Transmission Power Control based on temperature and relative humidity

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Symposium on Sustainable and Adaptive Sensor Networks Singapore, 21–24 April 2014

Transmission Power Control based on Temperature and Relative Humidity César Ortega-Corral2,1, Luis E. Palafox1, J. Antonio Garcia-Macias3, Jaime Sanchez Garcia4, Leocundo Aguilar1, Juan Ivan Nieto Hipolito5 1

Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja Californía, Calzada Tecnológico 14418, Mesa de Otay, Tijuana, B.C., C.P. 22390. México. 2

Tecnologías de la Información y Comunicación, Universidad Tecnológica de Tijuana Km. 10 Carretera Libre Tijuana-Tecate, Frac. El Refugio. Quintas Campestre. Tijuana, B.C., C.P. 22650. México. 3

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Depto de. Ciencias de la Computación. 4Depto. de Electrónica y Telecomunicaciones. Centro de Investigación Científica y de Educación Superior de Ensenada, Carr. Ensenada–Tijuana No. 3918, Zona Playitas, Ensenada, B.C. 22860. México

Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja Californía, Km. 103 Carretera Tijuana- Ensenada, Ensenada, B.C., C.P. 22860. México. {cesar.ortega,lepalafox,laguilar,jnieto}@uabc.edu.mx, [email protected],{jagm,jasan}@cicese.mx

Abstract— In this paper we present a novel Wireless Sensor Network (WSN) Transmission Power Control (TPC) scheme based on prevailing weather conditions: temperature and relative humidity (TRH). After the analysis of several days worth of experimental data containing received signal strength indicator (RSSI) values, a strong correspondence became evident between signal loss and wireless channel weather conditions. This led us to characterize a TRH normalized gradient that describes a relative amount of power loss that is incurred when using the wireless channel. After review, we deployed this gradient with certain modifications as a gain coefficient in a novel way of compensating wireless transmission power. Meaning that a TRH TPC scheme is proposed, for more dependable wireless communications, which determines power gain using RSSI feedback. Performance metrics of a prototype TRH TPC algorithm, with differing parametric values, are presented and compared to another node with no TPC, transmitting at the same time with a fixed power level. Finally, after weighing in the full range of results, we establish a set of conditions where this TRH compensator is effective in improving the RSSI and the overall packet received rate (PRR), while operating in harsh daylight and humid conditions. Keywords— Transmission power control, wireless sensor networks.

I. INTRODUCTION Multidisciplinary interest in marine habitat and environmental monitoring has grown substantially in the last couple of decades. Now, efforts are being done using wireless sensor networks (WSN), for this purpose. Although WSN research has dealt with a wide range of matters (medium access control, routing, application space, enegy harvesting, etc.) [1-6], there are few research articles that discuss TPC applied to WSN deployments. The control aspect tries to compensate signal fading by dynamically increasing or decreasing transmission power when necessary to ensure data

delivery in harsh environments [7]. This means that distance, terrain, climate and required reliability factors are taken into account to compensate against signal loss due to dispersion, absorption and multi-path fading (be it fast or slow fading) [8]. Likewise, harsh environments and places where terrain complexity in the radio path is high are challenging scenarios for RF automatic power compensation deployments. Common transmission power control techniques in WSN are stochastic in nature, some use a Packet Received Rate (PRR) feedback strategy, while others use Packet Error Rate (PER) as a performance metric to establish the amount of transmission power to compensate [9]. A disadvantage with an statistical approach is that the controller may take some time to converge, if the amount of transferred data is small, and sample rates are low. There are TPC deployments at a medium access control (MAC) level that use conventional control techniques, others are adaptive in nature using bit error rate (BER) feedback, with the trade-off of needing prior training to ensure convergence [10-11]. Other authors have dealt with thermal loss and TPC based on temperature, operating within a limited range (25oC to 45oC) and in no certain terms [12-13]. It is worth mentioning that although air temperature and relative humidity are known to add noise and dampen transmitted wireless signals, the amount of RF power loss related to weather conditions is not well documented [14-15], which means that in this regard there is much experimentation needed to fully understand weather effects on radio frequency propagation. II. TEMPERATURE AND RELATIVE HUMIDITY VS. RSSI VARIATION

