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1

Duari, Swagata, and Vasudha Bhatnagar. "sCAKE: Semantic Connectivity Aware Keyword Extraction." Information Sciences 477 (March 2019): 100–117. http://dx.doi.org/10.1016/j.ins.2018.10.034.

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Ghafoor, Huma, Insoo Koo, and Nasir-ud-Din Gohar. "Neighboring and Connectivity-Aware Routing in VANETs." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/789247.

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A novel position-based routing protocol anchor-based connectivity-aware routing (ACAR) for vehicular ad hoc networks (VANETs) is proposed in this paper to ensure connectivity of routes with more successfully delivered packets. Both buses and cars are considered as vehicular nodes running in both clockwise and anticlockwise directions in a city scenario. Both directions are taken into account for faster communication. ACAR is a hybrid protocol, using both the greedy forwarding approach and the store-carry-and-forward approach to minimize the packet drop rate on the basis of certain assumptions. Our solution to situations that occur when the network is sparse and when any (source or intermediate) node has left its initial position makes this protocol different from those existing in the literature. We consider only vehicle-to-vehicle (V2V) communication in which both the source and destination nodes are moving vehicles. Also, no road-side units are considered. Finally, we compare our protocol with A-STAR (a plausible connectivity-aware routing protocol for city environments), and simulation results in NS-2 show improvement in the number of packets delivered to the destination using fewer hops. Also, we show that ACAR has more successfully-delivered long-distance packets with reasonable packet delay than A-STAR.
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Jeong, Rae-Jin, and Kang-Whan Lee. "Context-aware Connectivity Analysis in Delay Tolerant Networks." Procedia Computer Science 63 (2015): 177–82. http://dx.doi.org/10.1016/j.procs.2015.08.330.

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4

Tamboli, Neelofer, and Mohamed Younis. "Coverage-aware connectivity restoration in mobile sensor networks." Journal of Network and Computer Applications 33, no. 4 (July 2010): 363–74. http://dx.doi.org/10.1016/j.jnca.2010.03.008.

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5

ZHANG, Li-Feng, Bei-Hong JIN, and Wei ZHUO. "On Connectivity-Aware and Reliable Data Dissemination in VANETs." Chinese Journal of Computers 36, no. 4 (March 13, 2014): 701–15. http://dx.doi.org/10.3724/sp.j.1016.2013.00701.

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6

Haider, Ammar, Iqbal Gondal, and Joarder Kamruzzaman. "Social-connectivity-aware vertical handover for heterogeneous wireless networks." Journal of Network and Computer Applications 36, no. 4 (July 2013): 1131–39. http://dx.doi.org/10.1016/j.jnca.2013.01.010.

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7

Liu, Xuxun. "Survivability-Aware Connectivity Restoration for Partitioned Wireless Sensor Networks." IEEE Communications Letters 21, no. 11 (November 2017): 2444–47. http://dx.doi.org/10.1109/lcomm.2017.2699174.

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8

Ibrahim, Ahmed S., Karim G. Seddik, and K. J. Ray Liu. "Connectivity-aware network maintenance and repair via relays deployment." IEEE Transactions on Wireless Communications 8, no. 1 (January 2009): 356–66. http://dx.doi.org/10.1109/t-wc.2009.080045.

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9

Zhao, Xin, Yaodong Zhang, Chunxiao Jiang, Jian Yuan, and Jiannong Cao. "Mobile-Aware Topology Control Potential Game: Equilibrium and Connectivity." IEEE Internet of Things Journal 3, no. 6 (December 2016): 1267–73. http://dx.doi.org/10.1109/jiot.2016.2587102.

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10

Hu, Jiafeng, Beihong Jin, Fusang Zhang, Zhaoyang Wang, and Yuwei Yang. "Towards connectivity-aware deployment and adjustment for roadside units." International Journal of Autonomous and Adaptive Communications Systems 9, no. 1/2 (2016): 119. http://dx.doi.org/10.1504/ijaacs.2016.075390.

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11

Prasad, Athul, and Andreas Maeder. "Backhaul-aware energy efficient heterogeneous networks with dual connectivity." Telecommunication Systems 59, no. 1 (November 20, 2014): 25–41. http://dx.doi.org/10.1007/s11235-014-9893-4.

