Cooperative perception is a crucial technology to improve the safety and robustness of autonomous driving systems. Existing works mainly utilize a pre-constructed 3D map or roadside infrastructures to estimate poses for cooperative perception. However, the pre-constructed map should be updated frequently, and roadside infrastructures can only be employed on specific roads. To overcome these limitations, we exploit the inherent features of external shapes of vehicles for relative pose estimation and design an Edge-assisted multi-Vehicle cOoperative perception system, called EVO. EVO adopts a suit of approaches to ensure the accuracy and real-time performance of cooperative perception: (1) a novel approach is proposed to estimate relative poses without requiring any pre-constructed 3D map or roadside infrastructures; (2) a hierarchical registration module is adopted to speed up pose estimation; (3) a Regions of Interest (RoIs) prediction module based on Dlinear and a registrations reduction module are designed to reduce the computation latency. We evaluate EVO on a CARLA-based dataset and demonstrate that EVO can perform multi-vehicle cooperative perception in real-time and maintain centimeter-level accuracy in pose estimation. Further, EVO extends the maximum perception distance from 32-39m to 38-67m and achieves about 2.43× improvement in perception area.

Continuous crowdsensing in Mobile CrowdSensing (MCS) involves ongoing monitoring to gather real-time data over extended periods. A key challenge is determining appropriate intervals between consecutive data samplings to capture temporal variations, especially in unknown dynamic environments. Traditional Age-of-Information (AoI) driven methods maintain data freshness but can be costly and result in data redundancy. To address this, we integrate the AoI metric with information entropy difference to create a novel indicator, Composite Data Value (CDV), balancing data freshness and redundancy. Based on it, we investigate the online adaptive sampling problem for continuous crowdsensing in unknown dynamic environments. This problem is challenging due to the vast space of sensing strategies, difficulty in estimating rewards with unknown distributions and varying rates of change, and the degradation of optimal strategies as the environment evolves. Using a multi-armed bandit framework, we propose AdaScs, an online adaptive sampling optimization approach to maximize long-term CDV performance. First, AdaScs develops compact sensing strategies through limited trials. Then, it adaptively performs online sampling based on evolving reward estimations, identifying optimal strategies, detecting environmental drifts, updating strategies, and adjusting cycle lengths. Our results show that AdaScs outperforms all baselines, with accuracy increasing by 22.6% and reaction time at last improving by 54.6%.

Along with the advance of low-altitude economy, a variety of applications based on Unmanned Aerial Vehicles (UAVs) have been developed to accomplish diverse tasks. In this paper, we focus on the scenario of multiple UAVs performing Federated Learning (FL) tasks. Specifically, a group of UAVs is scheduled to repeatedly visit some Points of Interest (PoIs), collect the data produced by these PoIs, and jointly train a machine learning model based on the collected data. The most challenging issue is how to schedule UAVs to collect data so as to optimize the generalization and convergence of model training under the case that the distributions of the data produced by PoIs have not been known in advance. To address this issue, we propose a novel framework for online FL on distributed unknown data, named OFL-UD2, which is dedicated to online decision-making for UAVs to optimize model training performance. Concretely, we formulate the optimization problem while considering the convergence and quality of trained models as well as energy constraints. Then, we define a utility metric for the data quality of different PoIs and conduct a rigorous convergence analysis for OFL-UD2. Based on the analysis results, we design a two-stage algorithm to determine the scheduling of UAVs. Extensive simulations demonstrate that OFL-UD2can improve model accuracy and speed up running time compared to existing benchmarks significantly.

Federated Graph Learning (FGL) enables collab-orative model training across decentralized graph data while preserving privacy. However, FGL faces severe performance degradation under out-of-distribution (OOD) shifts due to both feature distribution divergence and structural heterogeneity among clients. To address this, we propose FGOOD, a lightweight and effective framework that improves OOD generalization in FGL. FGOOD integrates two key components: (1) representation propagation, which enhances structural robustness by aggregating multi-hop topology while preserving local features, and (2) representation scattering, which regularizes node embeddings toward a uniformly dispersed distribution on the hypersphere, improving inter-class separation without requiring contrastive pairs. The theoretical analysis provides an upper bound on the generalization error under distribution shifts. Extensive experiments on three real-world datasets demonstrate that FGOOD outperforms existing state-of-the-art baselines, improving OOD accuracy by up to 5% while remaining lightweight and scalable.

As a revolutionary technology, autonomous driving is changing the traditional ways of transportation. However, due to the high complexity and uncertainty of the road environment, existing autonomous driving systems still face numerous challenges in practical applications. To address these challenges, we propose a fuzzy knowledge-driven with reward shaping Human-in-the-loop Reinforcement Learning (FR-HRL) framework. The framework incorporates human expert knowledge through a fuzzy rule base and utilizes a Lagrangian method to regulate expert intervention frequency. Additionally, reward shaping is introduced to integrate domain knowledge, while a dual experience replay mechanism is employed to separately manage expert demonstrations and self-generated experiences. Experimental results in an autonomous driving scenario show that FR-HRL achieves improvements of 19.5%-250.0% in success rate, 13.5%-115.3% in total reward, and 90.0%-97.8% in environmental cost reduction compared to baseline methods. The framework performs particularly well in early training stages, where expert guidance significantly accelerates learning while ensuring safety.

Federated learning (FL) is a distributed learning paradigm that enables large-scale IoT devices to collaboratively train a shared model while preserving the privacy of local data. To avoid the single-point-of-failure of the conventional parameter server architecture, the study concentrates on the decentralized FL (DFL) paradigm building on the device-to-device communication network. However, existing DFL frameworks encounter challenges related to resource limitations, privacy protection, and data heterogeneity. To overcome these challenges, the study proposes and implements DF$^{2}$-MPC in industrial IoT, an efficient DFL framework with personalized model pruning and adaptive communication. Specifically, a personalized pruning ratio determination approach is designed by exploiting the model pruning technique. This approach enables all devices to flexibly determine pruning ratios by themselves, thereby achieving both communication savings and privacy protection. Then, this study designs an adaptive neighbor selection scheme, which can enhance model performance and foster model consensus under resource constraints. In addition, the study theoretically proves the convergence performance of DF$^{2}$-MPC. Finally, extensive simulations on three real-world traces are conducted to corroborate the superiority of DF$^{2}$-MPC, demonstrating that the method can improve communication efficiency with satisfactory model accuracy and convergence performance.

The exponential growth of data in the Internet of Vehicles (IoV) has created opportunities to improve traffic safety and efficiency through data trading. However, establishing trust among highly mobile and resource-constrained vehicles poses significant challenges for effective data trading in IoV. To address this issue, we propose a lightweight blockchain-based data trading scheme (LBDT), which ensures secure and efficient data trading in IoV. We introduce a proof-of-reputation (PoR) consensus mechanism to establish trustworthiness for data trading. Specifically, we use a progressive reputation mechainism to support the PoR consensus. LBDT utilizes a parallel-chain structure for the PoR consensus to minimize communication and storage costs while reducing transaction confirmation latency. Additionally, we adopt a double auction mechanism as an incentivizing strategy to encourage vehicle participation in data trading. We evaluate the performance of LBDT through extensive experiments. The experimental results demonstrate that LBDT is highly effective and secure, achieving a transaction latency of approximately 4 seconds. Moreover, LBDT successfully mitigates communication and storage overheads by over 90%, thus establishing its superiority over state-of-the-art solutions under comparable conditions.

In recent years, deep neural networks have been applied in machine translation systems, resulting in the so-called neural machine translation (NMT) models that can improve translation quality significantly. However, due to the brittleness of deep neural network, machine translation systems could return erroneous translations that lead to misunderstandings or even cause serious losses. To detect translation errors, various testing techniques have been proposed. As a popularly used technique, metamorphic testing mainly relies on text or syntactic structure of translations while ignoring the meaning of sentences (i.e., semantic information). Compared with text and syntactic information, semantic information of sentences is more stable when dealing with languages that have rich vocabulary and flexible word order. Motivated by this observation, we propose semantic structure invariance-based metamorphic testing (SSIMT) for machine translation systems. The key insight is that contextually similar sentences should typically have translations of similar semantic structures. Experiments have been conducted to evaluate SSIMT on two widely used machine translation systems, Microsoft Bing Translator and Google Translate with 600 seed sentences crawled from well-known news websites covering six different corpus topics. The experimental results show that SSIMT is able to find thousands of erroneous translations in both translation systems with high accuracy (over 70%). Translation errors reported by SSIMT covers a wide variety of common error types.

The registration of LiDAR point clouds between Connected Vehicles (CV s) is the basis for vision tasks such as multi-vehicle cooperative perception and high-precision scene reconstruction. Due to the huge amount of LiDAR point cloud data and the complexity of computation, the registration has high transmission latency and execution time. Fast and accurate point cloud registration between CV s is urgently required by safe driving applications. To achieve faster registration, we propose partitioning overlapping point clouds for registration in advance. Meanwhile, for higher accuracy, we incorporate more geometric features during the registration. This paper demonstrates the Echo Registration System (ERS) developed to increase the speed of existing point cloud registration methods. ERS employs an initial positioning based on point cloud distributions, a density-consistent partition strategy, and a virtual geometric feature fusion. We deploy ERS to accelerate multiple state-of-the-art registration methods. The comprehensive results show that ERS improves the overall running time of current state-of-the-art baselines by 5.7 x with 42.1 % registration recall gains.

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative three-layer architecture. In this architecture, UAVs, inherently lacking data, serve as mobile clients and are tasked with periodically collecting desired data from a group of Points of Interest (PoIs) for FL training. Meanwhile, a Base Station (BS) coordinates these UAVs to efficiently train a high-quality global model. Our objective is to determine a data collection strategy for each UAV to minimize the time required to meet the global model’s loss requirement within the constraint of energy consumption. Through theoretical analysis, we establish a bound for the convergence speed of DDFL and quantify the impact of collecting data from different PoIs on the global model’s loss function. Leveraging these analyses, we formulate the PoI selection problem as a novel two-stage Combinatorial Multi-Armed Bandit (CMAB) problem with multiple constraints. We then propose an Adaptive Two-stage CMAB-based algorithm, named FedATC, to jointly optimize the data collection route and UAV velocity. Extensive simulations demonstrate that FedATC significantly reduces the time required to achieve desired model quality compared to state-of-the-art algorithms.

There is an increasing demand for analyzing live surveillance video streams via large-scale camera networks, particularly for applications in public safety and smart cities. To address the conflict between resource-intensive detection models and limited capabilities of cameras, a detection-with-tracking framework has gained prominence. However, since trackers are vulnerable to occlusions and new object appearances, frequent detections are required to calibrate the results, leading to varying detection demands that depends on video content. Consequently, we propose a mechanism for content-aware analytics on collaborative cameras, denoted as VisFlow, to increase the quality of detections and achieve the latency requirement by fully utilizing camera resources. We formulate such a problem as a non-linear, integer program with a long-term perspective, aimed at maximizing detection accuracy. An online mechanism, underpinned by a queue-based algorithm and randomized rounding, is then devised to dynamically orchestrate detection workloads among cameras, thus adapting to fluctuating detection demands. Via rigorous proof, both dynamic regret regarding overall accuracy and the transmission budget are ensured in the long run. The testbed experiments on Jetson Kits demonstrate that VisFlow improves accuracy by 18.3% over the baselines.

