Adaptive Sampling for Energy Conservation in Wireless Sensor Networks for Snow Monitoring Applications

Transcription

1 Adaptve Samplng for Energy Conservaton n Wreless Sensor Networks for Snow Montorng Applcatons Cesare Alpp *, Guseppe Anastas, Crstan Galpert *, Francesca Mancn, Manuel Rover * * Dp. d Elettronca e Informazone Poltecnco d Mlano, Italy {lastname}@elet.polm.t Abstract Energy conservaton technques for sensor networks typcally rely on the assumpton that data sensng and processng consume consderable less energy than communcaton. Ths assumpton does not hold n some practcal applcaton scenaros, where ad hoc developed sensor unts requre power consumpton comparable wth, or even larger than, that of the rado. In ths paper we focus on an embedded sensor for montorng snow composton n mountan slopes for avalanche forecastng. To lower the sensor energy consumpton we propose an adaptve samplng algorthm able to dynamcally estmate the optmal samplng frequency of the sgnal to be montored. In turn, ths mnmzes the actvty of both the sensor and the rado (hence savng energy) whle mantanng an acceptable accuracy on the acqured data. Smulaton experments show that the suggested soluton can save to 97% of the energy consumed for sensng when the sensor s always on, whle mantanng the error at acceptable levels.. Introducton A sensor network typcally conssts of a large number of sensor nodes deployed over a geographcal area. Each node s a low-power devce that ntegrates processng, sensng and wreless communcaton abltes. Sensor nodes acqure nformaton from the surroundng envronment, process locally the data, and/or send them to one or more collecton ponts (base statons) []. A man ssue for a credble deployment of sensor unts s energy consumpton as sensor nodes are, generally, battery powered. The battery has lmted capacty and, often, cannot be replaced or recharged, due to envronmental or cost constrants. Therefore, the desgn of any component n the sensor network should Work sported by the Italan Mnstry for Educaton and Scentfc Research (MIUR) under the FIRB ArtDeco and PRIN WseMaP projects. Dept. of Informaton Engneerng Unversty of Psa, Italy {frstname.lastname}@et.unp.t address mnmzaton of the energy consumpton. As the rado component of sensor nodes manly account for energy consumpton (n addton to flash memory storage), several energy management technques have been proposed that am at mnmzng the rado actvty. They nclude data compresson [][3] and aggregaton [4][5], predctve montorng [6], topology management [7][8], adaptve duty cycle [9], just to name the few. All the above technques, however, rely on the assumpton that the energy requred for data sensng and processng s neglgble wth respect to the energy consumed n data communcaton. Despte the fact that ths assumpton holds n most applcatons, some others requre ad-hoc sensors wth a power consumpton comparable wth, or even larger than that of the rado [0]. In ths paper we focus on a sensor board for snow montorng applcatons; data, sutably aggregated, can be used to forecast possble avalanches. Energy savngs mechansms for the rado have been extensvely studed n the past (e.g., see []). Here, we approach the problem at the sensor board level. Even f we focus the attenton to snow sensor to make the presentaton easy to follow, the methodology s qute general and can be appled possbly wth mnor adaptaton- to any unt characterzed by sensors wth non neglgble energy consumpton. Energy conservaton at the sensor level can be acheved by usng an adaptve duty cycle approach, whch conssts n () swtchng off the sensor board between two consecutve samples, and () usng the optmal samplng frequency for the physcal quantty to be montored. Specfcally, we propose an Adaptve Samplng Algorthm for estmatng the optmal sample frequency. The basc dea s to fnd dynamcally the mnmum sample rate compatble wth the montored sgnal. By reducng the samplng rate, the algorthm also reduces the amount of data to be transmtted and, hence, the energy consumed by the rado. The concept of adaptve samplng s not new [][3][4]. However, prevous proposals are amed /07/$ IEEE

