Selfish Users Research Articles

High-Efficient Resource Allocation in Data Relay Satellite Systems With Users Behavior Coordination

In data relay satellite (DRS) systems, users compete for limited intersatellite link antenna beam (ILAB) resources of data relay satellites by submitting excessive service requests to the mission scheduling center (MSC), which leads to serious scheduling conflicts and considerably degrades the QoS (quality of service). Two key challenges, the effective mission scheduling, and the coordination of multiple users’ behaviors in submitting service requests, are involved in this issue. Most of the previous works have only focused on the mission scheduling issue, while neglecting the impact of users’ selfish behavior on the QoS. In this paper, we investigate the resource allocation problem in DRS systems from the perspective of users’ behavior analysis, i.e., joint coordination of users’ selfish behavior in submitting service requests and mission scheduling. Since those selfish users have no incentive to cooperate in the one-shot interaction, we construct a repeated game framework with a proper punishment and forgiveness strategy to maximize users’ payoffs and meanwhile to reduce the resource conflicts through a cooperative way. Moreover, an efficient cheat-proof mechanism is proposed to make users self-enforced to submit his/her actual resource requirement. Simulation results confirm that the proposed scheme can significantly improve the system operational performance. Compared with the payoffs under the Nash equilibrium in the one-shot game which is the main method of the current coordination scheme for DRS systems, our proposed joint method based on the repeated game framework can acquire 1.36 to 3.46 times gain in respect to 2 to 10 users, and also effectively decrease the resource conflicts.

Designing Security-Aware Incentives for Computation Offloading via Device-to-Device Communication

Computation offloading via device-to-device (D2D) communication, or D2D offloading, can enhance mobile computing performance by exploiting spare computing resources of nearby user devices. The success of D2D offloading relies on user participation in collaborative service provisioning, which incurs extra costs to users providing the service, thus mandating an incentive mechanism that can compensate for these costs. Although incentive mechanism design has been intensively studied in the literature, this paper considers a much more challenging yet less investigated problem in which selfish users are also facing interdependent security risks, such as infectious proximity-based attacks. Security cost is significantly different in nature from conventional service provisioning costs such as energy consumption because security risks often depend on the collective behavior of all users. To this end, we build a novel mathematical framework by leveraging the combined power of game theory and epidemic theory to investigate the interplay between user incentives and interdependent security risks in D2D offloading, thereby enabling the design of security-aware incentive mechanisms. Our analysis discovers an interesting “less is more” phenomenon: although giving users more incentives promotes more participation, it may harm the network operator’s utility. This is because too much participation may foster persistent security risks, and as a result, the effective participation level does not improve.

A general framework for multiuser de-centralized cooperative spectrum sensing game

Over recent years, cognitive radio technology has been paid enormous attention as this innovatory communication archetype can utilize existing wireless spectrum resources more competently. Spectrum sensing is considered to be primary and most essential task for cognitive radio network operators. It has been acknowledged that cooperative spectrum sensing improves the overall sensing performance of the network. But selfish users be likely to act greedily to access the channel without involving in spectrum sensing. In this paper, we considered multiuser de-centralized cooperative spectrum sensing scenario and game theory is used to analyze the cognitive interaction process. A comprehensive expression is derived for throughput and it is, then, maximized through strategic interaction between cognitive users. Simulation result shows that taking part in sensing is not always good in terms of throughput. This work also evaluates network conditions to determine when to contribute in sensing to maximize throughput.

Braess paradox in a network with stochastic dynamics and fixed strategies

The Braess paradox can be observed in road networks used by selfish users. It describes the counterintuitive situation in which adding a new, per se faster, origin–destination connection to a road network results in increased travel times for all network users. We study the network as originally proposed by Braess but introduce microscopic particle dynamics based on the totally asymmetric exclusion process. In contrast to our previous work Bittihn and Schadschneider (2016), where routes were chosen randomly according to turning rates, here we study the case of drivers with fixed route choices. We find that travel time reduction due to the new road only happens at really low densities and Braess’ paradox dominates the largest part of the phase diagram. Furthermore, the domain wall phase observed in Bittihn and Schadschneider (2016) vanishes. In the present model gridlock states are observed in a large part of phase space. We conclude that the construction of a new road can often be very critical and should be considered carefully.

