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Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information. [ABSTRACT FROM AUTHOR]

A multi-user multiple-input single-output simultaneous wireless information and power transfer full-duplex system with imperfect channel state information is considered. Under the assumption that the unknown channel estimation error is within a certain range, the self-interference cannot be suppressed completely at the base station and at the multi-user nodes. The proposed design problem is formulated as maximizing the average harvested energy of multi-user nodes and maintaining the signal-to-interference-plus-noise ratio requirement, which is proved to be non-convex. Utilizing semidefinite relaxation and S-procedure methods, a convex optimization problem of optimal beamforming vector for the multi-antenna transmitter is obtained. Moreover, semidefinite relaxation is proved to be tight by showing the existence of rank-one optimal solutions. Simulation results are presented to verify the robustness and effectiveness of the proposed algorithm compared with the non-robust design. [ABSTRACT FROM AUTHOR]

Extensive delay-sensitive and computation-intensive tasks are involved in emerging vehicular applications. These tasks can hardly be all processed by the resource constrained vehicle alone, nor fully offloaded to edge facilities (like road side units) due to their incomplete coverage. To this end, we refer to the new paradigm of vehicular collaborative edge computing (VCEC) and make the best use of vehicles’ idle and redundant resources for energy consumption reduction within the VCEC system. To realize this target, we are faced with several nontrivial challenges, including short-term decision making coupled with long-term queue delay constraints, information uncertainty, and task offloading conflicts. Accordingly, we apply Lyapunov optimization to decouple the original problem into three sub-problems and then tackle them one by one: the first sub-problem is resolved by Lagrange multiplier method; the second is handled by UCB learning-matching approach; the third is addressed by a carefully designed greedy method. Scenarios without volatility and real-world road topology with realistic vehicular traffics are utilized to evaluate the proposed solution. Results from extensive numerical simulations demonstrate that our solution can achieve superior performances compared with the benchmark methods, in terms of energy consumption, learning regret, task backlog, and end-to-end delay. [ABSTRACT FROM AUTHOR]

Highly directional millimeter-wave (mmW) connectivity – especially in industry-grade scenarios with complex and unpredictable device mobility – requires a certain degree of structural redundancy in the network, which can be provided by utilizing multi-connectivity mechanisms. To lower the coordination complexity and overhead of tracking multiple directional beams, mmW networks can retrieve and leverage timely positioning information. In this paper, we develop a holistic framework for the co-design of networking and positioning in industrial 5G mmW deployments with multi-connectivity capabilities. In particular, we propose a flexible two-stage positioning solution – mindful of information uncertainty – that relies upon the 5G NR system design and can be seamlessly integrated into the mmW cellular infrastructure with reasonable overheads. We reproduce a typical 5G mmW network deployment featuring dissimilar device mobility patterns and assess the performance of the proposed architecture. In particular, we evaluate the precision of our positioning and base station orientation estimation methods as well as analyze the impact of the proposed scheme on the system-level performance. Our numerical results demonstrate that the proposed solution yields highly accurate position estimates and significantly improves the average network spectral efficiency. [ABSTRACT FROM AUTHOR]

Cognitive satellite–terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely \mathcalS-procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters. [ABSTRACT FROM PUBLISHER]

Fog node (FN) close to Internet of Things (IoT) devices can be exploited to offload the computing task of IoT devices. However, when lots of tasks are simultaneously offloaded from multiple IoT devices, FN can be overloaded. To mitigate this problem, we introduce a DYSON: DelaYed offloading System with Opportunistic fog Node (OFN) such as vehicles and mobile phones which can opportunistically reduce the load of static FN thanks to their mobility. In DYSON, to maximize the offloading effect of OFN, when IoT device cannot use OFN, it delays the task offloading with the expectation of future contacts of OFN. To assess the performance of DYSON, we develop an analytic model for the opportunistic offloading probability that the task can be offloaded to OFN. Based on the analytic model, we derive the optimal delay timer to maximize the opportunistic offloading probability while maintaining the probability that the task cannot be processed within the deadline below a target probability. Extensive simulation results are provided to show the superiority of DYSON. [ABSTRACT FROM AUTHOR]

