Efficient energy for one node and multi-nodes of wireless body area network

Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput

Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

[EN] Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Qureshi, KN.; Bashir, MU.; Lloret, J.; León Fernández, A. (2020). Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision. Journal of Sensors. 2020:1-19. https://doi.org/10.1155/2020/9040395S1192020Sneha, K., Kamath, R., Balachandra, M., & Prabhu, S. (2019). New Gossiping Protocol for Routing Data in Sensor Networks for Precision Agriculture. Soft Computing and Signal Processing, 139-152. doi:10.1007/978-981-13-3393-4_15Qureshi, K. N., Abdullah, A. H., Bashir, F., Iqbal, S., & Awan, K. M. (2018). Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks. International Journal of Communication Systems, 31(8), e3533. doi:10.1002/dac.3533Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122. doi:10.1016/j.comnet.2014.03.027Feng, X., Zhang, J., Ren, C., & Guan, T. (2018). An Unequal Clustering Algorithm Concerned With Time-Delay for Internet of Things. IEEE Access, 6, 33895-33909. doi:10.1109/access.2018.2847036Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. doi:10.1109/access.2019.2902371Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2020). Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Computer Communications, 149, 382-392. doi:10.1016/j.comcom.2019.10.030Kumar, S. A., & Ilango, P. (2017). The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review. Wireless Personal Communications, 98(1), 685-698. doi:10.1007/s11277-017-4890-zAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2014). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216-238. doi:10.1007/s11119-014-9371-8Long, D. S., & McCallum, J. D. (2015). On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture, 16(5), 492-504. doi:10.1007/s11119-015-9391-zFu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223-237. doi:10.1016/j.future.2018.08.031Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.2720Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(2), 551-591. doi:10.1109/surv.2012.062612.00084De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109-118. doi:10.1016/j.future.2018.09.034Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005-2020. doi:10.1007/s11276-016-1270-7Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4-19. doi:10.1016/j.inffus.2019.06.001Liu, X. (2015). Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review. IEEE Sensors Journal, 15(10), 5372-5383. doi:10.1109/jsen.2015.2445796Jan, N., Javaid, N., Javaid, Q., Alrajeh, N., Alam, M., Khan, Z. A., & Niaz, I. A. (2017). A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks. IEEE Access, 5, 6134-6150. doi:10.1109/access.2017.2676004Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300-311. doi:10.1016/j.jnca.2014.01.003Safa, H., Karam, M., & Moussa, B. (2014). PHAODV: Power aware heterogeneous routing protocol for MANETs. Journal of Network and Computer Applications, 46, 60-71. doi:10.1016/j.jnca.2014.07.035Liu, X. (2015). An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks. IEEE Sensors Journal, 15(6), 3484-3491. doi:10.1109/jsen.2014.2372340Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy Efficient Direction-Based PDORP Routing Protocol for WSN. IEEE Access, 4, 3182-3194. doi:10.1109/access.2016.2576475Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks. IEEE Sensors Journal, 15(8), 4576-4586. doi:10.1109/jsen.2015.2424296Huynh, T.-T., Dinh-Duc, A.-V., & Tran, C.