Low Earth Orbit (LEO) satellite networks such as Starlink and Project Kuiper are increasingly integrated with cloud infrastructures, forming an important internet backbone for global web services. By extending connectivity to remote regions, oceans, and disaster zones, these networks enable reliable access to applications ranging from real-time WebRTC communication to emergency response portals. Yet the resilience of these web services is threatened by space radiation: it degrades hardware, drains batteries, and disrupts continuity, even if the space-cloud integrated providers use machine learning to analyze space weather and radiation data. Specifically, conventional fixes like altitude adjustments and thermal annealing consume energy; neglecting this energy use results in deep discharge and faster battery aging, whereas sleep modes risk abrupt web session interruptions. Efficient network-layer mitigation remains a critical gap. We propose RALT (Radiation-Aware LEO Transmission), a control-plane solution that dynamically reroutes traffic during radiation events, accounting for energy constraints to minimize battery degradation and sustain service performance. Our work shows that unlocking space-based web services' full potential for global reliable connectivity requires rethinking resilience through the lens of the space environment itself.

Federated Unlearning (FU) has emerged as a promising paradigm for effectively removing the influence of specific data of clients from the global model in federated learning. It can further enhance personal data privacy for individual clients and eliminate the impact of malicious attacks like poisoning. Due to these benefits, many FU methods have been analyzed and proposed. Yet, they largely overlook external threats against FU systems, such as gradient inversion attacks that reconstruct client data from shared gradients, posing serious privacy risks to other participating clients. Motivated by this, we propose DIARY, a Differential prIvacy IntegrAted fedeRated recoverY framework to address these dual threats. DIARY presents a privacy budget allocation method, whose insight lies in adaptively assigning appropriate privacy budgets to various training and recovery phases to fully utilize the global privacy budget of each client, balancing the trade-off between privacy and utility. Furthermore, DIARY introduces a novel Federated noise-Immune aNomaly Detection (FIND) module. The deep integration of FIND with two-level selective storage and model rollback mechanisms contributes to model recovery, while significantly reducing the associated overhead. Finally, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods are conducted to validate the effectiveness of DIARY.

Federated Learning (FL) is a promising learning paradigm that allows for training a shared model by coordinating multiple distributed devices, namely, clients, without exposing their raw data. To mitigate excessive communication overhead and enhance practicality, a variant known as Hierarchical FL (HFL) has been introduced, which integrates edge servers between the cloud server and clients. In HFL, the number of potential clients is typically large, making full client participation impractical due to various resource constraints. As a result, it is essential to develop a sampling strategy that effectively selects suitable clients for federated optimization. While several methods have been proposed to protect the privacy of communicated models, we argue that the outcomes of client sampling are closely tied to the local data of clients, thereby raising privacy concerns, like the risk of differential attacks. Motivated by this, we propose a Two-step Privacy-Preserving client Sampling framework (TPPS) designed to protect against both attacks on communicated models and potential vulnerabilities in client sampling outcomes. Initially, we consider the diverse privacy requirements of clients by presenting a double-layer noise mechanism. We then conduct a thorough analysis of the impact of this noise mechanism, proposing a novel client sampling strategy that seeks to balance the trade-off between privacy and training performance. The insight lies in maintaining a real-time sampling probability for each client, which can be acutely tuned based on personalized privacy needs and previous training feedback. We provably show that TPPS achieves local differential privacy, a bounded sampling regret, and a privacy-related convergence rate. Furthermore, we conduct extensive simulations based on open datasets, showing the robustness and applicability of TPPS in enhancing privacy while optimizing HFL performance.

