Mobile Crowdsensing (MCS) is a new data acquisition method that has emerged with the proliferation of smart mobile devices. With the expanding scale of urban sensing, the locations of tasks and users become critical information, which plays a significant role in crowd-sensing and task scheduling areas. Tasks in areas with a high concentration of users can be completed quickly, whereas tasks in sparsely populated areas are challenging to accomplish. To address this issue, existing research has primarily focused on task assignment to designated users, assuming that users' motivations are rational, while neglecting the impact of psychological factors on their motivations. Therefore, we propose an incentive mechanism based on prospect theory, analyzing the decisions users might make under irrationality and then adjusting corresponding rewards to influence user decisions. This paper transforms the problem of maximizing the data value in crowdsensing into an ordered submodular function model. Our proposed incentive mechanism consists of three components: User Decision-Making, User Selection, and Payment Determination. In the User Decision-Making phase, users calculate the prospect value based on the auction results from the previous round to make decisions. In the User Selection phase, users are chosen based on marginal value. In the Payment Determination phase, rewards for winning users are designed based on the ordered submodular model. The platform provides auction results as a reference for the next round. In the experimental section, we demonstrate that the incentive mechanism can enhance the platform's value.

Location Dependent Mobile Crowdsensing (LDMC) often needs to collect data at different time points in various regions to ensure the coverage of sensing data. An incentive mechanism is needed to encourage participants to move to sparse areas and improve coverage. However, there are two problems: 1) most incentive mechanisms assume that the participants can get accurate information about tasks; 2) those mechanisms encourage participants through absolute utility so that the platform can obtain an improvement of incentive effect by increasing the reward. However, nodes usually get inaccurate information in reality. Moreover, behavioral economics finds that decision-making is often affected by relative utility rather than absolute utility. Path-dependence means that choices made on the basis of transitory conditions can persist long after those conditions change, which can solve the above problems. This study uses cognitive bias and the reference effect to explain the principle of path-dependence, and proposes a mechanism called Task Coverage promotion based on Path-dependence (TCPD). TCPD cultivates the cognitive bias of participants, causing an overestimation of expected utility. Then, it sets dynamic reference points to prevent participants from quitting early. The simulation results show that TCPD can improve the coverage and effectiveness of the platform.

Neural Networks’ performance on a sequence of incremental class tasks drops over time for Class Incremental Learning (IL). The gradient-based IL methods can simultaneously adapt to both new and previous tasks by promoting the update of model in the correct direction. However, existing methods simply consider the previous/new task gradients separately. In this paper, we propose Parallel Gradient Blend (PGB) paradigm. On the one hand, PGB uses the gradients generated by mixing previous and new samples in equal proportions with Batch-Normal layers to adjust a reasonable model update direction. By comparing gradient similarities, the model selects either the previous task gradient or mixed gradient to update. On the other hand, PGB uses the sample feature gradient distribution difference to construct a regularized gradient. Finally, we experimentally demonstrate that PGB outperforms state-of-the-art methods on class-IL benchmarks.

With the coming of the Internet of Things (IoT) era, malware attacks targeting IoT networks have posed serious threats to users. Recently, the emerging of edge computing have paved the way for new data processing paradigms in IoT networks, but it is still a challenge for deploying malware detection systems on the IoT devices. This paper develops an IoT malware detection system based on trust hybrid user-edge evaluation, namely MDHE. This system decomposes a large and complex deep learning model into two parts, which are deployed on edge servers and end devices, respectively. Specifically, a trust evaluation mechanism is used to select the trusted devices to participate the model training. Moreover, we develop a private feature generation that leverages a graph mining technology to extract the subgraph features, which then are perturbed by leveraging the differential privacy technology to prevent user privacy from leaking. Finally, we reconstruct the perturbed features on edge server, and propose a Capsule Network (CapsNet) to identify malware. Experimental results show that MDHE can effectively detect malware. Specifically, it can reduce sensitive inference while maintaining the utility of data.