In this paper, we propose a channel allocation algorithm for heterogeneous multi-cell cognitive radio 5G networks. The proposed algorithm considers channel-availability and channel-demand heterogeneity across CR cells. We use the theory of continuous-time Markov chains to develop mathematical expressions for channel availability and channel demand. Both expressions are incorporated in a binary integer program to find the optimal channel allocation. Further, we equip the proposed channel allocation algorithm with a novel interference domain identification scheme to detect interference range overlaps between cognitive radio cells. This scheme is essential for channel allocation, as it plays an important role in avoiding inter-cell interference. Our simulation results show significant performance gains compared to existing algorithms overlooking channel-availability. Furthermore, we use the theory of Dantzig-Wolfe decomposition to develop a framework for transforming centralized channel allocation algorithms into distributed ones. Through simulation experiments, we demonstrate the efficacy of the proposed framework. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC). [ABSTRACT FROM AUTHOR]

The exponential growth of data traffic from mobile devices leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we develop a fronthaul-aware upper confidence bound (FA-UCB) algorithm that is able to deal with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal benchmark with full a-priori knowledge, given that the backhaul delays are independently and identically distributed over time. Furthermore, under a restless martingale (RM) bandit condition, we put forth a generalized RM-FA-UCB algorithm that can achieve a sublinear regret bound even under non-stationary network dynamics. Numerical results demonstrate the merits of the proposed schemes and algorithms. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) has been expected as a promising scheme that can increase the computational capability of vehicles without relying on servers. Comparing with accessing the remote cloud, VFC is suitable for delay-sensitive tasks because of its low-latency vehicle-to-vehicle (V2V) transmission. However, due to the dynamic vehicular environment, how to motivate vehicles to share their idle computing resource while simultaneously evaluating the service availability of vehicles in terms of vehicle mobility and vehicular computational capability in heterogeneous vehicular networks is a main challenge. Meanwhile, tasks with different priorities of a vehicle should be processed with different efficiencies. In this work, we propose a task offloading scheme in the context of VFC, where vehicles are incentivized to share their idle computing resource by dynamic pricing, which comprehensively considers the mobility of vehicles, the task priority, and the service availability of vehicles. Given that the policy of task offloading depends on the state of the dynamic vehicular environment, we formulate the task offloading problem as a Markov decision process (MDP) aiming at maximizing the mean latency-aware utility of tasks in a period. To solve this problem, we develop a soft actor-critic (SAC) based deep reinforcement learning (DRL) algorithm for the sake of maximizing both the expected reward and the entropy of policy. Finally, extensive simulation results validate the effectiveness and superiority of our proposed scheme benchmarked with traditional algorithms. [ABSTRACT FROM AUTHOR]

Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme (ECOS) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme's performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost. [ABSTRACT FROM AUTHOR]

The 5G heterogeneous networks (HetNets) are capable of providing real-time computing services for autonomous vehicles (AVs) by deploying edge computing devices (ECDs) at macro cell base stations (MCBSs) and small cell base stations (SCBSs). With the imbalanced distribution and fast moving AVs contending intensely for computing services, how to efficiently exploit cooperations among participants in 5G HetNets to improve the service performance is therefore challenging. In this paper, we develop a game theoretic scheme for collaborative vehicular task offloading to facilitate the computing services in 5G HetNets. Specifically, we propose a two-stage vehicular task offloading mechanism to promote the cooperation among participants with the target of improving the task completion rate and the utilities of the participants, where the mechanism jointly considers the network architecture of the HetNets, the imbalanced distribution of AVs and the reuse of task results. In the first stage, an auction model is designed to help the MCBS select the optimal SCBS to execute the offloaded task based on the requirement of the task and the available computing resources of SCBSs. According to the task execution cost declared by the selected SCBS, the MCBS then bargains with the AV for the agreement of the task offloading service to maximize their utilities in the second stage. Using simulations, we show that the proposed collaborative task offloading scheme can achieve a higher task completion rate for the task offloading service and bring higher utilities to all participants than conventional schemes. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs’ CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity. [ABSTRACT FROM AUTHOR]