Many field experiments were done measuring air and water conditions with stationary wireless sensor nodes, transmitting at a fixed power level, far apart one another [16-17]. These nodes were originally developed for long range marine

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issue becomes apparent when considering the case when both temperature and RH are at their maximum, the direct factor would reach the maximum limit that would also saturate the transmitter. To avoid this, we propose using a "dampened" or "compressed" gain factor, represented by (3), which states:

monitoring. And after observing day-night results, as the ones shown in Fig. 1, it is inferred that changes of temperature and air humidity produce differing amounts of loss reflected on the resulting RSSI magnitude at the destination. If we simply factor in air temperature times relative humidity, the resulting scaled curve fits the instantaneous RSSI wave envelope and the average RSSI wave shape as shown in Fig. 2. The amounts of the gradient changes is relative to upper and lower limits imposed on both variables. For RH the natural limits are obviously 0% to 100%. But air temperature is a different matter, it depends on the part of the world where the radios will operate. In this case, we have opted to limit temperatures to the ones at the coasts of Baja California, Mexico, where our marine monitoring systems will operate. Because of the previous reasons, for practical purposes, the operating temperature limits taken into account are the following: ͳ ௢‫  ܥ‬൑ ܶ ൑ ͷͲ ௢‫ܥ‬

οே ൌ ሾͳ െ ‡ିఈబ ‫ ׏‬ሿ

(3)

Where Į0 is a dampening coefficient, which can take different values that affect the resulting compensation factors magnitude ¨N. In Fig. 3, a set of resulting curves for different Į0 values are shown against the direct normalized TRH gradient.

(1)

Fig. 3. TRH power gain factor against the normalized gradient, DELTA represents ¨N.

For large normalized T x RH% values, using particular Į0 coefficients, leads to small ¨N increments, which is intended to reduce chances of transmitter power saturation. Therefore, if this gain coefficient is to be used as an effective means of Tx power compensation, a set of optimal Į0 values has to be determined. IV. PROPOSED TRH TPC The goal of our controller is to keep the PRR as high as possible by controlling the transmitted power according to RSSI levels. Here, the proposal is to use short term temperature and humidity conditions to dictate the amount of transmition power compensation needed. In Fig. 4, the TRH TPC system with RSSI feedback is shown.

Fig. 1. Instantaneous RSSI and its average in dBm, along with scaled versions of measured T and RH.

Fig. 2. The scaled TRH gradient fits the RSSI average curve.

With temperature and RH limits established, the proposed gradient normalization is expressed by (2). ܶሺ‫ݐ‬ሻܴ‫ܪ‬ሺ‫ݐ‬ሻ (2) ‫׏‬ሺ–ሻ ൌ Ǣ Ͳ ൏ ‫׏‬൑ ͳ ܶ௠௔௫ ܴ‫ܪ‬௠௔௫ Fig. 4. TRH transmission power control system with RSSI feedback.

Eq. (2) represents the T and RH normalized product, which means that within the for mentioned bounds, the normalized T and RH product can only take real values between 0.02 and 1.

Considering a desired RSSI threshold, rThr in Fig. 4, the RSSI output variable, r(z), is fed back to the controller, G(z), which uses temperature and relative humidity data (T, RH)avg to regulate the Tx power level, PL(z), invested when sending messages through a wireless link, C(z), according to RSSI error, eRSSI(z), which is the difference between a particular RSSI threshold and the RSSI feedback, as expressed by (4).

