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        基于懲罰誤差矩陣的同步預(yù)測(cè)無(wú)線體域網(wǎng)節(jié)能方法

        2019-08-01 01:57:38鄭卓然鄭向偉田杰
        計(jì)算機(jī)應(yīng)用 2019年2期
        關(guān)鍵詞:懲罰路由無(wú)線

        鄭卓然 鄭向偉 田杰

        摘 要:針對(duì)傳統(tǒng)無(wú)線體域網(wǎng)(WBAN)預(yù)測(cè)模型對(duì)感知數(shù)據(jù)預(yù)測(cè)精度低、計(jì)算量大、能耗高的問題,提出一種基于懲罰誤差矩陣的自適應(yīng)三次指數(shù)平滑算法。首先在感知節(jié)點(diǎn)與路由節(jié)點(diǎn)之間建立輕量級(jí)預(yù)測(cè)模型,其次采用地毯式搜索方式對(duì)預(yù)測(cè)模型進(jìn)行參數(shù)優(yōu)化處理,最后采用懲罰誤差矩陣對(duì)預(yù)測(cè)模型參數(shù)作進(jìn)一步的細(xì)?;幚?。實(shí)驗(yàn)結(jié)果表明,與ZigBee協(xié)議相比,在1000時(shí)隙范圍內(nèi),所提方法可節(jié)省12%左右的能量;而采用懲罰誤差矩陣與地毯式搜索方式相比,預(yù)測(cè)精度提高了3.306%。所提方法在有效降低計(jì)算復(fù)雜度的同時(shí)能進(jìn)一步降低WBAN的能耗。

        關(guān)鍵詞:無(wú)線體域網(wǎng);懲罰誤差矩陣;輕量級(jí)預(yù)測(cè)模型;地毯式搜索;體域網(wǎng)

        中圖分類號(hào): TP393

        文獻(xiàn)標(biāo)志碼:A

        Abstract: To solve the problem that traditional Wireless Body Area Network (WBAN) prediction model has low prediction accuracy, large computational complexity and high energy consumption, an adaptive cubic exponential smoothing algorithm based on penalty error matrix was proposed. Firstly, a lightweight prediction model was established between the sensing node and the routing node. Secondly, blanket search was used to optimize the parameters of the prediction model. Finally, penalty error matrix was used to further refine the parameters of the prediction model. The experimental results showed that compared with the ZigBee protocol, the proposed method saved about 12% energy in 1000 time slot range; compared with blanket search method, the prediction accuracy was improved by 3.306% by using penalty error matrix. The proposed algorithm can effectively reduce the computational complexity and further reduce the energy consumption of WBAN.

        Key words: Wireless Body Area Network (WBAN); penalty error matrix; lightweight prediction model; blanket search; body area network

        0 引言本文的文字比較差

        作為信息通信技術(shù)和醫(yī)學(xué)的交叉領(lǐng)域,無(wú)線體域網(wǎng)(Wireless Body Area Network, WBAN)[1-2]旨在為公眾提供實(shí)時(shí)的健康服務(wù)[3-4],如臨床決策支持[5]、家庭健康監(jiān)測(cè)[6]等。

        為了給予WBAN用戶更寬廣的活動(dòng)空間與優(yōu)質(zhì)的用戶體驗(yàn),節(jié)點(diǎn)必須在多跳網(wǎng)絡(luò)環(huán)境中進(jìn)行通信[7]。路由節(jié)點(diǎn)負(fù)責(zé)接收和轉(zhuǎn)發(fā)監(jiān)測(cè)數(shù)據(jù),接收器負(fù)責(zé)分析來(lái)自路由節(jié)點(diǎn)的感知數(shù)據(jù),通過(guò)Serial Interface通信或TCP(Transmission Control Protocol)通信將監(jiān)測(cè)數(shù)據(jù)發(fā)送至數(shù)據(jù)處理中心[8-9]。在多跳環(huán)境下的WBAN中,將接收器作為協(xié)調(diào)器簡(jiǎn)化了復(fù)雜的同步過(guò)程的需要,能夠提高數(shù)據(jù)傳輸過(guò)程中的能量效率,但感知節(jié)點(diǎn)與路由節(jié)點(diǎn)將承受較大的計(jì)算消耗和報(bào)文轉(zhuǎn)發(fā)負(fù)擔(dān),因此降低感知節(jié)點(diǎn)與路由節(jié)點(diǎn)的能耗,延長(zhǎng)WBAN生命周期成為本文研究的關(guān)鍵問題。

