sensors for everyday life

Smart Sensors, Measurement and Instrumentation 23 Subhas Chandra Mukhopadhyay Octavian Adrian Postolache Krishanthi P. Jayasundera Akshya K. Swain Editors Sensors for Everyday Life Environmental and Food Engineering Smart Sensors, Measurement and Instrumentation Volume 23 Series editor Subhas Chandra Mukhopadhyay Department of Engineering, Faculty of Science and Engineering Macquarie University Sydney, NSW Australia e-mail: S.C.Mukhopadhyay@massey.ac.nz More information about this series at http://www.springer.com/series/10617 Subhas Chandra Mukhopadhyay Octavian Adrian Postolache Krishanthi P Jayasundera Akshya K Swain Editors Sensors for Everyday Life Environmental and Food Engineering 123 Editors Subhas Chandra Mukhopadhyay Department of Engineering, Faculty of Science and Engineering Macquarie University Sydney, NSW Australia Krishanthi P Jayasundera Institute of Fundamental Sciences Massey University Palmerston North New Zealand Octavian Adrian Postolache Instituto de Telecomunicaỗừes Lisbon Portugal Akshya K Swain Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand and ISCTE-IUL Lisbon Portugal ISSN 2194-8402 ISSN 2194-8410 (electronic) Smart Sensors, Measurement and Instrumentation ISBN 978-3-319-47321-5 ISBN 978-3-319-47322-2 (eBook) DOI 10.1007/978-3-319-47322-2 Library of Congress Control Number: 2016953322 © Springer International Publishing AG 2017 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Sensors play a pivotal role in our everyday life They gather data on environment, and information on weather, traffic congestion, air pollution, water pollution, etc is obtained; they gather data on human body, and information on health, treatment or therapy outcomes is obtained; they gather data on objects, and information for monitoring and control of these objects is obtained; they gather data on subjects or objects functions, and information for better decisions, control and action is obtained For instance, the weather information is used to choose adequate clothes, the battery level sensor permits smartphone power management optimization, and the level of blood glucose allows better healthcare management Data collected through sensors enhance our lives and our connections to each other and with our environment, allow real-time monitoring of many phenomena around us, provide information about quality of products and services, improve the equipment control based on sensorized interfaces and contribute to increase knowledge on physical and chemical world The advances in electronics, embedded controller, technology for communication as well as the progress towards a better informed, knowledge based society increase the demand for small size, affordable sensors that allow accurate and reliable data recording, processing, storing and communication The work contains invited chapters from renowned experts, working in sensors’ field, and it is split into two books that present several technologies and applications of sensors in Environmental and Food Engineering (ISBN 978-3-319-47322-2) and for Healthcare Settings (ISBN 978-3-319-47319-2) The book Sensors for Everyday Life—Environmental and Food Engineering describes novel sensors and sensing systems developed for environment monitoring and food production and quality assessment Environmental quality refers to characteristics from natural environment as well as from the built environment (i.e city air and/or water pollution, concentration of nitrate from the soil in cultivated fields) Environmental quality plays an important role in health and well-being of the populations Degraded environmental quality as produced by air and water pollution may affect our lives, directly or through the food we eat In food engineering various sensors are used for assessment of v vi Preface contaminants, adulterants, naturally occurring toxins or any other substance that may make food injurious to health on an acute or chronic basis as well as sensors that contribute for quality improvement of food New developed sensors and technology trend related air, water, food quality monitoring as well as for modern agronomy and food production are presented in this book How This Book is Organized In Chap 1, a novel method for the simultaneous determination of NOx and soot in the exhaust of diesel engines during the periodical technical inspection roadworthiness test is presented A multi-wavelength light extinction measurement, in a setup similar to an opacimeter with high sensitivity, and a mathematical inversion algorithm are used to obtain the concentrations from the extinction readings Analytical technique of the fine particles using atomic emission spectroscopy system for an environmental pollution monitoring is presented in Chap Laser-induced breakdown spectroscopy (LIBS) system and the helium-microwaveinduced plasma-atomic emission spectroscopy (He-MIP-AES) system are used for characterizations and real-time measurement of the air chemical compositions and particle size Chapter