The fast proliferation of Internet of Things (IoT) exacerbates the current spectrum shortage. As a promising solution, licensed spectrum sharing has received great attentions recently. However, due to the unpredictable activities of incumbent users, the availability of the shared spectrum is uncertain, making the quality of service (QoS) provisioning based on it a difficult task, especially under multi-hop scenarios. In this paper, considering the energy-efficiency issue, we propose an end-to-end (E2E) QoS guaranteed green traffic off-loading scheme over the uncertain shared spectrums in multi-hop networks. Specifically, we formulate it into a two-step problem, an off-loaded traffic maximization (OTM) problem and an energy-efficiency improvement (EEI) problem. The former one is to determine the admitted sessions. To achieve the E2E QoS guarantee under the uncertain spectrum supply, we develop a concept of equivalent link capacity, and construct a risk-averse joint chance constraint. Since the formulated problem is intractable, we first develop a data-driven conservative transformation to make the joint chance constraint a set of deterministic individual ones. Then, we design a convex-hull-based and a substitution-based linearization approach to make all the constraints tractable. For the second step, the EEI problem with the same constraints as the OTM problem is designed to refine the resource scheduling for the admitted sessions to optimize the energy-efficiency. To deal with the fractional-form energy-efficiency objective, a Dinkelbach method based iterative algorithm is further developed. Numerical results have shown the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption. [ABSTRACT FROM AUTHOR]

With the development of autonomous vehicular technologies, the execution tasks become more memory-consuming and computation-intensive. Simultaneously, a certain portion of tasks are latency-sensitive, such as collaborative perception, path planning, collaborative simultaneous localization and mapping, real-time pedestrian detection, etc. Because of the limited computation resources inside vehicles and restricted transmission bandwidth, edge computing can be an effective way to assist with the tasks execution. Considering from the perspective of business, the reservation or subscription cost is cheaper than real time requests. In order to minimize the expense of consuming edge services, the desirable situation is to reserve the resources as much as needed. However, the configuration of vehicular network is variational in practice due to the diversity of road maps, different time range like peak time and off-peak time, and the various task types, which makes it challenging to figure out a general machine learning model that is suitable for any case. Therefore, to predict the resource consumption in edge nodes accurately in different scenarios, we propose a two-stage meta-learning based approach to adaptively choose the appropriate machine learning algorithms based on the meta-features extracted on database. Besides, due to the deficiency of dataset for edge resource consumption, we program in game engine unity to generate the 3D model of Manhattan area. Meanwhile, we change the factors like different road maps and number of vehicles so as to get closer to practices. In the evaluation part, we adopt root mean square error, mean absolute percentage, and mean GEH as evaluation metrics to assess the performance of each model. Also, a quantitative analysis for the total cost and waste is also conducted. Eventually, we can find that the proposed meta-learning based method outperforms the non-meta ones. [ABSTRACT FROM AUTHOR]

Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this article, we first form an aviation Big Data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long short-term memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered. [ABSTRACT FROM AUTHOR]

Presents the 2019 subject/author index for this publication. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the publication. [ABSTRACT FROM AUTHOR]

The persistent awareness of dynamic links quality is of critical importance to shared access systems, e.g., promoting sensing accuracy or even enabling seamless sharing, which remains yet as a challenging task. In this paper, we consider the shared access in heterogeneous small-cells, which are likely to be the main infrastructure components by utilizing the carrier aggregation (CA) scheme, potentially combining both licensed and unlicensed bands. We suggest a promising sensing scheme, which permits the simultaneous assessment of multiple links quality when detecting vacant spectrum, and hence is able to solve the long-standing hidden terminal problem. We incorporate an additional objective of acquiring dynamic link state information of multiple incumbent transmitters/receivers, and thereby, structure spectrum sensing as a mixed classification and estimation problem. The main challenge is that multiple channels may become similar with each other, and existing joint estimation schemes are unable to distinguish which incumbent is active and what its associated channel state is. To address this indistinguishable problem and alleviate the error propagation in recursive estimations, a novel alternating Bernoulli filtering algorithm is proposed. By fully exploiting the group transitional behavior of incumbents, a multiple-chains inference framework is developed. Numerical simulations validate our new scheme. Occupancy states of multiple incumbents can be identified accurately, by tracking their links quality and exploiting the underlying dynamics, which lays the foundation for more effective shared access. [ABSTRACT FROM AUTHOR]