The discovery of protein-protein interaction (PPI) sites is vital for exploring the principles of PPIs. Developing computational approaches to predict PPI sites can effectively compensate for biological experiments, which are mostly time-consuming and vulnerable-to-noise. In recent years, deep learning has been used to predict PPI sites, by considering the contextual information of target amino acid residues and using a local protein sequence to represent the targets. However, traditional deep-learning techniques, e.g., DNNs and CNNs, disregard important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address PPI site prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other existing competing schemes in the four key metrics of F1, MCC, AUROC, and AUPR.

Understanding protein functions not only helps to deepen our understanding of biological processes, but also provides a crucial biological basis for disease diagnosis, treatment, and drug development. With the development of deep learning, various computational methods have been proposed to improve annotation efficiency. Most existing methods did not consider the inherent flaws of various data, including the inaccuracy of homology data due to protein variations, and the noise of protein-protein interaction data due to incompleteness and diversity of data sources. Moreover, most existing methods combine multimodal data sources by directly concatenating the features extracted from different modalities without considering the collaborative effect of these features. To address these issues, we propose a new deep learning framework, MFPFP, by designing a heterogeneous graph construction strategy that not only better integrates homology and interaction information, but also enriches the graph with potential latent relationships. Based on the constructed graph, we further design a graph attention mechanism that can leverage the collaborative effect of the two data sources for mutual verification, which can automatically extract important features and reduce noise interference. Moreover, we design a tailored gated fusion module to integrate network information and sequence data, ultimately obtaining comprehensive features for prediction. Compared with eight state-of-the-art methods, MFPFP outperforms in both Fmax and AUPR metrics. Ablation experiments further validate the important contribution of each module in the MFPFP model.

Existing sequence-based protein-protein interaction (PPI) prediction methods face a critical bottleneck: the quadratic computational complexity of standard Transformer attention mechanisms often necessitates models to truncate protein sequences, leading to information loss and performance limitations. This paper introduces ProtAgent-ESM2, a novel framework that overcomes this limitation by integrating a protein-specific Agent Attention mechanism into the ESM-2 backbone. Our framework decomposes dense attention interactions into two sparse stages through learnable agent tokens, reducing computational complexity from quadratic to near-linear while maintaining global context modeling capabilities. We implement protein-specific adaptations, including asymmetric rotary position embeddings, local structure modeling through depthwise separable convolutions, and a hybrid embedding strategy that combines static ESM-2 representations with dynamic task-specific features. To validate our framework, we construct Intra-2-Long, a challenging benchmark specifically designed to evaluate performance on long protein sequences. Experimental results demonstrate that ProtAgent-ESM2 achieves competitive performance on standard benchmarks while maintaining consistent performance across varying sequence lengths, advancing the state-of-the-art in computational PPI prediction.

Multi-UAV cooperative search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a multilayered aerial computing network (MACN) scenario, which consists of a low-altitude platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a high-altitude platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of search probability map (SPM), and meanwhile maximizing the number of target discovery and coverage rate. The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a multiagent deep reinforcement learning (MADRL) approach based on the parameter sharing and action mask (PSAM), called PSAMMA, where the state-action-reward-state-action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that 1) the proposed PSAMMA algorithm outperforms existing algorithms in the literature, and can increase the average utility by 9.89%–31.15% and 2) we evaluate the search performance by analyzing the average uncertainty, target rate, and coverage rate under different parameter settings.

Multi-UAV Cooperative Search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a Multi-layered Aerial Computing Network (MACN) scenario, which consists of a Low-Altitude Platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a High-Altitude Platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of Search Probability Map (SPM). The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach based on Parameter Sharing and Action Mask (PSAM), called PSAMMA, where the State-Action-Reward-State-Action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that the proposed PSAMMA algorithm outperforms existing methods in terms of the average SPM uncertainty, the target discovery rate, and the coverage rate.