        但遺憾的是,目前尚未見到在多跳WBAN環(huán)境中節(jié)點(diǎn)低功耗的解決方案,為此,降低感知節(jié)點(diǎn)能耗成為解決電池續(xù)航能力的一大出路。文獻(xiàn)[10]中提出了基于小波變換量最小二乘支持向量機(jī)(Wavelet Transform-Least Squares Support Vector Machine, WT-LSSVM)的輕量級(jí)預(yù)測(cè)模型,通過(guò)在感知節(jié)點(diǎn)與路由節(jié)點(diǎn)之間建立同步預(yù)測(cè)模型減少冗余數(shù)據(jù)的傳輸,但該算法不適合于在硬件資源嚴(yán)重受限的無(wú)線傳感網(wǎng)絡(luò)中應(yīng)用。目前無(wú)線體域網(wǎng)領(lǐng)域的專家提出了關(guān)于IEEE802.15.6[11]的改進(jìn)版以及在媒體訪問控制(Media Access Control, MAC)層中基于時(shí)分多址(Time Division Multiple Access, TDMA)的一種改進(jìn)方案,但并沒有針對(duì)無(wú)線體域網(wǎng)應(yīng)用層進(jìn)行有效的設(shè)計(jì)和改進(jìn)。

        通過(guò)對(duì)數(shù)據(jù)的同步分析預(yù)測(cè)可大大減少接收節(jié)點(diǎn)和發(fā)送節(jié)點(diǎn)打開收發(fā)機(jī)的次數(shù)以降低無(wú)線體域網(wǎng)能耗

        本文在應(yīng)用層部分通過(guò)對(duì)數(shù)據(jù)的同步分析預(yù)測(cè)減少接收節(jié)點(diǎn)和發(fā)送節(jié)點(diǎn)打開收發(fā)機(jī)的次數(shù)以降低無(wú)線體域網(wǎng)能耗。因此在本文研究中,選擇以ZigBee多跳樹形網(wǎng)絡(luò)架構(gòu)[12]為基礎(chǔ),在感知節(jié)點(diǎn)和路由節(jié)點(diǎn)之間建立以基于懲罰誤差矩陣的自適應(yīng)三次指數(shù)平滑算法[13]為骨架的輕量級(jí)預(yù)測(cè)模型,并針對(duì)預(yù)測(cè)模型權(quán)重參數(shù)進(jìn)行細(xì)粒化調(diào)節(jié)。該算法能自適應(yīng)調(diào)節(jié)權(quán)重參數(shù),提高預(yù)測(cè)準(zhǔn)確率,同時(shí)降低感知節(jié)點(diǎn)與路由節(jié)點(diǎn)能耗,可以為長(zhǎng)期健康監(jiān)測(cè)應(yīng)用提供更加長(zhǎng)久的網(wǎng)絡(luò)服務(wù)。

        本文首先采用自適應(yīng)三次指數(shù)平滑算法在感知節(jié)點(diǎn)與路由節(jié)點(diǎn)之間建立輕量級(jí)預(yù)測(cè)模型以減少冗余數(shù)據(jù)的轉(zhuǎn)發(fā),實(shí)現(xiàn)了有限的節(jié)能效果;然后通過(guò)引入懲罰誤差矩陣細(xì)?;A(yù)測(cè)模型參數(shù)自適應(yīng)調(diào)節(jié)權(quán)重參數(shù),實(shí)現(xiàn)了預(yù)測(cè)模型參數(shù)精度和預(yù)測(cè)效果的顯著提升;最后通過(guò)實(shí)驗(yàn)驗(yàn)證了引入懲罰誤差矩陣提升了自適應(yīng)三次指數(shù)平滑算法構(gòu)建的預(yù)測(cè)模型的預(yù)測(cè)精度,對(duì)整個(gè)WBAN網(wǎng)絡(luò)節(jié)能效果的提升有很大的幫助。

        1 輕量級(jí)預(yù)測(cè)模型融合

        1.1 自適應(yīng)三次指數(shù)平滑算法

        4 結(jié)語(yǔ)