Differential-privacy federated learning (DP-FL) has emerged as a promising paradigm capable of mitigating the inherent threat of traditional FL architectures that are vulnerable to inferential attacks due to the frequent exchange and updating of model parameters. However, existing DP-FL frameworks often assume that the client's perturbations remain constant throughout the FL process, while ignoring the varying influence of the client's perturbations in distinct communication rounds on the model performance. Besides, existing DP-FL frameworks posit the FL server as a fully rational actor, thereby neglecting the bounded rationality that the FL server may exhibit in the face of risk and uncertainty. In this paper, we propose a novel long-term (i.e., throughout the FL process) privacy-preserving FL framework to address the optimal incentive design, in the presence of the bounded rationality inherent in the FL server and the dynamic influence of perturbations on model performance. Specifically, we first investigate the impact of local perturbations of the client on the model's convergence performance in different communication rounds, elucidating the trade-off between learning performance and privacy loss. Then, to reconcile learning performance with privacy loss, we design a long-term privacy-preserving incentive scheme, where the interactions between clients and the FL server throughout the FL process are modeled as a multi-stage privacy-preserving game. Furthermore, by applying prospect theory (PT) to formulate the risk-aware behavior of the bounded rationality FL server, we employ contract theory to derive the equilibrium of the game, thereby ensuring optimality and fairness. Finally, extensive simulations illustrate that our scheme can motivate clients to provide high-quality models and improve the accuracy of the global model, compared with benchmarks.

The rapid evolution of wireless edge computing faces fundamental limitations from connection-oriented protocols, particularly in dynamic scenarios with distributed edge environments. In this paper, we present SWICE, a novel transmission system with modified frame injection techniques that eliminates connection establishment overhead while ensuring reliable data delivery in wireless distributed edge environments. Our system introduces a connection-free measurement strategy that uses minimal packets to assess communication status while keeping device identities private. We formulate an edge node selection problem for efficiency and develop a heuristic algorithm for optimal data delivery. Additionally, we implement a reliability mechanism that combines adaptive retransmission to enhance communication stability. We conduct real-world experiments with commercially available Wi-Fi devices and modified wireless radios. The results show that SWICE achieves up to a 36.88 % increase in goodput compared to conventional transmission methods, demonstrating its effectiveness through real-world experiments.

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.

As a fundamental tool for sequential decision-making, the Combinatorial Multi-Armed Bandits model (CMAB) has been extensively analyzed and applied in various online applications. However, the privacy concerns in budgeted CMAB are rarely investigated thus far. Few bandit algorithms have adequately addressed the privacy-preserving budgeted CMAB setting. Motivated by this, we study this setting using differential privacy as the formal measure of privacy. In this setting, playing an arm yields both a random reward and a random cost, and these values are kept private. In addition, multiple arms can be played in each round. The objective of the decision-maker is to minimize regret while subject to a budget constraint on the cumulative cost of all played arms. We demonstrate an exploration-exploitation-balanced bandit policy, which preserves the privacy of both rewards and costs under budgeted CMAB settings. This policy is proven differentially private and achieves an upper bound on regret. Furthermore, to provide incentives for the differentially private bandit policy so as to ensure that the reported costs are truthful, we introduce the concept of truthfulness and incorporate a payment mechanism that has been proven to be$\sigma$-truthful. Numerical simulations based on multiple real-world datasets validate the theoretical findings and demonstrate the effectiveness of our policy compared to state-of-the-art policies.

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing remote aerial vehicles (RAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers’ utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC; (2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as$O(\sqrt{T})$. Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches.

Inter-Satellite Links (ISLs) are pivotal for delivering global connectivity services and optimizing resource utilization in 6 G and beyond. However, delivering effective topology control services through ISL provisioning faces critical challenges insustainabilityandreliability. Reducing ISLs can conserve energy and extend satellite battery life for Low-Earth-Orbit (LEO) satellites where replacing batteries is impractical. Conversely, increasing ISLs can enhance service reliability but may lead to uneven traffic distribution, overloading nodes, and accelerating battery degradation, ultimately degrading the quality of 6 G services. To tackle this dilemma, we propose TASRI—a service-oriented framework forTraffic-Aware, Sustainable, and Reliable ISL provisioning. TASRI provides a dynamic topology control service by partitioning network topologies into logical zones, enabling flexible ISL activation and deactivation to adapt to varying service demands, ensuring efficient resource utilization and dynamic service orchestration. Using a sustainability-oriented weight model, we formulate the topology control service optimization problem and introduce a scalable on-demand topology evolving algorithm with a bounded approximation ratio. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability and excellent scalability with considerably fewer ISLs or ISL handovers.