Federated learning (FL) is emerging as a new privacy-preserving learning paradigm that allows multiple devices to collaborate in training a model without sharing their raw data, using a central server for coordination. However, device heterogeneity poses a challenge in FL, as participating devices often have different computing capacities. To address this issue, heterogeneous models need to be designed to accommodate different device computing capacities. The existing approach involves pre-designing multiple heterogeneous models and extracting sub-models from the server model. While such an approach effectively tackles device heterogeneity, it has several drawbacks such as high communication overhead and insufficient personalization. In each training round, the server distributes the entire model parameters to each device, and each device also transmits the entire model parameters to the server for aggregation. In this work, we propose FedPartial, a new framework that overcomes these challenges by introducing a partial model transmission and aggregation mechanism. The FedPartial framework eliminates the need for devices to transmit the entire model parameters in each training round while still benefiting from global model aggregation. Specifically, FedPartial divides the device model into two parts: the shallow part participates in the global aggregation of heterogeneous models, while the deep part remains on the device locally. By keeping the deep part of the model on the device, FedPartial reduces the communication overhead significantly and achieves a certain degree of personalization. Through extensive experiments, we demonstrate that FedPartial outperforms existing state-of-the-art methods, particularly in more complex and statistically heterogeneous scenarios.

This paper studies information freshness in collaborative surveillance scenarios operated with non-orthogonal multiple access (NOMA), where each monitoring device observes a portion of a common target and reports its latest status on the target to a common access point (AP) to recover the complete observation. We use age of collection (AoC) as a metric of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when the AP receives all partial updates from different devices (i.e., successfully receives a “joint” update). Conventional NOMA schemes typically use multiuser decoding (MUD) techniques to decode update messages from different devices. However, MUD does not work well when the signal-to-noise ratios (SNRs) of different NOMA users are (nearly) balanced. Therefore, we consider network-coded multiple access (NCMA), an advanced NOMA scheme that integrates MUD with physical-layer network coding (PNC). PNC is a technique that turns wireless interferences into useful network-coded information, which works well even when the SNRs of different users do not differ much. Experimental results on software-defined radios indicate that NCMA is a practical solution for achieving low average AoC under different channel conditions. This is the first study to show that NCMA, thanks to the combination of MUD and PNC, can receive joint update messages in a shorter period of time, thus significantly reducing the average AoC of the system.

While computing task offloading in mobile edge computing (MEC) has been extensively studied, existing research has primarily focused on the mobility-awareness arising from opportunistic contact between edge devices. However, in a crowdsourced MEC system, it is essential to incentivize edge users to relocate for offloading tasks to crowdsourced edge devices. This movement-aware task offloading is particularly important for the crowdsourced system operation and has not yet been thoroughly explored. Moreover, the situation becomes more complex when users are socially connected and D2D-enabled, yet few works have considered these characteristics in combination, particularly from an economic incentive perspective. Therefore, a new incentive framework is needed to analyze the economic issues comprehensively. In this work, we focus on designing a truthful incentive mechanism, where socially-connected D2D users can be incentivized to move around for offloading tasks. To truthfully elicit private information, we model the resource allocation between edge devices and users as a multi-seller multi-buyer double auction mechanism with realistic MEC constraints, such as delay and storage limitation. Theoretically, we show that the proposed mechanism is computationally efficient and achieves desirable economic properties, including truthfulness, individual rationality, and budget balance. Simulations demonstrate that the mechanism achieves good system efficiency, with a performance improvement of 20% compared to state-of-the-art baselines.

As a typical application of mobile crowdsourcing, streaming media has been attracting increasing attention since recent years. However, traditional streaming media platforms, such as Netflix, Disney+, and Hulu, may suffer some problems like inflexible billing modes, lacking sustainability in the incentive mechanisms, and management censorship. These problems may lead to a decrease in user participation rate, which will directly affect the interests of streaming media platforms. To address these issues, we propose a secure, efficient and flexible streaming media platform framework based on blockchain and well-designed smart contracts. In particular, we design a new billing model based on pay-as-you-go strategy and a new incentive mechanism with probabilistic payment technique. To improve the fairness of our incentive model, we introduce a secondary fee refund protocol where a user’s second consecutive payment could be refunded, which in turn can attract more users to participate in the platform. Since blockchain has the natural properties of decentralization and transparency, the proposed framework is resistant to censorship and enables the transactions to be publicly auditable. Based on the proposed framework, we have implemented two streaming media platform schemes. Scheme I relies primarily on smart contracts to implement the framework’s functionality, while Scheme II moves the main flow of framework to off-chain channels. As the execution of smart contracts requires transaction fees, Scheme I is more expensive but can provide much more security and accountability as well. Scheme II can execute the transaction process much faster and with only a small transaction fee. Finally, we deployed these two schemes on Ropsten and conduct a series of experiments. The results show the effectiveness and efficiency of the proposed schemes.