-H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580-588. doi:10.1109/jcn.2016.000081Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469-18479. doi:10.1109/access.2017.2749606Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Sajwan, M., Gosain, D., & Sharma, A. K. (2018). CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks, 25(5), 2603-2620. doi:10.1007/s11276-018-1689-0Varga, A. (2010). OMNeT++. Modeling and Tools for Network Simulation, 35-59. doi:10.1007/978-3-642-12331-3_3Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Studies in Classification, Data Analysis, and Knowledge Organization, 261-268. doi:10.1007/978-3-540-78246-9_31Mathur, P., Nielsen, R. H., Prasad, N. R., & Prasad, R. (2016). Data collection using miniature aerial vehicles in wireless sensor networks. IET Wireless Sensor Systems, 6(1), 17-25. doi:10.1049/iet-wss.2014.0120Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors, 16(1), 53. doi:10.3390/s16010053Song, Y., Ma, J., Zhang, X., & Feng, Y. (2012). Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System. Journal of Networks, 7(5). doi:10.4304/jnw.7.5.838-844Nikolidakis, S., Kandris, D., Vergados, D., & Douligeris, C. (2013). Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering. Algorithms, 6(1), 29-42. doi:10.3390/a6010029Ndzi, D. L., Harun, A., Ramli, F. M., Kamarudin, M. L., Zakaria, A., Shakaff, A. Y. M., … Farook, R. S. (2014). Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 105, 83-94. doi:10.1016/j.compag.2014.04.01

A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks

This is the peer reviewed version of the following article: Moravejosharieh, Amirhossein, Lloret, Jaime. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks.International Journal of Communication Systems, 29, 7, 1269-1292. DOI: 10.1002/dac.3098, which has been published in final form at http://doi.org/10.1002/dac.3098. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving[EN] Wireless body sensor networks are offered to meet the requirements of a diverse set of applications such as health-related and well-being applications. For instance, they are deployed to measure, fetch and collect human body vital signs. Such information could be further used for diagnosis and monitoring of medical conditions. IEEE 802.15.4 is arguably considered as a well-designed standard protocol to address the need for low-rate, low-power and low-cost wireless body sensor networks. Apart from the vast deployment of this technology, there are still some challenges and issues related to the performance of the medium access control (MAC) protocol of this standard that are required to be addressed. This paper comprises two main parts. In the first part, the survey has provided a thorough assessment of IEEE 802.15.4 MAC protocol performance where its functionality is evaluated considering a range of effective system parameters, that is, some of the MAC and application parameters and the impact of mutual interference. The second part of this paper is about conducting a simulation study to determine the influence of varying values of the system parameters on IEEE 802.15.4 performance gains. More specifically, we explore the dependability level of IEEE 802.5.4 performance gains on a candidate set of system parameters. Finally, this paper highlights the tangible needs to conduct more investigations on particular aspect(s) of IEEE 802.15.4 MAC protocol. Copyright (c) 2015 John Wiley & Sons, Ltd.Moravejosharieh, A.; Lloret, J. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks. International Journal of Communication Systems. 29(7):1269-1292. https://doi.org/10.1002/dac.3098S12691292297Alrajeh, N. A., Lloret, J., & Canovas, A. (2014). A Framework for Obesity Control Using a Wireless Body Sensor Network. International Journal of Distributed Sensor Networks, 10(7), 534760. doi:10.1155/2014/534760Lopes I Silva B Rodrigues J Lloret J Proenca M A mobile health monitoring solution for weight control International Conference on Wireless Communications and Signal Processing (WCSP) Nanjing / China 2011 1 5Singh, N., Singh, A. K., & Singh, V. K. (2015). Design and performance of wearable ultrawide band textile antenna for medical applications. Microwave and Optical Technology Letters, 57(7), 1553-1557. doi:10.1002/mop.29131Lan, K., Chou, C.-M., Wang, T., & Li, M.