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In intelligent IoT networks, an IoT user is capable of sensing the spectrum and learning from its observation to dynamically access the wireless channels without interfering with the primary user’s signal. The network, however, is potentially subject to primary user emulation and jamming attacks. In the existing works, various attacks and defense mechanisms for spectrum sharing in IoT networks have been proposed. This paper systematically conducts a targeted survey of these efforts and proposes new approaches for future studies to strengthen the communication of IoT users. Our proposed methods involve the development of intelligent IoT devices that go beyond existing solutions, enabling them not only to share the spectrum with licensed users but also to effectively thwart potential attackers. First, considering practical aspects of imperfect spectrum sensing and delay, we propose to utilize online machine learning-based approaches to design spectrum sharing attack policies. We also investigate the attacker’s channel observation/sensing capabilities to design attack policies using time-varying feedback graph models. Second, taking into account the IoT devices’ practical characteristics of channel switching delay, we propose online learning-based channel access policies for optimal defense by the IoT device to guarantee the maximum network capacity. We then highlight future research directions, focusing on the defense of IoT devices against adaptive attackers. Finally, aided by concepts from intelligence and statistical factor analysis tools, we provide a workflow which can be utilized for devices’ intelligence factors impact analysis on the defense performance.
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ZCNET: Achieving High Capacity in Low Power Wide Area Networks
In this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves over 40 times the network capacity of LoRa using similar or less resource under the most challenging channel conditions. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel, while still achieving high packet receiving ratios such as 0.9 or above. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.
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- Award ID(s):
- 1910268
- PAR ID:
- 10285183
- Date Published:
- Journal Name:
- 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
- Page Range / eLocation ID:
- 702 to 710
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MASS50613.2020.00090
