UAV-Empowered Edge Computing Environment for Cyber-Threat Detection in Smart Vehicles

Garg, Sahil; Singh, Amritpal; Batra, Shalini; Kumar, Neeraj; Yang, Laurence T.

doi:10.1109/mnet.2018.1700286

Cited by 161 publications

(59 citation statements)

References 10 publications

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“…Given the advantages of prompt deployment and their bird's-eye perspective, unmanned aerial vehicles (UAVs) have been widely invoked for environmental monitoring and data collection [1]- [3], in the fields of agriculture [4], disaster sensing [5], emergency management [6], border control [7], intelligent transportation systems [8] and crowd surveillance [9]. However, the decision-making applications relying on real-time video streaming and image processing tend to exceed the local data processing capability of low-cost UAVs or may excessively prolong the time required for executing their actions [10].…”

Section: A Motivation and Scopementioning

confidence: 99%

Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems

Bai

Wang

Ren

et al. 2019

IEEE Trans. Veh. Technol.

225

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Characterized by their ease of deployment and bird's-eye view, unmanned aerial vehicles (UAVs) may be widely deployed both in surveillance and traffic management. However, the moderate computational capability and the short battery life restrict the local data processing at the UAV side. Fortunately, this impediment may be mitigated by employing the mobile-edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV through a wireless transmission link. However, the offloaded information may become compromised by eavesdroppers. To address this issue, we conceive an energyefficient computation offloading technique for UAV-MEC systems, with an emphasis on physical-layer security. We formulate a number of energy-efficiency problems for secure UAV-MEC systems, which are then transformed to convex problems. Finally, their optimal solutions are found for both active and passive eavesdroppers. Furthermore, the conditions of zero, partial and full offloading are analyzed from a physical perspective. The numerical results highlight the specific conditions of activating the above three offloading options and quantify the performance of our proposed offloading strategy in various scenarios.

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Section: A Motivation and Scopementioning

confidence: 99%

Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems

Bai

Wang

Ren

et al. 2019

IEEE Trans. Veh. Technol.

225

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“…After obtaining the user association and UAV location by solving Problem (53), a feasible computing capacity allocation for Problem (42) is given by…”

Section: Function (53a) Represents the Sum Squared Distance Between Amentioning

confidence: 99%

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Yang¹,

Pan²,

Wang³

et al. 2019

IEEE Trans. Wireless Commun.

377

186

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In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing based algorithm is accordingly is proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering based algorithm is proposed.Numerical results show that the proposed algorithm achieves better performance than conventional approaches. ). 2 I. INTRODUCTION With high mobility and the explosive growth of data traffic, unmanned aerial vehicles (UAVs) assisted wireless communications have attracted considerable attention [1]. Compared to conventional wireless communications, UAV-enabled wireless communications can provide higher wireless connectivity in areas without infrastructure coverage. Besides, high throughput can always be achieved in UAV-enabled wireless communications due to the higher probability of line-of-sight (LoS) communication links between user equipments (UEs) and UAVs [2]-[5]. Due to the above distinctions, UAVs can be utilized in many applications, such as UAVenabled relaying [6]-[9], UAV-enabled data collection [10]-[13], UAV-enabled device-to-device communication networks [14], [15], UAV-enabled wireless power transfer networks [16] and UAV-enabled caching networks [17], [18].To fully exploit the design degrees of freedom for UAV-enabled communications, it is crucial to investigate the location and trajectory optimization in UAV-enabled wireless communication networks. In [19], the altitude of UAV was optimized to provide maximum radio coverage on the ground. To maximize the number of covered users using the minimum transmit power, an optimal location and altitude placement algorithm was investigated in [20] for UAV-base stations (BSs). With different quality-of-service (QoS) requirements of users, authors in [21] studied the three-dimension UAV-BS placement that maximizes the number of covered users.Considering the adjustable UAVs' locations, the UAV number minimization was considered in [22]. In [23] and [24], the UAV's trajectory was optimized by jointly considering both the communication throughput and the UAV's energy consumption. Further optimizing user-UAV association, [25] investigated the sum power minimization problem of the UAV. Different from [19]-[25] with fixed-beamwidth antenna, the beamwidth of the directional antenna was optimized in [26] with fixed bandwidth allocation to improve the system throughput. Through jointly optimizing beamwidth and bandwidth, the sum power was further minimized in [27]. Deployi...

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“…Optimizing the position and trajectory [126], [128], [133], [144], [176] Intelligent network layer Routing selection [15], [69], [89] Congestion control [177]- [181] Smart access [182], [183] Intelligent application layer High-level control [184]- [187] UAV-enabled intelligent aerial services…”

Section: Intelligent Communication Layermentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

179

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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UAV-Empowered Edge Computing Environment for Cyber-Threat Detection in Smart Vehicles

Cited by 161 publications

References 10 publications

Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems

Energy-Efficient Computation Offloading for Secure UAV-Edge-Computing Systems

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

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