Academic literature on the topic 'Wireless sensor body area network'
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Journal articles on the topic "Wireless sensor body area network"
Vandana. T, Santhi, and Sreenivasa Ravi. K. "A survey overview: on wireless body area network and its various applications." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 936. http://dx.doi.org/10.14419/ijet.v7i2.7.11428.
Full textHussein, Safa Saad, C. B. M. Rashidi, Hanan Ali Alrikabi, S. A. Aljunid, Muataz H. Salih, and Mohammed Sabri Abuali. "Wireless Body Area Sensor Network: Tutorial Review." Journal of Computational and Theoretical Nanoscience 16, no. 11 (November 1, 2019): 4839–52. http://dx.doi.org/10.1166/jctn.2019.8396.
Full textE. Ramya, Mrs, and Dr R. Gobinath. "Delay metric in wireless body area sensor net-works." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 448. http://dx.doi.org/10.14419/ijet.v7i2.33.14208.
Full textZong Chen, Dr Joy Iong, and Lu-Tsou Yeh. "Data Forwarding in Wireless Body Area Networks." June 2020 2, no. 2 (June 1, 2020): 80–87. http://dx.doi.org/10.36548/jei.2020.2.002.
Full textKhan, Rahat Ali, and Al-Sakib Khan Pathan. "The state-of-the-art wireless body area sensor networks: A survey." International Journal of Distributed Sensor Networks 14, no. 4 (April 2018): 155014771876899. http://dx.doi.org/10.1177/1550147718768994.
Full textREN, HONGLIANG, and MAX Q. H. MENG. "MODELING THE GROUP MOBILITY PATTERN IN WIRELESS BODY SENSOR NETWORKS." International Journal of Information Acquisition 03, no. 04 (December 2006): 259–70. http://dx.doi.org/10.1142/s0219878906001015.
Full textKargar, Mohammad Javad, Samaneh Ghasemi, and Omolbanin Rahimi. "Wireless Body Area Network." International Journal of Reliable and Quality E-Healthcare 2, no. 4 (October 2013): 38–47. http://dx.doi.org/10.4018/ijrqeh.2013100104.
Full textEl Azhari, Maryam, Nadya El Moussaid, Ahmed Toumanari, and Rachid Latif. "Equalized Energy Consumption in Wireless Body Area Networks for a Prolonged Network Lifetime." Wireless Communications and Mobile Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4157858.
Full textThabit, Ahmed A., Mahmoud Shuker Mahmoud, Ahmed Alkhayyat, and Qammer H. Abbasi. "Energy harvesting Internet of Things health-based paradigm: Towards outage probability reduction through inter–wireless body area network cooperation." International Journal of Distributed Sensor Networks 15, no. 10 (October 2019): 155014771987987. http://dx.doi.org/10.1177/1550147719879870.
Full textS, Sandeep K., Hari Krishnan, and Soumya K. N. C. R. Manjunath. "A Survey on Wireless Body Area Network Sensors and Security Issues." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 362–68. http://dx.doi.org/10.31142/ijtsrd12935.
Full textDissertations / Theses on the topic "Wireless sensor body area network"
Eljamaly, Omar. "Low-power wireless body area sensor network communication sub-systems." Thesis, University of Surrey, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479515.
Full textZincarelli, Nicola. "Applicazioni Wireless in Body Area Network." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9641/.
Full textNguyen, Viet-Hoa. "Energy-efficient cooperative techniques for wireless body area sensor networks." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S011/document.
Full textAmong various cooperative techniques aiming to reduce power consumption for transmissions between Wireless Body Area Networks (WBAN) and base stations, we present a new approach, named distributed max-dmin precoding (DMP), combining MIMO precoding techniques and relay communications. This protocol is based on the deployment of a virtual 2 × 2 max-dmin precoding over one source, one forwarding relay, both equipped with one antenna and a destination involving 2 antennas. In this context, two kinds of relaying, amplify and forward (AF) or decode and forward (DF) protocols, are investigated. The performance evaluation in terms of Bit-Error-Rate (BER) and energy efficiency are compared with non cooperative techniques and the distributed space time block code (STBC) scheme. Our investigations show that the DMP takes the advantage in terms of energy efficiency from medium transmission distances (after 10 meters). In order to maximise the energy efficiency, we propose a power allocation over the source and the relay. Thus, we derive the performance of our system, both for AF and DF, analytically. To further increase the performance of DF cooperative schemes, we also propose to design a new decoder at the destination that takes profit from side information, namely potential errors at the relay
Arrobo, Gabriel. "Improving the Throughput and Reliability of Wireless Sensor Networks with Application to Wireless Body Area Networks." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4279.
