Edge computing provides a platform facilitating edge servers to contribute to computation offloading while economizing their resources. Traditional offloading solutions are mostly centralized, which are unscalable for large-scale edge computing networks due to complex interactions among many edge servers. Meanwhile, dynamic pricing for an operator is equally, if not more, important to accommodate users’ time-varying demands for computing services. In this paper, we develop a decentralized online optimization framework to jointly minimize the server’s cost of workload scheduling while maximizing the operator’s utility of service pricing. Specifically, we employ the mean field game to model the collective scheduling behavior of all edge servers, thereby enabling optimal decision making only based on the server’s local information. Considering the service price in practice is not adjusted as frequently as the scheduling process, we establish a two-timescale optimization framework, where workload scheduling at a small timescale is tightly embedded into service pricing at a large timescale. Using mean field approximation, we derive the closed-form expression for the minimum scheduling cost, and the approximation error is$O\left ({\frac {1}{\sqrt {M}}}\right)$which declines as the number of edge servers$M$increases. By characterizing the influence of workload scheduling on dynamic pricing, we transform the complex service utility maximization into a succinct but equivalent problem, and thus we can make use of Lyapunov optimization to determine the optimal price over time. Extensive evaluations validate the effectiveness and optimality of our scheduling and pricing schemes.

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems of data collection and truth inference. Existing works have devised manifold techniques to discover truth from collected noisy data, but they frequently ignore various expertise of workers and dynamic information of crowdsensing system, thus leading to error-prone estimated truth and unqualified sensing data. In this paper, we design an online location-aware crowdsensing system to accurately estimate truth and efficiently assign tasks. Specifically, we unify diverse types of numerical and categorical tasks based on probabilistic graphical model, and then propose unsupervised learning methods which can dynamically infer ground truth and various worker expertise at the same time. Furthermore, we develop online task allocation schemes to gradually gather high quality data considering location awareness and inferred worker expertise. In particular, we convert the complicated task allocation into the additive form of probability improvement and entropy reduction, thereby solving the allocation problem via linearly selecting worker-task pair with low computation complexity. We finally carry out extensive evaluations using two datasets collected by our smartphones, where results demonstrate the superiority of our algorithms over the state-of-the-art approaches.

Wireless power transfer (WPT) enables a reliable and convenient charging paradigm. This article concerns the fundamental issue of energy saving in wireless rechargeable sensor networks (WRSNs), i.e., given a fixed number of rechargeable sensors (RSs) with their locations and charging demands, we focus on a minimal charging expenditure (MAP) problem with directional WPT to decrease the energy expenditure of the charger, on condition that the charging demands of all sensors are satisfied. In particular, we consider the anisotropic energy receiving property of RSs, which is closely related to the distance and the angle between the sensor and the charger antenna's orientation in directional WPT. We transform the MAP problem into an optimal function placement (OFA) problem, which can be geometrically analyzed in a rectangular coordinate system and is NP-hard. First, we study the OFA problem in the case of uniformly distributed sensors with identical charging demands, we develop the uniform charging strategy (UCS) to bound the total charging expenditure as Θ(1) for any number of sensors N. Based on the acquired insights, we further studied the OFA problem when the distribution of charging demands is Gaussian. We bound the total charging expenditure as Θ(1) for any number of sensors N, by developing the layered charging strategy (LCS). Extensive simulation results confirmed the performance of our design compared with two baseline algorithms. Both of the theoretical and simulations results reveal that the total energy expenditure of the charger is strongly related to the sensors' charging demands, however, is less affected by the number of sensors in the network.

Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue of matching workers to their assigned tasks. In this paper, we reexamine this issue by considering the spatiotemporal worker mobility and task arrivals, which more fits the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the nonlinear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Performance evaluation on real data set validates the proposed algorithm, where 116% gain of fairness is achieved at the expense of 12% loss of sensing value on average.

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

Coded caching has demonstrated the superiority in mitigating traffic pressure through jointly considering content delivery and storage schemes. However, existing works mainly focus on the situation where users have uniform demands with multiple layer of caches. In this paper, we propose a multilevel hierarchical coded caching scheme when users have nonuniform demands in a multi-hop content delivery network scenario. Specifically, to maintain the symmetry constraint of coded caching we utilize K-means to separate the file set with arbitrary distribution of popularity into several file subsets. We also formulate the Jensen's inequality and derive the upper bound of the transmission rate in each layer. To evaluate the system efficiency, we leverage the open-source Netflix dataset as our file set and conduct an experiment on a content delivery network with two layers of caches. Experimental results demonstrates that our multilevel hierarchical coded caching scheme performs much better than the baseline LFU caching scheme.

Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving, such as image classification, optical character recognition, and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of multi-class labeling tasks under strict budget constraint. We properly profile the tasks’ difficulty levels and workers’ quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational, and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.

Crowd sensing systems enable a wide range of data collection, where the data are usually tagged with private locations. How to incentivize users to participate in such systems while preserving location-privacy is coming up as a critical issue. To this end, we consider location-privacy protection when motivating users to sense data instead of viewing them separately. Without loss of generality,$k$-anonymity is utilized to reduce the risk of location-privacy disclosure. Specifically, we propose a location aggregation method to cluster users into groups for$k$-anonymity preserving, and meanwhile mitigating the incurred information loss. After that, an incentive mechanism is carefully designed to select efficient users and calculate rational compensations based on clustered groups obtained in location aggregation, where the influences of both the information loss and$k$-anonymity in location-privacy preserving are captured into group values and sensing costs. Through theoretical analysis and extensive performances evaluated on real and synthetic data, we find out that the incentive payment increases sharply with more stringent privacy protection and the information loss can be further mitigated compared with conventional methods.

Data volume has been increasing explosively in recent years and learning methods are vitally important to extract key information in such mass data. Traditional offline learning requires multiple traversals to the dataset, thus frequently suffering from lack of computational resources. Online learning can benefit in shrinking total time consumed by training model and lowering computational capacity. However they often converge slowly due to memory loss. Considering partial feedback, we uniquely propose online Confidence- Weighted learning in Bandit setting (CWB) for lower cumulative error and higher convergence rate. Specifically, historical information is preserved to adjust the weights of features for speeding up the convergence rate. Moreover, we novelly integrate the random sampling into the confidence-weighted learning, which can balance the exploitation and exploration in bandit setting. Extensive experiments demonstrate efficiency and effectiveness of our proposed scheme.

Crowdclustering clusters data items in a crowdsourcing manner, which makes discovered item categories more consistent with human perception. However, due to diversity of crowdsourcing workers and fluctuation of the number of tasks assigned to each worker, inferring stable and reliable clusters is challenging. Moreover, an item may be associated with multiple attributes, and such items should be put into different clusters, which makes inferring accurate clusters more complicated. To address the challenges above, in this paper we present a robust and fast crowdclustering scheme for finding overlapping clusters of items. Distinguished from existing works, we extract reliable and stable cluster information from workers' answers by majority voting. We then formulate an optimization problem to find overlapping clusters, and develop a nonnegative matrix factorization based approach to approximate the optimal solution. Experiments show the robustness, accuracy and efficiency of our approach.

Distributed TDMA medium access protocol plays an important role in stabilizing the throughput under various network states. The major challenge of implementing such protocol lies in the difficulty of slot assignment. Previous works provide a variety of mechanisms to implement the collision-free slot assignment. However, when network topology changes frequently, existing approaches suffer from low throughput due to the waste of idle slots. In this paper, we propose a novel distributed TDMA protocol called HD-MAC to improve the throughput performance under high dynamic change of network topology. Specifically, we come up with a frame structure with fixed length and establish a network join principle regarding the slot assignment for the newly added node, which avoid the overhead of maintaining extra frame length information among nodes. In addition, we adopt a contention-based channel access mechanism for enhancing the utilization of the idle slots, thus increasing the overall throughput. Finally, simulation results are demonstrated to validate the effectiveness of HD-MAC.

Lane-level vehicular localization has been regarded as a critical technology component for future vehicle navigation services. Current lane-level vehicular localization systems require dedicated devices, making the systems difficult to popularize. Moreover, most systems depend heavily on continuous accurate GPS data, which may be interfered under specific environments. Efficient lane-level map building is another problem to deal with. In this paper, we propose an integrated system with the capability of map building and lane- level localization using smartphones. This system employs a crowdsourcing-based approach to collect information from multiple sensors (including GPS, orientation sensor and acceleration sensor), such as lane changes and turns. Based on the information, a lane localization schemes is designed using the tool of machine learning. The experimental results show that the proposed system achieves high accuracy of map building and lane-level localization.

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure-less unmanned aerial vehicle (UAV) assisted VANET system called Vehicle-Drone hybrid vehicular ad hoc Network (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle-to-vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on-board UAV, which can deliver data message directly to destination, relay messages in a multi-hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end-to-end delay with controlled communication overhead. Copyright © 2016 John Wiley & Sons, Ltd.