III. TRH COMPENSATION FACTOR AND ITS SELECTION As previously shown in Fig. 2, the shape of resulting RSSI average matches the scaled temperature times relative humidity gradient, and vice versa. Meanwhile, eq. (2) represents the complete TRH expression in a normalized fashion. A scaling

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and Ethernet controller onboard, through which the RCM4300 software communicates and establishes Internet gateway In this scheme, we use the TRH compensator to establish connections with a custom web application for final sensor information storage. Performance metrics measured at the BS, the power level increment, ¨PL, as indicated by (5). were feed back to the CLH during the communication process. οܲ௅ ሺ݇ ൅ ͳሻ ൌ  οே ݁ோௌௌூ ሺ݇ሻ ൌ  οே ൣ‫்ݎ‬௛௥ െ ‫݅ݏݏݎ‬௔௩௚ ሺ݇ሻ൧ (5) In this case, the CLH besides expecting MAC layer After solving (5), the power level update is done with (6), acknowledgements, it also expects BS messages that contain which adds the power level increment to the previous power the last time-stamped RSSI value to be averaged by the CLH system. level value. To work on the conceptual aspect of the proposed TRH (6) ܲ௅ ሺ݇ ൅ ͳሻ ൌ ܲ௅ ሺ݇ሻ ൅ οܲ௅ ሺ݇ ൅ ͳሻ compensator, an experimental setup was arranged using two of To ensure power limits, after every RSSI assessment, these cluster-head wireless sensor nodes, designated CLH-1 bounds are imposed on the controllers power level output, and CLH-2, both transmitting in an alternate manner. PL(k). Noting that if a BIBO (Bounded Input / Bounded Output) Particularly, CLH-1 was configured with a fixed transmission system is ensured, then a stability condition is met for the power level (useful as the experimental reference), and CLH-2 controllers response, in this case easily imposed by software. was programmed with the TRH compensation algorithm Technology selection for this development was thought out prototype, for testing its behavior with different Į0 coefficients. in two ways: (1) the need for a radio with a wide transmission Furthermore, both cluster-heads were programmed to do power range, with several discrete levels, and (2) a digital host automatic (T and RH) sensor input sampling and RSSI processor with analog data acquisition capabilities, which is feedback assessments every five seconds. Because these common place. In this case, we chose the Laird Technologies AC4490 transceivers are designed for long range AC4490-1000M long range 900MHz transceiver [18-19], its communications, -54dBm is their starting point for sensing recent firmware permits 81 discrete transmission power levels RSSI values. Later on, we will show that in practice these PL={0,1,..80} that configure the actual transmission power radios have to be 75 meters apart while transmitting with a PTx={5, 10, ..., 1000} mW. In México, where this work was 30mW power level, to be able to measure true RSSI values of done, 902-928MHz frequency bands are part of the open 54dBm or less. Here we did distance and loss estimates with frequency ranges permitted under the law. Because the known wireless link power equations. Starting with the ideal AC4490 is made for outdoor use, it has a high -100dBm received or captured power (C) in dB, using (7), which sensitivity, which can be configured to an even higher expresses the difference between the effective irradiated power sensitivity of -110dBm, for detecting very weak signals, if the (EIRP) present at the transmitters antenna, against the incurred noise floor permits it. The A4490 power level curve is non- path loss (LP) due to distance and operating frequency range, linear, which means that a selected power level varies the which can be determined according to the well known Friis amount of real Tx power in a uneven manner, which depends approximation. on what side of the power scale the transceiver is operating.  ൌ  ሺ†ሻ െ ୮ ሺ†ሻ (7) ݁ோௌௌூ ሺ݇ሻ ൌ  ‫்ݎ‬௛௥ െ ‫݅ݏݏݎ‬௔௩௚ ሺ݇ሻ

(4)

At short to medium distance, d={5m, 75m, 1Km, 1.3Km, 2Km}, in Table 1, results of different EIRP, LP, C and RSSI estimates in dBm are shown. For simplicity, EIRP was fixed at 15dBm (according to the real transmitted power, just over PTx=30mW), which includes the antennas gain and line-feed loss. Also, the noise floor (N) was measured with a portable radio-frequency (RF) spectrum analyzer to be -90 dBm.