-W. (2012). Using body sensor networks for motion detection: a cluster-based approach for green radio. Transactions on Emerging Telecommunications Technologies, 25(2), 199-216. doi:10.1002/ett.2559Lloret, J., Garcia, M., Catala, A., & Rodrigues, J. J. P. C. (2016). A group-based wireless body sensors network using energy harvesting for soccer team monitoring. International Journal of Sensor Networks, 21(4), 208. doi:10.1504/ijsnet.2016.079172Garcia M Catala A Lloret J Rodrigues J A wireless sensor network for soccer team monitoring International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS) Barcelona / Spain 2011 1 6Penders J Gyselinckx B Vullers R De Nil M Nimmala V van de Molengraft J Yazicioglu F Torfs T Leonov V Merken P Van Hoof C Human++: from technology to emerging health monitoring concepts 5th International Summer School and Symposium ISSS-MDBS on Medical Devices and Biosensors Hong Kong 2008 94 98Penders J Van de Molengraft J. Brown L Grundlehner B Gyselinckx B Van Hoof C Potential and challenges of body area networks for personal health Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Minneapolis, U.S. 2009 6569 6572Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., … Kwak, K. S. (2010). A Comprehensive Survey of Wireless Body Area Networks. Journal of Medical Systems, 36(3), 1065-1094. doi:10.1007/s10916-010-9571-3Cao, H., Leung, V., Chow, C., & Chan, H. (2009). Enabling technologies for wireless body area networks: A survey and outlook. IEEE Communications Magazine, 47(12), 84-93. doi:10.1109/mcom.2009.5350373Hall, P. S., Yang Hao, Nechayev, Y. I., Alomainy, A., Constantinou, C. C., Parini, C., … Bozzetti, M. (2007). Antennas and propagation for on-body communication systems. IEEE Antennas and Propagation Magazine, 49(3), 41-58. doi:10.1109/map.2007.4293935Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456-2466. doi:10.1109/tbme.2011.2156795LAN-MAN Standards Committee the IEEE Computer Society IEEE standard for local and metropolitan area networks - part 15.4: low rate wireless personal area networks (LR-WPANs) 2011Petrova M Riihijarvi J Mahonen P Labella S Performance study of IEEE 802.15.4 using measurements and simulations IEEE Wireless Communications and Networking Conference (WCNC) Las Vegas, U.S. 2006 487 492Vaithiyanathan, J., Raju, R. K., & Sadayan, G. (2011). Performance Evaluation of IEEE 802.15.4 Using Association Process and Channel Measurement. Communications in Computer and Information Science, 409-417. doi:10.1007/978-3-642-22555-0_42Yazdi E Moravejosharieh A Willig A Pawlikowski K Coupling power and frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks: part II 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 105 110Pelegris P Banitsas K Investigating the efficiency of IEEE 802.15.4 for medical monitoring applications 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Boston, U.S. 2011 8215 8218Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Jianliang Zheng, & Lee, M. J. (2004). Will IEEE 802.15.4 make ubiquitous networking a reality?: a discussion on a potential low power, low bit rate standard. IEEE Communications Magazine, 42(6), 140-146. doi:10.1109/mcom.2004.1304251Toscano E Lo Bello L Cross-channel interference in IEEE 802.15.4 networks IEEE International Workshop on Factory Communication Systems, 2008. WFCS 2008 Dresden, Germany 2008 139 148Bashir F Baek WS Sthapit P Pandey D young Pyun J Coordinator assisted passive discovery for mobile end devices in IEEE 802.15.4 2013 IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2013 601 604Tabatabaei Yazdi E Willig A Pawlikowski K Shortening orphan time in IEEE 802.15.4: what can be gained 2013 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Park, T. R., Kim, T. H., Choi, J. Y., Choi, S., & Kwon, W. H. (2005). Throughput and energy consumption analysis of IEEE 802.15.4 slotted CSMA∕CA. Electronics Letters, 41(18), 1017. doi:10.1049/el:20051662Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210IEEE Computer Society LAN MAN Standards Committee Wireless LAN medium access control (MAC) and physical layer (PHY) specifications 1997Pollin, S., Ergen, M., Ergen, S. C., Bougard, B., Der Perre, L. V., Moerman, I., … Catthoor, F. (2008). Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer. IEEE Transactions on Wireless Communications, 7(9), 3359-3371. doi:10.1109/twc.2008.060057Xinhua Ling, Yu Cheng, Mark, J. W., & Xuemin Shen. (2008). A Renewal Theory Based Analytical Model for the Contention Access Period of IEEE 802.15.4 MAC. IEEE Transactions on Wireless Communications, 7(6), 2340-2349. doi:10.1109/twc.2008.070048Lee, C. Y., Cho, H. I., Hwang, G. U., Doh, Y., & Park, N. (2011). Performance modeling and analysis of IEEE 802.15.4 slotted CSMA/CA protocol with ACK mode. AEU - International Journal of Electronics and Communications, 65(2), 123-131. doi:10.1016/j.aeue.2010.02.007Wang, F., Zhao, Y., & Li, D. (2011). Analysis of CSMA/CA in IEEE 802.15.4. IET Communications, 5(15), 2187-2195. doi:10.1049/iet-com.2010.1007Zhu, J., Tao, Z., & Lv, C. (2011). Performance Evaluation of IEEE 802.15.4 CSMA/CA Scheme Adopting a Modified LIB Model. Wireless Personal Communications, 65(1), 25-51. doi:10.1007/s11277-011-0226-6Shu F Sakurai T Analysis of an energy conserving CSMA-CA GLOBECOM Washington DC, U.S. 2007 2536 2540Shu, F., & Sakurai, T. (2011). A new analytical model for the IEEE 802.15.4 CSMA-CA protocol. Computer Networks, 55(11), 2576-2591. doi:10.1016/j.comnet.2011.04.017Cano-Garcia, J. M., & Casilari, E. (2011). An empirical evaluation of the consumption of 802.15.4/ZigBee sensor motes in noisy environments. 2011 International Conference on Networking, Sensing and Control. doi:10.1109/icnsc.2011.5874886Baz, M., Mitchell, P. D., & Pearce, D. A. J. (2013). Versatile Analytical Model for Delay and Energy Evaluation in WPANs: A Case Study for IEEE 802.15.4 CSMA-CA. Wireless Personal Communications, 75(1), 415-445. doi:10.1007/s11277-013-1370-yLiu Q Czylwik A A priority-based adaptive service differentiation scheme for IEEE 802.15.4 sensor networks Proceedings of European Wireless 2014; 20th European Wireless Conference Barcelona, Spain 2014 1 6Golmie, N., Cypher, D., & Rebala, O. (s. f.). Performance evaluation of low rate WPANs for medical applications. IEEE MILCOM 2004. Military Communications Conference, 2004. doi:10.1109/milcom.2004.1494952Misic, J., Misic, V. B., & Shafi, S. (s. f.). Performance of IEEE 802.15.4 beacon enabled PAN with uplink transmissions in non-saturation mode - access delay for finite buffers. First International Conference on Broadband Networks. doi:10.1109/broadnets.2004.61Mišić, J., Shafi, S., & Mišić, V. B. (2005). The impact of MAC parameters on the performance of 802.15.4 PAN. Ad Hoc Networks, 3(5), 509-528. doi:10.1016/j.adhoc.2004.08.002Anastasi, G., Conti, M., & Di Francesco, M. (2011). A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 7(1), 52-65. doi:10.1109/tii.2010.2085440Lee, B.-H., Al Rasyid, M. U. H., & Wu, H.-K. (2012). Analysis of superframe adjustment and beacon transmission for IEEE 802.15.4 cluster tree networks. EURASIP Journal on Wireless Communications and Networking, 2012(1). doi:10.1186/1687-1499-2012-219Zimmerling, M., Ferrari, F., Mottola, L., Voigt, T., & Thiele, L. (2012). pTunes. Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN ’12. doi:10.1145/2185677.2185730Rohm, D., Goyal, M., Hosseini, H., Divjak, A., & Bashir, Y. (2009). Configuring Beaconless IEEE 802.15.4 Networks Under Different Traffic Loads. 2009 International Conference on Advanced Information Networking and Applications. doi:10.1109/aina.2009.84Jin-Shyan Lee. (2006). Performance evaluation of IEEE 802.15.4 for low-rate wireless personal area networks. IEEE Transactions on Consumer Electronics, 52(3), 742-749. doi:10.1109/tce.2006.1706465De Paz Alberola, R., & Pesch, D. (2012). Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks. Ad Hoc Networks, 10(4), 664-679. doi:10.1016/j.adhoc.2011.06.006Barbieri, A., Chiti, F., & Fantacci, R. (2006). WSN17-2: Proposal of an Adaptive MAC Protocol for Efficient IEEE 802.15.4 Low Power Communications. IEEE Globecom 2006. doi:10.1109/glocom.2006.989Jeon, J., Lee, J. W., Ha, J. Y., & Kwon, W. H. (2007). DCA: Duty-Cycle Adaptation Algorithm for IEEE 802.15.4 Beacon-Enabled Networks. 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring. doi:10.1109/vetecs.2007.35Kang, M., Chong, J., Hyun, H., Kim, S., Jung, B., & Sung, D. (2007). Adaptive Interference-Aware Multi-Channel Clustering Algorithm in a ZigBee Network in the Presence of WLAN Interference. 2007 2nd International Symposium on Wireless Pervasive Computing. doi:10.1109/iswpc.2007.342601Yi, P., Iwayemi, A., & Zhou, C. (2011). Developing ZigBee Deployment Guideline Under WiFi Interference for Smart Grid Applications. IEEE Transactions on Smart Grid, 2(1), 110-120. doi:10.1109/tsg.2010.2091655Tang, L., Wang, K.-C., Huang, Y., & Gu, F. (2007). Channel Characterization and Link Quality Assessment of IEEE 802.15.4-Compliant Radio for Factory Environments. IEEE Transactions on Industrial Informatics, 3(2), 99-110. doi:10.1109/tii.2007.898414Sha M Xing G Zhou G Liu S Wang X C-MAC: model-driven concurrent medium access control for wireless sensor networks IEEE INFOCOM 2009 Rio de Janeiro, Brazil 2009 1845 1853 10.1109/INFCOM.2009.5062105Peizhong Yi, Iwayemi, A., & Chi Zhou. (2010). Frequency agility in a ZigBee network for smart grid application. 2010 Innovative Smart Grid Technologies (ISGT). doi:10.1109/isgt.2010.5434747Torabi N Wong W Leung VCM A robust coexistence scheme for IEEE 802.15.4 wireless personal area networks IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2011 1031 1035 10.1109/CCNC.2011.5766322IEEE standard for local and metropolitan area networks - part 15.6: wireless body area networks IEEE Std 802.15.6-2012 2012 1 271 10.1109/IEEESTD.2012.6161600Kim, S., Kim, S., Kim, J.-W., & Eom, D.-S. (2012). Flexible beacon scheduling scheme for interference mitigation in body sensor networks. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON). doi:10.1109/secon.2012.6275772Bradai N Fourati LC Kamoun L Performance analysis of medium access control protocol for wireless body area networks 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA) Barcelona, Spain 2013 916 921Moravejosharieh A Yazdi ET Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part I: the need for enhancement IEEE 16th International Conference on Computational Science and Engineering (CSE) Sydney, Australia 2013 1226 1231Moravejosharieh A Yazdi ET Willig A Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part II: greedy channel utilization 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Moravejosharieh A Yazdi E Willig A Pawlikowski K Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: continuous hopping approach Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 93 98 10.1109/ATNAC.2014.7020880Moravejosharieh, A. H. (2015). Frequency-Adaptive Approach In IEEE 802.15.4 Wireless Body Sensor Networks: Continuous-Assessment or Periodic-Assessment? International Journal of Information, Communication Technology and Applications, 1(1), 19. doi:10.17972/ajicta2015113Moravejosharieh A Yazdi E Pawlikowski K Sirisena H Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: adaptive phase-shifting approach International Telecommunication Networks and Applications Conference (ITNAC) Sydney, Australia 2015 93 98Bian, K., Park, J.-M., & Gao, B. (2014). Channel Assignment for Multi-hop Cognitive Radio Networks. Cognitive Radio Networks, 101-116. doi:10.1007/978-3-319-07329-3_6Bian, K., Park, J.