Incentive mechanisms in Spatial Crowdsourcing (SC) have been widely studied as they provide an effective way to motivate mobile workers to perform spatial tasks. Yet, most existing mechanisms only involve single tasks, neglecting the presence of complementarity and substitutability among tasks. This limits their effectiveness in practice cases. Motivated by this, we consider task bundles for incentive mechanism design and closely analyze the mutual exclusion effect that arises with task bundles. We then develop a combinatorial incentive mechanism, including three key policies: In the offline case, we propose a combinatorial assignment policy to address the conflict between mutual exclusion and assignment efficiency. We next study the conflict between mutual exclusion and truthfulness, and build a combinatorial pricing policy to pay winners that yields both incentive compatibility and individual rationality. In the online case with unknown workers’ utilities, we present an online combinatorial assignment policy that balances the exploration-exploitation trade-off under the mutual exclusion constraints. Through theoretical analysis and numerical simulations using real-world mobile networking datasets, we demonstrate the effectiveness of the proposed mechanism.

Inter-Satellite Links (ISLs) are key for worldwide communication and efficient use of space networks in the future 6G network. However, they face challenges in sustainability and reliability. Reducing ISLs saves energy and extends battery life, which is critical since satellite batteries are hard to replace. More ISLs, however, can make the system more reliable but at the cost of higher energy use, especially problematic when traffic is uneven, speeding up battery wear. To tackle this dilemma, we for the first time develop a Traffic-Aware, Sustainable and Reliable ISL provisioning (TASRI) framework for LEO satellite constellation networks. In TASRI, ISLs can be flexibly switched on and off to better accommodate various traffic conditions as well as reliability and sustainability. We formulate the ISL provisioning problem based on the sustainability-oriented weight model and then propose an on-demand topology evolving algorithm. Extensive real-world deployment-based simulation results show that, compared to the state-of-the-art, our TASRI can substantially reduce battery life consumption, while achieving comparable reliability with considerably fewer ISLs.

Existing peer-to-peer data-sharing methods suffer from low data delivery efficiency and scalability due to the naive data request/response procedure and the high redundant data transmission rate. It becomes even worse in large-scale mobile networks considering the limited resources of mobile devices. To address this issue, this paper presents MobiShare, an efficient decentralized data-sharing approach for mobile devices, which allows users to not only share the data but also the data generation methods. To achieve MobiShare, we introduce a function block encoding method and a data request method to enhance sharing efficiency, minimizing costs for decentralized data sharing. We propose a credit payment mechanism where congested devices can send data vouchers instead of actual data, containing the expected transmission time. Based on the load and bandwidth of devices, we build the optimized dissemination tree with data vouchers in a decentralized way to improve scalability. Evaluation results show that MobiShare avoids redundant transmission. It greatly shortens transmission completion time and lowers energy consumption with limited network resources.

Satellite networks are believed to become an indispensable component in the forthcoming 6G network and beyond. The surging demands attract numerous satellite network operators into this market to compete, yet also cooperate via resource sharing for cost and performance improvement, which is similar to the growth trajectory of how the Internet becomes the network of networks. Hence, we envision a federated network of satellite networks (shortened as federated satellite network) in this paper, where satellite network operators will eventually federate with each other to achieve a win-win situation. However, the yet-to-come federated satellite network faces two unique challenges: sustainability and dynamic topology. As such, we propose a sustainable and service-differentiable framework named Federated Cross-orbit Satellite Network (FCSN). Different from most existing solutions which focused on the Internet or simple cooperation among satellites, the FCSN orchestrates network resources in the dynamic topology to improve sustainability, through service-differentiable offloading in the resource-limited scenario. We formulate the sustainability-oriented federated offloading problem based on the utility and cost models tailored for the FCSN and propose an efficient hardware-budget constrained auction algorithm with a bounded approximation ratio. Finally, we design a truthful and rational payment scheme to motivate the construction of the FCSN. Extensive simulation results based on real-world deployments show that our solution significantly improves sustainability and delay, making it one step further toward the vision of the federated network of satellite networks.