Full textLi, Kejia. "Custom biomedical sensors for application in wireless body area networks and medical device integration frameworks." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/14632.
Full textDepartment of Electrical & Computer Engineering
Steve Warren
The U.S. health care system is one of the most advanced and costly systems in the world. The health services supply/demand gap is being enlarged by the aging population coupled with shortages in the traditional health care workforce and new information technology workers. This will not change if the current medical system adheres to the traditional hospital-centered model. One promising solution is to incorporate patient-centered, point-of-care test systems that promote proactive and preventive care by utilizing technology advancements in sensors, devices, communication standards, engineering systems, and information infrastructures. Biomedical devices optimized for home and mobile health care environments will drive this transition. This dissertation documents research and development focused on biomedical device design for this purpose (including a wearable wireless pulse oximeter, motion sensor, and two-thumb electrocardiograph) and, more importantly, their interactions with other medical components, their supporting information infrastructures, and processing tools that illustrate the effectiveness of their data. The GumPack concept and prototype introduced in Chapter 2 addresses these aspects, as it is a sensor-laden device, a host for a local body area network (BAN), a portal to external integration frameworks, and a data processing platform. GumPack sensor-component design (Chapters 3 and 4) is oriented toward surface applications (e.g., touch and measure), an everyday-carry form factor, and reconfigurability. Onboard tagging technology (Chapters 5 and 6) enhances sensor functionality by providing, e.g., a signal quality index and confidence coefficient for itself and/or next-tier medical components (e.g., a hub). Sensor interaction and integration work includes applications based on the GumPack design (Chapters 7 through 9) and the Medical Device Coordination Framework (Chapters 10 through 12). A high-resolution, wireless BAN is presented in Chapter 8, followed by a new physiological use case for pulse wave velocity estimation in Chapter 9. The collaborative MDCF work is transitioned to a web-based Hospital Information Integration System (Chapter 11) by employing database, AJAX, and Java Servlet technology. Given the preceding sensor designs and the availability of information infrastructures like the MDCF, medical platform-oriented devices (Chapter 12) could be an innovative and efficient way to design medical devices for hospital and home health care applications.
Jobs, Magnus. "Wireless Interface Technologies for Sensor Networks." Doctoral thesis, Uppsala universitet, Fasta tillståndets elektronik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-239400.
Full textJobs, Magnus. "Design and Performance of Diversity based Wireless Interfaces for Sensor Network Nodes." Licentiate thesis, Uppsala universitet, Fasta tillståndets elektronik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198734.
Full textWISENET
WISEJET
Sheriff, Nathirulla. "Time Synchronization In ANT Wireless Low Power Sensor Network." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Data- och elektroteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-15068.
Full textCelik, Numan. "Wireless graphene-based electrocardiogram (ECG) sensor including multiple physiological measurement system." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15698.
Full textAli, Mohamad Jaafar. "Wireless body area networks : co-channel interference mitigation & avoidance." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB252/document.