2 manly at optmzng the communcaton between sensor nodes and Base Staton (BS). In [] the samplng rate s adapted to the characterstcs of the data stream so that more bandwdth s allocated to sensor nodes wth larger actvty. In [3] adaptve samplng s used to sport routng. However, no specfc samplng algorthm s proposed. In [4] adaptve samplng bascally conssts n actvatng the approprate number of sensor nodes to acheve a target error level, dependng on spatal correlaton and actvty. Instead, our proposal reles on the CUSUM change detecton test [5] and s amed at reducng energy consumpton for both sensng and communcaton. We show by a prelmnary analyss that t s practcal, and actually t reduces the energy consumpton of the snow sensor to 97%. The paper s organzed as follows. Secton descrbes the snow sensor board consdered n ths paper. Secton 3 ntroduces the Adaptve Samplng Algorthm. Secton 4 descrbes the smulaton envronment used for performance analyss, whle Secton 5 presents smulaton results.. Snow Montorng Applcatons Montorng the status of snow coverage s an mportant publc protecton ssue whch allows experts for forecastng avalanches. Moreover, nformaton regardng the snow coverage s welcome to quantfy the potental presence of water to be subsequently released and used; such nformaton s frutful for optmal plannng hydropower generaton. To gather nformaton related to snow nstabltes on mountans slopes t s crucal to dentfy the composton of layers of snow at dfferent heghts from the ground. Snow s a mx of ce, water and ar, and ts delectrc constant, measured at dfferent frequences n the range [0., 00] khz, vares wth the percentage of water and ce n the mx. Therefore, by estmatng the snow delectrc constant over tme, t s possble to acheve nformaton about the composton of dfferent snow layers [6]. The snow sensor s thus a capactance readng unt composed of an ad-hoc engneered probe to be left on the mountan (for example fxed on a pole) measurng snow capactance, and an electronc njecton board capable of drvng the probe and measurng capacty at dfferent frequences of exctaton (Patent pendng No. US 006/09568 A). For each sample cycle the sensor provdes measurements of snow capactance at 00 Hz (low frequency) and 00 khz (hgh frequency). At the same tme a second sensor provdes a measurement of the ambent temperature. The three readngs are then passed to the sensor node to be packed n a sngle message and sent over the wreless channel. For each measurement, the njecton board electroncs of the snow sensor makes several procedures (calbraton, electrode pre-chargng, charge sharng) n a cyclc way to obtan a reasonably stable and relable measure. Ths actvty makes the sensor very energy consumng: for nstance, by samplng data every 5s, the average energy consumed s 880 mj/sample. Such a hgh value can be explaned as follows: a) the sensor s an ad-hoc sensor not optmzed for energy consumpton; b) the sensor s always actve (no energy management s ently avalable on the sensor). We dscovered that a good duty cycle for the sensor s around s for a 50 mj/sample energy consumpton. Immedately, by ntegratng the duty cycle concept, the energy consumpton decreases by 83%. 3. Adaptve Samplng Algorthm In ths secton we descrbe the algorthm for estmatng the optmal samplng rate of snow capactance. Nyqust [7] defnes the mnmum samplng frequency F N guaranteeng the reconstructon of the sampled sgnal: F N > F where F s the mum frequency n the power spectrum of the sgnal. Unfortunately, the mum frequency F s generally not a pror avalable. Moreover, n a non-statonary process, the frequency spectrum of the sgnal (and as a consequence the mum frequency) may change over tme. The am of the proposed Adaptve Samplng Algorthm s thus to track the dynamc of the physcal process under montorng by adaptng the samplng frequency of the sensor to the process dynamc. The proposed soluton s based on the non-statonarty change detecton CUSUM test. Change detecton technques are statstcal tests that assess the statonary hypothess for a process under nvestgaton. Here, we modfed the tradtonal CUSUM test to assess the non-statonarty (and hence the change) of the mum frequency of the power spectrum of the sgnal (and not the sgnal tself). The algorthm ntally estmates, through a Fast Fourer Transform (FFT), the mum frequency F of the sgnal by relyng on the frst W samples comng from the process (e.g., W =400 samples). The estmated F becomes the startng reference value to be contrasted for new estmates, possbly assocated wth changes n the mum frequency of the sgnal