Mobile crowdsensing (MC), an excellent solution to large-scale spatiotemporal data sensing problems, has recently received lots of attention from both industry and academia. In the MC system, any requester can acquire the sensing data for his points of interest (PoIs) by offering some payments to attract a group of mobile users capable of completing these PoI-related sensing tasks. However, the current MC work neglected three vital factors, more or less. First, they assume that these distributed users are mutually independent in MC, ignoring the social effects. Actually, the sensing data collected by one user may be corroborated by others’ sensing data, so-called information corroboration. Second, all rational and selfish users are inclined to gather to perform these tasks due to information corroboration. Meanwhile, they may be strategic about their participation levels to maximize profits. However, more similar sensing data will undoubtedly lower the information value, so any user has a tradeoff between gather and scatter. Third, although mobile users can obtain some payments, privacy issues may still prevent them from participating in MC. In this article, we propose a secure blockchain-assisted socially-aware MC framework by adopting the smart contract technique of Ethereum. For this framework, we further devise a two-stage Stackelberg game model to assist the requester (i.e., the leader in the game) in properly pricing each PoI-related sensing task, so that mobile users (i.e., the followers in the game) can exactly select their tasks and determine their participation levels. To analyze the game equilibrium, we extend the traditional Hessian matrix method to a multidimension case involving the multiuser multitask hyperspace setting. We conduct extensive experiments to prove the equilibrium and effectiveness of the proposed solution. We also implement a prototype and deploy the smart contract to an official Ethereum test network to demonstrate the practicability of the proposed framework.

Over the last decade, the mobile crowdsourcing has become a paradigm to conduct the manual annotation and further analytics by recruited workers, with their rewards depending on the result quality. Existing dispatchers cannot precisely capture the resource-quality trade-off for video analytics, because the configurations supported by recruited workers are limited, and workers’ availability changes over time. To determine the most suitable configurations as well as workers for video analytics, we formulate a non-linear mixed program in long term, maximizing the crowdsourcing profit. Based on previous results under various configurations and workers, we design an algorithm via a series of subproblems to decide the configurations adaptively upon the prediction of workers’ feedbacks. Such prediction is based on volatile multi-armed bandit to capture workers’ availability and stochastic changes on resource uses. Furthermore, we extend the proposed algorithms to the multi-worker selection scenario where the platform needs to determine a candidate worker set instead of a single worker for video analytics. Via rigorous proof, the regret is ensured upon the Lyapunov optimization and the bandit, measuring the gap between the online decisions and the offline optimum. Extensive trace-driven experiments show that our proposed algorithm improves the profit by 37% compared with other algorithms.

In recent years, Crowdsensing Data Trading (CDT) has emerged as a new data trading paradigm, where buyers crowdsource data collection tasks to a group of mobile users with sensing devices (a.k.a., sellers) who sell the collected data to them, through a platform as the broker for a long-term data trading. One of the most critical issues in CDT is ensuring the stability of the matching between buyers and sellers in the data trading market. In this paper, we focus on privacy protection and the stability problems in the CDT market with unknown preference sequences of buyers. The goal is to protect sellers' data qualities and ensure the CDT market's stability while maximizing the cumulative data quality for each task. We model this problem as a differentially private multi-player multi-armed competing bandit problem and propose a novel metric of the approximate stability, called$\delta$-stability. We propose a privacy-preserving stable CDT mechanism called DPS-CB to solve this problem in the centralized setting, which is based on stable matching theory, and competing bandit strategy. Moreover, we extend it into decentralized setting in order to avoid the competitive matching conflicts caused in this setting and propose a Conflicts-avoiding DPS-CB mechanism, called CDPS-CB, by using Bernoulli probability and selecting feasible sets of sellers. In addition, we prove the security and stability of the CDT market under privacy concerns and analyze the regret performance of DPS-CB and CDPS-CB mechanisms, respectively. Finally, the significant performance of these two mechanisms is demonstrated through extensive simulations on a real-world dataset.

In this paper, we focus on the privacy preserving mechanism design for crowdsourced Federated Learning (FL), where a requester can outsource its model training task to some workers via an FL platform. A potential way to preserve the privacy of workers' local data is to leverage Differential Privacy (DP) mechanisms on local models. However, most of these studies cannot allow workers to dominate their own privacy protection levels by themselves. Thus, we propose a Personalized Privacy Preserving Mechanism, called P3M, to satisfy the heterogeneous privacy needs of workers, which consists of two parts. The first part includes a personalized privacy budget determination problem. We model it as a two-stage Stackelberg game, derive the personalized privacy budget for each worker and the optimal payment for the requester, and prove that they form a unique Stackelberg equilibrium. Second, we design a dynamic perturbation scheme to perturb model parameters. Through the theoretical analysis, we prove that P3M satisfies the desired DP property, and derive the bounds of the variance of average perturbed parameters and the convergence upper bound. This demonstrates that the global model accuracy can be controllable and P3M is endowed with the satisfactory convergence performance. In addition, we extend our problem to the scenario where the total privacy budget of all workers is limited, so as to prevent some workers from setting exorbitant privacy budgets. Under the privacy constraint, we re-determine the personalized privacy budget for each worker. Finally, exhaustive simulations of P3M are conducted based on real-world datasets, and the experimental results corroborate its effectiveness and practicability.

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

In this paper, we investigate the privacy-preserving task push problem with unknown popularity in Spatial Crowdsourcing (SC), where the platform needs to select some tasks with unknown popularity and push them to workers. Meanwhile, the preferences of workers and the popularity values of tasks might involve some sensitive information, which should be protected from disclosure. To address these concerns, we propose a Privacy Preserving Auction-based Bandit scheme, termed PPAB. Specifically, on the basis of the Combinatorial Multi-armed Bandit (CMAB) game, we first construct a Differentially Private Auction-based CMAB (DPA-CMAB) model. Under the DPA-CMAB model, we design a privacy-preserving arm-pulling policy based on Diffie-Hellman (DH), Differential Privacy (DP), and upper confidence bound, which includes the DH-based encryption mechanism and the hybrid DP-based protection mechanism. The policy not only can learn the popularity of tasks and make online task push decisions, but also can protect the popularity as well as workers’ preferences from being revealed. Meanwhile, we design an auction-based incentive mechanism to determine the payment for each selected task. Furthermore, we conduct an in-depth analysis of the security and online performance of PPAB, and prove that PPAB satisfies some desired properties (i.e., truthfulness, individual rationality, and computational efficiency). Finally, the significant performance of PPAB is confirmed through extensive simulations on the real-world dataset.

Recently, in response to the low efficiency and high transmission latency of traditional centralized content delivery networks, especially in congested scenarios, edge caching has emerged as a promising method to bring content caching closer to the edge of the network. However, traditional content delivery methods might still lead to low utilization of cache resources. To tackle this challenge, this paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we consider a multi-tier edge caching-enabled content delivery network architecture combined with a content recommendation system and formulate the optimization problem with the objective of minimizing long-term content delivery delay and maximizing cache hit rate. Second, considering time-varying system environments and uncertain content demands, we approximate the optimization process of content delivery and cache replacement for each agent as a Partially Observable Markov Decision Process (POMDP) and propose a single-agent Deep Deterministic Policy Gradient (DDPG)-based method. Subsequently, we extend the POMDP to a multi-agent scenario. To address the issue of agents converging to local optima and establish more personalized models, we propose a Federated Distributed DDPG-based method (FD3PG) to solve the corresponding problem in a multi-agent system. Finally, simulation results demonstrate that the proposed FD3PG achieves lower delivery delay and higher cache hit rate compared with other baselines in various scenarios. Specifically, compared with FADE, MADRL, and DDPG, FD3PG achieves a significant decrease in average delivery delay, approximately 10%, 11%, and 35% on the Synthetic dataset, and 12%, 14%, and 48% on the MovieLens Latest Small dataset, respectively.

With the increasing prevalence of video streaming media, users have higher requirements for the quality of video streams, which can be quantified as Quality of Experience (QoE). Caching and transcoding video chunks on edge servers are effective methods to improve users’ QoE and reduce the cost of retrieving video chunks. In this paper, we investigate how to cache and transcode video chunks for mobile users to maximize their QoE, while taking into consideration the cooperation between edge servers, users’ mobility, and heterogeneous preferences, simultaneously. We employ a Multi-Agent Reinforcement Learning (MARL) framework and propose an MARLbased Cache replacement and Transcoding (MACT) mechanism. More specifically, we formulate the problem as a multi-agent game and prove this game is a state-based potential game. Then, we decompose this multi-agent game into individual agent decision problems to be solved. Through extensive simulations, we demonstrate that the proposed MACT mechanism achieves about 19.3% performance improvement, compared to the state-of-the-art works.

Many crowdsourcing platforms are emerging, leveraging the resources of recruited workers to execute various outsourcing tasks, mainly for those computing-intensive video analytics with high quality requirements. Although the profit of each platform is strongly related to the quality of analytics feedback, due to the uncertainty on diverse performance of workers and the conflicts of interest over platforms, it is non-trivial to determine the dispatch of tasks with maximum benefits. In this paper, we design a decentralized mechanism for a Crowd of Crowdsourcing platforms, denoted as Crowd2, optimizing the worker selection to maximize the social welfare of these platforms in a long-term scope, under the consideration of both proportional fairness and dynamic flexibility. Concretely, we propose a video analytics dispatch algorithm based on multi-agent bandit, for which the more accurate profit estimates are attained via the decoupling of multi-knapsack based mapping problem. Via rigorous proofs, a sub-linear regret bound for social welfare of crowdsourcing profits is achieved while both fairness and flexibility are ensured. Extensive trace-driven experiments demonstrate that Crowd2improves the social welfare by 36.8%, compared with other alternatives.

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.

Crowdsensed Data Trading (CDT) is a novel data trading paradigm, where each data consumer can publicize its data demand as some crowdsensing tasks, and some mobile users (i.e., data sellers) can compete for these tasks, collect the corresponding data, and sell the results to the consumers. Existing CDT systems generally depend on a data trading broker, which will inevitably cause consumers concerns on the trustworthiness of the systems and truthfulness of the data. To address this problem, we propose a Blockchain-based Crowdsensed Data Trading (BCDT) system, mainly containing a smart contract, called BCDToken. First, we replace the broker with blockchain to guarantee the trustworthiness of data trading. Meanwhile, BCDToken adopts Blockchain-based Reverse Auction (BRA) to assign tasks to data sellers. BRA holds truthfulness and individual rationality, which can ensure the sellers to report costs honestly and prevent sellers to manipulate the auction. Moreover, we implement a Secure Truth Discovery and reliability Rating (STDR) mechanism in BCDToken based on homomorphic cryptography, which can incentivize sellers to upload the truthful data and consumers to rate truthfully the reliabilities of sellers without revealing any privacy of data. Additionally, we also deploy BCDToken to the test network to demonstrate its practicability.