Mobile-edge computing (MEC) is a promising computing scheme to support computation-intensive AI applications in vehicular networks, by enabling vehicles to offload computation tasks to edge computing servers deployed on road side units (RSUs) that approximate to them. In this work, we consider an MEC-enabled vehicular edge network (VEN), where each vehicle can offload tasks to edge/cloud computing servers via vehicle-to-infrastructure (V2I) links or to other end-vehicles via vehicle-to-vehicle (V2V) links. In such acloud-edge–endcollaborative offloading scenario, we focus on the joint task offloading, scheduling, and resource allocation problem for vehicles, which is challenging due to the online and asynchronous decision-making requirement for each task. To solve the problem, we propose aMultilayer deep reinforcement learning(DRL)-based approach, where each vehicle constructs and trains three modules to make different layers’ decisions: 1)Offloading Module(first layer), determining whether to offload each task, by using the dueling and double deepQ-network (D3QN) framework; 2)Scheduling Module(second layer), determining where and how to offload each task in the offloading queues, together with the transmission power, by using the parameterized deepQ-network (PDQN) framework; and 3)Computing Module(third layer), determining how much computing resource to be allocated for each task in the computation queues, by using classic optimization techniques. We provide the detailed algorithm design and perform extensive simulations to evaluate its performance. Simulation results show that our proposed algorithm outperforms the existing algorithms in the literature, and can reduce the average cost by 25.86%–72.51% and increase the average satisfaction rate by 3.48%–90.53%.

Mobile edge caching is promising to improve content delivery and alleviate backbone burden by caching contents at the network edges. The commercial deployment relies on a comprehensive understanding of the economic interactions involved. This paper studies the edge caching enabled Internet ecosystem including a Content Provider (CP), a Global ISP (G-ISP) providing backbone services, a Local ISP (L-ISP) providing access services, and End-Users (EUs). The CP serves EUs via Internet servers or L-ISP's edge cache. We formulate their multi-tiered interactions as a three-stage dynamic game. In Stage I, CP determines edge cache storage to purchase from L-ISP and cache access fee to charge EUs. In Stage II, G-ISP and L-ISP determine backbone and access prices. In Stage III, EUs decide whether to choose edge cache services, considering cache hit probability, cache access fee, and backbone access prices. We analyze the subgame perfect equilibrium by elaborately designing five cases of EUs' choices, five regions of ISPs' pricing, and three patterns of CP's caching, undercooperativeandcompetitiveISP pricing scenarios. Our analysis demonstrates that adopting edge caching leads to win-win outcomes for all parties involved. Furthermore, we find that competitive pricing is more advantageous for CP's profit when cache costs are low, while cooperative pricing is more beneficial when cache costs are high.

Mobile edge computing (MEC) is a promising technology for improving the efficiency and security of mobile blockchain networks, by allowing miners with limited computing resources to offload the computation-intensive mining tasks to edge computing servers that are proximate to them. Collaborative block mining can further improve the mining efficiency and increase the miner profit, by enabling multiple miners to pool their computation resources and transaction data together to mine new blocks collaboratively. Thus, an MEC-assisted collaborative blockchain network can leverage the advantages of both technologies, offering superior efficiency, security, and scalability for blockchains. While existing research in this area mainly focused on the single-coalition collaboration mode where each miner can only join one collaborative coalition, this work explores a more comprehensive multi-coalition collaboration mode, which allows each miner to join multiple collaborative coalitions. To analyze the miner behavior in such a scenario, we formulate a novel two-layer sequential game, consisting of a coalition formation game as the first-layer and an edge resource competition game (among the formed coalitions) as the second layer. Specifically, in the first layer, each miner acts as a game player and selects multiple coalitions to join, leading to an overlapping coalition formation (OCF) game among miners. In the second layer, each established coalition acts as a game player and decides the amount of edge computing resource to invest, leading to an edge resource competition (ERC) game among coalitions. We derive the closed-form Nash equilibrium for the ERC game, and propose an iterative algorithm that converges to a stable coalition structure for the OCF game. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 34.1% ~ 54.3%, compared to the single-coalition collaboration mode.