3 spectrum. Drectly from the Nyqust theorem, a sutable samplng frequency for F s F c = c F wth c > where c s a confdence parameter. We defned c =. for allowng the algorthm for detectng frequences hgher than F. To track the process dynamc, the proposed algorthm defnes the two alternatve hypotheses for the mum frequency as: F = + F ; F = F. Durng the operatonal lfe, the algorthm computes the ent mum frequency F of the sgnal on sequences of W samples and the change detecton test can then be appled: f F s closer to F or F than F for h consecutve samples, a change s detected n the mum frequency of the sgnal and a new samplng frequency s defned. More specfcally, the change detecton test s composed by the followng detecton rule: F F < F for h consecutve f ( F ) samples or f ( F F < F ) F for h consecutve samples, date the samplng frequency: F = c. c F An example of frequency change detecton s presented n Fgure, where t s possble to observe F, F and F. A change s detected when the mum frequency of the sgnal F overcomes one of the two thresholds (the horzontal dotted lnes): th = F + F th = ( ) ( F F ) for h consecutve samples. The choce of h s crtcal to the robustness of the algorthm: wth low values of h (e.g. or ), the algorthm quckly detects a varaton n the mum frequency of the sgnal but t mght suffer from false detectons whch can cause a contnuous change of the samplng frequency. On the contrary, very hgh values of h (e.g. 000 or 000) decrease the false alarm rate but the algorthm mght be less prompt n detectng the changes. We suggest to fx h =40 n ths applcaton to trade off robustness and quckness n the frequency change detecton. If avalable, a-pror nformaton about the process could provde the desgner wth a sutable parameter h. The proposed algorthm s summarzed n Algorthm, where L represents the total number of samples n the dataset (we suggest c =., h =40 and W =400). Algorthm : Adaptve Samplng Algorthm. Estmate F by consderng W ntal samples and set F c = c F ;. Defne F = + F F = F 3. h =0 and h =0; 4. for ( =W +; < L; ++) { 5. Estmate the ent mum frequency F on sequence ( -W, ) 6. f ( F F < F ) { 7. { h = h +; h = 0; } F F < F { h = h +; h =0; } 9. else { h =0; h = 0; } 0. f ( h > h ) ( h > h ) { F = c F ; F 8. else f ( ). c F. F F = + F ; = F ; } } Smulaton Set We carred out a prelmnary smulaton analyss to assess the performance of the Adaptve Samplng Algorthm. A smple network scenaro consstng of a cluster of sensor nodes eqped wth snow sensor boards was consdered; a star topology was envsaged for communcaton towards the base staton. Due to ts computaton demand, the Adaptve Samplng Algorthm s executed at the BS level. The latter computes the optmal sample rate for each sngle sensor node, based on the data receved, and communcates t back to the node. Communcaton

4 between sensor nodes and BS occurs n TDMA; ths allows for energy savng as the rado s swtched off durng slots allocated to other nodes. Fg.. Frequency change recognton of the adaptve samplng algorthm We tested our algorthm by usng four dfferent data sets, derved from real snow measurements n dfferent days and condtons. Each data set conssts of approxmately samples acqured wth a fxed perod of 5s. Ths value was chosen on the bass of a- pror knowledge of the sgnals to be measured (snow capactance and ambent temperature): t s large enough to capture quck varatons (we should note that t s larger than necessary snce we expect snow capactance and ambent temperature to have small varatons for most of the tme). To evaluate the performance of the algorthm we consdered the followng performance metrcs. Samplng Fracton, defned as the number of samples acqured by the Adaptve Samplng Algorthm dvded by the number of samples acqured usng fxed-rate over-samplng (.e., samplng every 5 s). Mean Relatve Error (MRE), defned as where MRE = N x denotes the data sequence, and x the N = x x x th sample n the orgnal th data sample n the data sequence reconstructed at the BS, and N the total number of data n the orgnal data sequence. The Samplng Fracton provdes an ndcaton of the energy saved