        本文提出了一種基于懲罰誤差矩陣的同步預(yù)測(cè)體域網(wǎng)節(jié)能方法,實(shí)現(xiàn)了數(shù)據(jù)有效傳輸與低功耗。通過(guò)在感知節(jié)點(diǎn)和路由節(jié)點(diǎn)之間建立基于懲罰誤差矩陣的自適應(yīng)三次指數(shù)平滑輕量級(jí)預(yù)測(cè)模型對(duì)周期非線性生理信息進(jìn)行更加有效的細(xì)?;A(yù)測(cè),節(jié)省了大量的能耗,避免了頻繁地更換電池,能實(shí)現(xiàn)對(duì)感知節(jié)點(diǎn)的低功耗管理和控制。

        參考文獻(xiàn):

        [1] BAE J, SONG K, LEE H, et al. A 0.24-nJ/b wireless body-area-network transceiver with scalable double-FSK modulation [J]. IEEE Journal of Solid-State Circuits, 2011, 47(1): 310-322.

        [2] CHEN S-L, LEE H-Y, CHEN C-A, et al. Wireless body sensor network with adaptive low-power design for biometrics and healthcare applications[J]. IEEE Systems Journal, 2010, 3(4): 398-409.

        [3] PENG Y, WANG X, GUO L, et al. An efficient network coding-based fault-tolerant mechanism in WBAN for smart healthcare monitoring systems [J]. Applied Sciences, 2017, 7(8): 817.

        [4] KHATUN F, HEYWOOD A E, HANIFI S M A, et al. Gender differentials in readiness and use of mHealth services in a rural area of Bangladesh [J]. BMC Health Services Research, 2017, 17(1): 573.

        [5] CHUNG K, PARK R C. PHR open platform based smart health service using distributed object group framework[J]. Cluster Computing, 2016, 19(1): 505-517.

        [6] GREEN M L, RUFF T R. Why do residents fail to answer their clinical questions? A qualitative study of barriers to practicing evidence-based medicine[J]. Academic Medicine Journal of the Association of American Medical Colleges, 2005, 80(2): 176-182.

        [7] FRANTZIDIS C A, GILOU S, BILLIS A, et al. Future perspectives toward the early definition of a multivariate decision-support scheme employed in clinical decision making for senior citizens [J]. Healthcare Technology Letters, 2017, 3(1): 41-45.

        [8] RANI S, MALHOTRA S, SANGWAN V. Detailed study of RS-232 serial interface [J]. Elena F Pérez Carrillo, 2014: 417-426.

        [9] KIM Y, LEE S, LEE S. Coexistence of ZigBee-based WBAN and WiFi for health telemonitoring systems [J]. IEEE Journal of Biomedical & Health Informatics, 2015, 20(1): 222-230.

        [10] 王汝言, 翟美玲, 吳大鵬. 就帶有同步預(yù)測(cè)的WBAN時(shí)序數(shù)據(jù)融合算法[J]. 通信學(xué)報(bào), 2015, 36(6):13-21.(WANG R Y,ZHAI M L, WU D P, Time series data aggregation algorithm with synchronous prediction for WBAN [J].Journal on Communications, 2015, 36(6): 13-21.)

        [11] 梁正友,姚玉梅.IEEE 802.15.6中能量有效的無(wú)線體域網(wǎng)拓?fù)浣Y(jié)構(gòu)優(yōu)化研究[J].通信學(xué)報(bào),2016,37(6):1-10.(LIANG Z Y,YAO Y M. Study of energy efficient WBAN topology optimization in IEEE 802.15.6 [J].Journal on Communications,2016, 37(6): 13-21.)

        [12] 張皛,鄔春學(xué),陳凱明.基于負(fù)載均衡的ZigBee動(dòng)態(tài)路由優(yōu)化算法[J].計(jì)算機(jī)工程,2016,42(3):138-142. (ZHANG X, WU C X, CHEN K M. ZigBee dynamic routing optimization algorithm based on load balance [J]. Computer Engineering, 2016, 42(3): 138-142.)

        [13] 王國(guó)權(quán),王森,劉華勇,等.基于自適應(yīng)的動(dòng)態(tài)三次指數(shù)平滑法的風(fēng)電場(chǎng)風(fēng)速預(yù)測(cè)[J].電力系統(tǒng)保護(hù)與控制,2014(15):117-122. (WANG G Q, WANG S, LIU H Y, et al. Self-adaptive and dynamic cubic ES method for wind speed forecasting[J]. Power System Protection and Control, 2014(15): 117-122.)

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