Mobile edge caching (MEC) allows edge devices to cache popular contents and deliver them to end-users directly, which can effectively alleviate increasing backbone loads and improve end-users’ quality of service. Peer content sharing (PCS) enables edge devices to share the cached contents with others, and can further increase the caching efficiency. While many efforts have been made to content caching or sharing technologies, it remains open to study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint MEC-PCS framework, and focus on capturing the strategic interactions between different types of edge devices. Specifically, we model their interactions as a non-cooperative game, where each device (player) can choose to be an agent (who caches and shares contents with others) or a requester (who doesn’t cache but requests contents from others) of each content. We analyze the existence and uniqueness of the game equilibrium systematically under the generic usage-based pricing (for PCS). To address the incomplete information issue, we further design a behavior rule that allows players to achieve the equilibrium via a dynamic learning algorithm. Simulations show that the joint MEC-PCS framework can reduce the total system cost by 60%, compared with the benchmark pure caching system without PCS.

The recognition of protein-protein interaction sites (PPIs) is beneficial for the interpretation of protein functions and the development of new drugs. Traditional biological experiments to identify PPI sites are expensive and inefficient, leading to the generation of various computational methods to predict PPIs. However, the accurate prediction of PPI sites remains a big challenge due to the existence of the sample imbalance issue. In this work, we design a novel model that combines convolutional neural networks (CNNs) with Batch Normalization to predict PPI sites, and employ an oversampling technique Borderline-SMOTE to address the sample imbalance issue. In particular, to better characterize the amino acid residues on the protein chains, we employ a sliding window approach for feature extraction of target residues and their contextual residues. We verify the effectiveness of our method by comparing our method with the existing state-of-the-art schemes. The performance validations of our method on three public datasets achieve accuracies of 88.6%, 89.9%, and 86.7%, respectively, all showing improved accuracies compared with the existing schemes. Moreover, the ablation experiment results suggest that Batch Normalization can greatly improve the generalization and the prediction stability of our model.

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task.

Power demand forecasting plays a significant role in the operation of power plants and utility companies. For data privacy, federated learning (FL) is widely adopted to aggregate local models of utility companies to a global model with very few data leaks. However, defects such as malicious updates, poisoning attacks, and low-quality data, may exist in multiple FL processes. As the general resistance to various defects is not considered by most FL approaches, a design with strong generalization is strongly needed. In this paper, we adopt DEfect-AwaRe federated soft actor-critic (DearFSAC), which dynamically assigns weights to FL's local models according to their quality. For fast and stable convergence, a deep neural network based on auto-encoder is designed for model quality evaluation and dimension reduction. Then, a deep reinforcement learning (DRL) algorithm soft actor-critic (SAC) is adopted to achieve the optimal weights assignment, considering SAC's near-optimum and sufficient exploration. We conduct simulations on power consumption data in real world. The results show that our approach performs well no matter if there exist defects or not.

Federated Learning (FL) is a novel distributed learning framework to train the global model locally without collecting the raw data of clients. However, the performance of FL is greatly restricted by the limited network communication capacity between the cloud and clients. MEC-assisted Hierarchical Federated Learning (HFL) can effectively relieve the network pressure in FL, by transmitting and aggregating model parameters at the network edge based on the idea of Mobile Edge Computing (MEC). The existing researches on HFL often adopt the traditional multiple access techniques (e.g., OFDMA) for the model transmission between clients and edge servers, which may be inefficient. In this work, we consider a novel Over-the-Air Computing (AirComp) based HFL framework, where clients send model parameters to edge servers simultaneously, and edge servers can directly complete the aggregation of model in the air by exploiting the superposition property of wireless channels. In such a scenario, we study the joint client association, transmission, and computation optimization problem, aiming at minimizing the overall energy consumption and latency. The problem is challenging due to the multi-level coupling between edge servers and clients. We decouple it into a client association subproblem and a resource optimization subproblem. We first show that the second subproblem is convex and can be easily solved by a coordinate descent algorithm. We then show that the first subproblem is a combinational optimization, and propose a near-optimal solution where each client is associated with the nearest edge server. Simulation results show that the AirComp-based HFL scheme outperforms the existing OFDMA-based schemes in terms of both energy consumption and latency.