by the Adaptve Samplng Algorthm, whle MRE gves a measure of the relatve error ntroduced n the data sequence reconstructed at the BS. When the samplng rate estmated by the algorthm s larger than the fxed over-samplng rate some samples n the orgnal sequence are skpped and, thus, not transmtted to the BS. In addton, when the message loss rate s greater than zero, some samples transmtted by the sensor node are mssed by the BS. In our experments we assumed that lost samples are replaced by the prevous ones (lost compensaton). Hence, n the computaton of MRE we assumed x = x f the th sample s (correctly) receved by BS, and x = x otherwse. In our experments messages loss was generated accordng to a Bernoull dstrbuton; to ncrease the accuracy of the smulaton results we used the replcaton method wth 90% confdence level [8]. 5. Smulaton Results We performed a prelmnary set of experments to tune the algorthm parameters W, c, and h (see Secton 3). To ths end we used four dfferent data sets measured n dfferent acquston days and condtons (hereafter referred to as Scenaro through Scenaro 4), and vared the message loss rate from 0% to 90%. We frst vared W n the range [00, 000] samples, n steps of 00. We found that for values lower than 400 the MRE s very large and exhbts an unstable behavor, whle ncreasng W beyond 400 and to 000 ncreases the energy consumpton (more samples are acqured) but does not provde a sgnfcant MRE reducton. Then we vared the parameter c n the range [., 3.0]. We observed that a value greater than. ncreases the samplng rate but t does not reduce sgnfcantly the MRE. Fnally, we nvestgated the behavor of h. We vared h between 0 and 00 and observed the best tradeoff between number of samples generated and MRE when h =40. Based on the above results, n all the subsequent experments we wll use as parameter settngs W =400, c =., h =40. Fgure shows the percentage of samples acqured by the snow sensor wth adaptve samplng - wth respect to the case of fxed over-samplng (.e., sample every 5 s) - for ncreasng values of the message loss rate. The Adaptve Samplng Algorthm reduces sgnfcantly the number of samples the snow sensor has to acqure. The percentage vares from 8 to 7% dependng on the scenaro taken nto consderaton. As a result, the algorthm can save approxmately 73-8% of the energy consumed when operatng at fxed over-samplng. Moreover, ths percentage does not depend sgnfcantly on the message loss rate of the wreless lnk between the sensor node and the base staton.

5 Fg.. Samplng Fracton as a functon of message loss. Fgure 3 and Fgure 4 show the MRE for snow capactance at low and hgh frequences, respectvely, whle Fgure 5 reports the same ndex for the temperature. The MRE for LF capactance s under 3% even when the message loss s extremely hgh. As far HF capactance, MRE remans always under 4% for all scenaros except n Scenaro 4. Ths s because the correspondng data sequence exhbts several spkes that are fltered out by the algorthm. Nevertheless, the data sequence reconstructed at the BS s always very close to the orgnal data sequence (see Fgure 6). Fg. 4. MRE for hgh frequency capactance as a functon of message loss. Fg 5. MRE for temperature as a functon of message loss. Summarzng, the Adaptve Samplng Algorthm s able to reduce the number of samples by 73-8% wth respect to fxed over-samplng. When used n combnaton wth a smple duty cycle technque that swtches off the sensor between consecutve readngs (as brefly descrbed n Secton ), the result s a savngs of the 95-97% of the energy consumed when the sensor s always kept on. Fg. 3. MRE for low frequency capactance as a functon of message loss. The MRE for ambent temperature s hgh n all the scenaros (see Fgure 5). Ths s because temperature values ranges from -3 to 3 C (measurements have been done durng sprng tme, and each data set comes from about 4-hours observaton), but t remans close to zero for a large fracton of tme (see Fgure 7). When the absolute value s small, small devatons cause hgh error too. However, Fgure 7 shows that the temperature data sequence, whch s reconstructed at the BS, s very close to the orgnal one even when the message loss s very hgh. Fg. 6. Orgnal and reconstructed hghfrequence capactance n Scenaro 4. Message loss = 40%.