Full textA Wireless Body Area Network (WBAN) is a short-range network that consists of a coordinator (Crd) and a collection of low-power sensors that can be implanted in or attached to the human body. Basically, WBANs can provide real-time patient monitoring and serve in various applications such as ubiquitous health-care, consumer electronics, military, sports, etc. [1]. As the license-free 2.4 GHz ISM band is widely used among WBANs and across other wireless technologies, the fundamental problem is to mitigate the resulting co-channel interference. Other serious problems are to extend the network lifetime and to ensure reliable transmission within WBANs, which is an urgent requirement for health-care applications. Therefore, in this thesis, we conduct a systematic research on a few number of research problems related to radio co-channel interference, energy consumption, and network reliability. Specifically, we address the following problems ranging from theoretical modeling and analysis to practical protocol design: • Intra-WBAN interference mitigation and avoidance • Cooperative inter-WBAN interference mitigation and avoidance • Non-cooperative inter-WBAN interference mitigation and avoidance • Interference mitigation and avoidance in WBANs with IoT Firstly, to mitigate the intra-WBAN interference, we present two mechanisms for a WBAN. The first is called CSMA to Flexible TDMA combination for Interference Mitigation, namely, CFTIM, which dynamically allocates time-slots and stable channels to lower the intra-WBAN interference. The second is called Interference Avoidance Algorithm, namely IAA that dynamically adjusts the superframe length and limits the number of channels to 2 to lower the intra-WBAN interference and save energy. Theoretically, we derive a probabilistic model that proves the SINR outage probability is lowered. Simulation results demonstrate the effectiveness and the efficiency of CFTIM and IAA in terms of lowering the probability of interference, extending network lifetime, improving throughput and reliability. Secondly, we address the problem of interference among cooperative WBANs through using orthogonal codes. Motivated by distributed time provisioning supported in IEEE 802.15.6 standard [2], we propose two schemes. The first is called Distributed Time Correlation Reference, namely, DTRC that provides each WBAN with the knowledge about which superframes overlap with each other. The second is called Orthogonal Code Allocation Algorithm for Interference Mitigation, namely, OCAIM, that allocates orthogonal codes to interfering sensors belonging to sensor interference lists (SILs), which are generated based on the exchange of power-based information among WBANs. Mathematically, we derive the successful and collision probabilities of frames transmissions. Extensive simulations are conducted and the results demonstrate that OCAIM can diminish the interference, improve the throughput and save the power resource. Thirdly, we address the problem of co-channel interference among non-cooperative WBANs through time-slot and channel hopping. Specifically, we propose two schemes that are based on Latin rectangles. The first is called Distributed Algorithm for Interference mitigation using Latin rectangles, namely, DAIL that allocates a single channel to a timeslot combination to each sensor to diminish inter-WBAN interference and to yield better schedules of the medium access within each WBAN. The second is called Channel Hopping for Interference Mitigation, namely, CHIM, which generates a predictable interference free transmission schedule for all sensors within a WBAN. CHIM applies the channel switching only when a sensor experiences interference to save the power resource. Furthermore, we present an analytical model that derives bounds on collision probability and throughput for sensors transmissions. (...)
Books on the topic "Wireless sensor body area network"
Li, Huan-Bang. Wireless body area network. Aalborg, Denmark: River Publishers, 2010.
Find full textRedouté, Jean-Michel, Kasun Maduranga Silva Thotahewa, and Mehmet Rasit Yuce. Ultra Wideband Wireless Body Area Networks. Springer, 2014.
Find full textRedouté, Jean-Michel, Mehmet Rasit Yuce, and Kasun Maduranga Silva Silva Thotahewa. Ultra Wideband Wireless Body Area Networks. Springer, 2016.
Find full textauthor, Iinatti Jari, and Mucchi Lorenzo editor, eds. Wireless UWB body area networks: Using the IEEE802.15.4-2011. Academic Press is an imprint of Elsevier, 2014.
Find full textInternet of Nano-Things and Wireless Body Area Networks (wban). Taylor & Francis Group, 2019.
Find full textAl-Turjman, Fadi. Internet of Nano-Things and Wireless Body Area Networks (WBAN). Auerbach Publishers, Incorporated, 2019.
Find full textCo-Operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications. Elsevier Science & Technology Books, 2014.
Find full textCo-Operative and Energy Efficient Body Area and Wireless Sensor Networks for Healthcare Applications. Elsevier, 2014. http://dx.doi.org/10.1016/c2013-0-18643-6.
Full textLiu, Donggang, and Peng Ning. Security for Wireless Sensor Networks. Springer, 2010.
Find full textSecurity for Wireless Sensor Networks (Advances in Information Security). Springer, 2006.
Find full textBook chapters on the topic "Wireless sensor body area network"
Kanagachidambaresan, G. R., R. Maheswar, R. Jayaparvathy, Sabu M. Thampi, and V. Mahima. "Fail Safe Routing Algorithm for Green Wireless Nano Body Sensor Network (GWNBSN)." In Body Area Network Challenges and Solutions, 131–49. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00865-9_7.
Full textAbidi, Bahae, Abdelillah Jilbab, and Mohamed E. L. Haziti. "Wireless Sensor Networks in Biomedical: Wireless Body Area Networks." In Advances in Intelligent Systems and Computing, 321–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46568-5_33.
Full textThotahewa, Kasun Maduranga Silva, Jean-Michel Redouté, and Mehmet Rasit Yuce. "An Ultra-Wideband Sensor Node Development with Dual-Frequency Band for Medical Signal Monitoring." In Ultra Wideband Wireless Body Area Networks, 83–115. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05287-8_5.