presents sensors and method for automatic fault detection in heating ventilating and air conditioning (HVAC) systems This is important mainly in smart buildings context as the indoor condition in these buildings is mainly related with the capabilities and reliability of HVAC systems New optical fiber humidity sensor is described in Chap Different humidity sensors that have been developed by now are presented focusing in the different optical structures and materials that have been used for improving sensitivity and resolutions of these sensors The measurement of air gas concentration represents an important field of application of sensing technologies In Chap of the book, a review on the oxygen gas sensing technologies is presented with focus on potentiometric, amperometric, paramagnetic and tunable diode laser spectroscopy (TDLS) sensors Theoretical aspects and operational basic of these technologies, system requirements as well as limitations of the methods are discussed in this chapter A low-cost sensor node based on interdigital capacitive sensor for nitrate and nitrite in surface and ground water concentration detection is presented in Chap This sensor is important for agronomy as well as for water pollution assessment Nitrates may be present in high concentration in ground and surface water as a result of intensive agriculture, disposal of human and animal sewage and industrial wastes In Chap an intelligent wireless sensor network system designed to monitor various parameter in palm oil plantation for improvement in the controlled pollination process is presented The system helps in making decision related to best time for pollination process The inaccuracy in determining pollination readiness Preface vii of the oil palm flower could potentially cause a detrimental effect on the palm oil industry in the long run The following two chapters present sensors for determination of quality and quantity of water for drinking purpose In Chap 8, a reflectometer and a Doppler radar systems for detection of water level in septic tank is described A novel S3 (Small Sensor System) nanowire device for the detection of complex mixtures of bacteria in potable water is presented in Chap Next three chapters describe sensors used in food productions and quality assessment A novel approach to monitor the quality of milk products, based on electromagnetic wave spectroscopy is presented in Chap 10 The system use vector network analyser to capture spectral signatures in the form of scattering parameters from electromagnetic wave sensors Data on reliability testing is presented A new, rapid, portable, easy-to-use, economic and non-destructive fouling based on nanowire technology device to control the presence of false grated Parmigiano Reggiano cheese is described in Chap 11 A review on the conventional techniques and dielectric spectroscopy for analyzing food products is presented in Chap 12, focusing on the application of dielectric spectroscopy in fats and oils adulteration detection Different wireless sensor network architecture is implemented nowadays to perform distributed measurement tasks for environment monitoring with increase in space resolution Big challenge in these implementations continues to be wireless interference and radio-frequency (RF) spectrum crowding Chapter 13 focuses on a technique for optical-based RF interference cancellation In this chapter several system architectures are presented and a sample of their experimental performance and the key characteristics of this technique and the future prospects for this technology, focusing specifically on photonic integrated circuits are discussed A scheme is proposed in Chap 14 that can reduce the performance difference between cluster heads (CHs) involved in inter-cluster communication on IEEE 802 15.4 cluster-based wireless sensor networks (WSNs) under spatial non-uniform traffic condition where the CHs have various amount of traffic This reduced the energy consumption and simplified processing mechanism to achieve long WSNs lifetime under limited network resource condition We sincerely hope that the readers will find this special issue interesting and useful in their research on sensors and wireless sensor networks for environment monitoring, food production and quality assessment We want to acknowledge all the authors for their contribution and for sharing of their knowledge We hope that the works presented in this book will stimulate further research related to sensors for everyday life Sydney, Australia Lisbon, Portugal Palmerston North, New Zealand Auckland, New Zealand Subhas Chandra Mukhopadhyay Octavian Adrian Postolache Krishanthi P Jayasundera Akshya K Swain About the Editors Dr Subhas Chandra Mukhopadhyay (M’97, SM’02, F’11) graduated from the Department of Electrical Engineering, Jadavpur University, Calcutta, India with a Gold medal and received the Master of Electrical Engineering degree from Indian Institute of Science, Bangalore, India He has Ph.D (Eng.) degree from