The location of participants in Location-based CrowdSensing (LCS) represents important information for task completion. Tasks in areas with high concentration of participants (AHCP) can be completed quickly, whereas task completion is difficult in areas with sparse participants (ASP). Incentive mechanisms are necessary to motivate participants to move toward ASP. Previous studies have faced two main problems. First, most incentive mechanisms assume that participant motivation is not affected by external factors. Second, when participants fail to complete tasks, only the cost of the participant is considered the loss. However, reference effect from behavioral economics proves that participants are influenced by both internal and external factors. Furthermore, loss aversion studies have shown that participant evaluations of loss are more severe than simple costs. Therefore we propose an incentive mechanism based on behavioral economics (IBE) consisting of two schemes for participant selection (IBE-PS) and payment decisions (IBE-PD). Based on reference effect, IBE-PS is proposed to control the task selection and pricing of participants. Based on loss aversion, IBE-PD is proposed to encourage participants to complete tasks in ASP many times. Theoretical analysis and simulation results demonstrate that IBE can improve the task completion rate, the participant utility, and the platform welfare.

In mobile crowdsensing, many mobile devices are collectively used to complete complex sensing tasks. Most tasks require users to consume resources to ensure continuous performance over multiple periods of time. Therefore, it is important to incentivize enough users to continuously participate in the tasks. However, there are two issues with current incentive mechanisms. First, most studies are designed for maximizing the revenue of a single round of tasks rather than long-term incentives. Second, although some studies use historical data to design mechanisms for long-term operation, the law of diminishing marginal utility is not considered; thus, the actual performance is lower than expected. In this study, the concepts of capital deposit and intertemporal choice from behavioral economics are introduced to explain the principle of addiction, which is a representative long-term incentive. Consequently, an Addiction Incentive Mechanism (AIM) is proposed. It influences the utility and demand functions of users by accelerating the accumulation of capital deposits and promoting users to become addicted to cooperative behavior. It also mitigates the effect of diminishing marginal utility through intertemporal choice theory to maintain user engagement. Simulations demonstrate that AIM improves participation and repetition rates compared with the state-of-the-art mechanisms.

The number of smart devices newly connected to the Internet has grown exponentially in recent years. These smart devices are interwoven into huge Internet of Things. There is a contradiction between mass data transmission and communication bandwidth, the distance between supercomputing power and processing object, and the demand of frequent interaction and real-time response. As a new computing paradigm, edge computing processes tasks on computing resources close to data sources. Considering the limited energy of the mobile terminal and the user's demand for low delay, making decisions about tasks executed locally and offloaded to edge computing servers. In the edge environment, resources are dynamically allocated to users on demand, and users need to pay for the resources they actually consume. By considering energy consumption, delay, and price, a user-centered joint optimization loading scheme is proposed to minimize the weighted cost of time delay, energy consumption, and price under the constraint of satisfying the advanced personalized needs of users. The optimization problem is modeled as a mixed-integer nonlinear programming problem, and a branch-and-bound algorithm based on linear relaxation improvement is proposed to solve the problem. Considering the complexity of the algorithm, a particle swarm optimization algorithm based on 0-1 and weight improvement is proposed to solve the problem. Simulation results show that the method proposed in this article can achieve higher performance in terms of delay, energy consumption, and price and provide personalized service for users.

Crowdsensing is a new paradigm of applications that takes advantage of mobile devices to collect sensing data. Tasks in crowdsensing will consume users' resources so that incentive mechanisms are necessary to encourage users' participation. Existing incentive mechanisms are based on traditional economics, which have two common problems: 1) the utility of different tasks is fungible and 2) users' behavioral preferences are consistent. Mental accounting (MA) theory in behavioral economics proves that the utility of tasks obtained in different ways is nonfungible and people's preferences of behavior are inconsistent. Reference dependence, loss aversion, and sensitivity decline are the three characteristics of MA. Reference dependence means people evaluate outcomes relative to a reference point, and then classify gains and losses. Loss aversion refers to people's tendency to prefer avoiding losses to acquiring equivalent gains. Sensitivity decline means that the marginal utility of MA about gains and losses is diminishing. Thus, this paper proposes an incentive mechanism called the MA auction incentive mechanism (MAAIM). Based on reference dependence, coupled with sensitivity decline, we establish an external reference environment and an internal reference point to motivate users. Based on loss aversion, we design a payment mechanism to encourage users to improve their data quality. The extensive simulation results show that MAAIM improves the number of users participating in sensing tasks, the utility of the sensing platform, and the quality of data collected by users.

