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With the rapid development of intelligent transportation systems, enormous amounts of delay-sensitive vehicular services have been emerging and challenge both the architectures and protocols of vehicular networks. However, existing cloud computing-embedded vehicular networks cannot guarantee timely data processing or service access, due to long propagation delay and traffic congestion at the cloud center. Meanwhile, the current distributed network architecture does not support scalable network management, leading the intelligent data computing policies to be undeployable. With this motivation, we propose to introduce fog computing into vehicular networks and define the Multiple Time-constrained Vehicular applications Scheduling (MTVS) issue. First, to improve the network flexibility and controllability, we introduce a Fog-based Base Station (FBS) and propose a Software-Defined Networking (SDN)-enabled architecture dividing the networks into network, fog, and control layers. To address MTVS issue, instead of normal centralized computing-based approaches, we propose to distribute mobile delay-sensitive task in data-level over multiple FBSs. In particular, we regard the fog layer of SDN-enabled network as an FBS-based network and propose to distribute the computing task based on the FBSs along multiple paths in the fog layer. By Linear Programming, we optimize the optimal data distribution/transmission model by formulating the delay computation model. Then, we propose a hybrid scheduling algorithm including both local scheduling and fog scheduling, which can be deployed on the proposed SDN-enabled vehicular networks. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the success rate for addressing MTVS issue. [ABSTRACT FROM AUTHOR]

In underwater acoustic sensor networks (UASNs), the sensors are deployed at different areas of the ocean, which perform information collection and delay-sensitive routing to the data center for further processing or industrial computing. However, in UASNs, the network states have spatiotemporal characteristics due to tides or autonomous underwater vehicles. To steadily route the traffic especially when the spatiotemporal characteristics of the UASNs are considered, a network architecture with intelligent traffic engineering or routing policies is indispensable. In this paper, we employ software-defined networking (SDN) technology and propose an SDN-enabled distributed architecture for UASNs. Based on the proposed architecture, we propose a scheme DSR-SDN for delay-sensitive spatiotemporal routing in SDN-enabled UASNs. The DSR-SDN includes three phases: First, topology awareness; second, spatiotemporal characteristics estimation; and third, routing computation. Particularly, with SDN features, DSR-SDN provides topology awareness based on a proposed software-defined beaconing scheme. Based on the detected topology, the spatiotemporal characteristics of the network states are estimated based on a proposed SDN-based hierarchical node localizing approach SDN-HL. Lead by the SDN controllers, SDN-HL makes use of the proposed “minimum weighted gap” formulation and Adam algorithm to optimize the localization and builds the indirect links to increase the localization rate. To route the traffic through the network with spatiotemporal characteristics, we adopt the time-expanded network approach, based on which a spatiotemporal route decision can be made before the routing starts. The simulation results demonstrate that the proposed scheme, i.e., DSR-SDN, can conduct accurate spatiotemporal characteristic estimation for the network states and provide delay-sensitive spatiotemporal routing for the sensed data. [ABSTRACT FROM AUTHOR]

Submarine pipelines are among the most efficient methods of marine oil transportation, and they are widely used in offshore oil exploitation. However, the complex environment of the ocean and malicious attackers may damage the pipeline. They can be efficiently monitored by deploying a multiplicity of sensor nodes, organized in underwater acoustic sensor networks (UASNs). UASNs is located near oil wells, so that UASNs can sense, broadcast, and receive data surveying the status of the submarine infrastructure timely. Inevitably, UASNs may experience security problems, owing to the open nature of the underwater environment. To address these security issues, this paper proposes a new source location privacy protection scheme based on an offshore oil acquisition platform. In the proposed scheme, the nodes are defined as pipeline and environment locations, and are allocated to different clusters. The concept of forming anonymous clusters by exchanging the identities of a source and a cluster head is introduced to determine fake confounding sources. In addition, a data fragmentation method is devised to reduce the energy consumption resulting from fake packet transmission. Particularly, we propose the deployment of disguised AUVs that collect data from fake sources, as to conceal and protect the location of the actual sources. Simulation results show that the safety time of the network is increased by about 6%. Due to the adoption of the data fragmentation method, the energy consumption of nodes is also decreased. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless communication due to its agility and altitude. UAVs can be used as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground devices (GDs) in various scenarios, such as emergency communication and traffic offloading in hotspots. However, due to the limited communication ranges and high prices of commercial UAV-BSs, covering a target area all the time with sufficient UAVs is quite challenging, especially under dynamic environment. We need to design the trajectory of the UAV-BSs to optimize system performance. Most existing works focus on the energy-efficient coverage and throughput maximization but ignore the fairness of communication service, especially the fairness at user-level. Besides, reinforcement learning is suitable for solving decision problems in dynamic environments. However, most existing works use centralized deep reinforcement learning (DRL) approaches. Due to the scalability and low time complexity, a distributed DRL approach is more suitable for multiple UAV-BSs communication system in dynamic environment. Unlike previous works, we characterize the fairness at user-level based on proportional fairness scheduling and formulate a weighted-throughput maximization problem via designing UAV-BSs’ trajectory. Then we model the dynamic deploymentproblem of UAV-BSs as a Markov game and propose a multi-agent deep reinforcement learning-based distributed UAV-BSs control approach named MAUC. MAUC approach adopts the framework of centralized training with distributed execution. Simulation results show that the MAUC can improve fairness of communication service by sacrificing a small amount of throughput. [ABSTRACT FROM AUTHOR]