V. EXPERIMENTAL PLATFORM, PERFORMANCE METRICS AND LINK BUDGET

In order to deploy and test this proposal, experimental wireless sensor nodes were assembled using the AC4490 long range radio on board, which has a 76.8Kbps transmission rate, using Frequency Shift Keying modulation under a Frequency Hopping Spread Spectrum (FSK-FHSS) collision avoidance approach [20]. The particular AC4490 series are transceivers that operate within the North American 900MHz open frequency bands. For rapid development, we chose the Arduino Mega open source processor system as the programmable host [21], we attached temperature and humidity sensors to it, along with another well known transceiver called the XBee Pro [22]. In our case, the XBee is used for short range wireless sensor cluster formations, for inexpensive WSN scaling purposes. In other words, the experimental platform is a so called Cluster-Head, CLH, with two radios on board: the AC4490 for long range base station (BS) communications, and the 2.4GHz XBee Pro for sensor grids organized in clusters. Emphasizing that our work with the TRH compensator centers only on controlling the AC4490 transmission power levels. On the destination side, we attached the receiving AC4490 sink node to the BS host RCM4300 processor module [23]. We selected this module because of its data logging capabilities

TABLE 1. RECEIVED SIGNAL POWER AT DIFFERENT DISTANCES, 915 MHZ. d Km

0.005 0.075 1 1.3 2

EIRP, dBm

LP dBm

C dBm

15

46 69 92 94 98

-31 -54 -77 -79 -83

Fm = Cmin-N dBm

RSSI ( C + Fm) dBm

-(100-90) = -10

-41 -64 -87 -89 -93

For the purpose of emulating distances, we attached a 25dB RF attenuator to the base stations antenna and its line feed. The result was as if we moved the BS 75 meters from the experimental CLH nodes. Furthermore, at each CLH, similar attenuators were fitted at their respective antenna. The result was as if we moved the CLH from the BS more than a kilometer away. This setup proved useful, with it the radios were transmitting as if they were far apart, emulating greater distance while communicating within the laboratory.

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VI. EXPERIMENTAL PLATFORM, PERFORMANCE METRICS AND LINK BUDGET

Initial experimentation was done to determine the proposed TRH compensators behavior with different alpha values, Į0={4, 3, 2, 1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.3, 0.2, 0.1}. These tests were done in the laboratory, where there was no sunlight interference, with a 50% to 60% RH and 25oC temperature average. For fairness, at both reference and control transceivers, we disabled their transmission retry feature, meaning that if at the first transmission the destination does not acknowledge receiving the packet, the transmitter does not try again and considers it a lost packet. It is worth mentioning that the AC4490 transceiver comes factory configured with ten retries, and in broadcast with three retransmissions. In this experimental deployment, we fixed the controller set point (or minimum RSSI) to -80dBm, and the maximum RSSI at -70dBm. Also, with antenna line attenuators attached to both CLH-1 and CLH-2 systems, we were expecting a -89dBm average, but the actual measured RSSI average was -95dBm, as shown in Fig. 5(a). The resulting PRR of the un-controlled CLH-1 was measured to be 82% with no retransmissions, as shown in Fig. 5(b). Meanwhile, RSSI results of the TRH controlled CLH-2 with Į0=4, shown in Fig. 6(a), started at 95dBm; and after the TRH controller converged, the RSSI average increased to -73dBm. Although TRH controlled CLH2 PRR graph is not shown here, the overall CLH-2 PRR was measured to be 98%, which obviously means that it outperformed CLH-1 by 16%. Most of CLH-2 lost packets occurred at the beginning of the exercise, before the TRH compensator converged. In Fig. 6(b), the TRH controlled radio started transmitting with a power level equal to 3 (nearly 30mW), and after the first ten transmissions, with RSSI feedback and TRH compensation, the transmission power value reached level 16. This represents a Tx power increase, from 30mW to almost 150mW, so with a large Į0=4 coefficient the power compensation increments are large as well. This may not be convenient because of posible wide RSSI variations, which makes the controller unstable. It then implies a trade-off between power consumption and dependable data delivery. Furthermore, with Į0=4 there is considerable Tx power usage which might be wasted if the PRR does not improve. Similar results to Į0=4 were obtained using Į0=3. In contrast, with Į0=2 results (ploted in Fig. 7(a) and 7(b)) show that with a smaller coefficient, the RSSI enhancement is smaller and power level variability is smaller as well, which implies that less power is wasted with a small enough Į0. Likewise in this experiment, CLH-1 (with no TPC) measured a 79% PRR performance, while CLH-2 with Į0=2 obtained a perfect 100% PRR. On the other hand, in figure 8(a), with a smaller Į0=0.5 value the average RSSI converged within the dessired limits, but some values dipped below -90dBm. And in figure 8(b), with Į0=0.5 the PRR averaged 92%. This means that by using a small Į0 coefficient, the trasnmission power variation is also smaller with the disadvantage that the PRR starts to decay if compensation is not done fast enough. This is evident in figure 9, where the power level rise was more gradual with Į0=0.5 compared to power level increments using larger Į0 coefficients, such as in figures 6(b) using Į0=4 and 7(b) using Į0=2.