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12

Shao, Caixing, Supeng Leng, Yan Zhang, Alexey Vinel, and Magnus Jonsson. "Performance Analysis of Connectivity Probability and Connectivity-Aware MAC Protocol Design for Platoon-Based VANETs." IEEE Transactions on Vehicular Technology 64, no. 12 (December 2015): 5596–609. http://dx.doi.org/10.1109/tvt.2015.2479942.

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13

Guoxia Zhang, Fuqiang Liu, Xinhong Wang, and Ping Wang. "Delay- and Connectivity-Aware Routing for Vehicular Ad Hoc Networks." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 3 (February 29, 2012): 277–86. http://dx.doi.org/10.4156/aiss.vol4.issue3.34.

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14

Bachir, Abdelmalik, Walid Bechkit, Yacine Challal, and Abdelmadjid Bouabdallah. "Joint Connectivity-Coverage Temperature-Aware Algorithms for Wireless Sensor Networks." IEEE Transactions on Parallel and Distributed Systems 26, no. 7 (July 1, 2015): 1923–36. http://dx.doi.org/10.1109/tpds.2014.2331063.

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15

Ozturk, Metin, Mona Jaber, and Muhammad A. Imran. "Energy-Aware Smart Connectivity for IoT Networks: Enabling Smart Ports." Wireless Communications and Mobile Computing 2018 (June 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/5379326.

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The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. In this work, we present a context-aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long-range technologies. We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.
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16

Pandana, Charles, and K. J. Ray Liu. "Robust connectivity-aware energy-efficient routing for wireless sensor networks." IEEE Transactions on Wireless Communications 7, no. 10 (October 2008): 3904–16. http://dx.doi.org/10.1109/t-wc.2008.070453.

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17

Gallina, Lucia, Andrea Marin, and Sabina Rossi. "Connectivity and energy-aware preorders for mobile ad-hoc networks." Telecommunication Systems 63, no. 2 (December 24, 2015): 307–33. http://dx.doi.org/10.1007/s11235-015-0122-6.

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18

Karthikeya, Surabhi Abhimithra, Revathy Narayanan, and Siva Ram Murthy C. "Power-aware gateway connectivity in battery-powered dynamic IoT networks." Computer Networks 130 (January 2018): 81–93. http://dx.doi.org/10.1016/j.comnet.2017.11.001.

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19

Kumar, Dileep, Jarkko Kaleva, and Antti Tolli. "Blockage-Aware Reliable mmWave Access via Coordinated Multi-Point Connectivity." IEEE Transactions on Wireless Communications 20, no. 7 (July 2021): 4238–52. http://dx.doi.org/10.1109/twc.2021.3057227.

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20

Jadoon, Rab Nawaz, Adnan Anwar Awan, Muhammad Amir Khan, WuYang Zhou, and Aqdas Naveed Malik. "PACR: Position-Aware Protocol for Connectivity Restoration in Mobile Sensor Networks." Wireless Communications and Mobile Computing 2020 (December 31, 2020): 1–15. http://dx.doi.org/10.1155/2020/8859256.

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Wireless Sensor Networks (WSNs) have gained global attention in recent times due to their vast applications in various fields. These networks can face the disruption of data transmission due to sensor node failures when placed in harsh, inaccessible, and adverse environments such as battlefields or monitoring in enemy territory. The specific tasks performed by the collaboration among the sensor nodes in WSNs by internode connectivity may be terminated. Besides this, due to the failure of sensor nodes, the area covered by the network may be limited, which can cause damage to the objectives for such a network, as there might be an unaware danger in the lost area. Connectivity is a big problem in mobile WSNs due to the mobility of nodes. Researchers have developed a lot of algorithms that are capable enough for connectivity problems, but they do not emphasize the loss of coverage. We try to fill these gaps by proposing the new hybrid algorithm PACR (Position-Aware protocol for Connectivity Restoration). The concept behind PACR is the same as a person who writes his will before death on a deathbed. In the same way, when the sensor energy is below the threshold, it is converted into a recovery coordinator and generates a recovery plan. This accelerates the recovery by decreasing the time needed for failure identification. For the recovery process, the neighbor’s nodes do not travel to the exact position of the failed node. Instead, they just move to the distance where they can build communication links with other nodes. This greatly prolongs the network lifetime. The simulation results show that PACR outperforms other techniques present in the literature.
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21

Maidorawa, Ahmadu, and Kamalrulnizam Abu Bakar. "An Enhanced Connectivity Aware Routing Protocol for Vehicular Ad hoc Networks." Research Journal of Applied Sciences, Engineering and Technology 7, no. 14 (April 12, 2014): 2935–45. http://dx.doi.org/10.19026/rjaset.7.624.

