Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

Cai, Zhipeng; Ji, Shouling; He, Jing; Lin, Wei; Bourgeois, Anu G.

doi:10.1109/tpds.2013.75

Cited by 46 publications

(13 citation statements)

References 54 publications

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“…Recently, studies in [15] have considered optimizing transmission schedules in sensory systems with dynamic traffic patterns. Sensory systems with a probabilistic network model have been investigated in [16] and [17]. Data collection processes in sensory systems with mobility have been studied in [18].…”

Section: Related Workmentioning

confidence: 99%

Concurrent Data Collection Trees for IoT Applications

Cheng

Ganganath

Fok

2017

IEEE Trans. Ind. Inf.

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Abstract-Internet of Things (IoT) systems comprise massive volumes of smart devices. Through exchanges of information, smart objects are capable of reasoning and generate higher level of intelligence. The effectiveness of data collection processes is a key factor to the success of IoT systems as it can seriously affect the freshness of the captured data. Efficient data collection processes have been well-studied on sensory systems with static topologies and single data extraction point. Smart devices in IoT systems are often shared by different parties, therefore concurrent data collection processes are always expected. Such a unique characteristic of IoT systems has imposed new challenges to the designs of efficient data collection processes. In this paper, concurrent data collection trees specifically designed for IoT applications are proposed. It is shown that, comparing with an existing single-user data collection structure, systems with the proposed tree structures can significantly shorten their concurrent data collection processes.

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Section: Related Workmentioning

confidence: 99%

Concurrent Data Collection Trees for IoT Applications

Cheng

Ganganath

Fok

2017

IEEE Trans. Ind. Inf.

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“…Although there exist numerous works on aggregation in traditional wireless networks (e.g., see [7] for a survey), we are only able to identify one paper [5] that explicitly targets aggregation in cognitive radio networks. However, the models and approaches employed by [5] are very different from the ones considered in this paper. Lastly, we note here that although rendezvous algorithms can potentially be used to solve data aggregation, COG-COMP is more efficient in many (if not all) cases.…”

Section: Data Aggregationmentioning

confidence: 99%

Efficient Communication in Cognitive Radio Networks

Gilbert

Kühn

Newport

et al. 2015

Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing

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Devices in a cognitive radio network use advanced radios to identify pockets of usable spectrum in a crowded band and make them available to higher layers of the network stack. A core challenge in designing algorithms for this model is that different devices might have different views of the network. In this paper, we study two problems for this setting that are well-motivated but not yet wellunderstood: local broadcast and data aggregation.We consider a single hop cognitive radio network with n nodes that each has access to c channels. We assume each pair of nodes overlaps on at least 1 ≤ k ≤ c channels.We first describe and analyze COGCAST, a randomized algorithm that solves local broadcast in O((c/k) · max{1, c/n} · lg n) time, with high probability, by spreading information in an epidemic manner through the network.We then propose COGCOMP, a randomized algorithm that solves data aggregation in O((c/k) · max{1, c/n} · lg n + n) time, with high probability. The COGCOMP algorithm uses COGCAST as a key primitive to establish a spanning tree among the nodes, so that data can be aggregated from leaves to root.We conclude with a collection of lower bounds that show COG-CAST is near optimal (in particular, within a lg n factor) in many cases. These bounds introduce new techniques of potential standalone interest for those concerned with proving fundamental limits in the cognitive radio network setting.

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“…They require channel-related information from all the candidate relay nodes, which is inefficient when the number of candidate relays is large or the time for relay selection is limited [10]. For example, channel state information and SNR are required by the relay selection approaches proposed in [11,12], respectively.…”

Section: Related Workmentioning

confidence: 99%

Enabling smartphone-based HD video chats by cooperative transmissions in CRNs

Jing

Cui

Cheng

et al. 2015

J Wireless Com Network

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Smartphones have been equipped with the cameras that can shoot HD videos, and the video chat apps such as Skype are becoming popular. We can, therefore, intuitively predict the trend that users are expecting to enjoy HD video chats via utilizing their smartphones. Most of the current Internet services, however, cannot support the live HD video transmissions because of their low uplink rate. In order to overcome this limit, we propose to offload the uplink transmissions to cooperative users via cognitive radio networks. Specifically, we first divide the video stream into several substreams according to the H.264/SVC standard and the cooperative users' uplink rates. Then, the cooperative users are selected by employing our proposed optimal multiple stopping method. Finally, the substreams are assigned to the selected cooperative users by a 0-1 Knapsack-based allocation algorithm. The simulation results demonstrate that our proposed scheme can successfully support 720P HD video chats.

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Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration

Cited by 46 publications

References 54 publications

Concurrent Data Collection Trees for IoT Applications

Concurrent Data Collection Trees for IoT Applications

Efficient Communication in Cognitive Radio Networks

Enabling smartphone-based HD video chats by cooperative transmissions in CRNs

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