Inconspicuous Manipulation for Social-Aware Relay Selection in Flying Internet of Things

Wang, Bowen; Sun, Yanjing; Sheng, Zhiqiang; Nguyen, Hien M.; Duong, Trung Q.

doi:10.1109/lwc.2019.2919536

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…First, we test and compare the total network energy consumption of three routing strategies, ECOR, OSP, and GreenOSPF, under different UAV load conditions, with the number of UAV requests increasing gradually. For each group of requests from source to destination in the UAV system [26], the experiment randomly generates 10000 packets of 10 kbit in size, and the data sending rate of each group of requests is [21]. The number of Exporter Router (ER) of GreenOSPF normally accounts for 5% to 10% of the total number of network nodes [21].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…For each group of requests from source to destination in the UAV system [26], the experiment randomly generates 10000 packets of 10 kbit in size, and the data sending rate of each group of requests is [21]. The number of Exporter Router (ER) of GreenOSPF normally accounts for 5% to 10% of the total number of network nodes [21]. Here, let ER = 10%.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…On the other hand, when the links and nodes in the UAV have multiple operating states with different service rates and the regulation of the operating states is closely related to the energy consumption of the UAV components, global planning and scheduling by packets are a better choice. For example, when the energy consumption of the working state of the UAV component obeys the subadditivity, i.e., f ðx + yÞ < f ðxÞ + f ðyÞ, to achieve the goal of optimizing energy consumption, it is obviously necessary to schedule the load in the network as much as possible and improve the utilization of the link, such as the GreenOSPF algorithm [21][22][23]. However, when the energy consumption of the operating state of the UAV components obeys superadditivity, i.e., f ðx + yÞ > f ðxÞ + f ðyÞ, to optimize the energy consumption of the UAV, it is necessary to balance the load in the UAV as much as possible.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

See 2 more Smart Citations

Multiwing UAV Communication and Energy Consumption Optimization Based on Energy Optimization

Xue

Tang

Fang

2022

Wireless Communications and Mobile Computing

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The UAV carries communication equipment to lift off as an aerial base station, which has the characteristics of flexibility, rapid deployment, etc. Reducing the energy consumption of UAVs is a key issue that needs to be urgently addressed in the current UAV field to build green UAVs. The transmission service rate adaptivity of UAV devices provides an effective way to optimize UAV energy consumption and improve UAV energy efficiency, and a global and distributed UAV energy optimization routing strategy based on routing algorithm is proposed in this paper. The strategy starts from the perspective of UAV global routing and abstracts the UAV components that provide transmission services for data into a processing domain according to the service characteristics of UAVs. In the simulation experiments, the distributed heuristic algorithm for energy-optimized routing proposed in the paper is compared with the OSPF and GreenOSPF energy-efficient routing algorithms in the related literature, and the comparison results of the algorithms in terms of energy consumption and delay are presented.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Simulation Experiments and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Multiwing UAV Communication and Energy Consumption Optimization Based on Energy Optimization

Xue

Tang

Fang

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Different from the game-theoretic solutions which focus on the interactions among different users, the matching theory is a powerful and special tool to solve the user pairing problems [18]. In [20], the authors formulated the single relay selection problem in FIoT as a one-to-one matching model, and the classical Gale-Shapley (GS) algorithm in [23] was invoked to find a stable matching, in which no agents can be better of by deviating to another choice. The stable matching can achieve the Pareto optimility.…”

Section: A Related Workmentioning

confidence: 99%

Popular Matching for Security-Enhanced Resource Allocation in Social Internet of Flying Things

Wang

Sun

Duong

et al. 2020

IEEE Trans. Commun.

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As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the socialaware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the "no pain no gain" principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize "win-win" situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

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“…Novel semi-automated driving technologies, collectively referred to as Cooperative Adaptive Cruise Control (CACC) [2], enable vehicles to drive very close together as a platoon. From Figure 1, we can see that vehicles in a platoon are virtually linked and communicate with each other through wireless communication technology [3], [4]. The leading vehicle is manually driven at the first position of the platoon and automatically followed by one or more following vehicles.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Chen

Jiang

et al. 2020

IEEE Access

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Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

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Inconspicuous Manipulation for Social-Aware Relay Selection in Flying Internet of Things

Cited by 12 publications

References 8 publications

Multiwing UAV Communication and Energy Consumption Optimization Based on Energy Optimization

Multiwing UAV Communication and Energy Consumption Optimization Based on Energy Optimization

Popular Matching for Security-Enhanced Resource Allocation in Social Internet of Flying Things

An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

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