Next-generation wireless technology and machine-to-machine technology can provide the ability to connect and share data at any time among IoT smart devices. However, the traditional centralized data sharing/trading mechanism lacks trust guarantee and cannot satisfy the real-time requirement. Distributed systems, especially blockchain, provide us with promising solutions. In this paper, we propose a blockchain based non-repudiation scheme for IoT data trading to resolve the credibility and real-time limits. The proposed scheme has two parts, i.e., a trading scheme and an arbitration scheme. The trading scheme employs a divide-and-conquer method and two commitment methods to support efficient IoT data trading, which runs in a two-round manner. The arbitration scheme first leverages a smart contract to solve disputes on-chain in real time. In case of on-chain arbitration dissatisfaction, the arbitration scheme also employs an off-line arbitration to make a final resolution. Short-term and long-term analysis show that the proposed scheme enforces non-repudiation among the data trading parties and runs efficiently for rational data owners and buyers. We implemented the proposed scheme. Experimental results confirm that the proposed scheme has an orders-of-magnitude performance speedup than the state-of-the-art scheme.

Cloud storage synchronization among different computing terminals has attracted large-scale uses among enterprise and individual users. It enables users to maintain the same copy of data in real time, which eases users the tedious yet error-prone data management burden. However, existing cloud storage synchronization systems are in a closed form. Users are fixed to a certain cloud service provider, which makes it hard to transfer from one provider to another when balancing factors such as performance, cost, security, etc. To bridge this gap, this article proposes a new synchronization system based on standard cloudobjectstorage. Specifically, we first formulate the cloud object storage synchronization problem by defining some useful concepts. We then use the idea of state encoding and a push-pull paradigm to propose a cloud object storage synchronization system. The proposed system supports real-time, multiple-terminal, and cloud-independent storage synchronization. We also prototyped the proposed system. The experimental results show that the proposed system is promising for practical usages.

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the incentive-related problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time. Based on the above, we further characterize the system equilibrium systematically. Simulation results show that the proposed scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulations, the total transmission cost can be reduced by 42.5%~55.2% and the cellular load can be reduced by 21.5%~56.4%.

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision making of different stakeholders in the ecosystem. In this article, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the CP designs the incentive scheme for EDs to cache and share contents, and EDs decide whether to cache and share contents for the CP. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue (as incentives) shared with EDs who choose to cache and share contents, aiming at maximizing its own profit. In Stage II, EDs choose to beagentswho cache and share contents, and meanwhile gain a certain revenue from the CP, orrequesterswho do not cache but request contents in the on-demand fashion. We first analyze the EDs’ best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the nonatomic game theory. Then, we identify the piecewise structure and the unimodal feature of the CP’s profit function, based on which we design a tailored low-complexity 1-D search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. The simulation results show that both the CP’s profit and the EDs’ total welfare can be improved significantly (e.g., by 120% and 50%, respectively,) by using the proposed Crowd-MECS system, comparing with the non-MEC system where the CP serves all EDs directly.

Mobile crowdsourced edge caching is emerging as a promising caching paradigm by crowdsourcing the storage resources of massive edge devices (EDs) for content caching. The successful technology adoption and commercial deployment rely on a comprehensive understanding of the economic interactions among different network entities involved in such a system. In this paper, we focus on the economic interactions between one content provider (CP) and a large number of EDs, where the CP shares a certain revenue with EDs as the incentive of caching contents, and EDs decide whether to cache contents and share the cached contents with others. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue shared with EDs, aiming at maximizing its own profit. In Stage II, each ED chooses to be an agent who caches contents and shares the cached contents with other EDs, or a requester who does not cache but requests contents from agents. e first analyze the existence and uniqueness of the Stage II subgame equilibrium by using the evolutionary game theory. Then, we identify the piece-wise structure of the CP's profit function, and derive the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that a higher revenue sharing ratio for EDs or a larger serving capacity of EDs can drive more EDs to choose to be agents and meanwhile achieve a higher total welfare for EDs at the equilibrium. Moreover, a larger content price of the CP will lead to a larger welfare loss for EDs.

Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.

