Edge caching strategy design and reward contract optimization for uAV-enabled mobile edge networks

Lu, Weidang; Yin, Baocai; Huang, Guoxiang; Li, Bo

doi:10.1186/s13638-020-1655-2

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…Thus, we can classify the ESs based on their participation willingness. If there are

types of participation willingness for the ESs, which are denoted as [ 20 ]:

where a larger value of

indicates a stronger desire for the ES to assist the intelligent telemedicine system. It is assumed that the probability of the ES with participation willingness

, and

.…”

Section: System Modelmentioning

confidence: 99%

“…Thus, we can classify the ESs based on their participation willingness. If there are I types of participation willingness for the ESs, which are denoted as [20]:…”

Section: Interaction Between MC and Essmentioning

confidence: 99%

“…u and a represent the slot allocation strategy for the WBAN and the data offloading strategy for the ECN, respectively. Constraint Equations ( 19) and (20) indicate that the slot allocation variable and the offloading strategy variable are binary variables. Constraint Equation (21) guarantees that the allocated slots cannot exceed the threshold T. Constraint Equation (22) shows that each GUN can only choose one ES.…”

Section: Resource Allocation and Data Offloading Based On Optimal Con...mentioning

confidence: 99%

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Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System

Wang

Chen

et al. 2023

Sensors

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Intelligent telemedicine technology has been widely applied due to the quick development of the Internet of Things (IoT). The edge-computing scheme can be regarded as a feasible solution to reduce energy consumption and enhance the computing capabilities for the Wireless Body Area Network (WBAN). For an edge-computing-assisted intelligent telemedicine system, a two-layer network architecture composed of WBAN and Edge-Computing Network (ECN) was considered in this paper. Moreover, the age of information (AoI) was adopted to describe the time cost for the TDMA transmission mechanism in WBAN. According to the theoretical analysis, the strategy for resource allocation and data offloading in edge-computing-assisted intelligent telemedicine systems can be expressed as a system utility function optimizing problem. To maximize the system utility, an incentive mechanism based on contract theory (CT) was considered to motivate edge servers (ESs) to participate in system cooperation. To minimize the system cost, a cooperative game was developed to address the slot allocation in WBAN, while a bilateral matching game was utilized to optimize the data offloading problem in ECN. Simulation results have verified the effectiveness of the strategy proposed in terms of the system utility.

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“…Thus, we can classify the ESs based on their participation willingness. If there are

types of participation willingness for the ESs, which are denoted as [ 20 ]:

where a larger value of

indicates a stronger desire for the ES to assist the intelligent telemedicine system. It is assumed that the probability of the ES with participation willingness

, and

.…”

Section: System Modelmentioning

confidence: 99%

“…Thus, we can classify the ESs based on their participation willingness. If there are I types of participation willingness for the ESs, which are denoted as [20]:…”

Section: Interaction Between MC and Essmentioning

confidence: 99%

Section: Resource Allocation and Data Offloading Based On Optimal Con...mentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System

Wang

Chen

et al. 2023

Sensors

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show abstract

“…Every smart computer that owns a CA can interpret intent, cache user information, contact data, and create activity profiles based on the user interface in the Internet of things (IoT) system. We see a related caching approach in [18], where the authors jointly suggest an edge caching technique that considers information awareness and a reward-based algorithm to encourage UAV-focused content sharing of the user equipment. Next, to realise the effectiveness of probabilistic caching, the authors in [19] investigated the service success probability in a heterogeneous network by implementing a base station (BS) in a UAV with different caching capacities and then maximising the probability caching placement.…”

Section: B Existing Research Workmentioning

confidence: 99%

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

et al. 2022

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Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system's energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.INDEX TERMS Caching, cognitive radio network, energy efficiency, human bond communication, nonorthogonal multiple access, optimisation, throughput, unmanned aerial vehicle. I. INTRODUCTION A. BACKGROUND AND MOTIVATIONSome of the key objectives of the potential universal communication environment are to enhance the standard of living by increasing convenience and enriching the experiences and circumstances of our daily activities. To realise this, several major technological advancements have centred on improving human interactions. The conventional approach to communication technology has always been to improve the information or signal so that the communication entities can manage them with the utmost efficiency. The limitation is that there is no transfer of information by such communication systems directly to the users' awareness that will allow for a more integrative sharing of sensory data. In relation to this, several articles have introduced the concept of human bond communication (HBC) to understand this dynamic growth

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“…Mobile edge computing (MEC) has been seen as a new evolution of cloud computing, in which the function of servers or access points moves towards the network edges to support the intensive computation needs of wireless devices in NGWCNs [14][15][16][17][18]. In this kind of computing approach, the edge servers serve as the computational access points to help accomplish the computation tasks of mobile computation-constrained devices through wireless links.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis for RF Energy Harvesting Mobile Edge Computing Networks with SIMO/MISO-NOMA Schemes

Ha¹,

Truong²,

Lee³

2021

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In this paper, we study an RF energy harvesting mobile edge computing network based on a SIMO/MISO system and NOMA schemes over Nakagami-m fading. Specifically, a multi-antenna user harvests RF energy from a power station by using a selection combining/maximal ratio combining scheme and offload its tasks to two MEC servers through downlink NOMA by employing transmit antenna selection/maximal ratio transmission scheme. Accordingly, we investigate the performance of six schemes, namely SC-TAS1, SC-TAS1, MRC-TAS1, MRC-TAS2, SC-MRT, and MRC-MRT, for this considered system. To evaluate the performance, exact closedform expressions of successful computation probability are derived. We further propose the optimal algorithm to find the best parameter sets to achieve the best performance. Moreover, the impacts of the network parameters on the system performance for these schemes are investigated. Finally, the simulation results are also provided to verify the accuracy of our analysis.

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Edge caching strategy design and reward contract optimization for uAV-enabled mobile edge networks

Cited by 11 publications

References 34 publications

Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System

Resource Allocation and Data Offloading Strategy for Edge-Computing-Assisted Intelligent Telemedicine System

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

Performance Analysis for RF Energy Harvesting Mobile Edge Computing Networks with SIMO/MISO-NOMA Schemes

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