(a)

(b)

Fig. 5. CLH-1 reference node with no TPC and no Tx retries: (a) RSSI plot, -95dBm average, and (b) PRR behaviour, 82% average.

(a)

(b)

Fig. 6. CLH-2 TRH controlled, no Tx retries, with Į0=4: (a) RSSI plot, -73dBm average, and (b) Tx power level behaviour.

(a)

(b)

Fig. 7. CLH-2 TRH controlled, no Tx retries, with Į0=2: (a) RSSI plot, with a -72dBm average, and (b) Tx power level behaviour.

(a)

(b)

Fig. 8. CLH-2 TRH controlled, no Tx retries, with Į0=0.5: (a) RSSI plot, -77dBm average, and (b) PRR behaviour, 92% average.

Fig. 9. CLH-2 power levels with TRH TPC, Į0=0.5.

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need short term PRR estimates, so it can fine tune the Į0 coefficient with changing wireless channel conditions.

Further experiments showed that below Į0=0.5, the TRH compensator stops working as such, and it just does unity power level adjustments. And as previously discussed, with a small Į0 coefficient the controllers response is too slow to compensate against deep and sudden fading. After doing an overall analysis, we found that this TRH TPC scheme works fine within the Į0 coefficient range expressed by (9): ͲǤͷ ൑ Ƚ଴ ൑ ʹ

(9)

Although these experiments were done in laboratory conditions, during short periods of time, preliminary results served to determine the adequate coefficient range expressed by (9).

(a)

(b)

Fig. 10. CLH-1 with no TPC: (a) -85dBm RSSI average and (b) PRR with a 75% average.

VII. HARSH ENVIRONMENT TRH TPC TESTING Several day outdoor TRH algorithm testing was done with different coefficients. Results corroborate laboratory observations, noting that the main difference compared to previous test results is the presence of a much higher RSSI variance and a higher noise floor. This time, three clusterheads were deployed simultaneously, CLH-1 with no TPC, CLH-2 operating with Į0=1 and CLH-3 with Į0=0.5. Fig. 10(a) illustrates RSSI values of CLH-1 transmitting at a fixed power level, and in fig. 10(b) shows the CLH-1 PRR plot, which averaged 75%. Meanwhile, in fig. 11(a) the resulting CLH-2 RSSI is plotted and in fig. 11(b) overall PRR reached a 93% average, which means that CLH-2 outperformed CLH-1 by 18%. Similarly, in Fig. 14(a) and 14(b) CLH-3 RSSI and PRR plots are illustrated, which show that CLH-3 also outperforms the fixed power CLH-1, this time by 17%. On the other hand, in Fig. 12(a) and 12(b), the overall recorded RH and T behaviors are shown, respectively. While in Fig. 13(a) the inverse TRH gradient is plotted, and when compared to CLH-2 power levels, shown in Fig. 13(b), there appears to be a match at several points in time. Similar results for the CLH-3 with Į0=0.5 are shown in figures 15(a), 15(b), 16(a) and 16(b). Although here we could not include all obtained results, of the many laboratory experiments, as well as of several real environment tests done with the wide range of selected Į0 coefficients, all of them showed an improvement of at least 10% and up to 20% in PRR performance. In table 2, comparative results are shown of the CLH harsh environment test bed. It summarizes day and night RSSI averages, Tx power level averages and the approximate Tx power average used, in mW and dBm. Between both CLH-2 and CLH-3 (with TPC), the resulting power average day consumption reached 306mW (approx.), which means that a larger fade margin needs to be applied to determine the minimum PTx considering the worst conditions. This would yield an excessive power consumption during night operation if no TPC is deployed. We consider these test bed results as preliminary, which now have served to verify the conceptual aspect of our proposal, more experimentation is needed to further understand the complete effects of the Į0 coefficient selection. Lastly, our pending work is now focused on determining a way of making the Į0 coefficient "adjustable", not in an adaptive sense, but rather its value will depend on short term PRR performance. This implies that the enhanced TRH algorithm, running on each CLH, besides needing RSSI feedback from the BS it will also

