Heterogeneous multi-channel neighbor discovery formobile sensing applications

Chen, Lin; Bian, Kaigui; Zheng, Meng

doi:10.1145/2632951.2632972

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…Second, if this situation continues, it makes the entire network partitioned or disconnected. A variety of asynchronous neighbor discovery protocols have been developed in order to address a neighbor discovery problem [16][17][18][19][20][21][22][23][24]. In this section, we present an asynchronous neighbor discovery protocol we proposed called BAND in the previous paper [25].…”

Section: Neighbor Discovery Protocolmentioning

confidence: 99%

Understanding people with human activities and social interactions for human-centered computing

Choi

2016

Hum. Cent. Comput. Inf. Sci.

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Computing technology has changed a lot of things in the world quickly. It is still affecting the way we think, the way we communicate with each other, the way we study, the way we work, and so on. Furthermore, computer hardware and software keep evolving every day and new paradigms and mechanisms continuously emerge into the world. There is no doubt that the main purpose of these technologies make human beings and our daily life convenient. In other word, the center of computing is all mankind.However, the main purpose of computing technology may not be human beings. Artificial intelligence (AI) could be one of the promising computing technologies in the future. Many AI researchers hope that this technology will help and assist people effectively and efficiently. In addition, they believe that AI dramatically change our life in the near future. Unfortunately, all of the change are not always positive. Rapid and frequent changing of computing technologies could be difficult to use especially for elderly people. Furthermore, it is well known that innovative AI technology could give people a great fear. Some famous movies dealing with AI issues reflected this phenomenon. In addition, there was the fight of the century to play game Go between an AI Go player (AlphaGo) and a professional human Go player in March, 2016. AlphaGo finally defeated the human Go player at that game. It is definitely possible that the result of the game surprised and frightened all of the people in the world. AbstractAn ultimate goal of human-centered computing is making human beings the center of computing technologies. To make people a core component of the technology, first of all, we need to understand people and society in which people live. In this paper, we propose two important factors in order to understand people and their social interactions. Proposed human activity recognition and neighbor discovery schemes help us comprehend human activities and their social behaviors. In addition, combination of these two mechanisms provide us with an opportunity for better understanding of people in the near future. Finally, it might be worth analyzing correlation between human activities and social interactions for the group of people using our proposed schemes.

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Section: Neighbor Discovery Protocolmentioning

confidence: 99%

Understanding people with human activities and social interactions for human-centered computing

Choi

2016

Hum. Cent. Comput. Inf. Sci.

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“…Work in [8,17,19,20,28,30,41,43] addresses the closely-related problem of rendezvous between two CR nodes. Intuitively, the rendezvous problem between two nodes involves ensuring that the two nodes tune to the same channel at the same time, which is necessary for nodes to discover each other.…”

Section: Related Workmentioning

confidence: 99%

“…Mobile sensing applications, ranging from mobile social networking to proximity-based gaming, involve collection and sharing of data among nearby users. The success of these applications depends on neighborhood information [20]. Neighbor discovery is extremely important in Underwater Acoustic (UWA) Networks and needs to be done frequently because nodes may move proactively due to the unpredictable underwater currents [68].…”

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confidence: 99%

Robust neighbor discovery in multi-hop multi-channel heterogeneous wireless networks

Mills

Gokhale

Mittal

et al. 2016

Journal of Parallel and Distributed Computing

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An important first step when deploying a wireless ad hoc network is neighbor discovery in which every node attempts to determine the set of nodes it can communicate within one wireless hop. In the recent years, cognitive radio (CR) technology has gained attention as an attractive approach to alleviate spectrum congestion. A cognitive radio transceiver can operate over a wide range of frequencies possibly spanning multiple frequency bands. A cognitive radio node can opportunistically utilize unused wireless spectrum without interference from other wireless devices in its vicinity. Due to spatial variations in frequency usage and hardware variations in radio transceivers, different nodes in the network may perceive different subsets of frequencies available to them for communication. This heterogeneity in the available channel sets across the network increases the complexity of solving the neighbor discovery problem in a cognitive radio network. In this work, we design and analyze several randomized algorithms for neighbor * A preliminary version of the work has appeared earlier in the discovery in such a (heterogeneous) network under a variety of assumptions (e.g., maximum node degree known or unknown) for both synchronous and asynchronous systems under minimal knowledge. We also show that our randomized algorithms are naturally suited to tolerate unreliable channels and adversarial attacks. IntroductionNeighbor discovery is an important step in forming a self-organizing wireless ad hoc network without any support from an existing communication infrastructure [8,9,19,20,29,42,51,54,57,58,63]. When deployed, nodes initially have no prior knowledge of other nodes that they can communicate with directly.The results of neighbor discovery can be used to solve other important communication problems such as medium access control [7,22], routing [15], broadcasting [50,54], clustering [16,32,40], collision-free scheduling [26,27], spanning tree construction [21], and topology control [37,62].Many algorithms for solving these problems implicitly assume that all nodes know their one-hop and sometimes even two-hop neighbors. Many location-based routing protocols (e.g. localized routing in Vehicular Ad hoc Networks (VANETs)) use the position of neighboring nodes to make routing/forwarding decisions. The neighborhood information is also used to update the reachability status of nodes. A better neighbor discovery algorithm, which uses fewer messages and has higher accuracy, can be used to improve the performance of location-based routing algorithms [10][11][12].Neighborhood information helps reduce the cost of flooding in multicast tree construction using flooding algorithms [39]. Mobile sensing applications, ranging from mobile social networking to proximity-based gaming, involve collection and sharing of data among nearby users. The success of these applications depends on neighborhood information [20]. Neighbor discovery is extremely important in Underwater Acoustic (UWA) Networks and needs to be done frequently because nodes...

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“…In addition, by extending Diff-Code to ADiff-Code, they try to deal with asymmetric neighbor discovery. In [18], they formulate a heterogeneous multi-channel neighbor discovery problem and establish a theoretical framework of the problem. Furthermore, they propose a novel multi-channel discovery protocol called Mc-Dis (Multi-channel Discovery).…”

Section: Related Workmentioning

confidence: 99%

Asymmetric Block Design-Based Neighbor Discovery Protocol in Sensor Networks

Choi

2016

Sustainability

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Abstract:Neighbor discovery is one of the emerging research areas in a wireless sensor network. After sensors are distributed, neighbor discovery is the first process to set up a communication channel with neighboring sensors. This paper proposes a new block design-based asymmetric neighbor discovery protocol for sensor networks. We borrow the concept of combinatorial block designs for our block combination scheme for neighbor discovery. First, we introduce an asymmetric neighbor discovery problem and define a target research question. Second, we propose a new asymmetric block design-based neighbor discovery protocol and explain how it works. Third, we analyze the worst-case neighbor discovery latency numerically between our protocol and some well-known protocols in the literature, and compare and evaluate the performance between the proposed protocol and others. Our protocol reveals that the worst-case latency is much lower than that of Disco and U-Connect. Finally, we conclude that the minimum number of slots per a neighbor schedule shows the lowest discovery time in terms of discovery latency and energy consumption.

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Heterogeneous multi-channel neighbor discovery formobile sensing applications

Cited by 10 publications

References 22 publications

Understanding people with human activities and social interactions for human-centered computing

Understanding people with human activities and social interactions for human-centered computing

Robust neighbor discovery in multi-hop multi-channel heterogeneous wireless networks

Asymmetric Block Design-Based Neighbor Discovery Protocol in Sensor Networks

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