6 Fg. 7. Orgnal and reconstructed temperature n Scenaro 4. Message loss = 80%. Moreover, the MRE remans at acceptable values even when the message loss s extremely hgh. In addton, t must be emphaszed that, by decreasng the number of samples, the Adaptve Samplng Algorthm reduces accordngly the amount of data to be transmtted by the sensor node, hence reducng the energy for communcaton by the same percentage (73-8%). 6. Conclusons and Future Work In ths paper we propose an Adaptve Samplng Algorthm to dynamcally estmate the optmal samplng frequency of a physcal quantty to be montored over tme. The algorthm has been conceved to reduce the energy consumpton of a prototype sensor for snow montorng applcatons. However, t can be used n all cases where the process to be montored exhbts slow varaton over tme. Our smulaton experments have shown that, when used n combnaton wth a duty cycle technque, the above algorthm can save to 97% of the energy consumed by the snow sensor when t s always on. In addton, by reducng the amount of data to be transmtted, the algorthm also reduces sgnfcantly the energy consumed by the rado. We have analyzed our proposal n a very smple network scenaro. The next step wll consst n explorng more complex network scenaros. 7. References [] I.F.Akyldz, W. Su, Y. Sankarasubramanam E. Caprc, Wreless Sensor Networks: a Survey, Computer Networks, Vol 38, N. 4, March 00. [] C. Tang, C. Raghavendra, Compresson Technques for Wreless Sensor Networks, Chapter 0 n Wreless Sensor Networks (C. Raghavendra, K. Svalngam, T. Znat, Eds.), Kluwer Academc Publshers, Boston, USA, 004. [3] C. Sadler, M. Martonos, Data Compresson Algorthms for Enegy-Constraned Devces n Delay Tolerant Networks, Proc. ACM SenSys 006, Boulder, Colorado, USA, November [4] S. Madden, M. Frankln, J.Hellersten, W. Hong. TAG: a Tny AGgregaton Servce for Ad-Hoc Sensor Networks, Proceedngs of OSDI, 00. [5] A. Bouls, S.Ganerwal, M. B. Srvastava, Aggregaton n Sensor Networks : an Energy-Accuracy Trade-off, Ad Hoc Networks, Vol. (003), pp [6] S. Goel, T. Imelnsky, Predcton-Based Montorng n Sensor Networks: Takng Lessons from MPEG, ACM Computer Communcaton Revew 00, Vol. 3, N. 5, October 00. [7] A. Cerpa, D. Estrn, ASCENT: Adaptve Self- Confgurng Sensor Networks Topologes, Proc. IEEE Infocom 00, New York, USA, 00. [8] C. Schurgers, V. Tsatss, S. Ganerwal, M. Srvastava, Optmzng Sensor Networks n the Energy-Latency- Densty Desgn Space, IEEE Trans. on Moble Computng, Vol., N., 00. [9] D. Ganesan, A. Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrn,, Networkng Issues n Wreless Sensor Networks, Journal of Parallel and Dstrbuted Computng, Vol. 64 (004). [0] V. Raghunathan, S. Ganerwal, M. Srvastava, "Emergng Technques for Long Lved Wreless Sensor Networks", IEEE Communcatons Magazne, Aprl 006, pp [] G. Anastas, M. Cont, M. D Francesco, A. Passarella, How to prolong the Lfetme of Wreless Sensor Networks n Moble Ad Hoc and Pervasve Communcatons, (M. Denko, L. Yang, Eds.), to appear. [] A. Jan, E. Y. Chang, Adaptve Samplng for Sensor Networks, Proc. Workshop on Data Management for Sensor Networks (DMSN 004), Toronto (CA), 004. [3] R. Wllet, A. Martn, R. Nowak, Backcastng: Adaptve Samplng for Sensor Networks, Proc. IPSN 004, Aprl 6-7, 004, Berkeley, USA. [4] J. Zhou, D. De Roure, S. Vvekanandan, Adaptve Samplng and Routng n a Floodplan Montorng Sensor Network, Proc. IEEE WMob 006, June 9-, 006 [5] M. Bassevlle, and I.V. Nkforov, Detecton of Abrt Changes: Theory and Applcaton, Prentce-Hall, Inc [6] M. Nang, M. Berner, Y. Gauther, G. Fortn, E. Van Bochove, M. Stacheder, and A. Brandelk, On the valdaton of snow denstes derved from snowpower probes n a temperate snow cover n eastern canada: Frst result, n 60th Eastern Snow Conference, Québec (CA), 003, pp [7] A. J. Jerr, "The Shannon Samplng Theorem Its Varous Extensons and Applcatons: A Tutoral Revew", Proc. of IEEE, 65: , Nov [8] J. Banks, Handbook of Smulaton, John Wley and Sons, New York, USA, 998.