(a)

(b)

Fig. 11. CLH-2 with TRH TPC, Į0=1: (a) RSSI values and (b) PRR with a 93% average.

(a) (b) Fig. 12. CLH-2: (a) relative humidity and (b) air temperature.

(a)

(b)

Fig. 13. CLH-2 (a) TRH upside down gradient and (b) Tx power levels.

(a) (b) Fig. 14. CLH-3 with TRH TPC, Į0=0.5: (a) RSSI values and (b) 92% PRR average.

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[3] Geoff V Merret and Yen Kheng Tan. Wireless Sensor Networks: Application Centric Design. Intech Open Access Publishing. www.intechopen.com last visited 07/05/2013. [4] N. Matthys, et. Al.. Towards Fine-Grained and Application-Centric Access Control for Wireless Sensor Networks. SAC’10 March 22-26, 2010, Sierre, Switzerland. 2010 ACM 978-1-60558-638-0/10/03. [5] Giuseppe Anastasi, Marco Conti, Mario Di Francesco, Andrea Passarella. Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, Elsevier. 2009. 537–568. [6] H. Erkal, et. al.. A Survey of Recent Work on Energy Harvesting Networks. 26th International Symposium on Computer and Information Sciences, 2012, p. 143 - 147. [7] Luiz H.A. Correia, Daniel F. Macedo, Aldri L. dos Santos, Antonio A.F. Loureiro, Jose Marcos S. Nogueira. Transmission power control techniques for wireless sensor networks. Computer Networks. Elsevier. 2007. [8] Samuel P. Mason. Atmospheric effects on radio frequency (RF) wave propagation in a humid, near-surface environment. Master's Thesis. Naval Postgraduate School. USA. 2010. [9] Chonggang Wang ,Sohraby, K., Bo Li, Daneshmand, M.. A survey of transport protocols for wireless sensor networks. Network IEEE, Vol. 20. Issue 3. 2006. [10] Javier Gomez and Andrew T. Campbell. Using Variable-Range Transmission Power Control in Wireless Ad Hoc Networks. IEEE Transactions on Mobile Computing, Vol. 6, No. 1, January 2007. [11] Shan Lin, et. al.. ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks. SenSys’06, November 1–3, 2006, Boulder, Colorado, USA. [12] Kenneth Bannister, Gianni Giorgetti, Sandeep K.S. Gupta. Wireless Sensor Networking for “Hot” Applications: Effects of Temperature on Signal Strength, Data Collection and Localization. HotEmNets ’08, June 2-3, 2008, Charlottesville, Virginia, USA. 2008 ACM 978-160558-209-2/08/0006. [13] Jungwook Lee and Kwangsue Chung. An Efficient Transmission Power Control Scheme for Temperature Variation in Wireless Sensor Networks. Sensors 2011, 11, 3078-3093; doi:10.3390/s110303078. [14] Carlo Alberto Boano, Nicolas Tsiftes, Thiemo Voigt, James Brown, and Utz Roedig. The Impact of Temperature on Outdoor Industrial Sensornet Applications. IEEE Transactions on Industrial,Informatics (TII), Volume 6, Number 3, pag. 451-459. August 2010. [15] C. A. Boano, et. al. Hot Packets: A Systematic Evaluation of the Effect of Temperature on Low Power Wireless Transceivers. ExtremeCom ’13, August 24-30, 2013, Thorsmork, Iceland. [16] C. Ortega-Corral, L. E. Palafox, J. A. García-Macías, L. Aguilar, J. Sánchez-García, A. C. Valenzuela-León, I. Chon-Aguiar A “Lighter” JSON For Message Exchange In Highly Resource Constrained Wireless Sensor Network Applications. Congreso Internacional de Electrónica ELECTRO 2012. Chihuahua, Chih. México. Oct. 2012. [17] C. Ortega-Corral, L. E. Palafox, J. A. García-Macías, J. SánchezGarcía, L. Aguilar. End-to-end message exchange in a deployable marine environment hierarchical wireless sensor network. Ubiquitous Sensor Networks and Their Application 2013. International Journal of Distributed Sensor Networks. Article - in press -. ISSN: 1550-1329. [18] Laird Tech. AC4490 Developer Kit User’s Manual. Version 3.3. 2005. [19] C. Ortega-Corral, Luis E. Palafox, J. Sánchez-García, J. Antonio García-Macías, J.J. Esqueda Elizondo, A. C. Valenzuela-León, I. Chon-Aguiar. Performance comparison of configurable transceiver technologies applied to long range wireless sensor network communications over seawater. Congreso Internacional de Electrónica ELECTRO 2012. Chihuahua, Chih. México. Oct. 2012. [20] Laird Tech. AC4490 900MHz Transceiver User’s Manual. Ver. 3.2.1, 2007. [21] Arduino microcontrollers. http://arduino.cc/en/Main/arduinoBoardMega. Last visit 05/04/2013. [22] Digi Inc. XBee™/XBee–PRO™ 2.4 GHz OEM RF Modules. Product Manual v1.xCx – 802.15.4. Digi Inc. USA. 2008. [23] Digi International Inc. Rabbit Core RCM4300. User’s Manual. 2007– 2009.