We study defending strategies against adversarial attacks onCombinatorial Multi-Armed Bandits(CMAB) algorithms. CMAB is an effective sequence decision making tool that has been broadly applied in online real-world applications. We consider a realistic CMAB setting, budgeted CMAB, in which multiple arms associated with pulling costs and unknown rewards are pulled per round, aiming to maximize the cumulative reward under a budget constraint. However, the adversarial attack against budgeted CMAB is rarely studied, posing a very important security issue. Specifically, a suboptimal arm that is not pulled (i.e., attacker) can hijack the budgeted CMAB algorithm's behavior, forcing itself to be pulled frequently by manipulating other arms' rewards. Existing strategies cannot prevent such attacks. Motivated by this, we closely study the adversarial attack against a popular budgeted CMAB algorithm, exposing a significant security threat to real-world applications. The attack extends to other algorithms with certain customization. To address this, we incorporate a truthful pricing-based defending strategy that prevents such attacks effectively and ensures arms share pulling costs truthfully. Extensive simulations have illustrated the proposed attack strategy can hijack the algorithm efficiently, while the defending strategy provides attack prevention, individual rationality, and asymptotic truthfulness guarantees.

Spatial Crowdsourcing (SC) has been adopted in various applications such as Gigwalk and Uber, where a platform takes location-based tasks (e.g., picking up passengers) from a requester and selects suitable workers to perform them. In most existing works, the platform selects workers based on the requester and worker information, which suffers from serious privacy issues. Some works have considered privacy issues, but they still suffer from either of two limitations: (i) Privacy of the requester and worker cannot be protected simultaneously; (ii) Third-party trusted entities are usually required. Motivated by this, we focus on protecting the privacy of both the requester and the worker without third-party entities while selecting workers. We use randomized response, a widely recognized and prevalent privacy model achieving Local Differential Privacy (LDP), to jointly protect the privacy of workers’ locations and charges based on the location-charge correlation. For the requester, we present a novel mechanism called randomized matrix multiplication to hide the real task locations. More importantly, we prove that the worker selection based on the protected information is non-submodular and NP-hard, which cannot be addressed in polynomial time. To this end, we present an approximate algorithm to solve the problem efficiently, of which the effectiveness is measured by the approximation ratio, i.e., the ratio of the optimal solution to the approximate solution. Finally, simulations based on real-world datasets illustrate that our worker selection outperforms the state-of-the-art method on both privacy protection and worker selection.

Mobile Crowdsensing (MCS) is a promising paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that the task completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. Furthermore, even though we can find a well-behaved group of users in the single-round scenario, while in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting uncertain user groups (exploration). Additionally, we consider that each user has a different cost, and the platform recruits users under a cost budget; thus, the problem becomes more challenging: users may report fake costs to gain more profits. To address these problems, assuming that the user's information is known, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the approximate solution. Then, in the multi-round scenario where the user's information is estimated from the previous rounds, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy under the budget constraint based on the combinatorial Multi-armed Bandit model (URMB), which is proven to achieve a tight regret bound. Next, we propose a graph-based payment strategy to achieve truthfulness and individual rationality of users. Finally, extensive experiments on three real-world datasets show that URMB always outperforms the state-of-th-art strategies.

Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted platforms to obtain a great quantity of information about workers and tasks’ locations, has raised numerous privacy concerns. In this paper, we investigate the privacy-preserving task assignment in the online scenario, where workers and tasks arrive at the platform in real time and tasks should be assigned to workers immediately. Traditional online task assignments usually make a benchmark to decide the following task assignment. However, when location privacy is considered, the benchmark does not work anymore. Hence, how to assign tasks in real time based on workers and tasks’ obfuscated locations is a challenging problem. Especially when many tasks could be assigned to one worker, path planning should be considered, making the assignment more challenging. To this end, we propose a Planar Laplace distribution based Privacy mechanism (PLP) to obfuscate real locations of workers and tasks, where the obfuscation does not change the ranking of these locations’ relative distances. Furthermore, we design a Threshold-based Online task Assignment mechanism (TOA), which could deal with the one-worker-many-tasks assignment and achieve a satisfactory competitive ratio. Simulations based on two real-world datasets show that the proposed algorithm consistently outperforms the state-of-the-art approach.

With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems assume a trusted centralized platform. However, we argue that the platform may collude with workers or requesters to trick others for achieving more benefits. For example, according to the workers’ uploaded data, the platform can modify the reward functions by colluding with the requester. Similarly, the platform might collude with workers to let them know the reward function, then workers could forge data. Meanwhile, requesters and workers may also be malicious. For example, requesters may post tasks but fail to pay and workers can upload wrong data to mislead the system. To solve the above problems, we combine the Crowdsensed Data Trading system with intelligent Blockchain (CDT-B), which contains a smart contract called CDToken. As a credible third-party, the CDToken is used to record the requesters’ reward function and workers’ data uploading function to avoid targeted trick. At the same time, we not only design a Data Uploading and Preprocessing (DUP) mechanism in CDToken to collect and process the workers’ sensed data, but also propose a Grouping Truth Discovery (GTD) to evaluate their data quality for determining the payments. Moreover, to hold a large number of requesters and workers in CDT-B, we propose a Layered Sharding blockchain based on Membership Degree (LSMD) to solve the blockchain inefficiency problem. Finally, we deploy CDToken to an experimental environment based on Ethereum and demonstrate its efficient performance and practicability.

Mobile crowdsensing (MCS) has attained much attention for gathering distributed mobile users to complete large-scale sensing tasks. To ensure the task completion, enough users should be motivated to participate in sensing tasks. Thus, much research in MCS focuses on proposing incentive mechanism, and these works usually focus on the offline scenario, where the information of all users is available to the platform. However, we argue that the actual MCS is usually an online scenario, where the platform does not know the user's information until they establish connections with the platform. Meanwhile, mobile users connect to the platform randomly and will cut off the connection at any time. Hence, when accessed by a user, the platform needs to make an irrevocable decision instantly about whether to select the user or not, and decides a remuneration for the user without knowing future information. In this paper, we first propose a reverse-auction framework to model the interaction between the platform and mobile users. Then, we present an online truthful incentive mechanism (OTIM) to motivate users, including online winner selection and remuneration determination strategies. Finally, massive simulations are conducted based on three real traces, and the simulation results illustrate that OTIM achieves truthfulness, individual rationality, computational efficiency and an approximately full budget utilization.

Mobile crowdsensing (MCS) has gained much attention due to the proliferation of smart devices equipped with powerful sensors. Large-scale users are the foundation of MCS, so designing incentive mechanisms to motivate users to participate in MCS is necessary. Existing works on incentive mechanisms usually assume a scenario where a group of tasks arrive at the platform at the same time and are immediately assigned to users. We argue that a more realistic MCS scenario can delay a task, which is called the assignment duration time, to wait for appropriate users. In this scenario, we focus on proposing a truthful incentive mechanism to reduce the overall social cost. Due to the uncertainty of coming users, the problems of selecting the appropriate users and calculating the payment for each recruited user (winner) are more complicated. To overcome these challenges, we design a dynamic truthful incentive mechanism (DTIM) including winner selection and payment decision processes. The former uniformly recruits users before the assignment deadline of tasks and dynamically readjusts the recruiting frequency of other tasks to select winners iteratively, which achieves an approximation ratio. Furthermore, the latter determines truthful payment for each winner to encourage user participation as well as avoid being deceived, which achieves truthfulness, individual rationality, and computational efficiency. Finally, massive simulations based on a real dataset roma/taxi validate the DTIM, which can effectively reduce the overall social cost and make a truthful payment for each winner.