Full textSharmila, Dhananjay Kumar, KumKum Som, Pramod Kumar, and Krista Chaudhary. "General Outlook of Wireless Body Area Sensor Networks." In Communications in Computer and Information Science, 58–67. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9942-8_6.
Full textDolmans, G., F. Bouwens, A. Breeschoten, B. Busze, P. Harpe, L. Huang, X. Huang, et al. "Ultra Low-Power Wireless Body-Area Sensor Networks." In Analog Circuit Design, 145–62. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1926-2_8.
Full textKhan, Rahat Ali, Shahzad Memon, and Qin Xin. "Enhancing Wireless Transmission Efficiency for Sensors in Wireless Body Area Sensor Networks." In Emerging Trends in ICT for Sustainable Development, 337–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53440-0_35.
Full textAli, Mohamad Jaafar, Hassine Moungla, Mohamed Younis, and Ahmed Mehaoua. "Interference Mitigation Techniques in Wireless Body Area Networks." In Mission-Oriented Sensor Networks and Systems: Art and Science, 677–718. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92384-0_19.
Full textDing, Yong, Hui Xu, and Yujue Wang. "Group Authentication for Sensors in Wireless Body Area Network." In Security, Privacy, and Anonymity in Computation, Communication, and Storage, 191–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68884-4_16.
Full textMehfuz, Shabana, Shabana Urooj, and Shivaji Sinha. "Wireless Body Area Networks: A Review with Intelligent Sensor Network-Based Emerging Technology." In Advances in Intelligent Systems and Computing, 813–21. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2250-7_81.
Full textGouda, Kanhu Charan, Santosh Kumar Das, Om Prakash Dubey, and Efrén Mezura Montes. "A GA-Based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks." In Nature Inspired Computing for Wireless Sensor Networks, 57–75. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2125-6_4.
Full textConference papers on the topic "Wireless sensor body area network"
Kaur, Harminder, and Sharvan Kumar Pahuja. "MAC Protocols for Wireless Body Sensor Network." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.33.
Full textPreeti, Kusum Grewal Dangi, and Kumari Bharti Sangwan. "Wireless body area sensor networks." In 2017 International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN). IEEE, 2017. http://dx.doi.org/10.1109/ic3tsn.2017.8284466.
Full textLauzier, Matthieu, Antoine Fraboulet, Jean-Marie Gorce, and Tanguy Risset. "Live Group Detection for Mobile Wireless Sensor Networks." In 9th International Conference on Body Area Networks. ICST, 2014. http://dx.doi.org/10.4108/icst.bodynets.2014.257026.
Full text"Wireless Body Area Sensor Network in Healthcare Applications." In SoutheastCon 2018. IEEE, 2018. http://dx.doi.org/10.1109/secon.2018.8479124.
Full textRong, Chunming, and Hongbing Cheng. "Authenticated Health Monitoring Scheme for Wireless Body Sensor Networks." In 7th International Conference on Body Area Networks. ACM, 2012. http://dx.doi.org/10.4108/icst.bodynets.2012.249945.
Full textArrabi, Saad, and John Lach. "Adaptive lossless compression in wireless body sensor networks." In 4th International ICST Conference on Body Area Networks. ICST, 2009. http://dx.doi.org/10.4108/icst.bodynets2009.6017.
Full textLee, Seokwon, Sungwoo Weon, Sooyong Choi, Jang-won Lee, Changsoon Park, Youngsoo Kim, Young-jun Hong, and Daesik Hong. "Increasing the Life-time of 802.15.4-based Wireless Sensor Networks." In 8th International Conference on Body Area Networks. ACM, 2013. http://dx.doi.org/10.4108/icst.bodynets.2013.253648.
Full textkotian, roshan, Georgios Exarchakos, and Antonio Liotta. "Assessment of Proactive Transmission Power Control for Wireless Sensor Networks." In 9th International Conference on Body Area Networks. ICST, 2014. http://dx.doi.org/10.4108/icst.bodynets.2014.258209.
Full textGhoshdastider, Unmesh, Reinhard Viga, and Michael Kraft. "Non-invasive synchronized spatially high-resolution wireless body area network." In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2014. http://dx.doi.org/10.1109/issnip.2014.6827593.
Full textMeng, Yun-Fan, Tuan-Fa Qin, and Jie Xing. "Sensor Cooperation Based on Network Coding in Wireless Body Area Networks." In 2014 International Conference on Wireless Communication and Sensor Network. IEEE, 2014. http://dx.doi.org/10.1109/wcsn.2014.80.
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