Relevance-Aware Information Dissemination in Vehicular Networks

Meuser, Tobias; Richerzhagen, Björn; Stavrakakis, Ioannis; Steinmetz, Ralf

doi:10.1109/wowmom.2018.8449751

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…The work in [19], for instance, proposes a priority-based content propagation scheme that accelerates safety content delivery for a set of moving vehicles and provides the forwarding of non-safety contents based on popularity. Similarly, in [20] an information-centric dissemination protocol for safety information in vehicular ad-hoc networks was proposed. In [20] a context-aware information dissemination scheme for vehicular networks was introduced, where instead of disseminating all events, only the most relevant ones are disseminated.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, in [20] an information-centric dissemination protocol for safety information in vehicular ad-hoc networks was proposed. In [20] a context-aware information dissemination scheme for vehicular networks was introduced, where instead of disseminating all events, only the most relevant ones are disseminated. The authors of [21] proposed an SDN architecture that uses a data cognitive engine to determine user priority (based on the users' health situation) and allocates edge resources (including edge caching resources) accordingly through a resource-cognitive engine.…”

Section: Related Workmentioning

confidence: 99%

“…However, none of the above-mentioned works consider the limited caching capability of edge caches in their solutions, and so none of them propose a strategy for content eviction. Even though both requirements 1 and 3 are satisfied in [19], [20], [21], requirements 2 and 4 remain unmet.…”

Section: Related Workmentioning

confidence: 99%

“…Parameters ϵ (exploration rate), and λ (a random value in range [0, 1]) are used to control these phases. A random action and its type are selected in the exploration phase (see lines [10][11][12][13][14][15][16][17][18][19][20][21][22][23], while in the exploitation phase, DQN − selection will determine the action (see line 25). The action, reward, and next state are then collected and stored in buffer D (line 29).…”

Section: F Ddqn With Lstm Cellsmentioning

confidence: 99%

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Deep Reinforcement Learning-Based Content Migration for Edge Content Delivery Networks With Vehicular Nodes

Malektaji

Ebrahimzadeh

Elbiaze

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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With the explosive demands for data, content delivery networks are facing ever-increasing challenges to meet end-users' quality-of-experience requirements, especially in terms of delay. Content can be migrated from surrogate servers to local caches closer to end-users to address delay challenges. Unfortunately, these local caches have limited capacities, and when they are fully occupied, it may sometimes be necessary to remove their lower-priority content to accommodate higher-priority content. At other times, it may be necessary to return previously removed content to local caches. Downloading this content from surrogate servers is costly from the perspective of network usage, and potentially detrimental to the end-user QoE in terms of delay. In this paper, we consider an edge content delivery network with vehicular nodes and propose a content migration strategy in which local caches offload their contents to neighboring edge caches whenever feasible, instead of removing their contents when they are fully occupied. This process ensures that more contents remain in the vicinity of end-users. However, selecting which contents to migrate and to which neighboring cache to migrate is a complicated problem. This paper proposes a deep reinforcement learning approach to minimize the cost. Our simulation scenarios realized up to a 70% reduction of content access delay cost compared to conventional strategies with and without content migration.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: F Ddqn With Lstm Cellsmentioning

confidence: 99%

See 2 more Smart Citations

Deep Reinforcement Learning-Based Content Migration for Edge Content Delivery Networks With Vehicular Nodes

Malektaji

Ebrahimzadeh

Elbiaze

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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“…The recent literature has stressed the importance of quantifying information relevance for V2X communication [30], [39]. In the following, let us explain how each vehicle can obtain its information relevance for cooperative driving.…”

Section: B Information Relevancementioning

confidence: 99%

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Bischoff¹,

Schiegg

Schuller³

et al. 2021

IEEE Access

Self Cite

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Cooperative driving is a promising approach to increase traffic efficiency and safety. However, cooperative driving requires high communication performance from the application perspective when coordinating and executing a cooperative maneuver, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized vehicular networks, e. g., from the European Telecommunications Standard Institute, maintain the radio communication performance under congested channel conditions but can severely degrade the communication performance from the application perspective. In this paper, we propose a collaborative resource allocation mechanism with content-aware congestion control for decentralized vehicular networks that prioritizes vehicles with relevant information under congested channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-agnostic congestion control mechanisms.

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