Mobile crowdsensing (MCS) is a novel and appealing sensing paradigm that leverages the diverse embedded sensors of massive mobile devices to collect different kinds of data. One of the key challenges in MCS is to efficiently schedule mobile device users to perform different sensing tasks. Prior effort to this problem mainly focused on the interaction between the task-layer and the user-layer, without considering the similar data requirements of tasks and the heterogeneous sensing capabilities of users. In this work, we introduce a new data-layer between tasks and users, and propose a three-layer data-centric MCS framework, which enables different tasks to reveal their common data requirements and hence reuse the common data items. We focus on studying the joint task selection and user scheduling problem under this new framework, aiming at maximizing the social welfare. Specifically, we first analyze theoretical performance gain due to data reuse in the ideal scenario with complete information. We then consider the practical scenario with private information of both tasks and users, and propose a two-sided randomized auction mechanism, which is computationally efficient, individually rational, incentive compatible (truthful) in expectation, and close-to-optimal. We further show that the proposed randomized auction may not be budget balanced, and hence introduce a reserve price into the auction to achieve the desired budget balance at the cost of certain welfare loss. Simulation results show that with data reuse, the social welfare achieved in the proposed randomized auction can be increased from 270 up to 4,500 percent, comparing with those without data reuse.

Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices' computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market, where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.

Mobile crowdsensing emerges as a promising sensing paradigm through leveraging the diverse embedded sensors in massive mobile devices. A key objective in mobile crowdsensing is to efficiently schedule mobile device users to perform multiple sensing tasks. Prior work mainly focused on the interactions between the task layer and the user layer, without considering the similarity of tasks' data requirements and the heterogeneity of users'sensing capabilities. In this work, we propose a three-layer data-centric crowdsensing model by introducing a new data layer between tasks and users, which allows us to effectively leverage both the task similarity and the user heterogeneity. We formulate a joint task selection and user scheduling problem on top of the data layer, aiming at maximizing the social welfare. This problem is difficult to solve due to the combinatorial nature as well as the two-sided private information of tasks and users. To address both issues, we propose a two- sided randomized auction mechanism, which is computationally efficient, individually rational, and incentive compatible in expectation. Simulations show that (i) the proposed randomized auction can achieve 90% of the maximum social welfare (benchmark), and (ii) the social welfare gain due to data reuse increases with the task similarity and reaches up to 1300% in our simulations.

This paper studies how to maximize a spectrum database operator's expected revenue in sharing spectrum to secondary users, through joint pricing and admission control of spectrum resources. A unique feature of our model is the consideration of the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and present the optimal solutions under both static and dynamic pricing schemes. In the case of static pricing, the prices do not change with time, although the admission control policy can still be time-dependent. In this case, we show that a stationary (time-independent) admission policy is in fact optimal under a wide range of system parameters. In the case of dynamic pricing, we allow both prices and admission control policies to be time-dependent. We show that the optimal dynamic pricing can improve the operator's revenue by more than 30% over the optimal static pricing, when secondary users' demands for spectrum opportunities are highly elastic.

Mobile crowdsensing is a new sensing paradigm relying on computation and storage capabilities of mobile devices. However, traditional server-client mobile crowdsensing models suffer from a high operational cost on the server, and hence a poor scalability. Peer-to- peer (P2P) mobile crowdsensing models can effectively reduce the server's operational cost, by leveraging the mobile devices' under-utilized computation and storage resources. In a P2P mobile crowdsensing model, the sensing data is saved and processed in mobile users' devices in a distributed fashion, and is shared among mobile users directly in a P2P manner. In this work, we focus on the incentive issue in such a P2P mobile crowdsensing model. Specifically, we propose a data market and a generic pricing scheme for the data sharing among data sensors and requesters. We analyze the user interactions in such a data market from a game theoretic perspective, and prove the existence and uniqueness of the market equilibrium. We further propose a generalized best response dynamics to reach the market equilibrium. Our theoretic analysis and numerical results indicate that the equilibrium social welfare decreases with the data transfer cost and data prices, while the ratio of the equilibrium social welfare to the maximum social welfare benchmark increases with the data transfer cost and data prices.

This paper solves the problem of long-term revenue maximization of a spectrum database operator, through joint pricing of spectrum resources and admission control of secondary users. A unique feature that we consider is the stochastic and heterogeneous nature of secondary users' demands. We formulate the problem as a stochastic dynamic programming problem, and consider the optimal solutions under both static and dynamic prices. In the case of static pricing, we constrain the prices to be time-independent while allowing the admission control policies to be time dependent. We show that in most cases a stationary (time independent) admission policy is in fact optimal in this case. We further look at the general case of dynamic pricing, where both the prices and admission control policies can be time dependent. We show that the flexibility of dynamic pricing can significantly improve the operator's revenue (by more than 30%) when secondary users have high demand elasticities.