The multi-armed bandit (MAB) models have always received lots of attention from multiple research communities due to their broad application domains. The optimal selection problem with unknown rewards in advance, such as ad recommendation in social networks, spectrum access in the cognitive radio field, etc., can be efficiently solved by using MAB models. In an MAB model, given$N$arms whose rewards are unknown in advance, the player selects exactly one arm in each round, and his goal is to maximize the cumulative rewards over a fixed horizon. Further, a more general model called combinatorial MAB (i.e., CMAB), where$K$arms can be played simultaneously in each round, is put forward. However, the existing CMAB models neglect the strategic behaviors of the$N$arms, which indicates that one arm might report false information to increase its own profits. In fact, in many applications such as user selection in crowdsensing, the arms are not the feelingless machines but the rational individuals. To this end, we combine the upper confidence bound (UCB) with auction theory to develop a new algorithm called auction-based UCB (AUCB). We divide the auction-based CMAB problem into two sub-problems: winning arm selection and payment computation problems. For AUCB, we derive an upper bound on regret and prove the truthfulness in one round, individual rationality, and computational efficiency. In addition, we consider an extended situation that some arms may be unavailable in some rounds and the arms will bid inconsistently in different rounds. We devise another algorithm called eAUCB to solve this problem. Extensive simulations are conducted to show the significant performance of the proposed algorithms.

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where mobile social-aware workers have unknown qualities and can share their answers to tasks via social networks. The objectives are to recruit high-quality workers and maximize all parties’ utilities simultaneously. However, most existing works assume that the qualities of workers are known in advance or cannot take all parties’ utilities into account together, especially having not considered the impact of social networks. Thus, we propose an incentive mechanism based on the multi-armed bandit and three-stage Stackelberg game, called TACT. We first design a greedy arm-pulling scheme to recruit workers, which not only can solve the exploration-exploitation dilemma but also takes workers’ social relations into account. Based on the recruitment results, we further design the utility functions incorporating with social benefits for workers, and model the payment computation problem as a three-stage Stackelberg game among all participants. Next, we derive the optimal strategy group so that each party can maximize its own utility to form a multi-win situation. Moreover, we theoretically prove the unique existence of Stackelberg equilibrium and the worst regret bound. Finally, we conduct extensive simulations on a real trace to corroborate the performance of TACT.

Crowdsourcing enables a paradigm to conduct the manual annotation and the analytics by those recruited workers, with their rewards relevant to the quality of the results. Existing dispatchers fail to capture the resource-quality trade-off for video analytics, since the configurations supported by various workers are different, and the workers’ availability is essentially dynamic. To determine the most suitable configurations as well as workers for video analytics, we formulate a non-linear mixed program in a long-term scope, maximizing the profit for the crowdsourcing platform. Based on previous results under various configurations and workers, we design an algorithm via a series of subproblems to decide the configurations adaptively upon the prediction of the worker rewards. Such prediction is based on volatile multi-armed bandit to capture the workers’ availability and stochastic changes on resource uses. Via rigorous proof, the regret is ensured upon the Lyapunov optimization and the bandit, measuring the gap between the online decisions and the offline optimum. Extensive trace-driven experiments show that our algorithm improves the platform profit by 37%, compared with other algorithms.

Edge computing is a new computing paradigm rising gradually in recent years. Applications, such as object detection, virtual reality and intelligent cameras, often leverage Deep Neural Networks (DNN) inference technology. The traditional paradigm of DNN inference based on cloud suffers from high delay because of the limited bandwidth. From the perspective of service providers, caching DNN models on the edge brings several benefits, such as efficiency, privacy, security, etc.. The problem we concerned in this paper is how to decide the cached models and how to allocate processors of edge servers to reduce the overall system cost. To solve it, we model and study the DNN Model Caching and Processor Allocation (DMCPA) problem, which considers user-perceived delay and energy consumption with limited edge resources. We model it as an integer nonlinear programming (INLP) problem, and prove its NP-Completeness. Since it is considered as a long-term average optimization problem, we leverage the Lyapunov framework to develop a novel online algorithm DMCPA-GS-Online with Gibbs Sampling. We give the theoretical analysis to prove that our algorithm is near-optimal. In experiments, we study the performance of our algorithm and compare it with other baselines. The simulation results with the trace dataset from real world demonstrate the effectiveness and adaptiveness of our algorithm.

The conventional centralized data trading system confront the problem that the Trusted Third Party (TTP) may be dishonest, which harms the fairness and transparency of the system. Besides, we notice that most data trading systems lack distinguishing between the copyright and the use-right in trading. To address these issues, we propose a novel blockchainbased data trading system with copyright-preserving, called CDTP, mainly including two blockchains and an agreement. The copyright chain, one of the blockchains, is designed for registering and trading copyrights stored in the form of atomic transactions. It adopts an auction-based Byzantine agreement, namely ABFT. Another is use-right blockchain, which records use-right transactions and stores data, combined with IPFS-based storage. Moreover, we carry out experiments to simulate the nerformance of ABFT when it is under attacks.

Nowadays, crowdsourcing has become an increasingly popular paradigm for large-scale data annotation. It is crucial to ensure label quality by selecting the most suitable workers for labeling tasks. Many previous works have studied the reliability of unknown workers for crowdsourcing tasks with a stochastic assumption. However, each worker's reliability varies when performing tasks with different categories. Meanwhile, the reliability of each worker is usually unknown and doesn't follow any stochastic distribution. In this paper, we propose an Adversarial multi-armed Bandit-base algorithm to handle the Unknown Worker Recruitment (ABUWR) problem without any prior stochastic assumption. In ABUWR, we determine suitable workers for each task to maximize the accumulated average accuracy of the labeling tasks under a limited budget. Specifically,$w$e model this unknown worker recruitment problem as an adversarial multi-armed bandit game and use the least confidence scheme to ensure the total accumulate accuracy. Meanwhile,$w$e theoretically prove that ABUWR has a sub-linear regret upper bound. Furthermore, we demonstrate its significant performance through extensive simulations on real-world data traces.

Fog computing is a potential technology that can solve computationally intensive, latency-sensitive, and energy-intensive tasks. This paper studies the task offloading problem for decentralized multi-users with unknown system-side information. On the basis, we design the distributed task offloading bandit (DTOB) algorithm to balance task offloading exploration and exploitation in fog computing (MFC) systems. Finally, the effectiveness of the offloading scheme based on bidirectional matching is verified by experiments.

Traditional spatial crowdsourcing (SC) systems employ a centralized server platform to provide services for requesters. Such a centralized design requires powerful resource capacity and often cannot accomplish the urgent demands due to the unpredictable network latency. In order to ensure the scalability of systems and the quality of services, we study the distributed SC (DSC), where a diversity of location-relative services provided by various service providers (SPs) can deploy on edge clouds (ECs) with low time latency. Since the edge resources are limited, SPs need to compete for edge resources so as to deploy their desired SC services, and the requested resources must be allocated together to meet the demand of the service. We first design a gated recurrent unit with particle filter (GRUPF) network for SPs to predict future resource demands so as to participate in the competitions judiciously. Then, we model the competitive edge resource allocation problem between SPs and ECs as a combinatorial auction process. Due to the NP-hardness of this problem, an approximation algorithm is proposed to tackle it. Moreover, the leakage of private information such as bids may incur severe economic damage, and most existing studies usually rely on a trusted third party to provide rigorous privacy protection. Therefore, we customize a novel differentially private resource auction (DRA) mechanism, and design a bid confusion strategy based on differential privacy. Through theoretical analysis, we prove that the DRA mechanism meets some desired properties, including$\epsilon $-differential privacy, individual rationality, computational efficiency, and$\gamma $-truthfulness. Additionally, we corroborate the significant performances of DRA through extensive simulations on synthetic and real-world data sets.

Federated learning (FL) is a newly emerging distributed machine learning paradigm, whereby a server can coordinate multiple clients to jointly train a learning model by using their private datasets. Many researches focus on designing incentive mechanisms in FL, but most of them cannot allow that clients flexibly determine privacy budgets by themselves. In this article, we propose a privacy-preserving incentive mechanism (NICE) based on differential privacy (DP) and Stackelberg game for FL systems in industrial Internet of Things. First, we design a flexible privacy-preserving mechanism for NICE, in which clients can add a Laplace noise into the loss function according to a customized privacy budget. Under this mechanism, we design two incentive utility functions for the server and clients. Next, we model the utility optimization problems as a two-stage Stackelberg game by seeing the server as a leader and the clients as followers. Finally, we derive an optimal Stackelberg equilibrium solution for both the stages of the whole game. Based on this solution, NICE can make the server and all clients achieve their maximum utilities simultaneously. In addition, we conduct extensive simulations on real-world datasets to demonstrate the significant performance of the proposed mechanism.

Mobile crowdsensing, through which a requester can coordinate a crowd of workers to complete some sensing tasks, has attracted significant attention recently. In this paper, we focus on the unknown worker recruitment problem in mobile crowdsensing, where workers’ sensing qualities are unknown a priori. We consider the scenario of recruiting workers to complete some continuous sensing tasks. The whole process is divided into multiple rounds. In each round, every task may be covered by more than one recruited workers, but its completion quality only depends on these workers’ maximum sensing quality. Each recruited worker will incur a cost and each task is attached a weight to indicate its importance. Our objective is to determine a recruiting strategy to maximize the total weighted completion quality under a limited budget. We model such unknown worker recruitment process as anovel combinatorial multi-armed bandit(CMAB) problem, and propose an unknown worker recruitment algorithm based on the modified upper confidence bound (UCB). Moreover, we extend the problem to the case where the workers’ costs are also unknown and design the corresponding algorithm. We analyze the regret bounds of the two proposed algorithms through rigorous proofs. In addition, we also study the unknown worker recruitment problem with fairness constraints. Here, the term “fairness” means that the platform must guarantee a minimum selection fraction for each registered worker, so that the platform can avoid the scenario where some workers are over-recruited but some others might be under-recruited. For this problem, we devise a fairness-aware unknown worker recruitment algorithm. Finally, we demonstrate the performance of the proposed algorithms through extensive simulations on real-world traces

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Many researches focus on the worker recruitment problem in MCS, but most of them either have the assumption that workers’ qualities are known ahead of time or cannot ensure that workers report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Based on CMABA, we ulteriorly propose an adaptive incentive mechanism, called ACMABA, to recruit workers via the alternative worker recruitment and quality update, which can achieve a higher total sensing quality and lower regret. Additionally, we also demonstrate significant performances of the CMABA and ACMABA mechanisms through extensive simulations on real-world data traces.

Major cities worldwide have millions of cameras deployed for surveillance, business intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to nearby servers, thus mitigating long latency incurred by data transmission via wide area networks. When offloading video frames from the front-end device to an edge server, the application configuration (i.e., frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration selection and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. We also extend the proposed algorithms to the multi-edge scenario in which each user or video stream has an additional choice about which edge server to connect. Extensive evaluation results show that the proposed solutions can effectively balance the analytics accuracy and energy consumption while keeping low system latency in a variety of settings.

