Q-007 the Effect of Lifestyle Modification for the Metabolic Syndrome

Tang, Meng-Bin

doi:10.1097/01.hjh.0000408119.75363.ac

Cited by 1 publication

(3 citation statements)

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“…ECDDMEC-A-2 is a greedy algorithm starting with initializing an empty set V * C2E to store C2E caching nodes (lines 1). Then, the algorithm iterates through a while loop (line [2][3][4][5][6][7][8][9][10][11] to approximately obtain the minimum C2E caching nodes set V * C2E . In each iteration of this while loop, the algorithm firstly traverses each node v i in V in a breadth-first search (BFS) order through a for loop (line 3-6) to compute the set N(v i , k) and the number of elements of set N(v i , k) which is denoted by n(v i ).…”

Section: Algorithm 1 Ecddmec-a-1: Construction Of Mobile Edge Computi...mentioning

confidence: 99%

“…The rapid advancement of mobile communication and Internet of Things (IoT) technologies has resulted in an exponential increase in the scale of mobile communication networks and the proliferation of mobile terminal devices which will generate a massive volume of data, leading to significant network traffic and congestion. Projections indicate that by 2025 the aggregate number of diverse terminal devices connected to these networks will soar to 34.1 billion and the global data volume will increase to 175 ZB [1,2]. The emergence of latency-sensitive applications, such as virtual reality, augmented reality, autopilot systems, and real-time navigation, further exacerbates this issue.…”

Section: Introductionmentioning

confidence: 99%

“…These applications demand high levels of real-time responsiveness, ultra-low latency, and substantial network throughput. The traditional centralized network architecture, predominantly based on cloud computing paradigms, is ill-equipped to meet the stringent requirements of end-users in terms of these performance metrics [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Edge Caching Data Distribution Strategy with Minimum Energy Consumption

Lin,

Liang

2024

Sensors

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In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

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