While encouraging progress has been made toward network-Cloud service composition, the impact of correlated network and Cloud services on the QoS of composite services, however, has not been sufficiently studied. In this paper, we address the challenging problem of QoS correlation-aware network and Cloud service composition. Specifically, we formulate this problem as a multi-constraint optimal path problem and propose a novel algorithm to solve it. We also evaluate the performance of the proposed algorithm with extensive simulations. The experimental results show that the proposed algorithm is effective and efficient and it is able to yield service composition solutions with better QoS guarantees through considering QoS correlations among different services.

As more and more data-intensive applications have been moved to the cloud, the cloud network has become the new performance bottleneck for cloud applications. To boost application performance, the concept of coflow has been proposed to bring application-awareness into the cloud network. A coflow consists of many individual data flows, and a coflow is completed only when all its component flows are transmitted. The network performance of a cloud application is dependent on the completion time of coflows, rather than the completion time of each individual flow. Existing coflow-aware optimization solutions employ flow preemption to reduce the completion time, which brings difficulty in practical implementation and non-negligible overhead. In this paper, we study the non-preemptive coflow scheduling and routing problem in the cloud network. We propose an offline optimization framework for coflow scheduling, as well as two subroutines for coflow routing using single-path routing and multi-path routing respectively. We also show that our proposed framework is easily extensible to the online scenario. Extensive evaluations show that the proposed solutions can greatly reduce coflow completion time compared to coflow-agnostic solutions, and are also computationally efficient.

The ever increasing video demands from mobile users have posed great challenges to cellular networks. To address this issue, video caching in radio access networks (RANs) has been recognized as one of the enabling technologies in future 5G mobile networks, which brings contents near the end-users, reducing the transmission cost of duplicate contents, meanwhile increasing the Quality-of-Experience (QoE) of users. Inspired by the emerging software-defined networking technology, recent proposals have employed centralized collaborative caching among cells to further increase the caching capacity of the RAN. In this paper, we explore a new dimension in video caching in software-defined RANs to expand its capacity. We enable the controller with the capability to adaptively select the bitrates of videos received by users, in order to maximize the number and quality of video requests that can be served, meanwhile minimizing the transmission cost. To achieve this, we further incorporate Scalable Video Coding (SVC), which enables caching and serving sliced video layers that can serve different bitrates. We formulate the problem of joint video caching and scheduling as a reward maximization (cost minimization) problem. Based on the formulation, we further propose a 2-stage rounding-based algorithm to address the problem efficiently. Simulation results show that using SVC with collaborative caching greatly improves the cache capacity and the QoE of users.

With the tremendous growth of cloud computing, it is increasingly critical to provide quantifiable performance to tenants and to improve resource utilization for the cloud provider. Though many recent proposals focus on guaranteeing job performance (with a particular note on network bandwidth) in the cloud, they usually lack efficient utilization of cloud resource, or vice versa. In this paper we present DCloud, which leverages the (soft) deadlines of cloud computing jobs to enable flexible and efficient resource utilization in data centers. With the deadline requirement of a job guaranteed, DCloud employs both time sliding (postponing the launching time of a job) and bandwidth scaling (adjusting the bandwidth associated with VMs) in resource allocation, so as to better match the resource allocated to the job with the cloud's residual resource. Extensive simulations and testbed experiments show that DCloud can accept much more jobs than existing solutions, and significantly increase the cloud provider's revenue with less cost for individual tenants.

Cloud computing has emerged as a prevailing platform for internet service hosting. To best utilize Cloud resources for profit making, Cloud providers rely on intelligent resource allocation algorithms when provisioning the virtualized environments for tenant service hosting. Conventional resource allocation proposals mainly focus on efficient allocation of the computing and storage resources, with little effort on ensuring the network performance of tenant services. To address this issue, a number of recent efforts abstract tenant services in the form of virtual infrastructures for resource allocation. A virtual infrastructure specifies the tenant's demand of both the computing resources for hosting virtual servers, and the network bandwidth for inter-virtual server communications. With the problem of resource allocation for virtual infrastructures being NP-hard in general networks, heuristic algorithms have been proposed for this problem. In this paper, we propose a novel optimization technique, named sequential rounding, to tackle the resource allocation problem for virtual infrastructures. The proposed technique extends the rounding technique used for the traditional virtual network embedding problem, while minimizing mapping conflicts introduced by the virtual infrastructure embed- ding problem. Experiments show that our proposed algorithm outperforms existing algorithms regarding both the acceptance ratio and average embedding cost of virtual requests.