FlopCoin: A Cryptocurrency for Computation Offloading

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

Towards sustainable heterogeneous wireless networks: A decision strategy for AP selection with dynamic graphs

This paper considers a problem of user and access point associations in heterogeneous wireless networks consisting of multiple radio access technologies. The current association policies are mostly based on a selfish user behavior optimizing their own decision criteria. Such simple strategies, however, might fail especially in a congested place with a large number of users. From this perspective, this study propounds a strategy for optimizing the problem that aims at sustainably and fairly maximizing the minimal utility of users. The minimal utilities can be obtained from bare essentials of the quality of experience and defined by considering different requirements of each user. In order to analyze characteristics of the proposed selection strategy, we conduct simulations comparing it with the other selection method under each evaluation criterion. Numerical results suggest that the proposed selection i) enhances the total minimal utility in sustainable and fair manners, ii) indicates a distinctive trend in load balancing and iii) requires a comparable computational time and handover frequency in contrast with the other selection.

A Secure Mobile Crowdsensing Game With Deep Reinforcement Learning

Mobile crowdsensing (MCS) is vulnerable to faked sensing attacks, as selfish smartphone users sometimes provide faked sensing results to the MCS server to save their sensing costs and avoid privacy leakage. In this paper, the interactions between an MCS server and a number of smartphone users are formulated as a Stackelberg game, in which the server as the leader first determines and broadcasts its payment policy for each sensing accuracy. Each user as a follower chooses the sensing effort and thus the sensing accuracy afterward to receive the payment based on the payment policy and the sensing accuracy estimated by the server. The Stackelberg equilibria of the secure MCS game are presented, disclosing conditions to motivate accurate sensing. Without knowing the smartphone sensing models in a dynamic version of the MCS game, an MCS system can apply deep Q-network (DQN), which is a deep reinforcement learning technique combining reinforcement learning and deep learning techniques, to derive the optimal MCS policy against faked sensing attacks. The DQN-based MCS system uses a deep convolutional neural network to accelerate the learning process with a high-dimensional state space and action set, and thus improve the MCS performance against selfish users. Simulation results show that the proposed MCS system stimulates high-quality sensing services and suppresses faked sensing attacks, compared with a Q-learning-based MCS system.

Mechanisms for Optimally Scheduling and Pricing Pleasingly Parallel Jobs in Service Clouds

Recently, more and more users begin to outsource their job executions to service clouds, which brings benefits for both users and providers. Unfortunately, the fixed-price models which are commonly used by most clouds have several shortcomings, such as requesting users to specify and monitor their resources as well as being difficult to set prices. These shortcomings limit the usage of fixed-price models for service clouds and, thus, service providers with finite resource urgently need effective approaches to schedule and price user’s jobs, with the goal of social welfare maximization. In response to the need of service providers, this paper designs new auction mechanisms for service clouds, with unique features of job-oriented users, pleasingly parallel jobs, and soft deadline constraints. However, several challenges should be addressed when designing mechanisms, such as the NP-hardness of finding the optimal job scheduling and possible misreports of selfish users for private information. To deal with these challenges, we first propose a new randomized scheduling mechanism for optimally scheduling and pricing pleasingly parallel jobs in service clouds. This mechanism is truthful in expectation, while achieving $\alpha $ -approximation to the social welfare. However, potential collusion among cloud users which may result in significant effects has been ignored by this mechanism. To handle the collusion problem, we further propose a collusion-resistant mechanism which achieves $(t,P)$ -truthful while scheduling and pricing jobs. Both of these two mechanisms are computationally efficient and individually rational, and they can schedule jobs in a way without preemption. Finally, the theoretical analysis and extensive simulations based on synthetic data and real-world job traces validate the effectiveness of our mechanism.

Social-Aware Rate Based Content Sharing Mode Selection for D2D Content Sharing Scenarios

Device-to-device (D2D) content sharing has become a promising solution to support the growing popularity of multimedia contents for local services. Considering the randomness of content location, the limited storage and transmission capability of devices, and the coexistence of altruistic and selfish user behaviors, how to optimally match the demanders to the providers of contents and how to stimulate an efficient cooperation are of importance for achieving the full benefits of D2D content sharing. Especially when the base-station-to-device (B2D), D2D, and novel multi-D2D sharing modes coexist, the issue of content sharing mode selection plays the predominant role in such matching. In this paper, we introduce a notion of social-aware rate, which combines the social selfishness from the social knowledge with the link rate to ensure the physical link quality and the effective cooperation together. Then, the social-aware rate-based content sharing mode selection problem is modeled as a maximum weighted mixed matching problem, which can be computationally reduced to a submodular welfare problem subject to a matroid constraint. Subsequently, we develop a best-effort distributed algorithm framework, which displays alternatives of various computation complexities and approximation ratios to satisfy the diverse practical needs.

Impact of Selfishness in Device-to-Device Communication Underlying Cellular Networks

In a device-to-device (D2D) communication underlying cellular network, a user equipment is desired to operate cooperatively and unselfishly to transmit data as relays. However, most users more or less behave in a selfish way, which makes user selfishness a key factor that affects the performance of the whole communication system. We focus on the impact of user selfishness on D2D communications. First, we distinguish user selfishness into two types in accordance with D2D transmission modes, namely the connected transmission and opportunistic transmission modes. The user selfishness in these two types of transmissions are modeled separately. Then, we propose a time-varying graph model that characterizes the impacts of both individual and social selfishness on the D2D communications. Simulation results obtained under the realistic networking settings indicate that the interaction between connected and opportunistic selfishness worsens the impairment caused by individual selfishness. Additionally, when concerning social selfishness, inside-community selfishness can be ignored in some occasions, while otherwise its role is heavily influenced by community numbers.

Incentive-Aware Time-Sensitive Data Collection in Mobile Opportunistic Crowdsensing

Data collection is a crucial operation in mobile opportunistic crowdsensing. The design of data collection is challenging due to the fact that smart devices have limited resources to conduct complex computation and sensing tasks in mobile opportunistic networks. In this paper, we propose a time-sensitive incentive-aware mechanism for mobile opportunistic crowdsensing data collection, in which each sensing data has an attached time-sensitive value that decays over time. We focus on the cooperative data collection problem in mobile opportunistic crowdsensing, where data collectors cooperate with mobile users to send data back to requesters. However, due to the selfishness of mobile users, we use an incentive mechanism to stimulate the mobile users to participate in the data collection. The objective is to maximize data collectors’ and mobile relay users’ rewards. We formulate the interaction among data carrier and mobile relay users as a two-user cooperative game, we apply the asymmetric Nash bargain solution to obtain the optimal cooperation decision and transfer payment. Extensive simulation results based on the real-world traces are provided to validate the superiority of our proposed schemes.

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

Taming the big to small: efficient selfish task allocation in mobile crowdsourcing systems

SummaryThis paper investigates the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperation among users to achieve balanced resource utilization in a platform‐centric view. In achieving fairly low communication and computational overhead, this work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the Proportional Allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users. Even with limited information, the balancing performance could be improved significantly. Moreover, theoretical analysis has been presented in convergence property. Extensive evaluations have been made to show that Chance‐Choice outperforms several existing algorithms. Typically, comparing with Proportional Allocation scheme, it could decrease the load gap between the maximum and the minimal in system by 50% to 80% and reduce the overhead complexity from O(n) to O(1) comparing with the Max‐weight Best Response algorithm, where n denotes the number of mobile users in a crowdsourcing system. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Using bilateral trading to increase ridership and user permanence in ridesharing systems

One of the main obstacles that has challenged peer-to-peer (P2P) ridesharing systems in operating as stand-alone systems is reaching a critical mass of participants. Toward this goal, we propose what we call the P2P ride exchange mechanism to increase matching rate and customer retention in a ridesharing system. This mechanism gives riders the opportunity to purchase other riders’ itineraries while it provides suitable alternative rides to the sellers, thus increasing the service rate in a ridesharing system. The proposed mechanism aims to maximize expected user surplus, is robust towards selfish user manipulation, and has very low information requirements. Using numerical experiments, we demonstrate what type of ridesharing systems can benefit the most from P2P ride exchange. Furthermore, we study the impact of customer flexibility on the rate of exchange. If implemented properly, P2P ride exchange can effectively increase the number of served riders and enhance customer loyalty by engaging customers in the ride-matching process.

CAIS: A Copy Adjustable Incentive Scheme in Community-Based Socially Aware Networking

Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.

Dynamic Routing for Social Information Sharing

Today mobile users are intensively interconnected thanks to the emerging mobile social networks, where they share location-based information with each other when traveling on different routes and visit different areas of the city. In our model the information collected is aggregated over all users' trips and made publicly available as a public good. Due to information overlap, the total useful content amount increases with the diversity in path choices made by the users, and it is crucial to motivate selfish users to choose different paths despite the potentially higher costs associated with their trips. In this paper we combine the benefits from social information sharing with the fundamental routing problem where a unit mass of non-atomic selfish users decide their trips in a non-cooperative game by choosing between a high-cost and a low-cost path. To remedy the inefficient low-content equilibrium where all users choose to explore a single path (the low-cost path), we propose and analyse two new incentive mechanisms that can be used by the social network application, one based on side payments and the other on restricting access to content for users that choose the low cost path. We also obtain interesting price of anarchy results that show some fundamental tradeoffs between achieving path diversity and maintaining greater user participation, motivating a combined mechanism to further increase the social welfare. Our model extends classical dynamic routing in the case of externalities caused from traffic on different paths of the network.

