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本科毕业设计外文文献及翻译

本科毕业设计外文文献及翻译
本科毕业设计外文文献及翻译

二 〇 一 年 月

本科毕业设计 外文文献及翻译 英文题目:Testing ZigBee Motes for Monitoring Refrigerated Vegetable Transportation under Real Conditions 中文题目:ZigBee 检测探头在冷藏蔬菜运输现实条件下的测试 学生姓名: 学 院:信息

工程学院

系 别:电子信息工程

专 业:

班 级: 指导教师: 教授

Abstract

Quality control and monitoring of perishable goods during transportation and delivery services is an increasing concern for producers, suppliers, transport decision makers and consumers. The major challenge is to ensure a continuou s …cold chain?from producer to consumer in order to guaranty prime condition of goods. In this framework, the suitability of ZigBee protocol for monitoring refrigerated transportation has been proposed by several authors. However, up to date there was not any experimental work performed under real conditions. Thus, the main objective of our experiment was to test wireless sensor motes based in the ZigBee/IEEE 802.15.4 protocol during a real shipment. The experiment was conducted in a refrigerated truck traveling through two countries (Spain and France) which means a journey of 1,051 kilometers. The paper illustrates the great potential of this type of motes, providing information about several parameters such as temperature, relative humidity, door openings and truck stops. Psychrometric charts have also been developed for improving the knowledge about water loss and condensation on the product during shipments.

1. Introduction

Perishable food products such as vegetables, fruit, meat or fish require refrigerated transportation. For all these products, Temperature (T) is the most important factor for extending shelf life, being essential to ensure that temperatures along the cold chain are adequate. However, local temperature deviations can be present in almost any transport situation. Reports from the literature indicate gradients of 5 °C or more, when deviations of only a few degrees can lead to spoiled goods and thousands of Euros in damages. A recent study shows that refrigerated shipments rise above the optimum temperature in 30% of trips from the supplier to the distribution centre, and in 15% of trips from the distribution centre to the stores. Roy et al. analyzed the supply of fresh tomato in Japan and quantified product losses of 5% during transportation and distribution. Thermal variations during transoceanic shipments have also been studied. The results showed that there was a significant temperature variability both spatially across the width of the container as well as temporally along the trip, and that it was out of the specification more than 30% of the time. In those experiments monitoring was achieved by means of the installation of hundreds of wired sensors in a single container, which makes this system architecture commercially unfeasible.

Transport is often done by refrigerated road vehicles and containers equipped with embedded cooling systems. In such environments, temperatures rise very quickly if a reefer unit fails. Commercial systems are presently available for monitoring containers and trucks, but they do not give complete information about the cargo, because they typically measure only temperature and at just one point.

Apart from temperature, water loss is one of the main causes of deterioration that reduces the marketability of perishable food products. Transpiration is the loss of moisture from living tissues. Most weight loss of stored fruit is caused by this process. Relative humidity (RH), T of the product, T of the surrounding atmosphere, and air velocity all affect the amount of water lost in food commodities. Free water or condensation is also a problem as it encourages microbial infection and growth, and it can also reduce the strength of packaging materials.

Parties involved need better quality assurance methods to satisfy customer demands and

to create a competitive point of difference. Successful transport in food logistics calls for automated and efficient monitoring and control of shipments. The challenge is to ensure a continuous …cold chain? from producer to consumer in order to guaranty prime condition of goods .

The use of wireless sensors in refrigerated vehicles was proposed by Qingshan et al. as a new way of monitoring. Specialized WSN (Wireless Sensor Network) monitoring devices promise to revolutionize the shipping and handling of a wide range of perishable products giving suppliers and distributors continuous and accurate readings throughout the distribution process. In this framework, ZigBee was developed as a very promising WSN protocol due to its low energy consumption and advanced network capabilities. Its potential for monitoring the cold chain has been addressed by several authors but without real experimentation, only theoretical approaches. For this reason, in our work real experimentation with the aim of exploring the limits of this technology was a priority.

The main objective of this project is to explore the potential of wireless ZigBee/IEEE 802.15.4 motes for their application in commercial refrigerated shipments by road. A secondary objective was to improve the knowledge about the conditions that affect the perishable food products during transportation, through the study of relevant parameters like temperature, relative humidity, light, shocking and psychrometric properties.

2. Materials and Methods

2.1. ZigBee Motes

Four ZigBee/IEEE 802.15.4 motes (transmitters) and one base station (receiver) were used. All of them were manufactured by Crossbow. The motes consist of a microcontroller board (Micaz) together with an independent transducer board (MTS400) attached by means of a 52 pin connector. The Micaz mote hosts an Atmel ATMEGA103/128L CPU running the Tiny Operating System (TinyOS) that enables it to execute programs developed using the nesC language. The Micaz has a radio device Chipcon CC2420 2.4 GHz 250 Kbps IEEE 802.15.4. Power is supplied by two AA lithium batteries.

The transducer board hosts a variety of sensors: T and RH (Sensirion SHT11), T and barometric pressure (Intersema MS5534B), light intensity (TAOS TSL2550D) and a two-axis accelerometer (ADXL202JE). A laptop computer is used as the receiver, and communicates with the nodes through a Micaz mounted on the MIB520 ZigBee/USB gateway board.

Each Sensirion SHT11 is individually calibrated in a precision humidity chamber. The calibration coefficients are used internally during measurements to calibrate the signals from the sensors. The accuracies for T and RH are ±0.5 °C (at 25 °C) and ±3.5% respectively.

