In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike conventional approaches, which require uniform sampling traditional CS theory, propose a random walk algorithm for WSNs. However, such an approach will conform to path constraints and result non-uniform selection measurements. It is still unknown whether method can be used recover sparse signals from perspectives theory graph provide mathematical foundations allow...

Cooperative offloading in mobile edge computing enables resource-constrained clouds to help each other with computation-intensive tasks. However, the power of such could not be fully unleashed, unless trust risks collaboration are properly managed. As tasks outsourced and processed at network edge, completion latency usually presents high variability that can harm offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided offloaDing...

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 label set binary under strict budget constraint. We properly profile tasks' difficulty levels workers' quality in crowdsourcing systems, where collected labels are aggregated with sequential Bayesian approach. To...

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. It quite challenging due to the complicated spatiotemporal dependency and essential uncertainty brought about by dynamic urban conditions. In most of existing methods, spatial correlation captured utilizing graph neural networks (GNNs) throughout fixed based on local proximity. However, road conditions are complex changeable, which leads interactions between roads should also be over time. addition,...

Electric vehicles (EVs), as part of sustainable transport, are believed to be helpful reduce global warming. In this article, we focus on the fast-charging station (FCS) deployment problem, which is one key issues EV ecosystem. Specifically, elastic demand considered, i.e., charging will suppressed because either long driving distance get or waiting time at station. A fixed-point equation proposed capture nature users' behavior. It considers both spatial and temporal penalties by...

Crowd sensing systems enable a wide range of data collection, where the are usually tagged with private locations. How to incentivize users participate in such while preserving location-privacy is coming up as critical issue. To this end, we consider protection when motivating sense instead viewing them separately. Without loss generality, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$...

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and secondary (SUs) with incomplete information. Inspired by matching theory, we model network as a market. In this market, each PU proposes certain proposal representing combination of relay power access time to attract SUs, while SU maximizes its utility selecting most suitable PU. derive sufficient necessary conditions stable in which none PUs or SUs would like change...

Delayed traffic offloading is a promising paradigm to alleviate the cellular network congestion caused by explosive demands. As we all know, in mobile networks, delay profile for remarkable due users’ mobility. How exploit user tolerance improve profit of operator as well users becomes big challenge. In this paper, model delayed process monopoly market based on contract theory, where acts monopolist setting up optimal statistical information satisfaction. We propose an incentive mechanism...

Real-time road traffic information brings great convenience for drivers. Various acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study problem extracting actual according to reports from an amount unknown contributors. To obtain accurate result with high probability, establish a reputation system evaluate reliability each contributor, which takes both...

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue matching workers their assigned tasks. In this paper, we reexamine by considering the spatiotemporal worker mobility and task arrivals, which more fits actual situation. Specifically, study location-aware location diversity based dynamic crowdsensing system, move over time tasks arrive stochastically. We first exploit offline proposing combinatorial...

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible train well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where model learned from data-sufficient leveraged benefit process data-scarce cities. However, spatio-temporal graphs among different...

We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where PU selects proper set to serve as relays for its transmission. In return, leases portion channel access time selected SUs their own The decides it will leave (i.e., relays), decide respective power level used help PU's transmission in order achieve proportional channel. assume that are rational selfish, i.e., they only aim at maximizing utility. As SU's utility is term...

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 system is that network suffers from frequent disconnections. second to adapt transmission route dynamic mobility pattern vehicles. Also, some infrastructural requirements hard meet when deploying widely. Facing these challenges, we devise an infrastructure-less unmanned...

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose Lyapunov-based security-aware defense mechanism backed by intelligence, where robust strategy-making is based acquired...

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues matching workers their assigned tasks and processing the collected data. In this paper, we reexamine these by considering spatio-temporal worker mobility task arrivals, which more fit actual situation. Specifically, study location-aware location diversity based dynamic crowdsensing system, move over time arrive stochastically. We first exploit offline...

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers' mobile devices equipped with on-board sensors for various tasks. Because importance ensuring satisfactory spatio-temporal coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize task completion rate under traveling constraints. Different from prior work, we propose Hector, a joint trajectory and incentive...

Mobile edge computing enables resource-limited clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset be placed at same time. This paper presents jointly optimized design cooperative placement scheduling framework, named JCPS, pursues <i>social cost minimization</i> over time while ensuring diverse user...

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how capture dynamic spatiotemporal correlation make accurate predictions very challenging. In most existing works, network often modeled as fixed graph based on local proximity. However, such modeling not sufficient describe dynamics global contextual information. this paper, we consider constructing weighted through attention mechanism....

<title>Abstract</title> Climate change and anthropogenic activities have exacerbated hypoxic conditions in the global ocean, threatening marine ecosystems earth sustainability. Investigating history of ocean deoxygenation is crucial for developing coping strategies. However, due to severe sparse in-situ observations spatiotemporal complexity mechanism, precise modelling oxygen trends, especially large scale, remains a long-standing problem. Here we propose Jingwei, hybrid graph learning...

The best arm identification problem in multi-armed bandit model has been widely applied into many practical applications, such as spectrum sensing, online advertising, and cloud computing. Although lots of works have devoted this area, most them do not consider the cost pulling actions, i.e., a player to pay some when she pulls an arm. Motivated by this, we study ratio-based problem, where each is associated with random reward well cost. For any \(\delta\in(0,1)\) , probability at least...

In this paper, we propose an agent-based spectrum trading model, where agent can play a third-party role in the process. Providing service to Secondary Users (SUs) with bought from Primary (PUs), make profits during process by providing secondary users. During each period, has decide how much it should lease PUs and what price charge SUs. Therefore, most significant challenge implement model is finding profitable strategy for agent(s). We address under two scenarios which: 1) single 2)...

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and spatial reusability simultaneously, which leaves room further enhance efficiency. In this paper, we introduce coalition double auction for efficient CRNs, where secondary users (SUs) partitioned several coalitions can be executed within each coalition. The partition...