In the multi-access edge computing environment, app vendors deploy their services and applications at the network edges, and edge users offload their computation tasks to edge servers. We study the user-perceived delay-aware service placement and user-allocation problem in edge environment. We model the MEC-enabled network, where the user-perceived delay consists of computing delay and transmission delay. The total cost in the offloading system is defined as the sum of service placement, edge server usage and energy consumption cost, and we need to minimize the total cost by determining the overall service-placing decision and user-allocation decision, while guaranteeing that the user-perceived delay requirement of each user is fulfilled. Our considered problem is formulated as a Mixed Integer Linear Programming problem, and we prove its NP-hardness. Due to the intractability of the considered problem, we propose a LOCal-search based algorithm for USer-perceived delay-aware service placement and user-allocation in edge environment, named LOCUS, which starts with a feasible solution and then repeatedly reduces the total cost by performing local-search steps. After that, we analyze the time complexity of LOCUS and prove that it achieves provable guaranteed performance. Finally, we compare LOCUS with other existing methods and show its good performance through experiments.

Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of$2(1+\xi)\ln (d+1)$with a considerable probability, where$d$is the maximum number of process slots of an edge node and$\xi$is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms.

Crowdsensing Data Trading (CDT), through which a platform can aggregate some data collected by a group of mobile users with sensing devices (a.k.a., data sellers) and sell the corresponding statistics to data consumers, has been recognized as a promising paradigm for large-scale data trading in recent years. It is critical to select sellers with high sensing qualities and maximize all trading participants' profits simultaneously. However, most existing CDT systems either assume that sellers' sensing qualities are known in advance or cannot realize concurrent profit maximization. In this paper, we propose a data trading mechanism based on Combinatorial Multi-Armed Bandit and three-stage Hierarchical Stackelberg game, called CMAB-HS, to tackle the problem of quality unknown seller selection and incentive strategy design. Our objective is to select a group of sellers to maximize the total sensing quality within time budget, and determine the optimal incentive strategy for each participant to maximize individual profit simultaneously. We theoretically prove that CMAB-HS achieves Stackelberg Equilibrium and a tight bound on regret. Additionally, we demonstrate its significant performances through extensive simulations on real data traces.

The proliferation of advanced mobile terminals opened up a new crowdsourcing avenue, spatial crowdsourcing, to utilize the crowd potential to perform real-world tasks. In this work, we study a new type of spatial crowdsourcing, called time-continuous spatial crowdsourcing (TCSC in short). It supports broad applications for long-term continuous spatial data acquisition, ranging from environmental monitoring to traffic surveillance in citizen science and crowdsourcing projects. However, due to limited budgets and limited availability of workers in practice, the data collected is often incomplete, incurring data deficiency problem. To tackle that, in this work, we first propose an entropy-based quality metric, which captures the joint effects of incompletion in data acquisition and the imprecision in data interpolation. Based on that, we investigate quality-aware task assignment methods for both single- and multi-task scenarios. We show the NP-hardness of the single-task case, and design polynomial-time algorithms with guaranteed approximation ratios. We study novel indexing and pruning techniques for further enhancing the performance in practice. Then, we extend the solution to multi-task scenarios and devise a parallel framework for speeding up the process of optimization. We conduct extensive experiments on both real and synthetic datasets to show the effectiveness of our proposals.

Traffic signal control is essential to efficient transportation networks since it can mitigate traffic congestion significantly. Trial-and-error approach in reinforcement learning will lead to traffic jams, even traffic accidents in the real scene, which is in violation of safety for traffic signal control. Besides, most signal control systems still rely on oversimplified information, which makes item challenging to adapt to dynamic traffic. In this paper, we focus on the edge coordinated optimization of large-scale traffic signal control, and propose a two-layeR edge-assisted pressUre balaNce (RUN) approach based on cooperative game. The external layer utilizes cooperative game to divide the traffic network into multiple coalitions. The internal layer uses pressure control and weighted queue to coordinate actions within each coalition and handle dynamic traffic situations over time. We derive a Pareto stable solution for the multi-intersection signal cooperative game with pressure control, and prove that it is non-superadditive. Moreover, we conduct extensive simulations to verify the significant performances of RUN based on both real data and synthetic data.

The multi-armed bandit (MAB) model has been deeply studied to solve many online learning problems, such as rate allocation in communication networks, Ad recommendation in social networks, etc. In an MAB model, given N arms whose rewards are unknown in advance, the player selects exactly one arm in each round, and his goal is to maximize the cumulative rewards over a fixed horizon. In this paper, we study the budget-constrained auction-based combinatorial multi-armed bandit mechanism with strategic arms, where the player can select K (<; N) arms in a round and pulling each arm has a unique cost. In addition, each arm might strategically report its cost in the auction. To this end, we combine the upper confidence bound (UCB) with auction to define the UCB-based rewards and then devise an auction-based UCB algorithm (called AUCB). In each round, AUCB selects the top K arms according to the ratios of UCB-based rewards to bids and further determines the critical payment for each arm. For AUCB, we derive an upper bound on regret and prove the truthfulness, individual rationality, and computational efficiency. Extensive simulations show that the rewards achieved by AUCB are at least 12.49% higher than those of state-of-the-art algorithms.

present, edge cloud is becoming more and more important as it provides computing resources required by mobile terminals. The online edge cloud resource trading mechanism has become one of the most important ways for edge cloud resource sharing, attracting much attention. In this paper, we have proposed a Blockchain-based Privacy-preserving Combinatorial Double Auction (BPCDA) mechanism, which combines trustworthiness, truthfulness and privacy protection for the first time. Firstly, BPCDA avoids dependence on third-party brokers by adopting blockchain. Secondly, we draw on the economic theories, and then adopt double auction to ensure the truthfulness of bids while participants in the auction are composed of multiple sellers and multiple buyers. Lastly, we make fell use of the differential privacy mechanism on the blockchain to avoid privacy leakage. Through simulated experiments, we verify the superior performance and practicality of our proposed edge cloud resource trading mechanism.

Crowdsourcing Data Trading is a novel paradigm, where a platform can aggregate data collected by a group of mobile users. Traditional crowdsourcing data trading systems rely on trusted data trading brokers, which increase costs and cannot prevent collusion. What’s more, they don’t take of data quality and truthfulness into consideration con-currently. The emergence of blockchain and smart contracts brings in a decentralized scheme. However, the selection of the most effective providers remains challenging. To tackle these problems, we propose a dynamic-game-with-complete-information-and-Blockchain-based Crowdsourcing data Trading system (CBCT), which mainly includes a smart contracts called CBCToken. Firstly, we replace the data trading broker and adopt the Stackelberg Game, a Dynamic Game with complete information (DGC), to manage the selection of providers. Moreover, homomorphic watermarking technology is applied to protect the data copyright. Lastly, we deploy BCDToken on an Ethereum test network to demonstrate its practicability and significant performances.

Federated Learning, through which a server can coordinate a crowd of clients to accomplish a machine learning task, has been recognized as a promising paradigm for privacy preserving decentralized learning in recent years. Most federated learning researches are on the basis of IID data, and more and more researches focus on the Non-IID data problem. However, they have not considered the process of data acquisition. In fact, in real applications, the training data are typically collected by clients in real scenarios. In this paper, we propose a Federated Learning framework based on Dynamic Client Selection, called FedDCS, to deal with the real scene Non-IID data machine learning problem in federated learning. The objective of FedDCS is to utilize a parameter estimation algorithm to select the optimal clients to join the collaboration and finally acquire a better global machine learning model. Our extensive experiments on several real-world data sets demonstrate the superior performance of FedDCS.

Mobile crowdsensing is a new paradigm by which a platform can recruit mobile workers to perform some sensing tasks by using their smart mobile devices. In this paper, we focus on a privacy-preserving unknown worker recruitment issue. The platform needs to recruit some workers without knowing the qualities of them completing tasks. Meanwhile, these quality information also needs to be protected from disclosure. To tackle these challenges, we model the unknown worker recruitment as a Differentially Private Multi-Armed Bandit (DP-MAB) game by seeing each worker as an arm of DP-MAB and the task completion quality contributed by each worker as the reward of pulling arm. Then, recruiting workers is equivalent to designing a bandit policy of pulling DP-MAB arms. Under this model, we propose a Differentially Private ϵ-First-based arm-pulling (DPF) algorithm and a Differentially Private UCB-based arm-pulling (DPU) algorithm, which can achieve the nearly optimal expected accumulative rewards under a given budget. We also analyze the regrets of the DPF and DPU algorithms and prove that both of them are δ-differentially private on the task completion qualities (δ > 0δ). Finally, we conduct extensive simulations to verify the significant performances of DPF and DPU based on both the real-trace and synthetic datasets.

Edge cloud computing is a new paradigm in which the computation and storage services of remote cloud data centers are moved to Edge Cloud Nodes (ECNs) in network edges. Compared to traditional cloud data centers, ECNs are geographically close to mobile users so the communication latency is significantly reduced. In this paper, we study the problem of allocating Virtual Machine (VM) resources in geo-distributed ECNs to mobile users by using the auction theory. First, we treat mobile users and ECNs as the buyers and sellers of the VM resource auction, respectively. Then, we model the VM resource allocation problem as an$n$-to-one weighted bipartite graph matching problem with 0-1 knapsack constraints. Since this problem is NP-hard, we design a greedy approximation algorithm to determine the winners of the auction, based on which we propose a truthful Auction-based VM resource Allocation (AVA) mechanism to solve the problem. Moreover, we prove that the AVA mechanism not only achieves an approximately optimal solution for winner selection, but also has the properties of truthfulness, individual rationality, and computational efficiency. Finally, we conduct extensive simulations on real traces to verify the significant performances of the proposed AVA mechanism.

Crowdsourcing data trading is a novel paradigm in which the crowdsourcing technology is adopted to collect big data for trading. At present, existing crowdsourcing data trading systems usually depend on a trusted broker and haven't considered the truthfulness and the quality of data (QoD) simultaneously. Besides, copyright protection is the another issue that has not been properly addressed. To tackle these problems, we propose a Copyright-Preserving crowdsourcing data trading framework based on Blockchain, named CPchain, which mainly includes a smart contract. We design an auction algorithm based on semantic similarity to guarantee the truthfulness and individual rationality while ensuring QoD. Moreover, we combine digital fingerprint technology with blockchain to protect data copyright without a third-party certification authority. Furthermore, we develop a simple prototype of our proposed trading framework on the Ethereum test network. We have carried out a lot of experiments to demonstrate the significant performances of our framework.

Mobile CrowdSensing (MCS), through which a requester can coordinate a crowd of workers to accomplish some data collection tasks, has been recognized as a promising paradigm for large-scale data acquisition in recent years. Although many MCS systems have been built for various applications, most of them either assume that workers’ qualities are known in advance or cannot ensure workers to report costs honestly. In this paper, we propose an incentive mechanism based on Combinatorial Multi-Armed Bandit and reverse Auction, called CMABA, to solve the multiple unknown workers recruitment problem in MCS. Our objective is to determine a recruiting strategy to maximize the total sensing quality under a limited budget, while ensuring truthfulness and individual rationality of sensing workers. We theoretically prove that our CMABA mechanism achieves truthfulness and individual rationality, and then analyze the regret of the mechanism. Additionally, we also demonstrate its significant performances through extensive simulations on real-world data traces.