Mobile CrowdSensing (MCS) has recently become a powerful paradigm that recruits users to cooperatively perform various tasks. In many realistic settings, users participate in real time and we have to recruit them in an online manner. The existing works usually formulate the online recruitment problem as a budgeted optimal stopping problem with submodular user utility, while we first argue that not only the budget but also the time constraints can jointly influence the recruitment performance. For example, if we have less budget but plenty of time, we should recruit users with more patience. Second, considering the user’s cooperative willingness, its contribution may be diminishing or even irregular. Hence, we also need to address not only submodular cases but also their non-submodular utility. In this paper, we study the online user recruitment problem with (non-)submodular utility under the budget and time constraints. To deal with the two constraints, we first estimate the number of users to be recruited and then recruit them in segments. Moreover, we extend the segmented strategy with a non-submodular utility, which has the submodularity ratio$\gamma $and the competitive ratio$\gamma ^{2}(1-e^{-1})/7$. Furthermore, to correct estimation errors and utilize newly obtained information, we dynamically re-adjust the segmented strategy and also prove that the dynamic strategy achieves a competitive ratio of${\gamma ^{2}(1-e^{-1})(1-e^{-\gamma /2})/7}$. Finally, a reverse auction-based online pricing mechanism is lightly built into the proposed user recruitment strategy, which achieves truthfulness and individual rationality. Extensive experiments on three real-world data sets validate the proposed online user recruitment strategy under the (non-) submodular utility and two constraints.

Mobile Crowdsensing (MCS) is a new paradigm that recruits users to cooperatively perform a sensing task. When recruiting users, existing works mainly focus on selecting a group of users with the best objective ability, e.g., the user's probability or frequency of covering the task locations. However, we argue that, for the cooperative MCS task, the completion effect depends not only on the user's objective ability, but also on their subjective collaboration likelihood with each other. In other words, in each single round, we prefer to recruit users with not only a strong objective ability but also good collaboration likelihood. Moreover, even though we could find a well-behaved group of users in a single round, in the multi-round scenario without enough prior knowledge, we still face the problem of recruiting previously well-behaved user groups (exploitation) or recruiting unknown user groups (exploration). To address these problems, in this paper, we first convert the single-round user recruitment problem into the min-cut problem and propose a graph theory based algorithm to find the optimal group of users. Furthermore, in the multi-round scenario, to balance the trade-off between exploration and exploitation, we propose the multi-round User Recruitment strategy based on the combinatorial Multi-armed Bandit model (URMB) and prove that it can achieve a tight regret bound. Finally, extensive experiments on three real-world datasets validate that the users recruited by URMB result in a better task completion effect than the state-of-the-art strategy.

Mobile crowdsensing recruits a group of mobile users to cooperatively perform a common sensing job with their smart devices. As a special issue, photo crowdsensing allows users to utilize the built-in cameras of mobile devices to take photos for an event or a target. Then, the photos can be used in numerous application areas, such as target reconstruction, scenario reduction, and so on. Therefore, photo crowdsensing has attracted considerable attention recently due to the rich information that can be provided by images. In this paper, we focus on using the photos to make reconstructions for specific targets. Furthermore, we develop a user recruitment system for efficient photo collecting in mobile crowdsensing (RSMC), where the task requesters publish a sensing task to the users, and the map is gridded according to the locations of the sensing targets. Then, we use a semi-Markov model to calculate the user's utility for the sensing task. Finally, a user recruitment strategy is devised to recruit the optimal k users for finishing the sensing task. We conduct extensive simulations based on three widely used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, RSMC takes the largest number of efficient photos for the sensing task.