-M., & Gao, B. (2014). Coexistence-Aware Spectrum Sharing for Homogeneous Cognitive Radio Networks. Cognitive Radio Networks, 61-75. doi:10.1007/978-3-319-07329-3_4Wu C Yan H Huo H A multi-channel MAC protocol design based on IEEE 802.15.4 standard in industry 2012 10th IEEE International Conference on Industrial Informatics (INDIN) Beijing, China 2012 1206 1211 10.1109/INDIN.2012.6300916Incel, O. D. (2011). A survey on multi-channel communication in wireless sensor networks. Computer Networks, 55(13), 3081-3099. doi:10.1016/j.comnet.2011.05.020Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th International Conference on Information Processing in Sensor Networks IPSN '08 St. Louis MO, U.S. 2008 53 63Demirkol, I., Ersoy, C., & Alagoz, F. (2006). MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 44(4), 115-121. doi:10.1109/mcom.2006.1632658Wykret T Correia L Macedo D Giacomin J Andrade L Evaluation and avoidance of interference in WSN: a multi-radio node prototype using dynamic spectrum allocation IFIP Wireless Days (WD) Valencia, Spain 2013 1 3 10.1109/WD.2013.6686533Doyle L Sutton P Nolan K Lotze J Ozgul B Rondeau T Fahmy S Lahlou H DaSilva L Experiences from the IRIS testbed in dynamic spectrum access and cognitive radio experimentation IEEE Symposium on New Frontiers in Dynamic Spectrum Singapore 2010 1 8 10.1109/DYSPAN.2010.5457835Ansari, J., Zhang, X., & Mahonen, P. (2010). Multi-radio medium access control protocol for wireless sensor networks. International Journal of Sensor Networks, 8(1), 47. doi:10.1504/ijsnet.2010.034066Liu Z Wu W A dynamic multi-radio multi-channel MAC protocol for wireless sensor networks 2nd International Conference on Communication Software and Networks (ICCSN) Singapore 2010 105 109Xu, W., Trappe, W., & Zhang, Y. (2008). Defending wireless sensor networks from radio interference through channel adaptation. ACM Transactions on Sensor Networks, 4(4), 1-34. doi:10.1145/1387663.1387664Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th IEEE Computer Society International Conference on Information Processing in Sensor Networks IPSN '08 Washington, DC, USA 2008 53 63Tae Hyun Kim, Jae Yeol Ha, & Sunghyun Choi. (2009). Improving Spectral and Temporal Efficiency of Collocated IEEE 802.15.4 LR-WPANs. IEEE Transactions on Mobile Computing, 8(12), 1596-1609. doi:10.1109/tmc.2009.85Chowdhury, K. R., Nandiraju, N., Chanda, P., Agrawal, D. P., & Zeng, Q.-A. (2009). Channel allocation and medium access control for wireless sensor networks. Ad Hoc Networks, 7(2), 307-321. doi:10.1016/j.adhoc.2008.03.004Deylami, M., & Jovanov, E. (2012). A distributed and collaborative scheme for mitigating coexistence in IEEE 802.15.4 based WBANs. Proceedings of the 50th Annual Southeast Regional Conference on - ACM-SE ’12. doi:10.1145/2184512.2184514Deylami, M. N., & Jovanov, E. (2014). A Distributed Scheme to Manage The Dynamic Coexistence of IEEE 802.15.4-Based Health-Monitoring WBANs. IEEE Journal of Biomedical and Health Informatics, 18(1), 327-334. doi:10.1109/jbhi.2013.2278217Deylami M Jovanov E An implementation of a distributed scheme for managing the dynamic coexistence of wireless body area networks Southeastcon, 2013 Proceedings of IEEE Jacksonville, U.S. 2013 1 6 10.1109/SECON.2013.6567446Cavallari, R., Martelli, F., Rosini, R., Buratti, C., & Verdone, R. (2014). A Survey on Wireless Body Area Networks: Technologies and Design Challenges. IEEE Communications Surveys & Tutorials, 16(3), 1635-1657. doi:10.1109/surv.2014.012214.00007Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless Body Area Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1658-1686. doi:10.1109/surv.2013.121313.00064Patel, M., & Wang, J. (2010). Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80-88. doi:10.1109/mwc.2010.5416354ULLAH, S., KHAN, P., ULLAH, N., SALEEM, S., HIGGINS, H., & Sup KWAK, K. (2009). A Review of Wireless Body Area Networks for Medical Applications. International Journal of Communications, Network and System Sciences, 02(08), 797-803. doi:10.4236/ijcns.2009.28093Boulis, A., Smith, D., Miniutti, D., Libman, L., & Tselishchev, Y. (2012). Challenges in body area networks for healthcare: the MAC. IEEE Communications Magazine, 50(5), 100-106. doi:10.1109/mcom.2012.6194389Pantelopoulos A Bourbakis N A survey on wearable biosensor systems for health monitoring 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, Canada 2008 4887 4890 10.1109/IEMBS.2008.4650309Takei, K., Honda, W., Harada, S., Arie, T., & Akita, S. (2014). Toward Flexible and Wearable Human-Interactive Health-Monitoring Devices. Advanced Healthcare Materials, 4(4), 487-500. doi:10.1002/adhm.201400546Caldeira, J. M. L. P., Rodrigues, J. J. P. C., & Lorenz, P. (2013). Intra-Mobility Support Solutions for Healthcare Wireless Sensor Networks–Handover Issues. IEEE Sensors Journal, 13(11), 4339-4348. doi:10.1109/jsen.2013.2267729Carrano, R. C., Passos, D., Magalhaes, L. C. S., & Albuquerque, C. V. N. (2014). Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(1), 181-194. doi:10.1109/surv.2013.052213.00116Sudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443-461. doi:10.1109/surv.2011.060710.00094Khanafer, M., Guennoun, M., & Mouftah, H. T. (2014). A Survey of Beacon-Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(2), 856-876. doi:10.1

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Distance Aware Relaying Energy-efficient: DARE to Monitor Patients in Multi-hop Body Area Sensor Networks

In recent years, interests in the applications of Wireless Body Area Sensor Network (WBASN) is noticeably developed. WBASN is playing a significant role to get the real time and precise data with reduced level of energy consumption. It comprises of tiny, lightweight and energy restricted sensors, placed in/on the human body, to monitor any ambiguity in body organs and measure various biomedical parameters. In this study, a protocol named Distance Aware Relaying Energy-efficient (DARE) to monitor patients in multi-hop Body Area Sensor Networks (BASNs) is proposed. The protocol operates by investigating the ward of a hospital comprising of eight patients, under different topologies by positioning the sink at different locations or making it static or mobile. Seven sensors are attached to each patient, measuring different parameters of Electrocardiogram (ECG), pulse rate, heart rate, temperature level, glucose level, toxins level and motion. To reduce the energy consumption, these sensors communicate with the sink via an on-body relay, affixed on the chest of each patient. The body relay possesses higher energy resources as compared to the body sensors as, they perform aggregation and relaying of data to the sink node. A comparison is also conducted conducted with another protocol of BAN named, Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop ProTocol (M-ATTEMPT). The simulation results show that, the proposed protocol achieves increased network lifetime and efficiently reduces the energy consumption, in relative to M-ATTEMPT protocol.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

A framework for obesity control using a wireless body sensor network

Low-cost low-power consumption small wireless sensor devices have empowered the development of wireless body area networks (WBANs). In WBANs many sensors are attached to human body for sensing particular health related information to improve healthcare and quality of life. Obesity is one of the most common problems all over the world, which is amongst main causes of cardiovascular diseases. In this research, we explore hardware and software architecture of WBAN for obesity monitoring. The proposed framework consists of few sensor nodes that monitor body motion, calories calculator, and a personal server running on a personal smart phone or a personal computer. The focus of this research is to make obesity patients easier to get rid of this disease.The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this Research Group no. RG-1435-037.Alrajeh, NA.; Lloret, J.; Cánovas Solbes, A. (2014). A framework for obesity control using a wireless body sensor network. International Journal of Distributed Sensor Networks. 