Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
Schools are amongst the most densely occupied indoor areas and at the same time children\nand young adults are the most vulnerable group with respect to adverse health effects as a result\nof poor environmental conditions. Health, performance and well-being of pupils crucially depend\non indoor environmental quality (IEQ) of which air quality and thermal comfort are central pillars.\nThis makes the monitoring and control of environmental parameters in classes important. At the\nsame time most school buildings do neither feature automated, intelligent heating, ventilation, and air\nconditioning (HVAC) systems nor suitable IEQ monitoring systems. In this contribution, we therefore\ninvestigate the capabilities of a novel wireless gas sensor network to determine carbon dioxide\nconcentrations, along with temperature and humidity. The use of a photoacoustic detector enables\nthe construction of long-term stable, miniaturized, LED-based non-dispersive infrared absorption\nspectrometers without the use of a reference channel. The data of the sensor nodes is transmitted via\na Z-Wave protocol to a central gateway, which in turn sends the data to a web-based platform for\nonline analysis. The results show that it is difficult to maintain adequate IEQ levels in class rooms\neven when ventilating frequently and that individual monitoring and control of rooms is necessary\nto combine energy savings and good IEQ....
Acognitive radio sensor network (CRSN) is a solution that enables sensor nodes to opportunistically access licensed radio channels.\nData transmitted over a network are divided into packets. In machine-to-machinecommunication,which is a heterogeneous nature\nof wireless networks, small-size packets are the common form of traffic. Due to the nature of CRSNs, small data packets will not\nallow a balance between optimal performance of the network and fulfilling the secondary network obligations towards the primary\nnetwork in terms of interference. Either interference or channelâ??s underutilization would result from employing data packets of\ninadequate size. In this paper, the appropriate packet size for adaptive CRSN is investigated by examining the performances of\nsmall, medium, and large packet size. In contrast to the trends of exploiting small packets of sizes up to 128 bytes, this study\ndemonstrates thatmedium-size packets aremore appropriate to yield the best performance in CRSNs. Simulation results show that\npackets of size 375 bytes outperform smaller and larger packets inmany CRSN protocols. The induced delay that is partially caused\nby interference is decreased at the same time the channels are efficiently utilized....
This paper proposes and investigates a piezoelectric energy harvesting system based on\nthe flow induced vibration of a piezoelectric composite cantilever pipe. Dynamic equations for the\nproposed energy harvester are derived considering the fluid-structure interaction and piezoelectric\ncoupling vibration. Linear global stability analysis of the fluid-solid-electric coupled system is\ndone using the numerical continuation method to find the neutrally stable vibration mode of the\nsystem. A measure of the energy harvesting efficiency of the system is proposed and analyzed.\nA series of simulations are conducted to throw light upon the influences of mass ratio, dimensionless\nelectromechanical coupling, and dimensionless connected resistance upon the critical reduced velocity\nand the normalized energy harvesting efficiency. The results provide useful guidelines for the practical\ndesign of piezoelectric energy harvester based on fluid structure interaction and indicate some future\ntopics to be investigated to optimize the device performance....
LEACH protocol randomly selects cluster head nodes in a cyclic manner. It\nmay cause network to be unstable, if the low energy node is elected as the\ncluster head. If the size of cluster is too large or too small, it will affect the\nsurvival time of the network. To address this issue, an improved solution was\nproposed. Firstly, the scheme considered the average and standard deviation\nof the nodesâ?? residual energy and the distance between the node and the base\nstation, then considered the distance between the node and the cluster head\nand the energy of the cluster head to optimize the cluster head selection and\nclustering. The performance analysis results showed this scheme could reduce\npremature deaths of the cluster heads and too high energy consumption of\nsome clusters. Thus, the proposed algorithm could prompt the stability and\nprolong the lifetime of the network....
The recently emerging cyber-physical-social system (CPSS) can enable efficient interactions\nbetween the social world and cyber-physical system (CPS). The wireless sensor network (WSN) with\nphysical and social sensor nodes plays an important role in CPSS. The integration of the social sensors\nand physical sensors in CPSS provides an advantage for smart services in different application areas.\nHowever, the dynamics of social mobility for social sensors pose new challenges for implementing\nthe coordination of transmission. Furthermore, the integration of social and physical sensors also\nfaces the challenges in term of improving energy efficiency and increasing transmission range.\nTo solve these problems, we integrate the model of social dynamics with collaborative beamforming\n(CB) technique to formulate the transmission optimization problem as a dynamic game. A novel\ntransmission scheme based on reinforcement learning is proposed to solve the formulated problem.\nThe corresponding implementation of the proposed transmission scheme in CPSS is presented by\nthe design of message exchange processes. The extensive simulation results demonstrate that the\nproposed transmission scheme presents lower interference to noise ratio (INR) and better signal to\nnoise ratio (SNR) performance in comparison with the existing schemes. The results also indicate\nthat the proposed method has effective adaptation to the dynamic mobility of social sensor nodes\nin CPSS....
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