Jadavpur University, India and Doctor of Engineering degree from Kanazawa University, Japan Currently he is working as Professor of Mechanical/Electronics Engineering and Discipline Leader of the Mechatronics Degree Programme of the Department of Engineering, Macquarie University, Sydney, Australia He has over 26 years of teaching and research experiences His fields of interest include smart sensors and sensing technology, wireless sensor networks, internet of things, electromagnetics, control engineering, magnetic bearing, fault current limiter, electrical machines and numerical field calculation He has authored/co-authored over 400 papers in different international journals, conferences and book chapter He has edited thirteen conference proceedings He has also edited fifteen special issues of international journals as lead guest editor and twenty-five books with Springer-Verlag He was awarded numerous awards throughout his career and attracted over NZ $4.2 M on different research projects He has delivered 272 seminars including keynote, tutorial, invited and special seminars He is a Fellow of IEEE (USA), a Fellow of IET (UK) and a Fellow of IETE (India) He is a Topical Editor of IEEE Sensors Journal and an Associate Editor IEEE Transactions on Instrumentation He has organized many international conferences either as general chair or technical programme chair He is the ix x About the Editors Ex-Chair of the IEEE Instrumentation and Measurement Society New Zealand Chapter He chairs the IEEE IMS Technical Committee 18 on Environmental Measurements Dr Octavian Adrian Postolache (M’99, SM’2006) graduated in Electrical Engineering at the Gh Asachi Technical University of Iasi, Romania, in 1992 and he received the Ph.D degree in 1999 from the same university, and university habilitation in 2016 from Instituto Superior Tecnico, Universidade de Lisboa, Portugal In 2000 he became principal researcher of Instituto de Telecomunicaỗừes where he is now Senior Researcher Since 2012 he joined Instituto Universitario de Lisboa/ISCTE-IUL Lisbon where he is currently Aux Professor His fields of interests include smart sensors for biomedical and environmental applications, pervasive sensing and computing, wireless sensor networks, signal processing with application in biomedical and telecommunications, non-destructive testing and diagnosis based on eddy currents smart sensors, computational intelligence with application in automated measurement systems He is active member of national and international research teams involved in Portuguese and EU and International projects He was principal researcher of different projects including EHR-Physio regarding the implementation of Electronic Health Records for Physiotherapy and he is currently principal researcher of TailorPhy project Smart Sensors and Tailored Environments for Physiotherapy Dr Postolache is author and co-author of patents, books, 16 book chapters, 66 papers in international journals with peer review, more than 220 papers in proceedings of international conferences He is IEEE Senior Member I&M Society, Distinguished Lecturer of IEEE IMS, chair of IEEEI&MSTC-13 Wireless and Telecomunications in Measurements, member of IEEEI&MSTC-17, IEEEI&MSTC-18, IEEEI&MSTC-25, IEEE EMBS Portugal Chapter and chair of IEEE IMS Portugal Chapter He is Associate Editor of IEEE Sensors Journal, and IEEE Transaction on Instrumentation and Measurements, he was general chair of IEEE MeMeA 2014, and TPC chair of ICST 2014, Liverpool and ICST 2015 in Auckland He received IEEE best reviewer and the best associate editor in 2011 and 2013 and other awards related to his research activity in the field of smart sensing 310 A Yamauchi et al High Start High traffic Short waiting CS Small BE priority More tx oppotunity Data tx CCH Short backoff window PCH Long waiting Large BE Suppress tx Low priority Less tx oppotunity CCH n Low traffic Long backoff window Fig Priority control based on backoff operation BE in order to increase the BW size and this parameter setting forces them to wait for a larger interval, leading to low priority state, being hard to get more transmission opportunity From these BE setting, the CCHs with higher traffic can obtain more transmission opportunity than the CCHs with lower traffic, as illustrated in Fig As a result, each CCH can transmit the data received from its SNs in proportion to its own traffic load, and then, WSNs can suppress the non-uniform transmission performance between CCHs, resulting in system performance improvement IEEE 802.15.4 MAC defines two control parameters for the BE; BEmin and BEmax The BEmin adjustment could impact the access priority at the earlier stage of backoff operation BEmax adjustment, however, cannot influence at the earlier stage, only at the final stage Therefore, in this work, we employ the BEmin to control the priority levels for CCHs 5.3 Traffic Status Estimation To achieve the above-mentioned priority access, each CCH needs to know its current traffic status relative to other CCHs competing the same CAP Therefore, as an indicator of the relative traffic status, the proposed scheme introduces the length LCAPend queue (packets) of transmission queue at the end point of the CAP assigned to the communication from the CCHs to their PCH At the end point of a given CAP, the data stored in the transmission queue shows the data which the CCH cannot complete to transmit during that CAP Monitoring the amount of the non-completed data allows each CCH to recognize its transmission status, good or poor status, reflecting the competition for the CAP with Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … Channel access at CCH1 Backoff CS Data Short backoff CCH Long Traffic estimation ON (monitoring) Tx queue Backoff CS High traffic Data PCH Short Long backoff Data ON CS BEmin decrease Tx queue Superframe 311 BEmin increase CCH n CS Data Low traffic Channel access at CCHn Fig Backoff exponent BE adjustment based on traffic status estimation other CCHs Hence, each CCH can estimate its traffic status relative to other CCHs by observing the transmission queue length LendCAP queue Figure partly illustrates an example for the proposed traffic status estimation, where the CCH1 (upper one) with many SNs detects a long LCAPend queue at the end of the current CAP Then, the CCH1 can estimate its traffic status at high traffic condition, where it obtain insufficient transmission opportunity compared with its current load Conversely, the CCHn (lower one) with a small number of SNs has a short LCAPend queue which shows it can get sufficient transmission opportunity during that CAP Therefore the CCHn can cognize that it is in low traffic status From these at the end of the CAP is considerations, the transmission queue length LCAPend queue useful for estimating traffic status relatively to other CCHs 5.4 Control Procedure In the proposed scheme, the backoff exponent BEmin is adjusted by a following procedure, as illustrated in Fig In every superframe, the CCHs observe the transmission queue length LCAPend queue at the end of the CAP used for data transmission to their PCH, and then estimate their traffic status based on the observed LCAPend queue After the traffic estimation, they adjusts their BEmin value which will be applied in the next superframe (CAP) As criteria for traffic status estimation, the proposed scheme introduces a predefined constant threshold THqueue for evaluating the observed LCAPend queue The CCHs 312 A Yamauchi et al estimate high traffic status relative to others when detecting the observed LCAPend queue is greater than the threshold THqueue In contrast, they estimate their low traffic status after detecting Nup consecutive LCAPend queue ≤ THqueue For the specific procedure for controlling the BEmin, the CCHs under the high traffic status decrease the value of BEmin by a decrement step ΔBEdown in order to increase their access priority, whereas CCHs with the low traffic status increase the BEmin by an increment step ΔBEup to lower their access priority These adjustment steps ΔBEdown and ΔBEup are predefined and constant control parameters The detailed controlling procedure for the BEmin and its adjustment range is given by: before > < BEmin + ΔBEdown after before BEmin = BEmin + ΔBEup > : before BEmin ; LCAPend queue > THqueue ; LCAPend queue ≤ THqueue in consecutive Nup ; otherwise, ð1Þ upperr lower subject to BEmin ≤ BEmin ≤ BEmin , upper lower where BEmin and BEmin are a minimum and maximum value for BEmin The optimal settings for Nup, ΔBEdown and ΔBEup will be discussed in Sect 7.2 Performance Evaluations The performance for the proposed autonomous traffic adaptive backoff control is evaluated through computer simulation with following assumptions The statistics are collected from the inter-cluster communication involving one PCH and its Ncch subordinate CCHs, as illustrated in Fig 6.1 Wireless Channel Model The radio channels are assumed to be ideal without any consideration of propagation loss, shadowing and fading fluctuation in this evaluation Packet transmission error occurs only due to packet collision caused from simultaneous transmission from multiple CCHs The CCA at each CCH is perfect without impact of hidden terminal problem 6.2 Traffic Model The packets are continuously generated at each CCH following Poisson distribution with an average cluster traffic load of Gx (packets/Lpkt), which corresponds to Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … 313 cluster traffic load The Lpkt is a packet length and is same for all generated packets After the packet generation, the packet is stored in transmission buffer with a length of Lbuffer (packets) at each CCH The packet is discarded due to buffer overflow when the buffer is full At the buffer overflow the oldest packet is discarded Three different cluster traffic loads, Gx (x = ‘low’, ‘mid’, or ‘high’) are defined to simulate spatial non-uniform traffic condition Ncch CCHs are equally separated into three CCH groups, and each CCH group has one of Gx without duplication each other Defining the ratio (Rlow : Rmid : Rhigh) for traffic loads (low : mid : high), the traffic load Gx for the cluster in group x is given by using average network traffic load G (packets/Lpkt): Gx = Rx ∑ Rx ⋅ G ̸ NCCH ð2Þ x = low, mid, high For transmission failure, the packets are retransmitted following IEEE 802.15.4 MAC specification 6.3 Evaluation Metric We evaluate the packet dropping probability Pdrop and transmission delay Dtx as transmission performance, where Pdrop and Dtx are given by a ratio of the number of dropping packets due to transmission failure or buffer overflow at CCHs to that of generated packets, and a time duration elapsed from packet generation at CCHs to successful reception at the PCH, respectively We also evaluate standard deviations (SDs) σ drop and σ delay for the packet dropping probability Pdrops and the transmission delay Dtxs observed at all the CCHs as an indicator showing uniformity in the transmission performance among CCHs with different cluster traffic In the performance evaluation, we compare the above each performance for the proposed scheme with that for the system employing fixed BEmin setting of 5, which is called ‘standard system’ in this evaluation Evaluation Results Using the parameter settings listed in Table 1, we evaluated the system performance for the proposed scheme, comparing with the standard system Note that MAC parameter settings except shown in Table employ their default values specified in the IEEE standard 314 A Yamauchi et al Table Simulation Parameter Settings Transmission rate aUnitBackoffPeriod Duration of aUnitBackoffPeriod Beacon order Superframe order Beacon interval CAP length Beacon frame length Data packet length ACK frame length macMaxCSMABackoffs Initial BE Range of BEmin Threshold for BEmin adjustment Tx buffer length 7.1 Symbol Value Rrate UBP TUBP BO SO BI LCAP Lbcon Lpkt Lack 250 0.32 48 12288 384 5, 2, BEmin, BEmax upperr lower BEmin , BEmin THqueue Lbuffer 10 (kbps) (ms) (UBPs) (UBPs) (UBPs) (UBPs) (UBPs) (UBPs) (packets) Non-uniform Performance Between CCHs with Different Traffic Loads This section clarifies the non-uniformity in transmission performance between CCHs with different cluster traffic loads for inter-cluster communication under non-uniform traffic condition with the number of CCHs Ncch = (thus, every CCH group has CCHs) and the traffic ratio of (Rlow : Rmid : Rhigh) = (1.0 : 1.5 : 2.0) Figure shows the packet dropping probability Pdrop for each CCH group with low, middle, or high cluster traffic load Each CCH group shows quite different Pdrop performance in the network traffic G of over around 0.01, and the Pdrop for high traffic CCHs clearly degrades compared with that for lower traffic ones This result clarifies that the transmission performance has clear non-uniformity between CCHs with different traffic loads for such spatial non-uniform traffic Hence, this non-uniformity caused from different cluster traffic should be mitigated even under spatial non-uniform traffic condition in order to improve overall system performance 7.2 Optimal Parameter Settings for Nup, ΔBEup, and ΔBEdown Before evaluating the performance of the proposed scheme by comparing with the standard system, optimal parameter settings for Nup, ΔBEup, and ΔBEdown should be obtained to achieve better system performance for the proposed scheme Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … 315 Fig Performance non-uniformity between CCHs with different cluster traffic loads Therefore, their optimal values are derived through computer simulation Here, we employ, as an evaluation metric, an average packet dropping probability Pave drop over all CCHs in the network traffic region of 0.002 ≤ G ≤ 0.03, and its standard deviation σ ave drop for Ncch CCHs Firstly we determine an optimum value for the number of detection times Nup ave required for estimating low traffic status Figure shows the Pave drop and σ drop performances for various Nup under the same conditions as Fig The packet dropping probability Pave drop in Fig is slightly better for smaller Nup However, the standard deviation σ ave drop degrades at Nup of and is better for Nup ≥ In addition it is almost the same for Nup ≥ 2, and thus, Nup of is concluded to be optimum value from these observations ave Next, Fig shows the Pave drop and σ drop performances for varying the increment step ΔBEup, to discuss its optimal value The σ ave drop performance degrades with increasing ΔBEup, while the Pave is almost equal for all ΔBEups Therefore, these drop results show the ΔBEup should be set to as its optimum value Then, for optimization of the decrement step ΔBEdown, Fig shows the Pave drop and σ ave performances for various ΔBE setting Roughly speaking, both the down drop ave ave Pdrop and σ drop indicate almost the same performance regardless of ΔBEdown value, Fig Packet dropping probability for varying Nup 316 A Yamauchi et al Fig Packet dropping probability for varying ΔBEup Fig Packet dropping probability for varying ΔBEdown however, the larger ΔBEdown gives slight better σ ave drop performances From these results, therefore, we conclude ΔBEdown of as its optimal setting Setting the optimum values derived above to Nup, ΔBEup, and ΔBEdown, Fig shows the BEmin behaviour for varying network traffic load G, under the same conditions as Fig From Fig 9, each CCH can adjust the BEmin to a smaller value with higher traffic load and a larger value with lower traffic load in middle and high network traffic load regions This BEmin behaviour proves the proposed scheme works well according to the traffic load estimated at each cluster 7.3 Transmission Performance Comparison The transmission performances Pdrop and Dtx are shown in Figs 10 and 11 for the proposed scheme (referred as ‘Traffic adaptive BE’ in Figs.) and the standard system under the same conditions as Fig The performance for each traffic group, Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … 317 Fig BEmin behaviour for each CCH group (low, mid, and high) Fig 10 Packet dropping probability Pdrop performance Fig 11 Transmission delay Dtx performance low, middle or high traffic, is separately shown when varying the network traffic load G in these figures In Fig 10, the packet dropping probability Pdrop is nearly equal between different traffic groups of CCHs for the proposed scheme, despite the standard system with different Pdrop performance, and thus, the proposed scheme can suppress 318 A Yamauchi et al Fig 12 Standard deviation of packet dropping probability Pdrop non-uniformity in Pdrop across CH groups with different cluster traffic For the transmission delay Dtx, almost the same behaviour can be found in Fig 11 as the Pdrop Hence, the non-uniformity in the transmission performances can be reduced for all network traffic load regions, applying the proposed scheme To provides quantitative analysis of the uniformity in transmission performance between CCH groups with different cluster traffic, Figs 12 and 13 show the standard deviations σ drop and σ delay of Pdrops and Dtxs observed at all the Ncch CCHs In Comparison with the standard system, both the standard deviations σ drop and σ delay are largely reduced for high network traffic load regions by applying the proposedscheme, and we can find in Figs 12 and 13 significant reduction against the transmission performance non-uniformity, provided by the proposed scheme This performance improvement is given by proper BEmin control based on current cluster traffic offered to each CCH, which is a new mechanism provided by the proposed scheme and is a main contribution of this work From the above results and their considerations, applying the proposed scheme can provide quite effective mitigation against the non-uniformity in the transmission performance between different traffic loaded CCHs on the inter-cluster communication in cluster-based WSNs under spatial non-uniform traffic 7.4 Performance for Various Network Conditions In the previous section the effectiveness of the proposed scheme is basically presented for the typical network condition Since the network has various operating condition, this section examines the performance for the proposed scheme under for various network operating conditions First, we examine the performance under various spatial non-uniform traffic conditions, and then the packet dropping probability Pdrop is evaluated for the conditions with spatial non-uniform traffic of (Rlow : Rmid : Rhigh) = (1.0: 1.0: 1.0), (1.0: 1.25: 1.5), (1.0: 1.5: 2.0), (1.0: 2.0: 3.0), (1.0: 2.5: 4.0), and (1.0: 3.0: 4.0) The Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … 319 Fig 13 Standard deviation of transmission delay Dtx standard deviation σtraffic in offered cluster traffic loads under these traffic conditions is 0.0, 0.2, 0.41, 0.82, 1.2, and 1.6, respectively, which indicates degree in non-uniformity of the offered cluster traffic Figure 14 shows the standard deviation ave σ ave drop of the Pdrop s at the CCHs, which is the same evaluation metric as shown in Fig 6, for varying the standard deviation σ traffic of offered traffic We can see from Fig 14 that the proposed scheme provides superior σ ave drop performance to the standard system for any σ traffic (spatial non-uniform traffic conditions) Moreover, the performance improvement obtained by the proposed scheme increases with the σ traffic This result concludes that the proposed scheme can provide the effectiveness under various spatial non-uniform traffic conditions, and the effectiveness gets larger for the larger spatial non-uniform traffic conditions Next, we examine the transmission performance for various network scale, which includes the number of CCHs Ncch competing the CAP in the inter-cluster communication The proposed scheme adjusts the BEmin according to the traffic load offered to each CCH, and higher traffic CCHs tend to use smaller BEmin to achieve higher priority in the channel access However, the smaller BEmin likely increases