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22

Shin, Seung Yeon, Soochahn Lee, Il Dong Yun, and Kyoung Mu Lee. "Topology-Aware Retinal Artery–Vein Classification via Deep Vascular Connectivity Prediction." Applied Sciences 11, no. 1 (December 31, 2020): 320. http://dx.doi.org/10.3390/app11010320.

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Retinal artery–vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.
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23

Hassan, Ahmed Nazar, Omprakash Kaiwartya, Abdul Hanan Abdullah, Dalya Khalid Sheet, and Ram Shringar Raw. "Inter Vehicle Distance Based Connectivity Aware Routing in Vehicular Adhoc Networks." Wireless Personal Communications 98, no. 1 (November 28, 2017): 33–54. http://dx.doi.org/10.1007/s11277-017-4831-x.

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24

Jiang, Feng, Jianqi Wang, and A. Lee Swindlehurst. "Interference-Aware Scheduling for Connectivity in MIMO Ad Hoc Multicast Networks." IEEE Transactions on Vehicular Technology 61, no. 4 (May 2012): 1762–78. http://dx.doi.org/10.1109/tvt.2012.2185858.

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25

Alsharif, Nizar, and Xuemin Shen. "$i$CAR-II: Infrastructure-Based Connectivity Aware Routing in Vehicular Networks." IEEE Transactions on Vehicular Technology 66, no. 5 (May 2017): 4231–44. http://dx.doi.org/10.1109/tvt.2016.2600481.

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26

Pei, Yuanteng, Matt W. Mutka, and Ning Xi. "Connectivity and bandwidth-aware real-time exploration in mobile robot networks." Wireless Communications and Mobile Computing 13, no. 9 (July 8, 2011): 847–63. http://dx.doi.org/10.1002/wcm.1145.

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27

Dahnil, Dahlila Putri, Yaswant Prasad Singh, and Chin Kuan Ho. "Connectivity-Aware and Minimum Energy Dissipation Protocol in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 9, no. 7 (January 2013): 153089. http://dx.doi.org/10.1155/2013/153089.

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28

Senturk, Izzet Fatih. "Partition-aware centrality measures for connectivity restoration in mobile sensor networks." International Journal of Sensor Networks 30, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijsnet.2019.099218.

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29

Senturk, Izzet Fatih. "Partition-aware centrality measures for connectivity restoration in mobile sensor networks." International Journal of Sensor Networks 30, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijsnet.2019.10020666.

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30

Shafiee, Kaveh, and Victor C. M. Leung. "Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs." Ad Hoc Networks 9, no. 2 (March 2011): 131–41. http://dx.doi.org/10.1016/j.adhoc.2010.06.003.

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31

Sardis, Fragkiskos, Glenford Mapp, Jonathan Loo, Mahdi Aiash, and Alexey Vinel. "Investigating a Mobility-Aware QoS Model for Multimedia Streaming Rate Adaptation." Journal of Electrical and Computer Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/548638.

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Supporting high quality multimedia streaming on wireless devices poses several challenges compared to wired networks due to the high variance in network performance encountered in the mobile environment. Although rate adaptation is commonly used in multimedia applications to compensate for fluctuations in network performance, it is a reactive mechanism which is not aware of the frequently changing connectivity that may occur on mobile devices. This paper proposed a performance evaluation model for multimedia streaming applications that is aware of user mobility and network performance. We presented an example of mathematical solution to the model and demonstrated the functionality using common mobility and connectivity examples that may be found in an urban environment. The proposed model is evaluated based on this functionality and how it may be used to enhance application performance.
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32

Parikh, Lisa, Dongju Seo, Cheryl Lacadie, Renata Belfort-Deaguiar, Derek Groskreutz, Muhammad Hamza, Feng Dai, et al. "Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes." Journal of Clinical Endocrinology & Metabolism 105, no. 1 (September 12, 2019): 1–13. http://dx.doi.org/10.1210/clinem/dgz004.

