Revisiting Routing Protocols to Design Energy Aware Wireless Body Area Network

Wireless body area networks (WBANs) a special type of wireless sensor networks (WSNs) in which sensor nodes to actualize continuous wearable wellbeing observing of patients are able to provide improved healthcare services in a distributed infrastructure less environments. However, the mobile node, due to less battery power, can easily suffer from the problem of energy level when control packets are transfer among nodes—a problem that can occurs by the fact that some sensor nodes may select wrong cluster head with inappropriate path and waste the resources. Although many energy efficient methods have been designed for the traditional sensor networks, there has been limited focus on incorporating WBANs into energy efficient schemes. Therefore, in order to incorporate above issue we revisit the already designed traditional energy efficient methods with cluster head selection protocols and optimal path transformation. Therefore, we encourage researchers to insert WBANs with existing methods to improve performance. However, some work has been done in WBANs that uses energy efficient methods to manage the routing issue, this research domain requires further research attention. Therefore, we discuss the current research work and purpose many future directions of research

Modelling of novel-structured copper barium tin sulphide thin film solar cells

[EN] In this work, a novel structured Cu2BaSnS4 (CBTS)/ZnS/Zn(O, S) photovoltaic device is proposed. A nontoxic, earth-abundant and auspicious quaternary semiconductor compound copper barium tin sulphide (Cu2BaSnS4) is used as an absorber layer. We propose a novel Zn(O, S) buffer layer for a high-power conversion efficiency (PCE) of CBTS-based thin film photovoltaic cells. Solar cell capacitance simulator software is used for device modelling and simulations are performed under a 1.5 AM illumination spectrum. The proposed device is investigated by means of numerical modelling and optimized the parameters to maximize its efficiency. Promising optimized functional parameters had been achieved from the proposed structure with back surface field layer with a PCE of 18.18%, a fill factor of 83.45%, a short-circuit current of 16.13 mA cm¿2 and an open-circuit voltage of 1.3 V. The promising results give an imperative standard for possible manufacturing of high efficiency, eco-friendly inorganic CBTS-based photovoltaic cells.This work was supported by Ministerio de Economia y Competitividad (ENE2016-77798-C4-2-R) and Generalitat valenciana (Prometeus 2014/044).Hameed, KY.; Baig, F.; Toura, H.; Marí, B.; Beg, S.; Khani, NAK. (2019). Modelling of novel-structured copper barium tin sulphide thin film solar cells. Bulletin of Materials Science. 42(5):1-8. https://doi.org/10.1007/s12034-019-1919-9S18425Ge J, Koirala P, Grice C R, Roland P J, Yu Y, Tan X et al 2017 Adv. Energy Mater. 7 1601803Khattak Y H, Mahmood T, Alam K, Sarwar T, Ullah I and Ullah H 2014 Am. J. Electr. Power Energy Syst. 3 86Steinmann V, Brandt R E and Buonassisi T 2015 Nat. Photonics 9 355Jackson P, Hariskos D, Wuerz R, Kiowski O, Bauer A, Friedlmeier T M et al 2015 Phys. Status Solidi: Rapid. Res. Lett. 9 28Shin D, Saparov B and Mitzi D B 2017 Adv. Energy Mater. 7 1602366Paper C, Le A, Universit D, Universit B, Universit M A, Marchionna S et al 2017 Eur. Photovolt. Sol. Energy Conf. 33 25Khattak Y H, Baig F, Ullah S, Marí B, Beg S and Ullah H 2018 J. Renew. Sustain. Energy 10 033501Fontané X, Izquierdo-Roca V, Saucedo E, Schorr S, Yukhymchuk V O, Valak M Y et al 2012 J. Alloys Compd. 539 190Zhang X, Bao N, Ramasamy K, Wang Y-H A, Yifeng Wang B L and Gupta A 2012 Chem. Commun. 48 4956Adewoyin A D, Olopade M A and Chendo M 2017 Optik—Int. J. Light Electron Opt. 133 122Boutebakh F Z, Zeggar M L, Attaf N and Aida M S 2017 Optik—Int. J. Light Electron Opt. 144 180Ananthakumar S, Ram Kumar J and Moorthy Babu S 2016 Optik—Int. J. Light Electron Opt. 127 10360Jianjun L, Dongxiao W, Xiuling L and Zeng Y 2018 Adv. Sci. 5 1700744Khattak Y H, Baig F, Ullah S, Marí B, Beg S and Ullah H 2018 Optik—Int. J. Light Electron Opt. 164 547Xiao Z, Meng W, Li J V. and Yan Y 2017 ACS Energy Lett. 2 29Shin D, Saparov B, Zhu T, Huhn W P, Blum V and Mitzi D B 2016 Chem. Mater. 28 477Repins I L, Romero M J, Li J V, Wei S-H, Kuciauskas D, Jiang C-S et al 2013 J. Photovoltaics 3 439Zhou H, Hsu W-C, Duan H-S, Bob B, Yang W, Song T-B et al 2013 Energy Environ. Sci. 6 2822Khattak Y H, Baig F, Toura H, Ullah S, Marí B, Beg S et al 2018 Curr. Appl. Phys. 18 633Ge J, Roland P J, Koirala P, Meng W, Young J L, Petersen R et al 2017 Chem. Mater. 29 916Ge J and Yan Y 2017 J. Mater. Chem. C 5 6406Hong F, Lin W, Meng W and Yan Y 2016 Phys. Chem. Chem. Phys. 18 4828Todorov T, Gunawan O and Guha S 2016 Mol. Syst. Des. Eng. 1 370Baig F, Ullah H, Khattak Y H and Mari Soucase B 2016 Int. Ren. Sus. En. Conf. 596, https://doi.org/10.1109/IRSEC.2016.7983899Lin L-Y, Qiu Y, Zhang Y and Zhang H 2016 Chinese Phys. Lett. 33 10780Platzer B C, Törndahl T, Abou-Ras D, Malmström J, Kessler J and Stolt L 2006 J. Appl. Phys. 100 044506Persson C, Platzer-Björkman C, Malmström J, Törndahl T and Edoff M 2006 Phys. Rev. Lett. 97 146403Burgelman M, Nollet P and Degrave S 2000 Thin Solid Films 361 527Khattak Y H, Baig F, Soucase B M and Beg S 2018 Mater. Focus 84 758Simya O K, Mahaboobbatcha A and Balachander K A 2015 Superlattices Microstruct. 82 248Shin D, Zhu T, Huang X, Gunawan O, Blum V and Mitzi D B 2017 Adv. Mater. 29 1Saha U and Alam M K 2018 Phys. Status Solidi: Rapid Res. Lett. 12 1Zhu T, Huhn W P, Wessler G C, Shin D, Saparov B, Mitzi D B et al 2017 Chem. Mater. 29 7868Ge J, Grice C R and Yan Y 2017 J. Mater. Chem. A 5 2920Baig F, Khattak Y H, Marí B, Beg S, Gillani S R and Ahmed A 2018 Optik—Int. J. Light Electron Opt. 170 463Khattak Y H, Baig F, Ullah S, Marí B, Beg S and Gillani S R 2018 Optik—Int. J. Light Electron Opt. 171 45

Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation

Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation