=Paper=
{{Paper
|id=Vol-1730/p09
|storemode=property
|title=WiFi Field Monitoring for E-Pollution Detection
|pdfUrl=https://ceur-ws.org/Vol-1730/p09.pdf
|volume=Vol-1730
|authors=Tatjana Sidekerskiene,Robertas Damaševičius
|dblpUrl=https://dblp.org/rec/conf/system/SidekerskieneD16
}}
==WiFi Field Monitoring for E-Pollution Detection==
https://ceur-ws.org/Vol-1730/p09.pdf
WiFi Field Monitoring for E-Pollution Detection
Tatjana Sidekerskienė Robertas Damaševičius
Department of Applied Mathematics Department of Software Engineering
Kaunas University of Technology Kaunas University of Technology
Kaunas, Lithuania Kaunas, Lithuania
tatjana.sidekerskiene@ktu.lt robertas.damasevicius@ktu.lt
Abstract— The paper presents an outline of the development systems, can penetrate thick concrete block walls. People
of WiFi field monitoring maps using the Internet-of-Things (IoT) working in offices or students studying in schools are exposed
technology. The negative impacts of signals generated by the to 1600 hours of WiFi radiation during an academic year. This
WiFi access points on health and measurement metrics are value is larger than the 1640 hours of cell phone use in the
discussed. The experimental system for collecting WiFi signal INTERPHONE study associated with a 40% increase in brain
data is presented. Finally, the construction of WiFi signal tumors (glioma) [3]. In 2011, the radio frequencies of EMF
strength heatmap is discussed and some preliminary results using were qualified by IARC and WHO as possibly increasing the
a combination of real worlds and simulated data are presented.
risk of malignant brain tumor [4]. Rats exposed to pulsed
digital WiFi frequencies (2.4 GHz) for a long-term (25
Keywords—WiFi; field monitoring, e-health, m-health, s-
health, e-pollution.
months), had a higher rate of both primary and metastatic
cancers [5] though other studies did not confirm these findings
[6, 7].
I. INTRODUCTION1
WiFi has been linked to electromagnetic hypersensitivity
Recently there has been a significant increase of the
or ‘idiopathic environmental intolerance to electromagnetic
availability of wireless broadband internet access in public
fields’ (IEI-EMF). People suffering from IEI-EMF usually
spaces. Providers and points of access take the form of
have a diverse range of nonspecific physical symptoms (e.g.,
municipal WiFi networks, community wireless networks,
burning skin, headache, dizziness) that they attribute to their
advanced mobile phone networks (e.g. 4G), and WiFi cafes,
exposure to the EMF emitted by, e.g., mobile phones, mobile
restaurants, bookstores and related spaces. The ubiquitous
phone base stations, power lines and WiFi [8]. There is some
availability of wireless Internet access encourage greater
evidence of potential adverse effects including headaches,
participation in public spaces such as cafes [1] as free WiFi
increased blood pressure, and disturbances to
hotspots attract people. The problem is also important in the
electroencephalographic (EEG) activity during sleep [9].
domain of Ambient Assisted Living (AAL) and other similar
Several papers have been discussing the effects of
domains such as Smart Homes to avoid negative impact of
radiofrequency radiation (RFR) [10, 11, 12]. In 2011, the
massive use of wireless transceivers for Body Area Networks
WHO's International Agency for Research on Cancer (IARC)
(BAN), Personal Area Networks (PAN), etc. in terms of daily
reclassified RF-EMR as potentially carcinogenic to humans
electromagnetic (EM) exposure to radiofrequency
[13]. Also the EM radiation has been called as the fourth
electromagnetic radiation (RF-EMR), ranging between 0 Hz
pollution source besides air, water and noise [14].
and 300 GHz in frequency, as well as interference emission
compliance. In this context the Wi-Fi devices generally work However, long-time effects of these electromagnetic fields
in close proximity to persons, which can lead to higher risks on human and animal health are still unknown. Several studies
related to electromagnetic field (EMF) exposure [2]. conducted on the effects of RFR on human health have
provided contradictory and inconsistent findings regarding the
Electromagnetic fields (EMF) of all frequencies is one of
actual health risks associated with RFR [15-22].
the most fastest growing environmental pollutant. All people
are now exposed to varying degrees of EMF, and the levels Summarizing, when considering the health-related risks of
are expected continue to increase in future. Wireless access the use of WiFi technology in public spaces there is the need
points (APs) and wireless laptops are also often close to to perform modeling of the locations of WiFi access points in
humans. WiFi enabled tablets such as iPads or SmartPhones public buildings as well as in private houses to evaluate and
are handheld and thus provide more radiation directly into minimize the exposure of people to EM radiation while
human body. The exposure in public spaces and buildings can ensuring the quality and signal strength of WiFi connections.
be even worse than in homes as hundreds of people are Proposing the computational intelligence methods that allow
simultaneously connecting to the internet. to minimize the effects of e-pollution is a growing research
stream [23-29].
EMF radiation form industrial grade WiFi systems, which
are more than 10 times more powerful as domestic WiFi This paper presents an initial research towards developing
such system using Wireless Sensor Network (WSN) and the
1
Copyright © 2016 held by the authors. Internet-of-Things (IoT) technology.
51
II. RELATED WORKS development of wireless technology, including mobile phones,
A number of systems were developed to support the Wi-Fi, and various kinds of inter-connected devices making
measurement of WiFi fields both in outside environment as up the Internet-of-Things. Biologic material readily interferes
well as in buildings. For example, Bell and Jung [30] used with HF-EMF in a way that depends upon its shape, the
Netstumbler 0.4.0 for detecting available WLAN service and conductivity and density of the tissue, and the frequency and
collecting WiFi signal strength data. Netstumbler observes all amplitude of the EMF leading to an elevation of the tissue
APs within the wireless card’s visible range. Spatial and signal temperature and thermal-associated metabolic responses [35].
strength data were integrated after data was collected. Chan et When RF exposures are taken into account, the main
al. [31] detect the IEEE 802.11b Wi-Fi signal strength and mechanism to be considered is the ability of RF fields to
collect into a database. They also create a fuzzy color map to increase an average temperature through the vibration of
visualize the distribution of Wi-Fi signal. StumbVerter [32] is atoms and molecules in the biological tissue. The heat effect
a wireless visualization tool that relies on Microsoft’s depends on water content of the biological target material, as
MapPoint mapping library. It plots wireless transmitters on a well as on the frequency and intensity of the electromagnetic
street map using color to indicate signal strength. However, it (EM) radiation. The characteristic quantity is the Specific
lacks signal range mapping and it does not provide imagery Absorption Rate (SAR) [36]. SAR can be calculated as
data. Rensburg [33] use of GPS (Global Positioning System) follows [2]:
device, PDA and a tool to measure wireless signal
characteristics. Rose [34] describe Argos, the urban-scale σ 2
SAR = E
WSN designed explicitly to support measurement of ambient ρ
WiFi traffic across an entire city. Argos allows urban-scale
monitoring of wireless networks. To achieve high spatial here σ = 10.18 m/s is skin conductivity, ρ =1043 kg/m3
coverage, this requires multiple sensor nodes deployed is skin density, and E is the electric field strength.
throughout a city that can capture ambient wireless network
traffic. Exposure to RF radiation (mainly from mobile phones) has
been postulated to trigger a variety of neurological effects,
Several large-scale WiFI databases exist, which could be including headaches, changes in sleep pattern, modification in
used for researching the harms of exposure to WiFi fields: the neuronal electrical activity, and disturbance in the
neurotransmitter release [37]. Increasing evidence indicates
• Wigle (http://wigle.net/): a website for collecting
that oxidative stress may be involved in the adverse effects in
information about the different wireless hotspots
the nervous system. Ilhan et al. [38] reported a marked
around the world;
oxidative damage in brain tissues of rats exposed to 900 MHz
• IGiGLE: Irongeek's WiGLE: WiFi Database to signal for GSM (Global System for Mobile communications)
Google Earth Client for Wardrive Mapping (SAR of 2 Wkg−1 in the brain) for 7 days.
(http://www.irongeek.com/); The SAR values are not directly measurable and depend on
• Skyhook (http://www.skyhookwireless.com): a the frequency. Therefore, so-called reference levels have been
database containing unique IDs of more than 16 defined that are comparatively easy to measure. For the
million wireless routers and their locations. frequency range 0.8–2.8 GHz, the reference levels are
approximately 33–62 Vm−1 (general public) and 49–92 Vm−1
(occupational) [39]. Mobile phones are legally limited to a
III. CHARACTERISTICS OF WIFI SIGNALS
specific absorption rate (SAR) of 2.0 W/kg [40], while most
Usually Wi-Fi systems are based on the IEEE Standards have a SAR of ~1.4 W/kg [41].
802.11b and 802.11g and operate in the 2.4 GHz frequency
band. According to the various local legislations and IV. DEVELOPMENT OF WIFI FIELD MONITORING SYSTEM
regulations, Wi-Fi devices which are designed for private
(domestic) use should emit low power (less than 20 dBm or The system is implemented the following technologies and
100 mW) and should work in a frequency band also used by methods:
other communication devices (such as cordless phones). Wi-Fi 1) Internet-of-Things: smart things and devices (e.g.
devices based on the IEEE Standard 802.11a operate in the smartphones) that have necessary means to measure WIFI
frequency band of 5.8 GHz and are suitable to be used in field intensity.
public environment. The IEEE Standard 802.11n works in
both frequency bands of 2.4 and 5.8 GHz. Tthe most 2) Web services. Availability of free web services to
commonly used technologies are 802.11b and 802.11g (2.4 share data.
GHz, maximum output power 100 mW) and the 802.11a (5.8 3) Crowdsourcing. A community based effort using
GHz, maximum output power 1 W). contribution of multiple users which
High frequency (i.e., frequencies from 300 MHz to 3 GHz) We use a standard three-tiered architecture consisting of:
electromagnetic fields are mainly human-produced,
nonionizing electromagnetic radiations that do not naturally 1) Data gathering layer: a potentially large number of
occur in the environment, excluding the cosmic radiation. HF- devices that gather information about Wi-Fi field strength and
EMF are present in the environment because of the active sense it with geodata to the data feeds.
52
2) Data feed layer that publishes gathered data online here: d - distance, f - frequency, K - constant that depends
for further use by anyone including any applications beyond on the units used for d and f. If d is measured in kilometers, f
Wi-Fi mapping. in MHz, then K=32.44.
3) Data aggregator layer that aggregates and integrates From Eq. (1), we can find out the distance as follows:
all data from data feeds and represents it as a map.
We used Litepoint IQView equipment to measure WiFi d ( km ) = 10 ( FSPL – 32.44 – 20log10 ( f ) ) / 20 (2)
signal strength. A LitePoint IQview 802.11a/b/g WLAN tester
was used to sample the ISM band at 66 M/s, centered around
2.412 GHz (WLAN channel 1). The Litepoint IQView device The Fresnel Zone is the area around the visual line-of-sight
digitizes the received signal and records the data onto the that radio waves spread out into after they leave the antenna.
laptop using UDP transfer connection. The results were You want a clear line of sight to maintain strength, especially
processed using Matlab 8.1 (R2013a) to generate heat map of for 2.4GHz wireless systems. This is because 2.4GHz waves
signal strength. are absorbed by water, like the water found in trees. The rule
of thumb is that 60% of Fresnel Zone must be clear of
A computer is connected with the measurement device via obstacles. Typically, 20% Fresnel Zone blockage introduces
UTP cables. Communication with these devices is performed little signal loss to the link. Beyond 40% blockage the signal
using the TCP / IP protocol. RF connectors are connected to loss will become significant.
the measuring device using special RF cables. The computer is
running agent software for communications with the
measuring device and the RF transmitter adjusting system. FSPLr = 17.32 d / 4 f (3)
The system deployment diagram is shown in Fig. 1.
here: d - distance [km], f - frequency [GHz], r - radius [m].
Following the model proposed by Ocana et al. [Ocana], the
WiFi map can be calculated using a radio propagation model.
This model is difficult to obtain for indoor environments, due
to multipath effects and temporal variability of the WiFi
signal.
RSL = TSL + GTX + GRX +
(4)
20log ( 4λ ) − 10nWlog ( d ) − X a
here RSL is the received signal level, TSL is the transmitted
signal level, GTX and GRX are the transmitter and receiver
Fig. 1. Package diagram of an experimental system antennas gain respectively, λ is the wavelength (12.5cm for the
2.4GHz of the WiFi signal), nW is a factor that depends on the
walls effect, Xa is a random variable and d is the distance
V. MEASURED VALUES between the emitter and the receiver [42].
Using the developed system prototype we measure: 1) The
number of access points visible from a device. 2) The signal VI. CREATION OF WIFI MAPS
strength of the strongest field. 3) Aggregate signal strength.
Creating a WiFi signal coverage map for a given
Attenuation can be defined as the decrease of the transmitter using this approach involves: (1) fitting a
amplitude of a signal between its transmission and reception semivariogram function, which describes the amount of
points. As the radio waves propagate through the air it loses expected variation as a function of distance between
power over a distance. Therefore signal strength is less. The measurements and (2) predicting the value at each unmeasured
loss a signal will undergo between the transmitter and receiver location (pixel).
is referred to as Free Space Path Loss (FSPL). FSPL can be
understood as power lost as energy disperses into the air. Wi-Fi mapping is based on the signal scanning in different
FSPL depends on two parameters: the frequency of radio places at different times. Ideally, measuring the signal strength
signals and the wireless transmission distance. The following of all possible points at the same time allows to obtain the
formula can reflect the relationship between them: perfect Wi-Fi access point (Access Point, AP) map. However,
in practice signal values are measured only in a number of
selected location points, while in other points the signal values
FSPL ( dB ) = 20log10 ( d ) + 20log10 ( f ) + K (1) are interpolated
α +toβ create
= χ. large (1)areas (1)
of maps. Such WiFi
mapping has many uses such as for open access points search;
signal versus time comparison; finding the signal problem
areas; and optimizing the coverage area.
53
Time signal detection and interpretation must be carried assess dynamical changes of AP coordinates at some point in
out within the time for all supported frequency band. In order time.
to evaluate the signal quality is assessed the following
• Radio map (radio-map) constructs a map by measuring
parameters Signal to Noise Ratio (SNR), and Signal to
Interference Ratio (SIR). Since packet data networks are the signal strength to a number of points. This technique is
used for internal mapping and usually requires 2 stages. The
prevalent in wireless networks therefore one needs to assess
and receive data transmission at higher layers. Since the first stage is to collect AP signal strength at predetermined
points of location and save them to the database. In the second
higher layers are analyzed to transmit data, so they need to be
checked for each channel or frequency after they arrive. stage, the signals are compared and the most likely signal at
each site used as a good signal for display.
Wireless networks have different frequencies each with the
further frequency width, which may be 5, 10, 20 or 40 MHz After collecting the data described above is possible the
wide. In assessing signal quality is necessary to take into data shown on the map in different ways:
account these parameters. Wi-Fi mapping is necessary to
evaluate the signal in different places, and to do so at different 1. Survey Map - signal analysis map to display data
frequencies. collection points and signal strength in these points.
The WiFi signal detection procedure is shown in Fig. 2. 2. AP Signal heatmap – shows signal strength variation in
The data collected can include SSID: Service Set Identifier; space except only at one selected access point, and all other
MAC address: AP identifier; Signal strength: the access point access points are ignored. Interpolation is used to obtain full
signal strength; Quality: the strength of the surrounding access map coverage.
points; set of parameters describing the connection quality; 3. Signal heatmap - displays the total number of access
Longitude and Latitude of AP coordinates. points and variation of the signal strength in space.
4. AP Coverage – the map divided into zones, featuring
dominating point. Also signal strengths are measured,
assessing all the signals with a power greater than 70dBm.
5. Frequency, data speeds and other parameters of signal
strength maps representative of two or more of the selected
attributes dominance zones and overlapping areas in assessing
the strength of the signals which exceed the predetermined
values.
VII. RESULTS IN WIFI MONITORING
Due to the small number of transmitting devices in the
area, it is not possible to apply simple propagation models,
such as free space, to relate the received power to distance.
For this reason, we need to consider more complex
propagation models accounting for the geometry of the
environment. Here we consider the multiwall path loss model
[43] which accounts for propagation at 2.4 GHz. It is based on
generalization of the classical one slope loss model including
an additional attenuation term due to losses introduced by the
walls and floors encountered by the direct path between the
transmitter and the receiver. The signal power is defined as:
I Nd N fd
M w = lc + ∑ kwi li + ∑ χ n ld + ∑ λn l fd , (5)
Fig. 2. Algorithm of WiFi signal capture
i =1 n =1 n =1
After collecting this data, WiFi maps can be formed in where lc is a constant, kwi is the number of penetrated walls
many ways, such as maximum bandwidth, minimum delay, of type i, li is the attenuation due to the wall of type i, i = 1, 2,
the best coverage, etc. Wi-Fi detection techniques can be . . . , I, Nd and Nfd are the numbers of normal and thick doors
divided into two groups: encountered by the direct path, and χn(λn) are binary variables
accounting for the state (opened or closed) of the n-th door.
• Model-based (model-based) uses the detected signals AP
locations and radio frequency measurement model as The data was obtained by the authors within the office
triangulation help from all the points determined by the access building of Kaunas University of Technology (KTU). The
point location. This technique has the great advantage of the experimental results of modeling the SAR values of WiFi
external mapping. Mapping is a long process, and using this signals are presented in Figs. 3, 4 & 5, respectively.
technique, a small amount is sufficient to find enough points
with precise AP coordinates. However, the model is unable to
54
most by the features of the building thus provided safer
locations for office workers, e.g., for placing permanent work
places such as office desks (see Fig. 5, see a lighter shaded
area at the top right corner of the building).
VIII. CONCLUSIONS
WiFi needs to be used intelligently due to health concerns.
This involves limiting the spatial range of exposure,
establishing WiFi-free areas, providing wired access to those
who choose not to use wireless, and limiting the duration of
exposure in public spaces. The developed prototype allows
measuring the WiFi field strength and constructing WiFi
signal maps in public spaces. Using such maps one can plan
the layout of work desks in offices, or tables in cafes to
minimize prolonged exposure to high frequency EM radiation.
Future work will involve expanding the prototype system
Fig. 3. Example of WiFi signal strength map (with walls) with the GSM module to allow sending SMS to people’s
phones to anyone registered, who want to avoid the WiFi
hotspots with high levels of EM radiation.
ACKNOWLEDGEMENT
The authors would like to acknowledge the contribution of
the COST Action IC1303 – Architectures, Algorithms and
Platforms for Enhanced Living Environments (AAPELE).
REFERENCES
[1] K.N. Hampton, and N. Gupta, “Community and Social Interaction in the
WirelessCity: Wi-Fi use in Public and Semi-Public Spaces”, New Media
& Society, 2008, vol. 10 no. 6, pp. 831-850.
[2] S. de Miguel-Bilbao, and E. Aguirre, P.L. Iturri, L. Azpilicueta, J.
Roldán, F. Falcone, and V. Ramos, “Evaluation of Electromagnetic
Interference and Exposure Assessment from s-Health Solutions Based
on Wi-Fi Devices,” BioMed Research International, vol. 2015, Article
ID 784362, 9 pages, 2015. doi:10.1155/2015/784362
Fig. 4. Example of WiFi signal strength map (without considering walls) [3] K.R. Foster, "Radiofrequency exposure from wireless LANs utilizing
Wi-Fi technology", Health Physics 92 (3): 280–289, 2007.
doi:10.1097/01.HP.0000248117.74843.34.
[4] International Agency for Research on Cancer, “IARC classifies radio
frequency electromagnetic fields as possibly carcinogenic to humans,”
PressRelease No. 208, May 31, 2011.
[5] C.-K., Chou, A.W. Guy, L.L. Kunz, R.B. Johnson, J.J. Crowley, and
J.H. Krupp, “Long-term, low-level microwave irirradiation of rats,”
Bioelectromagnetic 13, 1992, pp. 469-596.
[6] H. Bartsch, C. Bartsch, E. Seebald, F. Deerberg, K. Dietz, L. Vollrath,
and D. Mecke, “Chronic exposure to a GSM-like signal (mobile phone)
does not stimulate the development of DMBA-induced mammary
tumors in rats: results of three consecutive studies,” Radiat. Res., 157 (2)
(2002), pp. 183–190.
[7] M.H. Repacholi, “Low level exposure to radiofrequency electromagnetic
fields: health effects and research needs,” Bioelectromagnetics, 19 (1)
(1998), pp. 1–19
[8] M. Witthöft, and G.J. Rubin, “Are media warnings about the adverse
health effects of modern life self-fulfilling? An experimental study on
idiopathic environmental intolerance attributed to electromagnetic fields
(IEI-EMF),” J Psychosom Res., 2013, 74(3), pp. 206-12. doi:
Fig. 5. Example of WiFi signal attenuation map 10.1016/j.jpsychores.2012.12.002.
[9] J.A. Adams, T.S. Galloway, D. Mondal, S.C. Esteves, and F. Mathews,
Finally, we show a difference map between the signal “Effect of mobile telephones on sperm quality: a systematic review and
attenuated by walls and other features of the building (Fig. 3) meta-analysis,” Environ Int., 2014, 70, pp. 106-12. doi:
and the modelled signal strength map if the signal would not 10.1016/j.envint.2014.04.015.
be attenuated (Fig. 4). These maps can allow us to reveal the [10] A. Pyrpasopoulolou, V. Kotoula, A. Cheva, P. Hytiroglou, E.
Nikolakaki, N.M. Ioannis, T.D. Xenos, T.D. Tsiboukis, and G.
locations in the building where the WiFi signals are shielded
55
Karkavelas, “Bone morphogenetic protein expression in newborn rat [26] T. Rolich, and D. Grundler, “Genetic Algorithm Aided Antenna
kidneys after prenatal exposure to radiofrequency radiation,” Placement in 3D and Parameter Determination Considering
Bioelectromagnetics, 25 (2004), pp. 216–227 Electromagnetic Field Pollution Constraints,” Journal of Computing and
[11] Z. Forgacs, Z. Somosy, G. Kubinyi, J. Bakos, A. Hudak, A. Surjan, and Information Technology, 20(1), pp. 41-49, 2012.
G. Thuroczy, “Effect of whole-body 1800 MHz GSM-like microwave [27] A. Esposito, L. Tarricone, and M. Zappatore, “A Linked Data Approach
exposure on testicular steroidogenesis and histology in mice,” Reprod. to Electromagnetic Pollution Monitoring,” Proc. of 2013 IEEE 10th
Toxicol., 22 (2006), pp. 111–117 International Conference on Ubiquitous Intelligence and Computing and
[12] A. Tomruk, G. Guler, Sepici, and A.S. Dincel, “The Influence of 1800 2013 IEEE 10th International Conference on Autonomic and Trusted
MHz GSM-like signals on hepatic oxidative DNA and lipid damage in Computing, UIC/ATC 2013, pp. 321-328.
nonpregnant, pregnant, and newly born rabbits,” Cell Biochem. [28] S. Salcedo-Sanz, P. García-Díaz, J. Antonio Portilla-Figueras, J. Del Ser,
Biophys., 56 (1), 2010, pp. 39–47. and S. Gil-Lopez, A Coral Reefs Optimization algorithm for optimal
[13] G. Grigor'ev, “The probability of developing brain tumours among users mobile network deployment with electromagnetic pollution control
of cellular telephones (scientific information to the decision of the criterion,” Appl. Soft Comput. (ASC) 24, pp. 239-248, 2014.
International Agency for Research on Cancer (IARC) announced on [29] L. Ioriatti, M. Martinelli, F. Viani, M. Benedetti, and A. Massa,”Real-
May 31, 2011,” Radiat. Biol. Radioecol., 51 (5), 2011, pp. 633–638. time Distributed Monitoring of Electromagnetic Pollution in Urban
[14] S.A. Abdulrazzaq, and J.S. Aziz, “SAR simulation in human head Environments,” IEEE International Geoscience & Remote Sensing
exposed to rf signals and safety precautions,” Int. J. Comput. Sci. Eng. Symposium, IGARSS 2009, pp. 100-103.
Technol., 3 (9), 2013, pp. 334–340. [30] S. Bell, and W. Jung, “Mapping WLAN Coverage As A Potential
[15] D. Brusick, R. Albertini, D. McRee, D. Peterson, G. Williams, P. Complementary Source for GPS-based Navigation in Indoor
Hanawalt, and J. Preston, “Genotoxicity of radiofrequency radiation,” Environments,” Canadian Geomatic Conference 2010, Calgary, Alberta.
Environ. Mol. Mutagen, 32, 1998, pp. 1–16. [31] E.C.L. Chan, G. Baciu, and S.C. Mak, “Using fuzzy color maps to
[16] A.M. Sommer, K. Grote, T. Reinhardt, J. Streckert, V. Hansen, A. increase the positioning accuracy in poor Wi-Fi coverage regions,” IEEE
Lenchl, “Effects of radiofrequency electromagnetic fields (UMTS) on 7th International Conference on Wireless and Mobile Computing,
reproduction and development of mice, a multi-generation study,” Networking and Communications, WiMob 2011, pp. 165-171.
Radiat. Res., 171 (1), 2009, pp. 89–95. [32] StumbVerter, http://www.sonar-security.com/sv.html,
[17] W. Joseph, P. Frei, M. Röösli, G. Vermeeren, J. Bolte, G. Thuróczy, P. [33] J.J. van Rensburg, and B. Irwin, “Wireless Network Visualization Using
Gajšek, T. Trček, E. Mohler, P. Juhász, W. Finta, L. Martens. Between- Radio Propagation Modelling,” Proc. of Information Security South
country comparision whole-body SAR from personal exposure data in Africa Conference.
Urban Areas. Bioelectromagnetics, 33 (8) (2012), pp. 682–694 [34] I. Rose, and M. Welsh, “Mapping the urban wireless landscape with
[18] I. Ros-Llor, M. Sanchez-Siles, F. Camacho –Alonso, and P. Lopez- Argos,” Proc. of the 8th International Conference on Embedded
Jornet, “Effect of mobile phones on micronucleus frequency in human Networked Sensor Systems, SenSys 2010, pp. 323-336.
exfoliated oral mucosal cells,” Oral Diseases, 18 (8) (2012), pp. 786– [35] A. Vian, E. Davies, M. Gendraud, and P. Bonnet, “Plant Responses to
792. High Frequency Electromagnetic Fields,” BioMed Research
[19] J.W. Finnie,”Expression of the immediate early gene, c-fos, in mouse International, vol. 2016, Article ID 1830262, 13 pages, 2016.
brain after acute global system for mobile communication microwave [36] C. Consales, C. Merla, C. Marino, and B. Benassi, “Electromagnetic
exposure,” Pathology, 37 (3), 2005, pp. 231–233. Fields, Oxidative Stress, and Neurodegeneration,” International Journal
[20] J. Jing, Z. Yuhua, Y. Xiao-gian, J. Rongping, G. Dong-mei, and C. Xi, of Cell Biology, vol. 2012, Article ID 683897, 16 pages, 2012.
“The influence of microwave radiation from cellular phone on fetal rat [37] K. A. Hossmann and D. M. Hermann, “Effects of electromagnetic
brain,” Electromagn. Biol. Med., 31 (1), 2012, pp. 57–66. radiation of mobile phones on the central nervous system,”
[21] D.M. Hermann, and K.A. Hossmann. Neurological effects of microwave Bioelectromagnetics, vol. 24, no. 1, pp. 49–62, 2003.
exposure related to mobile communication, J. Neurol. Sci., 152 (1), [38] A. Ilhan, A. Gurel, F. Armutcu et al., “Ginkgo biloba prevents mobile
1997, pp. 1–14 phone-induced oxidative stress in rat brain,” Clinica Chimica Acta, vol.
[22] R.W. Habash, J.M. Elwood, D. Krewski, W.G. Lotz, J.P. McNamee, and 340, no. 1-2, pp. 153–162, 2004.
F.S. Prato, “Recent advances in researh on radiofrequency fields and [39] A. Lerchl, “Electromagnetic pollution: another risk factor for infertility,
health: 2004–2007,” J. Toxicol. Environ. Health B Crit. Rev., 12 (4), or a red herring?,” Asian Journal of Andrology, 15(2), 2013, pp. 201–
2009, pp. 250–288. 203. http://doi.org/10.1038/aja.2012.104
[23] L. Paolino, M. Sebillo, G. Tortora, and G. Vitiello, “Monitoring [40] ICNIRP, “Guidelines for limiting exposure to time-varying electric,
Electromagnetic Pollution: A GIS-Based Visual Approach,” Proc.of. magnetic, and electromagnetic fields (up to 300 GHz),” Health Phys, 74,
Multimedia Databases and Image Communication (MDIC 2001), 2001, 1998, pp. 494–522.
pp. 90-104.
[41] A. Agarwal, A. Singh, A. Hamada, and K. Kesari, ”Cell phones and
[24] L. Albini, P. Burrascano, and S. Fiori, “A feasibility study for male infertility: a review of recent innovations in technology and
electromagnetic pollution monitoring by electromagnetic-source consequences,” Int Braz J Urol, 37, 2011, pp. 432–454.
localization via neural independent component analysis”,
Neurocomputing (IJON), 55(3-4), pp. 451-468, 2003. [42] M. Ocaña, L.M. Bergasa, M.Á. Sotelo, R. Flores, E. López, and R.
Barea, “Comparison of WiFi map construction methods for WiFi
[25] D.A. El Seoud, S. Nouh, R.A. Abbass, N.A. Ali, R.M. Daoud, H.H. POMDP navigation systems,” Proc. of the 11th Int. conference on
Amer, and H.M. Elsayed, “Monitoring electromagnetic pollution using Computer aided systems theory (EUROCAST'07), 2007, pp. 1216-1222.
Wireless Sensor Networks,” Proc. of 15th IEEE International
[43] D. De Luca, F. Mazzenga, C. Monti, and M. Vari, “Performance
Conference on Emerging Technologies and Factory Automation, ETFA
2010, pp. 1-4. evaluation of indoor localization techniques based on RF power
measurements from active or passive devices,“ EURASIP J. Appl.
Signal Process., 2006, pp. 160-160.
56