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Abstract Context Individuals with type 1 diabetes mellitus (T1DM) have alterations in brain activity that have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study. Setting Academic medical center. Participants 27 individuals with T1DM and 12 HC volunteers participated in the study. Intervention All participants underwent blood oxygenation level dependent (BOLD) resting state functional magnetic brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dL)–hypoglycemic (60 mg/dL) clamp. Outcome Changes in resting state functional connectivity. Results Using 2 separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia between HC, T1DM-Aware, and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes among individuals with T1DM.
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33

Venkatesh, Manasij, Joseph Jaja, and Luiz Pessoa. "Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification." NeuroImage 207 (February 2020): 116398. http://dx.doi.org/10.1016/j.neuroimage.2019.116398.

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34

Mohamed, Abdelrahim, Muhammad Ali Imran, Pei Xiao, and Rahim Tafazolli. "Memory-Full Context-Aware Predictive Mobility Management in Dual Connectivity 5G Networks." IEEE Access 6 (2018): 9655–66. http://dx.doi.org/10.1109/access.2018.2796579.

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35

Varaprasad, G. "Network connectivity based power-aware routing algorithm for Mobile Ad Hoc Networks." International Journal of Ad Hoc and Ubiquitous Computing 7, no. 2 (2011): 71. http://dx.doi.org/10.1504/ijahuc.2011.038993.

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36

Meng, Yun, Yuan Dong, Xinyi Liu, and Yi Zhao. "An Interference-Aware Resource Allocation Scheme for Connectivity Improvement in Vehicular Networks." IEEE Access 6 (2018): 51319–28. http://dx.doi.org/10.1109/access.2018.2867745.

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37

Zhiwei, HUANG, ZHENG Zimu, LI Zhicheng, and PENG Xinyi. "CONNECTIVITY-AWARE TOPOLOGY CONTROL WITH CYCLIC-LIKE STRUCTURES IN WIRELESS SENSOR NETWORKS." International Journal on Smart Sensing and Intelligent Systems 7, no. 4 (2014): 1663–82. http://dx.doi.org/10.21307/ijssis-2017-726.

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38

Senel, Fatih. "Coverage-aware connectivity-constrained unattended sensor deployment in underwater acoustic sensor networks." Wireless Communications and Mobile Computing 16, no. 14 (February 11, 2016): 2052–64. http://dx.doi.org/10.1002/wcm.2667.

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39

Kampffmeyer, Michael, Nanqing Dong, Xiaodan Liang, Yujia Zhang, and Eric P. Xing. "ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation." IEEE Transactions on Image Processing 28, no. 5 (May 2019): 2518–29. http://dx.doi.org/10.1109/tip.2018.2886997.

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40

Luo, Xiaojuan, Huiqun Yu, and Xiang Wang. "Energy-aware self-organisation algorithms with heterogeneous connectivity in wireless sensor networks." International Journal of Systems Science 44, no. 10 (October 2013): 1857–66. http://dx.doi.org/10.1080/00207721.2012.670297.

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41

Bellavista, Paolo, Antonio Corradi, and Carlo Giannelli. "Mobility-aware middleware for self-organizing heterogeneous networks with multihop multipath connectivity." IEEE Wireless Communications 15, no. 6 (December 2008): 22–30. http://dx.doi.org/10.1109/mwc.2008.4749744.

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42

Bellavista, Paolo, Antonio Corradi, and Carlo Giannelli. "Mobility-aware Management of Internet Connectivity in Always Best Served Wireless Scenarios." Mobile Networks and Applications 14, no. 1 (October 10, 2008): 18–34. http://dx.doi.org/10.1007/s11036-008-0106-9.

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43

Kalupahana Liyanage, Kushan Sudheera, Maode Ma, and Peter Han Joo Chong. "Connectivity aware tribrid routing framework for a generalized software defined vehicular network." Computer Networks 152 (April 2019): 167–77. http://dx.doi.org/10.1016/j.comnet.2019.01.040.

