Crowdsourcing becomes a popular data-collection method to relieve the burden of high cost and latency for data-gathering. Since the involved users in crowdsourcing are volunteers, need incentives to encourage them to provide data. However, the current incentive mechanisms mostly pay attention to the data quantity, while ignoring the data quality. In this paper, we design a Data-quality awaRe IncentiVe mEchanism (DRIVE) for collaborative tasks based on the Stackelberg game to motivate users with high quality, the highlight of which is the dynamic reward allocation scheme based on the proposed data quality evaluation method. In order to guarantee the data quality evaluation response in real-time, we introduce the mobile edge computing framework. Finally, one case study is given and its real-data experiments demonstrate the superior performance of DRIVE.

Peer-to-peer (P2P) technology has gained popularity as a way to enhance system performance. Nodes in a P2P network work together by providing network resources to one another. In this study, we examine the use of P2P technology for video streaming and develop a distributed incentive mechanism to prevent free-riding. Our proposed solution combines WebTorrent and the Solana blockchain and can be accessed through a web browser. To incentivize uploads, some of the received video chunks are encrypted using AES. Smart contracts on the blockchain are used for third-party verification of uploads and for managing access to the video content. Experimental results on a test network showed that our system can encrypt and decrypt chunks in about 1/40th the time it takes using WebRTC, without affecting the quality of video streaming. Smart contracts were also found to quickly verify uploads in about 860 milliseconds. The paper also explores how to effectively reward virtual points for uploads.

Device-to-device (D2D) relay enhances the capacity of a mobile network. If the channel quality of a user equipment (UE) is bad, the UE asks a neighbor to get its data from the base station and forward the data to it by using D2D communication. Since cellular and D2D communication can share spectrum resources, the spectral efficiency will rise. As UEs are owned by self-interested users, they may not provide relay services gratis. Thus, some incentive methods let UEs exchange tokens to buy and sell relay services. However, they assume that each relay service is worth one token and offers a fixed data rate, which lacks flexibility. Through the law of supply and demand, this paper proposes an economy aware token-based incentive (EAT-BI) strategy. A supplier (i.e., the service provider) charges different prices for its relay service with different rates. A consumer (i.e., the service requestor) takes different policies to choose a supplier based on its tokens and may bargain with suppliers to avoid starvation. Simulation results show that EAT-BI can efficiently promote D2D relay use and increase throughput under different mobility models of UEs.

Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.

The demand response is attracting attention to perform electric power load leveling. In this paper, we consider a power consumption reduction problem with an aggregator that requests electric power consumption reduction to consumers by allocating a part of its profit to them as an incentive. We formulate interactions among consumers as a game, where the incentive to each consumer is determined by his/her contribution to the total power consumption reduction, and the consumer determines his/her own reduction amount selfishly to maximize his/her payoff. The uniqueness of best responses of each consumer and an equilibrium condition of the game are also derived. By using numerical simulations, we show relationship among incentive allocation rate, realized total reduction amount through the game, and the aggregator's payoff for the cases with the for-profit and the nonprofit aggregator.

In this paper, we propose a new dynamic load balancing scheme according to load threshold adjustment and incentives mechanism. The proposed scheme adjusts the load threshold of a node by comparing it with a mean threshold of adjacent nodes, thereby increasing the threshold evenly. We also assign the incentives and penalties to each node through a comparison of the mean threshold of all the nodes in order to increase autonomous load balancing participation.

In this paper, an infinite-horizon team-optimal incentive Stackelberg strategy is investigated for a class of stochastic linear systems with many non-cooperative leaders and one follower. An incentive structure is adopted which allows for the leader's team-optimal Nash solution. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati equations (CCSAREs). In order to demonstrate the effectiveness of the proposed strategy, a numerical example is solved.

Our prior papers proposed a traffic engineering scheme to further localize traffic in peer-assisted content delivery networks (CDNs). This scheme periodically combines the content files and allows them to obtain the combined content files while keeping the price unchanged from the single-content price in order to induce altruistic clients to download content files that are most likely to contribute to localizing network traffic. However, the selection algorithm in our prior work determined which and when content files should be combined according to the cache states of all clients, which is a kind of unrealistic assumption in terms of computational complexity. This paper proposes a new concept of virtual local server to reduce the computational complexity. We could say that the source server in our mechanism has a virtual caching network inside that reflects the cache states of all clients in the ‘actual’ caching network and combines content files based on the virtual caching network. In this paper, without determining virtual caching network according to the cache states of all clients, we approximately estimated the virtual caching network from the cache states of the virtual local server of the local domain, which is the aggregated cache state of only altruistic clients in a local domain. Furthermore, we proposed a content selection algorithm based on a virtual caching network. In this paper, we used news life-cycle model as a content model that had the severe changes in cache states, which was a striking instance of dynamic content models. Computer simulations confirmed that our proposed algorithm successfully localized network traffic.

In P2P applications, networks are formed by devices belonging to independent users. Therefore, routing hotspots or routing congestions are typically created by an unanticipated new event that triggers an unanticipated surge of users to request streaming service from some particular nodes; and a challenging problem is how to provide incentive mechanisms to allocation bandwidth more fairly in order to avoid congestion and other short backs for P2P QoS. In this paper, we study P2P bandwidth game — the bandwidth allocation in P2P networks. Unlike previous works which focus either on routing or on forwarding, this paper investigates the game theoretic mechanism to incentivize node's real bandwidth demands and propose novel method that avoid congestion proactively, that is, prior to a congestion event. More specifically, we define an incentive-compatible pricing vector explicitly and give theoretical proofs to demonstrate that our mechanism can provide incentives for nodes to tell the true bandwidth demand. In order to apply this mechanism to the P2P distribution applications, we evaluate our mechanism by NS-2 simulations. The simulation results show that the incentive pricing mechanism can distribute the bandwidth fairly and effectively and can also avoid the routing hotspot and congestion effectively.

As one innovative research that heavily depends on the network virtualization for its realization and deployment on an Internet-scale, we propose an approach to utilize user resources in information-centric network (ICN). We try to fully benefit from the in-network cache that is one attractive feature of ICN by expanding the in-network cache indirectly based on the user resources. To achieve this, in this paper, we focus on how to encourage users to contribute their resources in ICN. Through simulations, we examine a feasibility of our approach and an effect of user participation on the content distribution performance in ICN. We also briefly discuss how the network virtualization technique can be utilized for our research in terms of its evaluation and deployment.

In this letter, we argue that user resources will be still useful in the information-centric network (ICN). From this point of view, we first examine how P2P utilizing user resources looks like in ICN. Then, we identify challenging research issues to utilize user resources in ICN.

When P2P systems are used for data sensitive systems, the data availability has become an important issue. The availability-based replication using individual node availability is the most popular method keeping high data availability efficiently. However, since the individual node availability is derived by the individual lifetime information of each node, the availability-based replication may select useless replicas. In this paper, we explore the relative MTTF (Mean Time To Failure)-based incentive scheme for the more efficient availability-based replication. The relative MTTF is used to classify the guaranteed replicas which can get the incentive node availability, and these replicas help reduce the data traffic and the number of replicas without losing the target data availability. Results from trace-driven simulations show that the replication using our relative MTTF-based incentive scheme achieves the same target data availability with 41% less data traffic and 24% less replicas.

In peer-to-peer (P2P) content sharing, users can share their content by contributing their own resources to one another. However, since there is no incentive for contributing contents or resources to others, users may attempt to obtain content without any contribution. To motivate users to contribute their resources to the service, incentive-rewarding mechanisms have been proposed. On the other hand, emerging wireless technologies, such as IEEE 802.11 wireless local area networks, beyond third generation (B3G) cellular networks and mobile WiMAX, provide high-speed Internet access for wireless users. Using these high-speed wireless access, wireless users can use P2P services and share their content with other wireless users and with fixed users. However, this diversification of access networks makes it difficult to appropriately assign rewards to each user according to their contributions. This is because the cost necessary for contribution is different in different access networks. In this paper, we propose a novel incentive-rewarding mechanism called EMOTIVER that can assign rewards to users appropriately. The proposed mechanism uses an external evaluator and interactive learning agents. We also investigate a way of appropriately controlling rewards based on the system service's quality and managing policy.

When the number of users in a service area increases in mobile multimedia services, no individual user can obtain satisfactory radio resources such as bandwidth and signal power because the resources are limited and shared. A solution for such a problem is user-position control. In the user-position control, the operator informs users of better communication areas (or spots) and navigates them to these positions. However, because of subjective costs caused by subjects moving from their original to a new position, they do not always attempt to move. To motivate users to contribute their resources in network services that require resource contributions for users, incentive-rewarding mechanisms have been proposed. However, there are no mechanisms that distribute rewards appropriately according to various subjective factors involving users. Furthermore, since the conventional mechanisms limit how rewards are paid, they are applicable only for the network service they targeted. In this paper, we propose a novel incentive-rewarding mechanism to solve these problems, using an external evaluator and interactive learning agents. We also investigated ways of appropriately controlling rewards based on user contributions and system service quality. We applied the proposed mechanism and reward control to the user-position control, and demonstrated its validity.

Wireless content sharing where peers share content and services via wireless access networks requires user contributions, as in fixed P2P content sharing. However, in wireless access environments, since the resources of mobile terminals are strictly limited, mobile users are not as likely to contribute as ones in fixed environments. Therefore, incentives to encourage user contributions are more significant in wireless access environments. Although an incentive service differentiation architecture where the content transfer rate is adjusted according to the contributions of each downloading user has been already proposed for fixed P2P, it may not work well in wireless access environments because several factors effect wireless throughput. In this paper, we propose a novel architecture for contribution-based transfer-rate differentiation using wireless quality of service (QoS) techniques that motivates users to contribute their resources for wireless content sharing. We also propose a radio resource assignment method for our architecture. Computer simulations and game-theoretic calculations validate our architecture.

Flash bulk files downloading in style of P2P through perpendicular pattern becomes more popular recently. Many peers download different pieces of shared files from the source in parallel. They try to reconstruct complete files by exchanging needed pieces with other downloading peers. The throughput of entire downloading community, as well as the perceived downloading rate of each peer, greatly depends on uploading bandwidth contributed by every individual peer. Unfortunately, without proper built-in incentive mechanism, peers inherently tend to relentlessly download while intentionally limiting their uploading bandwidth. In this paper, we propose a both effective and efficient incentive approach--Reciprocity, which is only based on end-to-end measurement and reaction: a peer caps uploading rate to each of its peers at the rate that is proportional to its downloading rate from that one. It requires no centralized control, or electronic monetary payment, or certification. Preliminary experiments' results reveal that this approach offers favorable performance for cooperative peers, while effectively punishing defective ones.

In this letter, we take advantage of both types of Interactive and Batch services by considering a hybrid multimedia-on-demand (MOD) system which provides both services. Also, we propose two serving methods with an incentive charging scheme to optimize the coexistence of both services. Numerical results show that the proposed hybrid system outperforms both individual systems. In particular, the system provider receives more revenue by serving more users concurrently, while the users have a better viewing experience due to higher availability of contents.