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.
Recently, Park and van der Schaar @cite_4 introduced an intervention device that can take a variety of actions to influence users to cooperate and avoid inefficiency , under the assumption that the device can monitor a random access network, such as a CSMA network, perfectly. In our work, we focus on a different context, participatory sensing, and will show that our designed scheme achieves Pareto efficiency.
@cite_4
2152350936 2152859483 2129951658 2054671542
Overcoming the inefficiency of non-cooperative out-comes poses an important challenge for network managers in achieving efficient utilization of network resources. This paper studies a class of incentive schemes based on intervention, which are aimed to drive self-interested users towards a system objective. A manager can implement an intervention scheme by introducing in the network an intervention device that is able to monitor the actions of users and to take an action that influences the network usage of users. We consider the case of perfect monitoring, where the intervention device can immediately observe the actions of users without errors. We also assume that there exist actions of the intervention device that are most and least preferred by all users and the intervention device, regardless of the actions of users. We derive analytical results about the outcomes achievable with intervention and optimal intervention rules, and illustrate the results with an example based on random access networks. Recently, it has been shown that carrier-sense multiple access (CSMA)-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an idealized continuous-time CSMA protocol where collisions can never occur. In addition, simulation results indicate that the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, in general they may only achieve a fraction of the capacity region. In this paper, we propose a discrete-time version of the CSMA algorithm. Central to our results is a discrete-time distributed randomized algorithm that is based on a generalization of the so-called Glauber dynamics from statistical physics, where multiple links are allowed to update their states in a single timeslot. The algorithm generates collision-free transmission schedules while explicitly taking collisions into account during the control phase of the protocol, thus relaxing the perfect CSMA assumption. More importantly, the algorithm allows us to incorporate heuristics that lead to very good delay performance while retaining the throughput-optimality property. Participatory sensing is becoming more popular with the help of sensor-embedded smartphones to retrieve context-aware information for users. However, new challenges arise for the maintenance of the energy supply, the support of the quality-of-information (QoI) requirements, and the generation of maximum revenue for network operator, but with sparsely research exposure. This paper proposes a novel efficient network management framework to tackle the above challenges, where four key design elements are introduced. First is the QoI satisfaction index, where the QoI benefit the queries receive is quantified in relation to the level they require. Second is the credit satisfaction index, where the credits are used by the network operator to motivate the user participation, and this index is to quantify its degree of satisfaction. Third is the Gur Game-based distributed energy control, where the above two indexes are used as inputs to the mathematical framework of the Gur Game for distributed decision-making. Fourth is the dynamic pricing scheme, based on a constrained optimization problem to allocate credits to the participants while minimizing the necessary adaptation of the pricing scheme from the network operator. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7 detection outage, saves 80 of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4 gap if compared with optimal solution. Random-access algorithms such as CSMA provide a popular mechanism for distributed medium access control in large-scale wireless networks. In recent years, tractable stochastic models have been shown to yield accurate throughput estimates for CSMA networks. We consider a saturated random-access network on a general conflict graph, and prove that for every feasible combination of throughputs, there exists a unique vector of back-off rates that achieves this throughput vector. This result entails proving global invertibility of the non-linear function that describes the throughputs of all nodes in the network. We present several numerical procedures for calculating this inverse, based on fixed-point iteration and Newton's method. Finally, we provide closed-form results for several special conflict graphs using the theory of Markov random fields.