the possibility of channel access collision between competing CCHs, and thus, the impact of smaller BEmin setting to the performance should be clarified for Fig 14 Packet dropping probability for various spatial non-uniform traffic conditions 320 A Yamauchi et al Fig 15 Standard deviation of packet dropping probability for varying the number of competing CCHs Fig 16 Packet dropping probability performance for varying the number of competing CCHs the proposed scheme Figures 15 and 16 show the packet dropping probability performance for various number of competing CCHs From the standard deviation σ ave drop shown in Fig 15, the proposed scheme show better performance and can achieve a reduction in the performance non-uniformity compared with the standard system for any values of the Ncch Therefore, an effectiveness of reducing the performance non-uniformity can be obtained even under the conditions with larger Ncch However, the improvement is getting smaller with increasing Ncch, and thus, the effectiveness gradually disappears with increasing Ncch Additionally, the packet dropping probability Pave drop for the proposed scheme is slightly getting worse with increasing Ncch, compared with the standard system, and therefore, the network scale, such as the Ncch, gives no small impact to the transmission performance for the proposed scheme Hence, the proposed scheme has to pay some cost to achieve uniformity in the transmission performance and requires countermeasure against this drawback, which is left as our future work Traffic Adaptive Channel Access Scheme for IEEE802.15.4 … 321 Conclusion This chapter has discussed non-uniformity in transmission performance between CCHs with various cluster traffic loads on inter-cluster communication in cluster-based WSNs employing IEEE 802.15.4 MAC This chapter has firstly quantitatively-clarified appearance of the performance non-uniformity among different traffic loaded CCHs under spatial non-uniform traffic condition, and then has proposed the countermeasure technique, the autonomous traffic adaptive channel access, which controls the backoff exponent BEmin in backoff operation based on the current traffic status estimated at each CCH, in order to mitigate such performance non-uniformity The transmission performances, packet dropping probability, transmission delay and their standard deviations as an indicator showing performance uniformity, have been evaluated for the proposed scheme through computer simulation under various network conditions These simulation results prove that the proposed scheme can significantly reduce the performance non-uniformity between different traffic CCHs, and thus, can achieve fairer transmission performance under spatial non-uniform traffic conditions Consequently, the proposed autonomous traffic adaptive channel access scheme is quite effective for cluster-based WSNs under spatial non-uniform traffic conditions Acknowledgments This work is partly supported by Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (c) 25420366, 2013–2015 References I.F Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci, Wireless sensor network: a survey Comput Netw 38(4), 393–422 (2002) doi:10.1016/S1389-1286(01)00302-4 R Verdone, D Dardari, G Mazzini, A Conti, Wireless sensor and actuator networks: technologies analysis and design Academic Press 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Matthew P Chang, 273 Matias, Ignacio R., 55 Monica Lu, 273 Mukhopadhyay, Subhas, 109 Mukhopadhyay, Subhas Chandra, 55 C Carmona, Estefania Núñez, 229 Corres, J.M., 55 E Eichberger, B., H Haji-Sheikh, Michael, 157 Harun, Ahmad Nizar, 137 Hashim, Dzulkifly Mat, 245 Hideo Kobayashi, 303 I Ikezawa, Satoshi, 21 Ismail, Alyani, 245 J Jiaming Li, 39 Jingyi (Jenny) Sun, 273 Joshi, Keyur H., 205 Jun Yamamoto, 21 L Latiff, Nurul Adilah Abdul, 245 N Nizar, Nina Naquiah Ahmad, 245 Núñez Carmona, Estefanía, 179 P Prucnal, Paul R., 273 Pulvirenti, Andrea, 229 R Rokhani, Fakhrul Zaman, 245 S Sairin, Masyitah Amat, 245 Sberveglieri, Veronica, 179, 229 Shuk, P., 81 Soprani, Matteo, 179 T Taherinezahdi, Mansour, 157 Toshitsugu Ueda, 21 © Springer International Publishing AG 2017 S Mukhopadhyay et al (eds.), Sensors for Everyday Life, Smart Sensors, Measurement and Instrumentation 23, DOI 10.1007/978-3-319-47322-2 323 324 W Wall, Josh, 39 West, Sam, 39 X Xie Li, 109 Author Index Y Ying Guo, 39 Z Zinger, Donald, 157 ... Settings (ISBN 978-3-319-47319-2) The book Sensors for Everyday Life Environmental and Food Engineering describes novel sensors and sensing systems developed for environment monitoring and food production... embedded controller, technology for communication as well as the progress towards a better informed, knowledge based society increase the demand for small size, affordable sensors that allow accurate... Austria © Springer International Publishing AG 2017 S Mukhopadhyay et al (eds.), Sensors for Everyday Life, Smart Sensors, Measurement and Instrumentation 23, DOI 10.1007/978-3-319-47322-2_1 H