In this paper, we consider a fundamental problem: given one mobile charger that can charge multiple sensor nodes simultaneously, how we can schedule it to charge a given WSN to maximize the energy usage effectiveness (EUE)? We propose a novel charging paradigm-Overlapped Mobile Charging (OMC)- the first of its kind to the best of our knowledge. Firstly, OMC clusters sensor nodes into multiple non-overlapped sets using k-means evaluated by the Davies-Bouldin Index, such that the sensor nodes in each set have similar recharging cycles. Secondly, for each set of sensor nodes, OMC further divides them into multiple overlapped groups, and charges each group at different locations for different time durations to make sure that each overlapped sensor node just receives its required energy from multiple charging locations.

With the development of deep learning, artificial intelligence applications and services have boomed in the recent years, including recommendation systems, personal assistant and video analytics. Similar to other services in the edge computing environment, artificial intelligence computing tasks are pushed to the network edge. In this paper, we consider the multi-user edge-assisted video analytics task offloading (MEVAO) problem, where users have video analytics tasks with various accuracy requirements. All users independently choose their accuracy decisions, satisfying the accuracy requirement, and offload the video data to the edge server. With the utility function designed based on the features of video analytics, we model MEVAO as a game theory problem and achieve the Nash equilibrium. For the flexibility of making accuracy decisions under different circumstances, a deep reinforcement learning approach is applied to our problem. Our proposed design has much better performance compared with some other approaches in the extensive simulations.

Federated Learning (FL) is a newly-emerging distributed ML model, where a server can coordinate multiple workers to cooperatively train a learning model by using their private datasets, while ensuring these datasets not to be revealed to others. In this paper, we focus on the incentive mechanism design for FL systems. Taking the incentives into consideration, we first design two utility functions for the server and workers, respectively. Then, we model the corresponding utility optimization problem as a two-stage Stackelberg game by seeing the server as a leader and the workers as some followers. Next, we derive an optimal Equilibrium solution for the both stages of the whole game. Based on this solution, we design an incentive mechanism that can ensure the server to achieve the optimal utility, while stimulating workers to do their best to train the ML model. Finally, we conduct extensive simulations to demonstrate the significant performance of the proposed mechanism.

Mobile crowdsensing, through which a requester can coordinate a crowd of workers to complete some sensing tasks, has attracted significant attention recently. In this paper, we focus on the unknown worker recruitment problem in mobile crowdsensing, where workers' sensing qualities are unknown a priori. We consider the scenario of recruiting workers to complete some continuous sensing tasks. The whole process is divided into multiple rounds. In each round, every task may be covered by more than one recruited workers, but its completion quality only depends on these workers' maximum sensing quality. Each recruited worker will incur a cost, and each task is attached a weight to indicate its importance. Our objective is to determine a recruiting strategy to maximize the total weighted completion quality under a limited budget. We model such an unknown worker recruitment process as a novel combinatorial multi-armed bandit problem, and propose an extended UCB based worker recruitment algorithm. Moreover, we extend the problem to the case where the workers' costs are also unknown and design the corresponding algorithm. We analyze the regrets of the two proposed algorithms and demonstrate their performance through extensive simulations on real-world traces.

Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to the nearby server, thus mitigating long latency incurred by data transmission via wide area networks. When offloading frames from the front-end device to the edge server, the application configuration (frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration adaption and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. Simulation results show that JCAB can effectively balance the analytics accuracy and energy consumption while keeping low system latency.

Along with the generation of Internet of Things (IoT), the values of tremendous volumes of sensing data will be slowly unlocked. Thus, crowd-sensed data trading as a new business paradigm has recently attracted increasing attention. A typical data trading system contains a platform, data consumers, and crowd workers. The platform recruits crowd workers to collect data and then sells the data to consumers. In this article, we design a differentially private crowd-sensed data trading mechanism, called DPDT, to preserve the identity privacy of consumers and the task privacy against crowd workers during the data collection process, simultaneously. DPDT consists of a differentially private auction-based data pricing algorithm and a differentially private data collection algorithm. The data pricing algorithm achieves a good approximation to the maximum revenue. Meanwhile, it guarantees (e2- 1)ϵ-truthfulness and 2ϵ-differential privacy, where ϵ > 0 is a small constant. The data collection algorithm is able to effectively protect the data collection task privacy against crowd workers. We prove that this data collection algorithm achieves δ-approximate ϵ-differential privacy, where δ <; 1/e is a small constant, and meanwhile guarantees a tight bound of the expected approximation ratio. At last, extensive simulations are conducted to verify the significant performance of DPDT.

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems and protecting workers' private sensitive information. We first model the task assignment problem as a reverse auction process. We formalize the winning bid selection of reverse auction as an n-to-one weighted bipartite graph matching problem with multiple 0-1 knapsack constraints. Since this problem is NP-hard, we design an approximation algorithm to select winning bids and determine corresponding payments. Based on this, a Secure Reverse Auction (SRA) protocol is proposed for this novel spatial crowdsourcing. We analyze the approximation performance of the proposed protocol and prove that it has some desired properties, including truthfulness, individual rationality, computational efficiency, and security. To the best of our knowledge, this is the first theoretically provable secure auction protocol for spatial crowdsourcing systems. In addition, we also conduct extensive simulations on a real trace to verify the performance of the proposed protocol.

Mobile Augmented Reality (MAR) applications usually contain computation-intensive tasks which far outstrip the capability of mobile devices. One way to overcome this is offloading computation-intensive MAR tasks to remote clouds. However, the wide area network delay is hard to reduce. Thanks to edge computing, we can offload MAR tasks to nearby servers. Prior studies focus on either single-task MAR applications offloading or dependent tasks offloading for a single user. In this article, we study the offloading decision of MAR applications from multiple users, each of which is comprised of a chain of dependent tasks, over a generic cloud-edge system consisting of a group of heterogeneous edge servers and remote clouds. We formulate the Multi-user Multi-task MAR Application Scheduling (M3AS) problem, which is NP-hard. We present Mutas, an efficient scheduling algorithm that jointly optimizes server assignment and resource management. We also consider the online version of M3AS and present OnMutas. Extensive evaluations demonstrate that both Mutas and OnMutas can significantly reduce the service delays of MAR applications when compared to three other heuristics.

Mobile crowdsensing is a new paradigm in which a requester can recruit a group of mobile users via a platform and coordinate them to perform some sensing tasks by using their smartphones. In mobile crowdsensing, each user might perform multiple tasks with different sensing qualities. Meanwhile, the users participating in the crowdsensing will ask for sufficient rewards to compensate for their expenditures. Hence, an important problem is how to recruit the users with minimum cost while achieving a satisfactory sensing quality for each task. Furthermore, in order to ease users' worries about privacy disclosures, the user recruitment process needs to protect each user's sensing quality and recruitment cost information from being revealed to other users or to the platform. In this paper, we propose two secure user recruitment problems for the cases where the recruitment costs of users are homogeneous and heterogeneous. After proving the NP-hardness of the problems, we design two secure user recruitment protocols by using secret sharing scheme. Both of the proposed protocols adopt greedy strategies, which can recruit nearly optimal users while ensuring that the total sensing quality of each task is no less than a given threshold. The difference lies in that the two greedy strategies are based on two unique utility functions. We analyze the approximation ratios of the two protocols and prove the security under the semi-honest model. Finally, we demonstrate the significant performance of the proposed protocols through extensive simulations and executions on real smartphones.

Virtualization techniques help edge environments separate the role of the traditional edge providers into two: edge infrastructure providers (EIPs), who manage the physical edge infrastructure, and edge service providers (ESPs), who aggregate resources (especially, compute resources) from multiple EIPs to place service entities and offer value-added services to end users (EUs). In such an environment, end users submit their data analysis jobs to ESPs; ESPs process the data analysis jobs using their service entities. One fundamental and critical problem for an ESP is to decide how much compute resources to rent from each edge server under the constraint that the total amount of rental resources is no more than a specified budget threshold, so that the average makespan of the data analysis jobs submitted to it is minimized. This Edge Resource Allocation (ERA) problem is proven to be NP-complete by reducing the set cover problem to a special case of it. To design an approximation algorithm for ERA, we perform two transformations on ERA: first, we transform ERA into mERA by replacing minimization with maximization; second, we transform mERA into dmERA by limiting the possible amounts of rental resources to a finite set of values. We find that dmERA has several tractable properties that allow us to design Hermes, a provably efficient algorithm that approximates the optimal allocation. We demonstrate that the gap between Hermes and the optimum in simulations and Android-based testbed experiments are no larger than 4.78% and 12.43%, respectively. Hermes can also output a curve showing the trade-off between the average makespan and the budget threshold, so that an ESP can choose the right balance.

The ever-increasing number of IoT devices in our surrounding environment bring us tremendous amount of opportunities but also challenges including limited battery life, low computational capability and scalability of multiple access. Recent advances in backscatter communication have enabled ubiquitous IoT devices to communicate in a cost-and power-efficient way. However, most of the proposed backscatter solutions nowadays focus on the single tag paradigm, i.e., multiple tags do not transmit simultaneously and thus the solutions have difficulties to scale with a large number of tags. This work presents CBMA, a backscatter system that enables multiple concurrent backscatter tags to communicate reliably and efficiently. For the first time, we demonstrate that multiple tags can backscatter concurrently and efficiently with novel impedance-based power control at the tag, and can be successfully decoded with commodity WiFi devices without affecting the existing WiFi communication. We present the design details of CBMA and build a prototype with off-the-shelf WiFi devices and FPGA. The CBMA system achieves a 10-tag bit rate of 8Mbps while supporting a communication distance up to 10m. Compared to single-tag solutions, CBMA improves the backscatter throughput by more than 10× even in challenging indoor scenarios with rich multipath and interference.

Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multi-armed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.

The ride-hailing system has become popular around the world. The Service Providers (SPs) such as Uber and Didi dispatch passenger orders based on their location information. However, one concern from the public is whether the SPs could protect the location privacy of passengers. In this paper, we propose an order dispatch scheme that could preserve the location privacy of passengers based on their requirements. Our scheme uses cloaking regions in which the SPs cannot distinguish actual locations of passengers. The trade-off is the loss of matching performance or social welfare, i.e., the increase in the overall pick-up distance. We formulate the problem as maximizing the social welfare (or minimizing the overall pick-up distances) under privacy requirements of passengers. A bipartite-matching-based scheme is investigated, and we provide a theoretical bound on the matching performance under specific privacy requirements. Nevertheless, minimizing the overall pick-up distances does not consider the interest of each individual passenger. Passengers with low privacy requirements may be matched with drivers far from them. Therefore, we further propose a pricing scheme that could make up for the individual loss by allocating discounts on their riding fares. Especially, three discount allocation strategies are proposed in this paper. Experiments on both real-world and synthetic datasets show the efficiency of our scheme.

Recently, Mobile Crowdsourcing (MC) has aroused great interest on the part of both academic and industrial circles. One of the key problems in MC is designing the proper mechanisms to incentivize user participation, as users are typically self-interested and must consume a substantial amount of MC resources/costs. Although considerable research has been devoted to this problem, the majority of studies have neglected the privacy issue in mechanism design. In this study, we consider the scenario where a mobile crowdsourcing platform aims to maximize the crowdsourcing revenue under a budget constraint, and users are interested in maximizing their utility while keeping their cost private. We design differentially-private mechanisms for such a scenario under an offline setting where users bid their costs simultaneously and under an online setting where user bids are revealed one by one. We show that our mechanisms simultaneously achieve provable performance bounds with respect to several measures, including revenue, differential privacy, truthfulness, and individual rationality. Finally, we also conduct extensive numerical experiments to demonstrate the effectiveness of our approach.

Edge computing is gaining popularity these years, more service providers are shifting their services from clouds to the edge for better QoS provision. Recent studies in NFV also tend to deploy Network Function Virtualization (NFV) services in the edge network. However, the NFV deployment in the edge network is a challenging problem and differs from the similar problem in data centers. We mainly focus on a new NFV Chain Placement (NCP) problem in the paper. It is well known that the edge of the network is dynamic, and edge computing aims to utilize the physical edge resources efficiently and quickly. We first prove that the NCP problem is NP-complete. Then we propose a novel metric that can better measure the balance condition of the physical resources. We also analyze its advantages. Finally, we design an efficient algorithm, MINI, based on this metric. We evaluate MINI using extensive simulations. The results show that MINI has great advantages over a genetic algorithm (GA) in terms of physical resource utilization, acceptance rate, and running time.

In data center networks (DCN), large scale flows produced by parallel computing frameworks form many coflows semantically. Most inter-coflow schedulers only focus on the remaining data of coflows and attempt to mimic Shortest Job First (SJF). However, a coflow may consist of multiple stages. In this paper, we consider the Multi-stage Inter-Coflow Scheduling problem and try to give an efficient online scheduling scheme. We first explore a short-sighted algorithm with the greedy strategy. This gives us an insight into utilizing the network resources. Based on that, we propose a far-sighted heuristic, which schedules sub-coflows to occupy network bandwidth in turn. Through simulations in various network environments, we show that, compared to a state-of-the-art scheduler - Varys, a multi-stage aware scheduler can reduce the coflow completion time by up to \pmb4.81× even though it is short-sighted. Moreover, the far-sighted scheduler can improve the performance by nearly \pmb7.95 × reduction.

Electric vehicle (EV) is a promising technological tool for diminishing environmental impact caused by gasoline-consumed transportation. Due to the limited battery capacity, EVs need to be charged frequently in a static charging station and thus waste large amounts of time being out of service. Research previously conducted in this topic have proposed solutions for deployment of charging lanes that can charge in-motion EVs. However, they cannot guarantee that every EV can be operational in their respective entire route. Meanwhile, we observe that EVs have repetitive motions and may cyclically encounter with each other which no prior research having been investigated. In addition, the development on the circuit design of energy transmit antennas can render EVs to be able to bi-directionally, highly efficiently transfer energy between themselves. These two observations enable us to distribute energy among EVs in a collaborative and interactive manner. We consider the cases of both loss-less and lossy energy transfer between EVs. In both cases, we formulate the problem of minimizing the time needed (or energy transferred) to reach a given energy distribution into a series of linear programming problems. When compared with a state-of-the-art algorithm, extensive simulation results show that the proposed algorithms can reduce the balancing time and energy loss by up to 70.60% and 36.59%, respectively.

Mobility-assisted opportunistic routing in mobile social network is an interesting research topic with real world applications. It allows users to exchange large chunks of data without relying on stationary network infrastructures. This paper proposes a scalability and privacy preserving routing algorithm. The core of a multi-hop routing scheme is the prediction of the next relay router, which ideally is the next hop on the shortest path to the destination of the message. The proposed routing algorithm contains a machine learning based prediction model that is trained on a trace of nodes connection events collected during a warm up period in a network where the mobility is assumed to exhibit a certain degree of regularity as in general human social networks. The algorithm is scalable since the prediction model, i.e. the control plane of the algorithm, only needs to maintain and broadcast the trained model that implicitly encodes the possible mobility locations and patterns within the mobility area, which is a constant to the number of nodes in the network. The algorithm is privacy preserving because that it does not require the nodes to disclose to other nodes any explicit information about its previous mobility or its personal preferences. Privacy preserving is achieved by using distributed node representations that jointly encode the mobility pattern of the node together with the prediction model, instead of using explicit statistical information to represent previous connectivity patterns as in prior work. This on one hand make attacker hard to find out any exact information about any particular node, and on the other hand enables the routing algorithm to extrapolate to the prediction of nodes of unseen mobility pattern, which is difficult if hand designed statistical mobility information is used. To perform large scale experiments, we collect very large synthetic mobility trace from university course registration information, this trace contain a total of about 35,000 nodes and 300 million message forwarding links. Experiments are conduct to examine several prediction accuracies of our algorithm.

Recently, influence maximization in social networks has attracted great attention. In this paper, we consider that a company intends to select some users to promote its multiple products (called influences) in online social network consisting of many communities, in which each user has different preferences for each influence. We focus on the Minimum Cost Seed Selection (MCSS) problem for multiple influences, that is, how to select some seeds with minimum cost so that the average influenced probability of all users in each community is not less than a threshold. To solve the MCSS problem, we design a submodular utility function, based on which we turn our problem into a non-trivial set cover problem with non-linear constraints. After proving the NP-hardness of MCSS, we propose a greedy algorithm, called G-MCSS, to solve it. We analyze the approximation ratio of G-MCSS. Additionally, we extend the MCSS problem to a complex case, where the number of acceptable influences for each user is limited, and the cost is proportional to the number of allocated influences. We further propose another greedy algorithm to solve the extended problem. Finally, we demonstrate the significant performances of our algorithms through extensive experiments based on real social network traces.

In this paper, we focus on the incentive mechanism design for a vehicle-based, nondeterministic crowdsensing system. In this crowdsensing system, vehicles move along their trajectories and perform corresponding sensing tasks with different probabilities. Each task may be performed by multiple vehicles jointly so as to ensure a high probability of success. Designing an incentive mechanism for such a crowdsensing system is challenging since it contains a non-trivial set cover problem. To solve this problem, we propose a truthful, reverse-auction-based incentive mechanism that includes an approximation algorithm to select winning bids with a nearly minimum social cost and a payment algorithm to determine payments for all participants. Moreover, we extend the problem to a more complex case in which the Quality of sensing Data (QoD) of each vehicle is taken into consideration. For this problem, we propose a QoD-aware incentive mechanism, which consists of a QoD-aware winning-bid selection algorithm and a QoD-aware payment determination algorithm. We prove that the proposed incentive mechanisms have truthfulness, individual rationality, and computational efficiency. Moreover, we analyze the approximation ratios of the winning-bid selection algorithms. The simulations, based on a real vehicle trace, also demonstrate the significant performances of our incentive mechanisms.

The lines between IaaS, PaaS, and SaaS are becoming blurred as datacenter providers seek to create cloud platforms that can widen their appeal to developers. With this kind of hybrid datacenter, resource requests from tenants are increasingly transforming into hybrid requests that may simultaneously demand IaaS, PaaS, and SaaS resources. This paper tackles the challenge of modeling and deploying hybrid tenant requests in datacenter networks, for which we coin ``networklet" to represent a set of VMs that collaboratively provide some PaaS or SaaS service. Through extracting networklets from tenant requests and thus sharing them between multiple tenants, we can achieve a win-win situation for datacenter providers and tenants. Extensive evaluations show that, the proposed model and deployment algorithm indeed improve DCN resource utilization while maintaining performance guarantee.

In today's datacenters, resource requests from tenants are increasingly transforming into hybrid requests that may simultaneously demand IaaS, Paas, and SaaS resources. This paper tackles the challenge of modeling and deploying hybrid tenant requests in datacenters, for which we coin "networklet" to represent a set of VMs that collaboratively provide a PaaS or SaaS service. Through extracting networklets from tenant requests and thus sharing them between tenants, we can achieve a win-win situation for datacenter providers and tenants.

Mobile crowdsensing is a new paradigm in which a requester can recruit a group of mobile users via a platform and coordinate them to perform some sensing tasks by using their smartphones. In mobile crowdsensing, each user might perform multiple tasks with different sensing qualities. An important problem is recruiting the minimum number of users while achieving a satisfactory sensing quality for each task. Meanwhile, in order to ease users' worries about privacy disclosures, the user recruitment process needs to protect each user's sensing quality information from being revealed to other users or to the platform. We prove that this problem is NP-hard. To solve this problem, we first propose a Basic User Recruitment (BUR) protocol based on a greedy strategy, which can recruit nearly the minimum amount of users while ensuring that the total sensing quality of each task is no less than a given threshold. Based on BUR, we further propose a Secure User Recruitment (SUR) protocol by using secret sharing schemes. We analyze the approximation ratio and prove the security of the SUR protocol in the semi-honest model. Moreover, we extend SUR to deal with a more general case where the total sensing quality of each task might be an increasing submodular function. Finally, we demonstrate the significant performance of the proposed protocol through extensive simulations and execution in real smartphones.

Channel state information (CSI) is now popular in many emerging applications over WLAN, such as gesture recognition, indoor localization, line-of-sight (LOS) identification and ranging. Traditional power-based ranging approaches usually utilize received signal strength indicator (RSSI) because of its low cost and wide applicability. However, the coarse-grained RSSI is sensitive to the multipath effect and performs poorly in the non-line-of-sight (NLOS) situation. Compared with RSSI, CSI is a finer-grained value, and it depicts the amplitude and phase of each frequency-domain subcarrier in an OFDM channel. Besides, MIMO technology leveraged in 802.11 infrastructures extends the spatial diversity of WiFi signals and guarantees reliable communications. To obtain more stable and precise distance of direct path (DDP) between the transmitter and the receiver, we employ both the frequency diversity and spatial diversity of CSI and propose a new ranging system over WLAN. We consider multiple influence factors when implementing the system on commodity WiFi devices, and we evaluate its performance in diverse indoor environments. The results demonstrate that our system outperforms the baselines in all the pre-set scenarios, especially under the conditions of NLOS and dynamic multipaths.

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart phones to conduct complex sensing tasks. In this paper, we focus on the makespan sensitive task assignment problems for the crowdsensing in mobile social networks, where the mobility model is predicable, and the time of sending tasks and recycling results is non-negligible. To solve the problems, we propose an Average makespan sensitive Online Task Assignment (AOTA) algorithm and a Largest makespan sensitive Online Task Assignment (LOTA) algorithm. In AOTA and LOTA, the online task assignments are viewed as multiple rounds of virtual offline task assignments. Moreover, a greedy strategy of small-task-first-assignment and earliest-idle-user-receive-task is adopted for each round of virtual offline task assignment in AOTA, while the greedy strategy of large-task-first-assignment and earliest-idle-user-receive-task is adopted for the virtual offline task assignments in LOTA. Based on the two greedy strategies, both AOTA and LOTA can achieve nearly optimal online decision performances. We prove this and give the competitive ratios of the two algorithms. In addition, we also demonstrate the significant performance of the two algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

Due to the explosive proliferation of mobile cloud computing applications, much data needs to be transmitted between mobile users and clouds, incurring a huge traffic demand on cellular networks. Mobile offloading is a promising approach to address this challenge. In this paper, we focus on the problem of offloading many deadline-sensitive data items to some WiFi networks with capacity constraints; that is, how to schedule each data item to the WiFi networks, so that we can offload as many data items before their deadlines as possible, while taking the constraints of transmission capacity into consideration. This problem involves a probabilistic combination of multiple 0-1 knapsack constraints, which differs from existing problems. To solve this problem, we propose a greedy oFfline Data Offloading (FDO) algorithm, achieving an approximation ratio of 2. Also, we propose an oNline Data Offloading (NDO) algorithm, which has a competitive ratio of 2. Additionally, we extend our problem to a more general scenario where WiFi transmission costs are heterogeneous. We design a Heterogeneous Data Offloading (HDO) algorithm to solve the extended problem, and give its performance analysis. Finally, we demonstrate the significant performances of our algorithms through extensive simulations based on some real-world and synthetic WiFi datasets.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their carried smart devices to cooperatively perform a large-scale sensing job over urban environments. In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing, in which mobile users perform sensing tasks with certain probabilities, and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time be no larger than a deadline. In order to solve this problem, we propose a greedy approximation algorithm, which can achieve the logarithmic approximation ratio.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job over urban environments. In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing. Unlike previous works, mobile users in this problem perform sensing tasks with probabilities, and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time is no larger than a deadline. Owing to such a probabilistic collaboration, DUR can be formalized as a non-trivial set cover problem with non-linear programming constraints and an optimization objective of real function. We first prove that the DUR problem is NP-hard. Then, we propose a greedy DUR algorithm, called gDUR, to solve this problem. Next, we prove that the gDUR algorithm can achieve a logarithmic approximation ratio. Furthermore, we extend the problem to a more complex case where sensing duration is taken into consideration, and we propose a sensing-duration-aware user recruitment algorithm, called dDUR. Finally, we validate the performance of the proposed algorithms through extensive simulations, based on a real mobile social network trace and a synthetic trace.

Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.

Nowadays, vehicles have shown great potential in crowdsensing. To guarantee a good Quality of Service (QoS), stimulating enough vehicles to participate in crowdsensing is very necessary. In this paper, we focus on the incentive mechanism design in the vehicle-based nondeterministic crowdsensing. Different from existing works, we take into consideration that each vehicle performs sensing tasks along some trajectories with different probabilities, and each task must be successfully performed with a joint probability no less than a threshold. Designing an incentive mechanism for such a nondeterministic crowdsensing system is challenging, which contains a non-trivial set cover problem with non-linear constraints. To solve the problem, we propose a truthful incentive mechanism based on reverse auction, including an approximation algorithm to select winning bids with a nearly minimum social cost, and a payment algorithm to determine the payments for all participants. Through theoretical analysis, we prove that our incentive mechanism is truthful and individual rational, and we give an approximation ratio of the winning bid selection algorithm. In addition, we conduct extensive simulations, based on a real vehicle trace, to validate the performances of the proposed incentive mechanism.

With the explosive proliferation of smartphones, many mobile cloud computing applications have emerged in recent years. These applications generally involve many data transmissions between mobile users and the cloud side. In order to reduce the monetary cost of these data transmissions, an effective approach is to offload partial data traffic from cellular networks to WiFi networks, when mobile users pass by some WiFi Access Points (APs). In this paper, we focus on the problem of offloading many deadline-sensitive data items to some WiFi APs with capacity constraints; that is, how to schedule each data item to the WiFi APs, so that we can offload as many data items before their deadlines as possible, while taking the constraints of transmission capacity into consideration. This problem involves a probabilistic combination of multiple 0-1 knapsack constraints, which differs from existing problems. To solve this problem, we propose a greedy oFfline Data Offloading (FDO) algorithm, and prove that this algorithm can achieve an approximation ratio of 2. Moreover, we extend our data offloading strategy to the online decision case, and propose an oNline Data Offloading (NDO) algorithm, which has a competitive ratio of 2. Finally, we demonstrate the significant performances of our algorithms through extensive simulations.

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.

Performing efficient data aggregation while keeping the property of privacy preservation of user-related data is of high concern. Extensive research has been conducted to address this problem in multiple areas. As a fact, the security of a number of privacy-preserving protocols are threatened by collusion between participants. Therefore, security analysis plays a fundamental role in privacy-preserving data aggregation protocols. In this paper, we present a new kind of collusion attack strategy called Hamburger Attack. It is of logical simplicity, but has the advantage of being effective and efficient. We employ it to check the security of several existing privacy-preserving data aggregation schemes. We show that under hamburger attack, some prior privacy-preserving data aggregation protocols will disclose part, even all, of the private data that they intended to protect. On the other hand, the hamburger attack is beneficial to designing new privacy-preserving data aggregation schemes. It can assist them in avoiding this kind of collusion attack.

A cyclic mobile social network (MSN) is a new type of delay tolerant network, in which mobile users periodically move around, and contact each other through their carried short-distance communication devices. In this paper, we introduce utility-based routing into cyclic MSNs, and propose a deadline-sensitive utility-based routing model. If a message is successfully delivered to its destination before a deadline, its source will receive a positive benefit as the reward. Otherwise, the source receives zero benefit. Also, each message delivery incurs a forwarding cost, no matter whether it succeeds or fails. The utility of a message delivery is defined as the benefit minus the forwarding cost. Under this model, we propose a deadline-sensitive opportunistic utility-based single-copy routing algorithm, DOUR. Each node first determines an optimal forwarding sequence, which is composed of a series of forwarding opportunities, in a distributed and greedy manner. Then, it forwards messages via these forwarding opportunities. Theoretical analysis and extensive simulations prove that DOUR can achieve the optimal utility for each message delivery. Moreover, we extend our algorithm to the case of multi-copy routing, and show that our proposed algorithms can inherently make a good tradeoff among the benefit, delay, and cost for each message delivery.

We study a spectrum auction problem where each request from new spectrum users has spatial, temporal, and spectral features. Our goal is to design truthful auction mechanisms that maximize either the overall social efficiency of new users (a.k.a buyers) or the revenue of the spectrum owner (a.k.a seller). Given that the optimal conflict-free spectrum allocation problem is NP-hard, this paper proposes a series of near-optimal auction mechanisms based on the following approximation techniques: linear programming (LP) relaxation, randomized rounding, derandomized rounding, monotone derandomization, and Lavi-Swamy method. Comparing with the prior art, we make two significant advances: First, our auction mechanisms are not only truthful but also provide theoretically-provable performance guarantee, an important feature that existing work under the same auction model does not have. Second, our auction mechanisms support both spatial and temporal spectral reuse, which makes the problem more challenging than existing work that deals with only spatial or temporal reuse. We perform extensive simulations to study the performance of the proposed mechanisms, and the simulation results corroborate our theoretical analysis.

Because data dissemination is crucial to wireless sensor networks (WSNs), its energy efficiency and reliability are of paramount importance. While achieving these two goals together is highly nontrivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it. To facilitate our algorithm design, we propose the novel concept of Time-Reliability-Power (TRP) space as a general data structure for designing data dissemination algorithms in WSNs, and the performance ratios of our algorithms based on the TRP space are proven to be$ {\cal O}(\log \Delta \log k)$for both multicast and broadcast, where$\Delta $is the maximum node degree in the network and$k$is the number of source/destination nodes involved in a data dissemination session. We also conduct extensive simulations to firmly demonstrate the efficiency of our algorithms.

A mobile social network (MSN) is a special kind of delay tolerant network (DTN) composed of mobile nodes that move around and share information with each other through their carried short-distance wireless communication devices. A main characteristic of MSNs is that mobile nodes in the networks generally visit some locations (namely, community homes) frequently, while visiting other locations less frequently. In this paper, we propose a novel zero-knowledge multi-copy routing algorithm, homing spread (HS), for homogeneous MSNs, in which all mobile nodes share all community homes. HS is a distributed and localized algorithm. It mainly lets community homes spread messages with a higher priority. Theoretical analysis shows that HS can spread a given number of message copies in an optimal way when the inter-meeting time between any two nodes and between a node and a community home follows independent and identical exponential distributions, respectively. We also extend HS to the heterogeneous MSNs, where mobile nodes have different community homes. In addition, we calculate the expected delivery delay of HS, and conduct extensive simulations. Results show that community homes are important factors in message spreading. By using homes to spread messages faster, HS achieves a better performance than existing zero-knowledge MSN routing algorithms, including Epidemic (with a given number of copies), and Spray&Wait.

Utility-based routing is a routing scheme based on a special composite utility metric. The existing utility-based routing algorithms have not yet considered the delivery delay, so that they cannot work well in low-duty-cycle wireless sensor networks (WSNs). In this paper, we present a time-sensitive utility model. A successful end-to-end message delivery will obtain a positive benefit, which linearly decreases along with an increasing delivery delay; otherwise, a failed delivery will receive zero benefit. The utility is the benefit minus the total transmission costs, no matter if the message delivery succeeds or fails. Such a utility model is analogous to the postal service in the real world. Under this novel utility model, we design two optimal time-sensitive utility-based routing algorithms for the non-retransmission setting and the retransmission-allowed setting, respectively. In our designs, we derive an iterative formula to compute the expected utility of each message delivery, and we present a binary search method to determine the optimal retransmission times. As a result, the two algorithms can achieve the optimal expected utility for each message delivery, which is the optimal balance among the concerned factors, including benefit, reliability, delay, and cost. The simulation results also prove the significant performances of our proposed algorithms.

A mobile opportunistic social network (MOSN) is a new type of delay tolerant network (DTN), in which the mobile users contact each other opportunistically. While cooperative caching in the Internet has been studied extensively, cooperative caching in MOSNs is a considerably different and challenging problem due to the probabilistic nature of contact among the mobile users in MOSNs. In order to reduce the total access delay, we let the mobile users cooperatively cache these data items in their limited buffer space. We balance between selfishness (caching the data items according to its own preference) and unselfishness (helping other nodes to cache). The friends with higher contact frequency may share similar interests, hence, caching the data items for friend users can lead to some benefit. In this paper, we present a hierarchical cooperative caching scheme, which divides the buffer space into three components: self, friends, and strangers. In the self component, mobile users cache the data items according to their preference. In the friends component, mobile users help their friends to cache some data items. In the strangers component, mobile users randomly cache the remaining data items. We formally analyze the access delay of the proposed scheme. The effectiveness of our approach is verified through extensive real world trace-driven simulations.

With the recent technical advances and popularization of smartphones, which are able to store, display, and transmit various types of media content, message forwarding in opportunistic mobile social networks has become a hot topic. In this paper, we propose a social-aware single-copy routing approach, which leverages the internal social feature information to resolve the social distance between the source and destination step-by-step. We introduce an optimal social feature forwarding set selection scheme for routing guidance, which is proven to achieve small message forwarding delay. We convert the routing process into an iterative 2-hop routing scheme, with a novel transition probability to measure the delivery delay of each hop. The transition probability is according to the community structure in each social feature space. Extensive simulations on both real and synthetic traces are conducted in comparison with several existing state-of-art approaches.

Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, these algorithms tend to forward messages to the nodes with locally optimal social characteristics, and thus cannot achieve the optimal performance. In this paper, we propose a distributed optimal Community-Aware Opportunistic Routing (CAOR) algorithm. Our main contributions are that we propose a home-aware community model, whereby we turn an MSN into a network that only includes community homes. We prove that, in the network of community homes, we can still compute the minimum expected delivery delays of nodes through a reverse Dijkstra algorithm and achieve the optimal opportunistic routing performance. Since the number of communities is far less than the number of nodes in magnitude, the computational cost and maintenance cost of contact information are greatly reduced. We demonstrate how our algorithm significantly outperforms the previous ones through extensive simulations, based on a real MSN trace and a synthetic MSN trace.