2014:1-6. https://doi.org/10.1155/2014/534760S162014Schmidt, R., Norgall, T., Mörsdorf, J., Bernhard, J., & von der Grün, T. (2002). Body Area Network BAN – a Key Infrastructure Element for Patient-Centered Medical Applications. Biomedizinische Technik/Biomedical Engineering, 47(s1a), 365-368. doi:10.1515/bmte.2002.47.s1a.365Garcia, M., Catala, A., Lloret, J., & Rodrigues, J. J. P. C. (2011). A wireless sensor network for soccer team monitoring. 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS). doi:10.1109/dcoss.2011.5982204Sun, G., Qiao, G., & Xu, B. (2012). Link Characteristics Measuring in 2.4 GHz Body Area Sensor Networks. International Journal of Distributed Sensor Networks, 8(10), 519792. doi:10.1155/2012/519792Tomas, J., Lloret, J., Bri, D., & Sendra, S. (2011). Sensors and their Application for Disabled and Elderly People. Handbook of Research on Personal Autonomy Technologies and Disability Informatics, 311-330. doi:10.4018/978-1-60566-206-0.ch020Latré, B., Braem, B., Moerman, I., Blondia, C., & Demeester, P. (2010). A survey on wireless body area networks. Wireless Networks, 17(1), 1-18. doi:10.1007/s11276-010-0252-4Zasowski, T., Meyer, G., Althaus, F., & Wittneben, A. (2006). UWB signal propagation at the human head. IEEE Transactions on Microwave Theory and Techniques, 54(4), 1836-1845. doi:10.1109/tmtt.2006.871989Bri, D., Lloret, J., Turro, C., & Garcia, M. (s. f.). Measuring Specific Absorption Rate by using Standard Communications Equipment. Telemedicine and E-Health Services, Policies, and Applications, 81-111. doi:10.4018/978-1-4666-0888-7.ch004Di Renzo, M., Buehrer, R. M., & Torres, J. (2007). Pulse Shape Distortion and Ranging Accuracy in UWB-Based Body Area Networks for Full-Body Motion Capture and Gait Analysis. IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference. doi:10.1109/glocom.2007.717Neirynck D.Channel characterisation and physical layer analysis for body and personal area network development [Ph.D. thesis]2006Bristol, UKUniversity of BristolSendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Tang, Q., Tummala, N., Gupta, S. K. S., & Schwiebert, L. (2005). Communication Scheduling to Minimize Thermal Effects of Implanted Biosensor Networks in Homogeneous Tissue. IEEE Transactions on Biomedical Engineering, 52(7), 1285-1294. doi:10.1109/tbme.2005.847527Bag, A., & Bassiouni, M. (2006). Energy Efficient Thermal Aware Routing Algorithms for Embedded Biomedical Sensor Networks. 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Sysetems. doi:10.1109/mobhoc.2006.278619Quwaider, M., & Biswas, S. (2012). Delay Tolerant Routing Protocol Modeling for Low Power Wearable Wireless Sensor Networks. Network Protocols and Algorithms, 4(3). doi:10.5296/npa.v4i3.2054Machado, T. M. F., Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., & Lloret, J. (2012). Performance evaluation of cooperation mechanisms for m-health applications. 2012 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2012.6503353Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Macias, E., Suarez, A., & Lloret, J. (2013). Mobile Sensing Systems. Sensors, 13(12), 17292-17321. doi:10.3390/s131217292Meghanathan, N., & Mumford, P. (2013). Centralized and Distributed Algorithms for Stability-based Data Gathering in Mobile Sensor Networks. Network Protocols and Algorithms, 84. doi:10.5296/npa.v5i4.4208Hanson, M. A., Powell, H. C., Barth, A. T., Ringgenberg, K., Calhoun, B. H., Aylor, J. H., & Lach, J. (2009). Body Area Sensor Networks: Challenges and Opportunities. Computer, 42(1), 58-65. doi:10.1109/mc.2009.5Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Lopes, I. M., Silva, B. M., Rodrigues, J. J. P. C., Lloret, J., & Proenca, M. L. (2011). A mobile health monitoring solution for weight control. 2011 International Conference on Wireless Communications and Signal Processing (WCSP). doi:10.1109/wcsp.2011.6096926Nachman, L., Huang, J., Shahabdeen, J., Adler, R., & Kling, R. (2008). IMOTE2: Serious Computation at the Edge. 2008 International Wireless Communications and Mobile Computing Conference. doi:10.1109/iwcmc.2008.19

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted