Participatory sensing has emerged recently as a promising approach to large-scale data collection. However, without incentives for users to regularly contribute good quality data, this method is unlikely to be viable in the long run. In this paper, we link incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches. With this demand-based principle, we design two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively. Our study shows that the IDF scheme is max-min fair and can score close to 1 on the Jain's fairness index, while the ITF scheme maximizes social welfare and achieves a unique Nash equilibrium which is also Pareto and globally optimal. We adopted a game theoretic approach to derive the optimal service demands. Furthermore, to address practical considerations, we use a stochastic programming technique to handle uncertainty that is often encountered in real life situations.
In the context of wireless ad hoc networks, incentive was studied as a means to stimulate each node to forward packets for other nodes, under the assumption that nodes are self-interested and try to conserve their own energy and transmission bandwidth. The approaches can be broadly classified into credit-based and reputation-based categories. For instance, Butty ' a n and Hubaux @cite_13 proposed a credit-based mechanism using a virtual currency called nuglet: forwarding one packet for others will earn one nuglet while sending one own packet will consume one nuglet. Marbach @cite_11 , on the other hand, added flexibility by allowing each node to freely decide on a forwarding price as well as sending rate in an adaptive manner. In reputation-based systems such as @cite_15 , each node's behavior is observed and evaluated by its neighbors and will further induce rewards or punishments based on the evaluation.
@cite_15 @cite_13 @cite_11
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In an ad-hoc network, intermediate nodes on a communication path are expected to forward packets of other nodes so that the mobile nodes can communicate beyond their wireless transmission range. However, because wireless mobile nodes are usually constrained by limited power and computation resources, a selfish node may be unwilling to spend its resources in forwarding packets which are not of its direct interest, even though it expects other nodes to forward its packets to the destination. It has been shown that the presence of such selfish nodes degrades the overall performance of a non-cooperative ad hoc network. To address this problem, we propose a secure and objective reputation-based incentive (SORI) scheme to encourage packet forwarding and discipline selfish behavior. Different from the existing schemes, under our approach, the reputation of a node is quantified by objective measures, and the propagation of reputation is efficiently secured by a one-way-hash-chain-based authentication scheme. Armed with the reputation-based mechanism, we design a punishment scheme to penalize selfish nodes. The experimental results show that the proposed scheme can successfully identify selfish nodes and punish them accordingly. This paper addresses the problem of fairness in wireless ad-hoc networks. Usually, the problem of fairness in wireless ad-hoc networks is addressed in a classic approach inherited from wired networks. The common assumption is that nodes flows have pre-assigned fair shares. The task becomes then to modify wired networks' fair queueing algorithms to address wireless network nature. Wired networks have efficient means of allocating fiar shares through admission control and additionally, the fair shares remain constant throughout the session duration due to the static nature of the nodes. In ad-hoc networks, it is meaningless to assume statically pre-assigned fair shares, since on one hand, not only the nodes move, but also the routers are mobile, and on the other hand, contention is location dependent, such that in terms of absolute guarantees, fairness would at most mean "avoiding starvation": thus applying rate proportional fiar queueing algorithms is beyond the original goal of such algorithm, viz. flow isolation protection and bandwidth guarantee. In this work we argue in favor of multi-level scheduling for wireless ad-hoc networks with max-min fair allocation of the fair shares at the lower-most layer (MAC layer). This paper, mainly lays down the theoretical framework by which one can calculate the fair shares that would achieve max-min fairness in an ad-hoc network. We design distributed algorithms that allow each node to determine its max-min per-link fair share in a global ad-hoc network without knowledge of the global topology of the network. The results are then used in conjunction with a novel practical scheduling algorithm for IEEE 802.11 to show how fairness is achieved Mobile ad-hoc networks rely on the cooperation of nodes for routing and forwarding. For individual nodes there are however several advantages resulting from noncooperation, the most obvious being power saving. Nodes that act selfishly or even maliciously pose a threat to availability in mobile adhoc networks. Several approaches have been proposed to detect noncooperative nodes. In this paper, we investigate the effect of using rumors with respect to the detection time of misbehaved nodes as well as the robustness of the reputation system against wrong accusations. We propose a Bayesian approach for reputation representation, updates, and view integration. We also present a mechanism to detect and exclude potential lies. The simulation results indicate that by using this Bayesian approach, the reputation system is robust against slander while still benefitting from the speed-up in detection time provided by the use of rumors. Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally define the notion of fairness of a beaconing rate allocation in vehicular networks. The NUM model provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. In this context, we propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. Simulation results validate our approach and show that, unlike recent proposals, FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios.