Enabling multi-hop ad hoc networks through WiFi Direct multi-group networking

Funai, Colin; Tapparello, Cristiano; Heinzelman, Wendi

doi:10.1109/iccnc.2017.7876178

Cited by 58 publications

(31 citation statements)

References 9 publications

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“…7) Routing: Each user makes the routing decision by selecting the flow to serve and the next hop for transmission by applying the backpressure algorithm, which optimizes a spectrum utility depending on traffic congestion and link rate (computed in Step 6). Lower layers of DeepWiFi are transparent to the routing algorithm and can be combined with other efforts for multi-hop networking such as the extension of WiFi Direct for multi-group networking [26]. The output of the DeepWiFi protocol is specified as: a. TX power specifies the transmit power.…”

Section: Overview Of the Deep Wifi Protocolmentioning

confidence: 99%

DeepWiFi: Cognitive WiFi with Deep Learning

Davaslıoğlu

Soltani

Erpek

et al. 2021

IEEE Trans. on Mobile Comput.

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We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiFi is interoperable with baseline WiFi and builds upon the existing WiFi's PHY transceiver chain without changing the MAC frame format. Users run DeepWiFi for i) RF front end processing; ii) spectrum sensing and signal classification; iii) signal authentication; iv) channel selection and access; v) power control; vi) modulation and coding scheme (MCS) adaptation; and vii) routing. DeepWiFi mitigates the effects of probabilistic, sensing-based, and adaptive jammers. RF front end processing applies a deep learningbased autoencoder to extract spectrum-representative features. Then a deep neural network is trained to classify waveforms reliably as idle, WiFi, or jammer. Utilizing channel labels, users effectively access idle or jammed channels, while avoiding interference with legitimate WiFi transmissions (authenticated by machine learning-based RF fingerprinting) resulting in higher throughput. Users optimize their transmit power for low probability of intercept/detection and their MCS to maximize link rates used by backpressure algorithm for routing. Supported by embedded platform implementation, DeepWiFi provides major throughput gains compared to baseline WiFi and another jamming-resistant protocol, especially when channels are likely to be jammed and the signal-to-interference-plus-noise-ratio is low.

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Section: Overview Of the Deep Wifi Protocolmentioning

confidence: 99%

DeepWiFi: Cognitive WiFi with Deep Learning

Davaslıoğlu

Soltani

Erpek

et al. 2021

IEEE Trans. on Mobile Comput.

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“…Once the group is established, the nodes can disseminate their data to other nodes in the group. WiFi Direct does not define the communication between clients [3], as each client does not know the information of other clients including IDs, MAC or IP addresses by its original design. Therefore, one has to implement additional function along with the MSN application to allow the data of all nodes being shared with others.…”

Section: B Go-coordinated Disseminationmentioning

confidence: 99%

“…In a group, one of the WiFi Direct devices is selected as group owner (GO) to control the group like a conventional access point (AP), while other nodes connect to the GO as clients. Recently, researchers have demonstrated the feasibility of using WiFi Direct as the medium for opportunistic networking [2], multi-hop networking [3], and multi-group networking [4] which are candidate underlying networking techniques for MSNs. In addition, there are already a few WiFi-Direct-based MSN applications that feature content dissemination, such as CAMEO [5], public safety [6], and social commerce service [7].…”

Section: Introductionmentioning

confidence: 99%

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Fair airtime allocation for content dissemination in WiFi-direct-based mobile social networks

Mao

Jiang

2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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The vast penetration of smart mobile devices provides a unique opportunity to make mobile social networking pervasive by leveraging the feature of short-range wireless communication technologies (e.g. WiFi Direct). In this paper, we study local content dissemination in WiFi-Direct-based mobile social networks (MSNs). We propose a simple GO-coordinated dissemination strategy, as WiFi Direct does not originally support content dissemination. Due to mobility and the short transmission range, the duration of nodes in contact tends to be limited and consequently they compete for the limited airtime to disseminate their own data. Therefore, fair allocation of the limited airtime among the nodes is required. We focus on fairness in content dissemination rate, which is a key application-layer metric, rather than fairness in throughput or airtime and formulate the allocation problem as a generalized Nash bargaining game wherein the nodes bargain for a share of the limited airtime. The game is proved to have a unique optimal solution, and an algorithm with low complexity is designed to find the optimal solution. Furthermore, we propose a detailed scheduling approach to implement the optimal solution. We also present numerical results to evaluate the Nash bargaining based allocation and scheduling.

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“…To overcome the limitations of Wi-Fi Direct, several systems (Duan et al, 2014;Jung et al, 2014;Casetti et al, 2015;Funai et al, 2015;Felice et al, 2016) have been proposed. Authors in (Duan et al, 2014) have developed a content centric ad hoc network that includes mobile devices equipped with Wi-Fi Direct technology.…”

Section: Related Workmentioning

confidence: 99%

A Mobile Ad hoc Cloud Computing and Networking Infrastructure for Automated Video Surveillance System

Shah¹

2017

Journal of Computer Science

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Mobile automated video surveillance system involves application of real-time image and video processing algorithms which require a vast quantity of computing and storage resources. To support the execution of mobile automated video surveillance system, a mobile ad hoc cloud computing and networking infrastructure is proposed in which multiple mobile devices interconnected through a mobile ad hoc network are combined to create a virtual supercomputing node. An energy efficient resource allocation scheme has also been proposed for allocation of realtime automated video surveillance tasks. To enable communication between mobile devices, a Wi-Fi Direct based mobile ad hoc cloud networking infrastructure has been developed. More specifically, a routing layer has been developed to support communication between Wi-Fi Direct devices in a group and multi-hop communication between devices across the group. The proposed system has been implemented on a group of Wi-Fi Direct enabled Samsung mobile devices.

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Enabling multi-hop ad hoc networks through WiFi Direct multi-group networking

Cited by 58 publications

References 9 publications

DeepWiFi: Cognitive WiFi with Deep Learning

DeepWiFi: Cognitive WiFi with Deep Learning

Fair airtime allocation for content dissemination in WiFi-direct-based mobile social networks

A Mobile Ad hoc Cloud Computing and Networking Infrastructure for Automated Video Surveillance System

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