Crowdsensing is a system which collects sensing data through recruiting a large number of participants to perform the sensing tasks. It consists of two parts: 1) the platform side and 2) the user side. The incentive mechanism is necessary to recruit a sufficient number of participants for the utility of the platform, and to guarantee the utility of the participants for the users. Current mechanisms for crowdsensing are based on traditional economics with two basic hypotheses. The first is that the preference of people is constant, which implies that their preference for an item does not change under different circumstances; the second is that the utility of people is equal to the payment they obtain. However, decoy effect from behavioral economics shows that people's preferences change as the alternative set changes. And resulting from fairness preference theory, people's utility depends not only on the actual income they receive but also on the fairness of the income of all people. Thus, current incentive mechanisms will lead to users' wrong selection making. To solve these problems, we design the framework for publishing tasks based on decoy effect (FPDE) mechanism on the platform side. And we design the payoff allocation based on fairness preference (PAFP) mechanism for the user side. We compare our mechanisms with a new and advanced mechanism called incentive mechanism for crowdsensing-centric (IMCC) model. The simulations show that FPDE can increase the utility of the platform by approximately 20.8%, and the utility of the users will increases by 14.8% under PAFP.

With the rapid growth of mobile traffic, Internet of Things requires a large number of access points (APs) to provide data offloading capabilities. Owing to their selfishness, most APs refuse to participate. Therefore, an effective incentive mechanism is necessary. There are two common problems with current incentive mechanisms: 1) they generally assume that APs make decision by calculating expected utility and 2) they also do not consider the time restrictions of the mechanisms themselves. Thus, this paper proposes an incentive mechanism comprising the anchoring effect and loss aversion on offloading (AELAO). The creative of AELAO is its use of anchoring effect (i.e., the influence of referencing a user's decision) and loss aversion (i.e., consequences become more intolerable when facing the same losses and benefits). In order to solve the first problem, this paper proposes the reference factor and price-break discounts factor based on the anchoring effect. The reference factor is used as the anchor value (i.e., reference point) to determine the number of APs participating in data offloading. The design of the price-break discounts factor is based on the reference factor. For the second problem, this paper presents two new concepts: 1) time pressure and 2) regret value. Based on APs' loss aversion, time pressure can encourage them to participate in data offloading as soon as possible within the given time limit. Theoretical analysis and simulation results show that AELAO can increase the amount of data offloading while improving the offloading value, the average utility, and the participation rate of APs.

The Internet of Vehicles (IoV) is composed of road side units (RSUs) nodes and vehicle nodes. Because the limited bandwidth of RSUs can not satisfy massive requests from vehicle nodes, offloading technology is proposed. Generally, spare vehicle nodes (SVs) with redundant resources are motivated to cache data from RSUs. However, due to the selfishness, the participation of SVs is generally low. Hence, an effective incentive mechanism to motivate SVs to offload is important. There are two problems with current incentive mechanisms: 1) all of them ignore loss aversion, which is the act of preferring avoidance of loss over acquiring equivalent gains. This act can lead to deviation in the final decision and 2) no attention is paid to the critical effects that initial allocation of property have on the final resource allocation. However, researches on behavior economics have found that loss aversion exists and the final resource allocation is affected by the initial configuration. Therefore, we propose an incentive mechanism called reverse auction based on endowment effect (RABEE). To the first problem, we introduce the endowment effect from behavior economics, and propose a term called endowment compensation for increasing the participation rate of SVs. For the second problem, by changing the initial allocation of endowment compensation, we illustrate through theoretical analysis and simulations the significance the initial configuration has in terms of the final resource allocation. Simulations show that RABEE increases the average utility of SVs by 6.8% compared with the conventional scheme, and it also improves participation rate by 2%.

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

P2P overlay potentially provides an efficient routing architecture that is self-organizing, massively scalable, and robust in a wide area. To improve the requesting nodes' QoS routing and to reduce the bandwidth and processing consumption of the nodes in a P2P system, three correlative requirements should be considered and satisfied: (1) to make system structure to be scalable; (2) to locate and route information at a low cost and efficiently without global view of the system; (3) to make the overlay robust in front of the dynamic topologies. However, not all P2P systems are compatible with multimedia streaming. Unstructured systems are designed more specifically than structured systems for the heterogeneous Internet environment, where the nodes' persistence and availability are not guaranteed. In this paper, three correlative standards for evaluating the compatibility of a P2P system with multimedia streaming are presented. A detailed measurement study of three popular unstructured P2P overlays and our overlay called MPO is performed. Our method is to analyze performances of classical searching algorithms in various overlays. Key factors in content locations including scalability, query success rate, query messages, cost, disturbed times and fault tolerance are considered carefully. The simulation results show some characteristics in unstructured P2P overlay and prove that MPO is a highly efficient, low cost and fault tolerant overlay and a good structure for applications in multimedia streaming.