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44

Qin, Qin, Yong-qiang He, and Li-ming Nie. "Cooperative Cloud Service Aware Mobile Internet Coverage Connectivity Guarantee Protocol Based on Sensor Opportunistic Coverage Mechanism." Journal of Electrical and Computer Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/128691.

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In order to improve the Internet coverage ratio and provide connectivity guarantee, based on sensor opportunistic coverage mechanism and cooperative cloud service, we proposed the coverage connectivity guarantee protocol for mobile Internet. In this scheme, based on the opportunistic covering rules, the network coverage algorithm of high reliability and real-time security was achieved by using the opportunity of sensor nodes and the Internet mobile node. Then, the cloud service business support platform is created based on the Internet application service management capabilities and wireless sensor network communication service capabilities, which is the architecture of the cloud support layer. The cooperative cloud service aware model was proposed. Finally, we proposed the mobile Internet coverage connectivity guarantee protocol. The results of experiments demonstrate that the proposed algorithm has excellent performance, in terms of the security of the Internet and the stability, as well as coverage connectivity ability.
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45

Riana, Yuliana, and Cornelia Alverina. "HIGHER EDUCATION WITHIN ASEAN CONNECTIVITY." Jurnal Asia Pacific Studies 2, no. 1 (June 4, 2018): 45. http://dx.doi.org/10.33541/japs.v2i1.669.

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ASEAN member countries are well aware of the importance of education to be one of the decisive factors in developing a high quality of human resources. This is reflected in the inclusion of education in ASEAN socio-cultural cooperation dimension. This review will highlight the implementation of higher education in ASEAN connectivity described in five steps. Infrastructure connectivity makes the mobility of young people in ASEAN members become easier in pursuing education across ASEAN region. In addition, the improvement of facilities and infrastructure in educational institutions could support a conducive atmosphere in the teaching and learning process. Institutional connectivity could also develop human resources who are not only knowledgeable but also have compatible skills with industry needs in ASEAN. In regional level, connectivity through ASEAN University Network will develop quality standards between AUN members which can be used as a benchmark standard of university quality in ASEAN. Young generation connectivity through students’ exchange program between many universities in ASEAN not only for knowledge exchange but also to understand each other’s cultural diversity. Connectivity is the success key in building the ASEAN community. Connectivity plays a role in helping to reduce education gaps among ASEAN member countries. Higher education within ASEAN connectivity will strengthen regional cooperation between university institutions and students in ASEAN, also will enhance the internationalization of competitive and high-quality education. Keywords: connectivity, ASEAN, education
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46

Chen Yue, 陈悦, 华楠 Hua Nan, and 郑小平 Zheng Xiaoping. "Inner-Port Connectivity Aware Dynamic Routing Schemes in Optical Wavelength Division Multiplex Networks." Chinese Journal of Lasers 40, no. 4 (2013): 0405005. http://dx.doi.org/10.3788/cjl201340.0405005.

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47

Kumar, Rakesh, Manoj Misra, and Anil K. Sarje. "A Proactive Load-Aware Gateway Discovery in Ad Hoc Networks for Internet Connectivity." International journal of Computer Networks & Communications 2, no. 5 (September 15, 2010): 120–39. http://dx.doi.org/10.5121/ijcnc.2010.2508.

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48

Triki, Bayrem, Slim Rekhis, and Noureddine Boudrigua. "Connectivity-Aware and Adaptive Multipath Routing Algorithm for Mobile Adhoc and Sensor Networks." International Journal of Wireless & Mobile Networks 7, no. 1 (February 28, 2015): 55–74. http://dx.doi.org/10.5121/ijwmn.2015.7104.

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49

Venkatramana, Dharani Kumari Nooji, Shylaja Banagiri Srikantaiah, and Jayalakshmi Moodabidri. "SCGRP: SDN‐enabled connectivity‐aware geographical routing protocol of VANETs for urban environment." IET Networks 6, no. 5 (September 2017): 102–11. http://dx.doi.org/10.1049/iet-net.2016.0117.

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50

Bellavista, Paolo, Antonio Corradi, and Carlo Giannelli. "A Unifying Perspective on Context-Aware Evaluation and Management of Heterogeneous Wireless Connectivity." IEEE Communications Surveys & Tutorials 13, no. 3 (2011): 337–57. http://dx.doi.org/10.1109/surv.2011.060710.00060.

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