Crowdsourcing incentive mechanism design has raised numerous interests from research communities in recent years. While most research focuses on contribution-based payment allocation, a solid crowdsourcing incentive mechanism should encourage users to both devote efforts to complete the task and refer other users to join into participation. In this paper, we adopt a data structure called incentive tree which has a unique advantage in incentivizing participants for solicitation. Furthermore, we consider the crowdsourcing scenario where the contribution model is submodular and time-sensitive, which is more realistic compared to the linear summation model adopted by previous works. Under this model, we design a reward mechanism based on the incentive tree, and prove that this mechanism satisfies several economic properties such as continuing contribution incentive, continuing solicitation incentive, θ-reward proportional to contribution, early contribution incentive, and sybil-proofness. We implemented our incentive mechanism and conducted extensive performance evaluations. The evaluation results confirm our theoretical analysis.

Auction is an effective method of allocating scarce spectrum resources in cognitive radio networks, where the primary users are sellers and the secondary users are buyers. In order for the buyers and sellers to act honestly during the auction, truthfulness has been identified as an important property. Current research focuses on the truthfulness and spatial reusability by either assuming that a conflict graph is given under the protocol model, or assuming that the grouping result is given under the physical interference model without power control. To fill this void, we design a framework of truthful double auctions, named TSA, for spectrum sharing in cognitive radio networks. TSA finds a feasible grouping profile such that users in the same group can be assigned to the same channel while each gets a satisfactory SINR value by an appropriate transmitting power allocation. We prove that TSA guarantees all the desired economic properties: individual rationality, budget-balance, computational efficiency, and truthfulness. Extensive performance evaluation also supports our theoretic analysis.

With the prosperity of smart devices, crowdsourcing has emerged as a new computing/networking paradigm. Through the crowdsourcing platform, service requesters can buy service from service providers. An important component of crowdsourcing is its incentive mechanism. We study three models of crowdsourcing, which involve cooperation and competition among the service providers. Our simplest model generalizes the well-known user-centric model studied in a recent Mobicom paper. We design an incentive mechanism for each of the three models, and prove that these incentive mechanisms are individually rational, budget-balanced, computationally efficient, and truthful.

Crowdsourcing is an emerging paradigm where users can have their tasks completed by paying fees, or receive rewards for providing service. A critical problem that arises in current crowdsourcing mechanisms is how to ensure that users pay or receive what they deserve. Free-riding and false-reporting may make the system vulnerable to dishonest users. In this paper, we design schemes to tackle these problems, so that each individual in the system is better off being honest and each provider prefers completing the assigned task. We first design a mechanism EFF which eliminates dishonest behavior with the help from a trusted third party for arbitration. We then design another mechanism DFF which, without the help from any third party, discourages dishonest behavior. We also prove that DFF is semi-truthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing transaction-wise budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no user could have a utility gain by unilaterally being dishonest.

Crowdsourcing is an emerging paradigm where users can pay for the services they need or receive rewards for providing services. One example in wireless networking is mobile crowdsourcing, which leverages a cloud computing platform for recruiting mobile users to collect data (such as photos, videos, mobile user activities, etc) for applications in various domains, such as environmental monitoring, social networking, healthcare, transportation, etc. However, a critical problem arises as how to ensure that users pay or receive what they deserve. Free-riding and false-reporting may make the system vulnerable to dishonest users. In this paper, we aim to design schemes to tackle these problems, so that each individual in the system is better off being honest. We first design a mechanism EFF which eliminates dishonest behavior with the help from a trusted third party for arbitration. We then design another mechanism DFF which, without the help from any third party, discourages free-riding and false-reporting. We prove that EFF eliminates the existence of free-riding and false-reporting, while guaranteeing truthfulness, individual rationality, budget-balance, and computational efficiency. We also prove that DFF is semi-truthful, which discourages dishonest behavior such as free-riding and false-reporting when the rest of the individuals are honest, while guaranteeing budget-balance and computational efficiency. Performance evaluation shows that within our mechanisms, no dishonest behavior could bring extra benefit for each individual.

Cloud computing is one of the most significant latest efforts in the field of information technology, which may change the way how information services are provisioned. In a Cloud environment, different types of resources need to be virtualized as a collection of Cloud services using virtualization technology. End-users in the Cloud are usually provided with customized Cloud services that involve not only different kinds of computing services but also the networks interconnecting those computing services. Therefore, a set of Cloud computing services and the networking services should be modeled as a composite customized Cloud service. In this paper, we present an improved model for Cloud service provisioning based on our previous Network-Cloud proposal, and propose a procedure with several QoS-aware service selection algorithms for composing different services offered by a Cloud. Our analysis with numerical experiments show that the presented algorithms can select services appropriately that deal with different requirements of service provisioning.