Mobile crowdsensing recruits a massive group of mobile workers to cooperatively finish a sensing task through their smart devices (mobile phones, ipads, etc.). In this paper, the communication in social network for delivering the sensing data of mobile crowdsensing is considered, where some requesters publish the sensing tasks to all the Point of Interests (PoIs), and the workers are recruited to take the sensing data in the PoI until they could communicate with the requester through an offline and online connection. We first use the semi-Markov model to predict the offline encounter situation. Then, the worker's utility is decided by both the offline encounter and social connection probabilities. The Worker Recruitment for Self-organized MSC (WEO) is further presented through recruiting a set of workers, who have the maximum communication probability with the requesters. We prove that the optimal recruitment problem is NP-hard, and we introduce a practical greedy heuristic method for this problem, the performance of the greedy method is also tested. Two real-world traces, roma/taxi and epfl are tested in our simulations, where WEO always achieves the highest delivery ratio of sensing tasks among different recruitment strategies.

In this paper, we investigate energy cooperation among sustainable cooperative relay nodes, which adopt decode- and-forward (DF) relaying. The relay nodes work on either information decoding (ID) mode or energy harvesting (EH) mode when receiving the signal from the source. Due to different harvested energy at the relay nodes, effective energy cooperation can benefit the energy efficiency, but suffer from practical energy loss when achieving energy cooperation. By solving the problem using Lagrangian duality, we obtain the optimal energy cooperation policy for the relay nodes and further discuss its two-level waterfilling structure. Then we derive the optimal and low-complexity EH/ID mode selecting algorithm with the two-level waterfilling energy cooperation policy. Finally, we compare the throughput between the systems with and without energy cooperation to show the benefit of energy cooperation.

Inspired by the green communication trend of next-generation wireless networks, we propose an opportunistic wireless information and energy transfer relaying scheme for sustainable cooperative relaying, in which the relay nodes are powered only by their independent harvested energy to forward the desired information to the destination. Thus there exists an inherent tradeoff between the information decoding (ID) and the energy harvesting (EH) for forwarding. We first analyze the tradeoff and formulate an optimization problem on joint EH/ID receive mode selection and transmit power control for the relay nodes. By Lagrangian optimality, we obtain the optimal solution in opportunistic relay and power control. Due to the NP-hard property of the EH/ID mode selection, we propose a low-complexity algorithm by relaxation for EH/ID mode selection. The simulation results show that the performance of the low-complexity algorithm is close to the optimal solution, and outperforms the baselines.

Wireless information and energy transfer (WIET) is a prominent technology to prolong the lifetime of battery-charging wireless networks. In this paper, we exploit the benefit of massive MIMO for WIET under external interference, and propose the antenna partition for information decoding and energy harvesting. Considering the effects of the external interference, i.e., interfering the information reception and benefiting the energy harvesting, we analyze the tradeoff between the data rate and the harvested energy, and obtain the achievable rate-energy (R-E) region. Then, we propose a low-complexity receive antenna partition algoritinterference mitigationhm for WIET in massive MIMO systems with the consideration of interference mitigation. The algorithm maximizes the data rate while guaranteeing a minimum harvested energy. It is found that the SNR of the low-complexity algorithm is at least an approximable half of the optimal SNR. Simulation results verify our theoretical claims and show the effectiveness of the proposed low-complexity antenna partition algorithm.

The increase of antennas can greatly improve the performance of MIMO systems, in the meanwhile, the precise channel state information is difficult to obtain since the channel matrix of massive MIMO is much more complex than the traditional one. In this paper, we propose a limited feedback strategy named hybrid limited feedback with selective eigenvalue information (HLFSEI), which jointly considers the conventional quantized feedback and codebook based feedback. In HLFSEI, because of the large amount of the elements of channel matrix, the quantized information of only selective eigenvalue elements is fed back from the receiver to the transmitter with feedback-link capacity constraint. We study the optimal feedback bit allocation of both feedback methods to maximize the throughput by theoretic deduction. Finally, we evaluate the performance of the proposed limited feedback scheme by simulation and show its performance gain compared to conventional feedback strategies.