Collaborative content dissemination based on game theory in multimedia cloud

With the development of multimedia clouding applications and broadband technology, multimedia group communication applications, such as multi-point video conference, major news and events live, internet-on-demand TV, network video monitoring and so on, continue to emerge. However, limited network bandwidth, burst of incoming service requests, and imbalance of content dissemination seem to be the most important factors that affect users’ experience. To this end, this paper proposes a collaborative content dissemination method based on game theory in multimedia cloud. In order to obtain the optimal number of service user in each group, integrated utility value of each user is calculated, and the improved particle swarm optimization algorithm is used. Moreover, the matching problem between service users and non-service users is formulated into game theory. In order to avoid the blindness and selfishness of non-service users to choose the service users, an evolutionary game is aimed at achieving the stability by learning the connection from the non-service users. A Stackelberg game is aimed at obtaining optimal pricing policy and bandwidth allocation of service users. The numerous experiment results show that the proposed method can effectively improve users’ experience of those who have weak system capabilities, reduce the average time of the content dissemination and decrease the total cost of the multimedia cloud user.

Trust-Oriented Partner Selection in D2D Cooperative Communications

To meet the rapid growing requirement of data services and offload the heavy base station load, device-to-device (D2D) communication is proposed as one of the key technologies for future cellular network, which can enable users to communicate directly and thus effectively improve the bearing capacity of the network. However, when D2D users transmit packets in the manner of cooperative communication, the cooperative users with non-cooperative behavior, such as the selfishness of user, may lead to a sharp decline in network performance. For this reason, in our paper, a trust-oriented partner selection mechanism (TPSM) is proposed to avoid choosing those users with non-cooperative behavior. In particular, the related psychological researches show that the psychology structure of users can be divided into three aspects: cognition, emotion, and behavior. Considering the psychology structure of users, we build multi-dimensional trust relationships between sending users and cooperative users by evaluating the cognition trust, emotion trust, and behavior trust. Furthermore, aiming at the multi-dimensional trust relationships between sending users and cooperative users, we classify the cooperative users into three categories, namely reliable users, observed users, and unreliable users, by combining the decision-theoretic rough sets based on Naive Bayes. Finally, based on different cooperative transmission scenarios, an optimal partner selection mechanism is proposed in this paper. The optimal partner can be chosen from the reliable cooperative users, according to the different scenarios and factors. The numerical results show that the proposed TPSM can effectively identify the selfish users and notably enhance the packet delivery rate.

Energy-aware relay selection in cooperative wireless networks: An assignment game approach

In the future connected societies, everyone and everything will be inter-connected, under the umbrella of the Internet of Things, where tens to hundreds of devices will be serving every single citizen. However, connecting this massive number of energy-constrained devices pose a serious challenge on the wireless networking paradigm; energy efficiency still represents a major challenge within the design of the future generation of wireless networking (5G). In this paper, we address reducing energy consumption of energy-constrained wireless devices, through energy-aware cooperative relaying in future heterogeneous networks (HetNets). Using game theory concepts, we formulate the problem as an assignment game. In the first stage of the game, the optimal relay selection is formulated as a linear programming problem, whose solution assigns suitable relays to their perspective sources. In the final stage, the core solution is derived, which guarantees fair distribution of the payoff among players, to keep them satisfied and discourage them from quitting the coalition. Our solution also proposes a credit based system to reward cooperative players; hence possibly excluding selfish users from cooperative coalitions. The proposed relay selection algorithm is evaluated using extensive NS2 simulations. The simulation results show significant energy savings using cooperative relaying, reaching up to 22%, with extensive increase in battery lifetime.

User-Network Cooperation-Based Sleep Scheduling for Communication Networks

The redundant design and dynamic nature of traffic raise an energy inefficiency issue in communication networks. We exploit the selfishness of both users and the network to schedule cooperatively the idle links and nodes into sleep to save energy. We first formulate the sleep scheduling problem from a perspective of routing, and then propose a greedy algorithm to solve the problem. To reduce the complexity of centralized computation, we further propose a user-network cooperation-based mechanism, where the network publishes a proportionally weighted cost-sharing rule related to energy consumption, while the users selfishly choose their routes with the least cost accordingly. The proposed cooperation mechanism attracts users to aggregate their traffic on fewer links and nodes. The network then simply puts the idle links and nodes into sleep. Selfish routing behaviors are modeled by an $\alpha $ -approximate routing game, where the $\alpha $ factor is adopted to consider the energy consumption, packet losses, and delay during re-routing. We prove the equilibrium existence, convergence, and convergence speed of the best responses, and evaluate the lower bound performance in terms of price of anarchy with further improvement by an advertisement method. Distributed algorithms based on the best responses are also developed to implement the cooperative mechanism. Simulation results over network instants from SNDlib show that our game-based algorithms outperform the greedy and heuristic centralized algorithms in saving energy.