A mobile social network (MSN) is a special delay tolerant network (DTN) composed of mobile nodes with social characteristics. Mobile nodes in MSNs generally visit community homes frequently, while other locations are visited less frequently. We propose a novel zero-knowledge MSN routing algorithm, homing spread (HS). The community homes have a higher priority to spread messages into the network. Theoretical analysis shows that the proposed algorithm can spread a given number of message copies in an optimal way when the inter-meeting times between any two nodes and between a node and a community home follow exponential distributions. We also calculate the expected delivery delay of HS. In addition, extensive simulations are conducted. Results show that community homes are important factors in efficient message spreading. By using homes to spread messages faster, HS achieves a better performance than existing zero-knowledge MSN routing algorithms, including Epidemic, with a given number of copies, and Spray&Wait.

In this paper, we propose a time-sensitive utility model for delay tolerant networks (DTNs), in which each message has an attached time-sensitive benefit that decays over time. The utility of a message is the benefit minus the transmission cost incurred by delivering the message. This model is analogous to the postal service in the real world, which inherently provides a good balance between delay and cost. Under this model, we propose a Time-sensitive Opportunistic Utility-based Routing (TOUR) algorithm. TOUR is a single-copy opportunistic routing algorithm, in which a time-sensitive forwarding set is maintained for each node by considering the probabilistic contacts in DTNs. By forwarding messages via nodes in these sets, TOUR can achieve the optimal expected utilities. We show the outstanding performance of TOUR through extensive simulations with several real DTN traces. To the best of our knowledge, TOUR is the first utility-based routing algorithm in DTNs.

Because data dissemination is crucial to Wireless Sensor Networks (WSNs), its energy-efficiency and reliability are of paramount importance. While achieving these two goals together is highly non-trivial, the situation is exacerbated if WSN nodes are duty-cycled (DC) and their transmission power is adjustable. In this paper, we study the problem of minimizing the expected total transmission power for reliable data dissemination (multicast/broadcast) in DC-WSNs. Due to the NP-hardness of the problem, we design efficient approximation algorithms with provable performance bounds for it.

Web services (WSs) can cooperate with each other to provide more valuable WSs. Current approaches for WS cooperation have typically assumed that WSs are always willing to participate in some form of cooperation, and have undermined the fact that WSs are autonomous in this open environment. This assumption, however, becomes more problematic in community-based WS cooperation due to the dynamic nature of WS community. It is, therefore, important to devise a cooperation scheme respecting WS autonomy for community-based WS cooperation. In this paper, we model the community-based cooperation among autonomous WSs as a coalitional game in graph form. We show this game is non-cohesive and design a distributed coalition formation algorithm. We prove that the proposed algorithm can lead to an individually stable coalition partition, which indicates that every WS can maximize its benefit through cooperation without decreasing other WSs' benefit. We also conduct extensive simulations, and the results show that the proposed algorithm can greatly improve the average payoff per WS and average availability per coalition when compared with other cooperation schemes.

Utility-based routing is a special routing approach, which takes the reliability and transmission costs into account at the same time. However, the existing utility-based routing algorithms have not yet considered the delivery delay. Thus, they cannot work well in duty-cycle wireless sensor networks (WSNs) since delay is an important factor in such WSNs. In this paper, we propose a novel utility model time-sensitive utility model. Unlike previous work, the utility of a message delivery in our model is not only affected by the reliability and transmission costs but also by the delivery delay. Under the time-sensitive utility model, we derive an iterative formula to compute the time-varying utility of each message delivery. Based on the formula, we propose an optimal time-sensitive utility-based routing algorithm. The theoretical analysis and simulation results show that our proposed algorithm can maximize the average utility of message deliveries, which makes a good tradeoff among reliability, delay, and cost.

Service recommendation in a Web of services with uncertain QoS is a challenging problem. In this paper, we propose a reputation-based service recommendation framework. We formally define a service reputation model that analyzes the relations between uncertain QoS and reputation. We also devise a two-phase planning approach to constructing a composite service as the recommendation when none of existing services can fulfill the user's requirements alone. Furthermore, we design a utility difference based approach that can fairly distribute the overall rating of a composite service to its component services and theoretically prove its fairness. We evaluate the efficiency and fairness of our framework on a publicly available dataset: ICEBE05.

Recent years, researchers have proposed numerous positioning algorithms for wireless sensor networks, in which range-free schemes are more attractive because of their low-cost advantage. However, in practical circumstance, the raw range-free information (neighboring set and hop-counts et al.) is usually unreliable due to combined factors such as irregular signal patterns, environment noise and so on. To make matters worse, the unreliable localization information will lead to the reduction of localization accuracy. In order to tackle this problem, we propose a Reputation-based Revising Scheme (RRS) to preprocess the raw localization information before applying any positioning algorithm. In RRS, we reevaluate the reliability of raw information by utilizing the neighboring relationship. After being processed through RRS, the raw information is close to the information collected in ideal environment. According to our detailed simulations, we demonstrate that the revised localization information has 27% lower average error rate than the raw one. Based on the revised information, the traditional range-free localization algorithms are capable to achieve higher accuracy.

Along with the standardization of Web services composition language and the widespread acceptance of composition technologies, Web services composition is becoming an efficient and cost-effective way to develop modern business applications. As Web services are inherently unreliable, how to deliver reliable Web services composition over unreliable Web services is a significant and challenging problem. In this paper, we propose FACTS, a framework for fault-tolerant composition of transactional Web services. We identify a set of high-level exception handling strategies and a new taxonomy of transactional Web services to devise a fault-tolerant mechanism that combines exception handling and transaction techniques. We also devise a specification module and a verification module to assist service designers to construct fault-handling logic conveniently and correctly. Furthermore, we design an implementation module to automatically implement fault-handling logic in WS-BPEL. A case study demonstrates the viability of our framework and experimental results show that FACTS can improve fault tolerance of composite services with acceptable overheads.

Topology control problem has been deeply studied in wireless sensor networks. Some methods try to minimize the maximal or average link interference, which may result in larger accumulative interference than the optimum for some node pairs. In this paper, we define the concept of path interference formally. And the path interference of the topology constructed by LIFE (Low Interference Forest Establisher) algorithm is even about O(D) factor of the optimum, where D is the network diameter. Thus, a localized algorithm, called LDPA, is presented to construct an efficient topology which guarantees the minimal path interference among any node pair. Moreover, we study the trade-off between the path interference and energy cost, thus define the problem of ε -interference. And an efficient algorithm, called ε -PI, is presented to solve it. The paper also theoretically proves that the proposed algorithms can satisfy the certain properties, such as minimal path interference or approximate path interference as well. And the experimental results show that the average path interference of LDPA is less than 1/2 of that of LIFE algorithm.

It is significant to deploy a mobile sensor network periodically sweeping from the upriver to the downriver to monitor the water quality of the third large river in China. An important problem in this network is how to maximize the dynamic coverage efficiency while maintain the connectivity of network. To solve this problem, we borrow the idea of flocking-birds model in artificial life field and propose a distributed coverage and connectivity control algorithm DC3. The simulation result shows that the dynamic coverage efficiency of this algorithm is significantly better than that of the random mobility model.

Although topology control has been extensively studied for stationary sensor networks, few theoretical results on topology control for mobile sensor networks (MSN) have been reported yet. In this paper, we propose two O(n3) time topology control algorithms, centralized and distributed versions respectively, under a variant rate mobile network model (VRMN). In VRMN each sensor node may move at a variant speed and associated with each node is its starting and ending positions in the unit time interval. Our algorithms are proactive such that it can guarantee the topology of a given instance of MSN can continuously connected in the unit time interval, regardless of how much the moving speeds of the sensor nodes are. Extensive simulations have been conducted and the simulation results reveal that the resultant topology after applying our distributed topology control algorithm shows good performances in terms of average transmission radius and connectivity.

QoS-aware Web services composition has recently received much attention. While most work focused on service selection, we study QoS in another stage of the life cycle of composite services, namely, scheduling. An interesting problem is whether we can obtain better QoS via scheduling even when the component services have been fixed. In this paper, we propose an approach to find an optimal (near-optimal) schedule with the least cancellation cost, which can further improve the overall QoS of composite services. An approach to analyze the expected cancellation cost of a schedule of a composite service is proposed and QoS-Aware service scheduling is formalized as a Constraint Satisfaction Optimization Problem (CoSOP). Two algorithms - heuristic back tracking and genetic algorithm - are presented to find an optimal (near-optimal) schedule, and their performance is studied by simulations. Preliminary experimental results show that our approach is effective.

In multi-radio wireless mesh networks, each node can be equipped with multiple network interface cards tuned to different channels. In this paper, we present a routing and channel selection algorithm for reducing the broadcast redundancy in multi-radio wireless mesh networks. In our approach, the concept of Relaying Channel Redundancy is proposed, which is the sum of the number of different channels selected by each forward node in a broadcast tree. Our aim is to build a broadcast tree with minimum Relaying Channel Redundancy. We prove that building such a broadcast tree is a NP-hard problem, and propose an approximate algorithm for it. Our algorithm has an approximation ratio of at most 20delta5+2, wheredeltais the number of available non-overlapping channels. Finally, the distributed implementation of our algorithm is also presented.

Currently, BPEL is the de-facto standard for the web service composition. Because web services are autonomous and loosely coupled, BPEL processes are susceptible to a wide variety of faults. However, BPEL only provides limited constructs for handling faults, which makes fault handling a time-consuming and error-prone task. In this paper, we propose a declarative approach to enhancing the reliability of BPEL processes. Our solution specifies fault handling logic through a set of Event- Condition-Action (ECA) rules which build on an extensible set of fault-tolerant patterns. These ECA rules are integrated with normal business logic before deployment to generate a fault-tolerant BPEL process. We also develop a GUI tool to assist designers to specify ECA rules. Experiments show our approach is feasible.

This paper proposes an efficient algorithm for P2P file sharing systems based on WiMAX mesh mode and Wi-Fi technologies. Wireless networks in our system are hierarchically divided into three layers: Wi-Fi based wireless local area networks under a subscriber station; the mesh network of subscriber stations under a base station; the network of base stations. File lookup procedure may go through three steps: the requesting end host firstly looks up for the requested file within it's neighbor end hosts under the same subscribe station; if fails, then it sends lookup messages to the nearest subscriber stations in the mesh network following to a changed dynamic source routing protocol; if both the steps fail, then the requesting end host searches the requested file through the chord-based base stations' overlay. By statistical analysis and simulations, we prove that our layered P2P file sharing system can reduce the number of lookup messages in physical networks and prevent over expenses of precious bandwidth in wireless metropolitan area networks

For the problem of decision tree classification with privacy concerns, we propose several efficient secure multi-party computation protocols to construct a privacy preserving ID3 algorithm over horizontally partitioned data among multiple parties. Our algorithm presents the first solution to privacy preserving decision tree classification among more than two parties. We also make a performance comparison with the existing solution, which is only applicable to the twoparty case. The result shows that our solution has a significantly better performance.