(b) (a) Fig. 15. CLH-3: (a) relative humidity and (b) air temperature.

(b) (a) Fig. 16. CLH-3 (a) TRH upside down gradient and (b) Tx power levels. TABLE 2. PERFORMANCE COMPARISON AND TPC RESULTS. CLH

RSSI avg Day / Night dBm

PRR %

PL

PTx mW Non-linear Day / Night

PTx (dBm)

1 (no TPC)

-95 / -85

75

3

31

14.9

2 (Į0 = 1)

-78 / -74

93

35 max 13 min

357 / 133

25.5 / 21.2

3 (Į0 = 0.5)

-78 / -75

92

25 max 14 min

255 / 143

24 / 21.5

VIII. CONCLUSIONS In this article, we propose a novel way of determining a relative amount of signal loss due to a short term normalized temperature and humidity gradient. Our TPC scheme was put to the test using RSSI feedback to compensate against poor wireless reception. Indirectly, PRR performance was enhanced, with a 10% to 25% improvement over a node operating under the same circumstances with a fixed Tx power value. This proposal includes a "dampening" Į0 gain coefficient, incorporated to avoid transmitter power saturation. Through experimentation, we found that the Į0 coefficient values should be between 0.5 and 2. Coefficient values below 0.5 makes the compensator a simple unity gain incremental controller. We also found that, with Į0 coefficients greater than 2, the controller becomes unstable. Future work would involve a selfadjusting Į0 coefficient according to short term PRR, with the goal of obtaining an improved system performance. REFERENCES [1] C.Y. Chong and S.P. Kumar. Sensor networks: Evolution, opportunities, and challenges, Proceedings of the IEEE, vol. 91, n.8, 2003, pp. 1247–1256. [2] Azzedine Boukerche. Algorithms and Protocols for Wireless Sensor Network. Book Ed. John Wiley & Sons Inc. 2009.

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