Refine
Has Fulltext
- yes (22)
Is part of the Bibliography
- yes (22)
Document Type
- Working Paper (9)
- Journal article (8)
- Report (2)
- Book (1)
- Conference Proceeding (1)
- Doctoral Thesis (1)
Language
- English (22)
Keywords
- Datennetz (4)
- Rechnernetz (3)
- Echtzeit (2)
- Ethernet (2)
- IoT (2)
- Latenz (2)
- 5G core network (1)
- Anwendungsfall (1)
- BitTorrent (1)
- Content Distribution (1)
Institute
Sonstige beteiligte Institutionen
In this work, we describe the network from data collection to data processing and storage as a system based on different layers. We outline the different layers and highlight major tasks and dependencies with regard to energy consumption and energy efficiency. With this view, we can outwork challenges and questions a future system architect must answer to provide a more sustainable, green, resource friendly, and energy efficient application or system. Therefore, all system layers must be considered individually but also altogether for future IoT solutions. This requires, in particular, novel sustainability metrics in addition to current Quality of Service and Quality of Experience metrics to provide a high power, user satisfying, and sustainable network.
Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group.