The Intersema MS5534B is a SMD-hybrid device that includes a piezoresistive pressure sensor and an ADC-Interface IC. It provides a 16 bit data word from a pressure and T (?40 to +125°C) dependent voltage. Additionally the module contains six readable coefficients for a highly accurate software calibration of the sensor.

The TSL2550 is a digital-output light sensor with a two-wire, SMBus serial interface. It combines two photodiodes and an analog-to digital converter (ADC) on a single CMOS integrated circuit to provide light measurements over a 12-bit dynamic range. The ADXL202E measures accelerations with a full-scale range of ±2 g. The ADXL202E can measure both dynamic acceleration (e.g., vibration) and static acceleration (e.g., gravity).

2.2. Experimental Set Up

The experiment was conducted in a refrigerated truck traveling during 23 h 41 m 21 s from Murcia (Spain) to Avignon (France), a distance of 1,051 km. The truck transported approx.14,000 kg of lettuce var. Little Gem in 28 pallets of 1,000 × 1,200 mm . The lettuce was packed in cardboard boxes with openings for air circulation.

The length of the semi-trailer was 15 m with a Carrier Vector 1800 refrigeration unit mounted to the front of the semi-trailer. For this shipment the set point was 0 °C.

The truck was outfitted with the wireless system, covering different heights and lengths from the cooling equipment, which was at the front of the semi-trailer. Four motes were mounted with the cargo (see Figure 1): mote 1 was at the bottom of the pallets in the front side of the semi-trailer, mote 2 was in the middle of the semi-trailer, mote 3 was in the rear at the top of the pallet, and mote 4 was located as shown in Figure 1, about a third of the distance between the front and the rear of the trailer. Motes 1, 2 and 3 were inside the boxes beside the lettuce. The program installed in the motes collects data from all the sensors at a fixed sample rate (7.2 s), with each transmission referred to as a “packet”. The RF power in the Micaz can be set from ?24 dBm to 0 dBm. During the experiment, the RF power was set to the maximum, 0dBm (1mW approximately).

2.3. Data Analysis

A specialized MATLA

B program has been developed for assessing the percentage of lost packets (%) in transmission, by means of computing the number of multiple sending failures for a given sample rate (SR). A multiple failure of m messages occurs whenever the elapsed time between two messages lies between 1.5 ×m ×SR and 2.5 ×m ×SR. For example, with a sample rate of 11 s, a single failure (m = 1) occurs whenever the time period between consecutives packets is longer than 16.5 s (1.5 × 1 × 11) and shorter than 27.5 s (2.5 × 1 × 11). The total number of lost packets is computed based on the frequency of each failure type. Accordingly, the total percentage of lost packets is calculated as the ratio between the total number of lost packets and the number of sent packets.

The standard error (SE) associated to the ratio of lost packets is computed based on a binomial distribution as expressed in Equation 1, where n is the total number of packets sent, and p is the ratio of lost packets in the experiment.

2.4. Analysis of Variance

Factorial Analysis of Variance (ANOV A) was performed in order to evaluate the effect of the type of sensor in the registered measurements, including T (by means of Sensirion and Intersema), RH, barometric pressure, light intensity and acceleration module. ANOV A allows partitioning of the observed variance into components due to different explanatory variables. The STATISTICA software (StatSoft, Inc.) was used for this purpose [14]. The Fishers?s F ratio compares the variance within sample groups (“inherent variance”) with the variance between groups (factors). We use this statistic for knowing which factor has more influence in the variability of the measurements.

2.5. Psychrometric Data

Psychrometry studies the thermodynamic properties of moist air and the use of these properties to analyze conditions and processes involving moist air. Psychrometric charts

show a graphical representation of the relationship between T, RH and water vapor pressure

in moist air. They can be used for the detection of water loss and condensation over the product.

In our study, the ASAE standard D271.2 was used for computing the psychrometric properties of air. Equations 2–5 and Table 1 enable the calculation of all psychrometric data of air whenever two independent psychrometric properties of an air-water vapour mixture are known in addition to the atmospheric pressure:where Ps stands for saturation vapor pressure (Pa), T is the temperature (K), Pv is the vapor pressure (Pa), H the absolute humidity (g/kg dry air), Patm is atmospheric pressure (Pa) and A, B, C, D, E, F, G and R are a series of coefficients used to compute Ps, according to Equation 3.

3.Results and Discussion

3.1. Reliability of Transmission

Signal propagation through the lettuce lead to absorption of radio signals, resulting in great attenuations in RF signal strength and link quality at the receiver. During the experiment, only motes 3 and 4 were able to transmit to the coordinator. No signals were received from mote number 1, at the bottom of the first pallet, and number 2, in the middle of the pallet. Mote 3 was closer to the coordinator than mote 4, but mote 3 was surrounded by lettuce which blocks the RF signal. However between mote 4 and the coordinator there was free space for transmission. Thus, the maximum ratio of lost packets found was 100% for two of the motes and the minimum 4.5% ± 0.1%, for mote 4.

Similar ratios were reported by several authors who performed experiments with WSN under real conditions, like for example in monitoring vineyards. Also, Baggio and Haneveld, after one year of experimentation in a potato field using motes operating at the band of 868/916MHz, reported that 98% of data packets were lost. However, during the second year the total amount of data gathered was 51%, which represents a clear improvement. Ipema et al. monitored cows with Crossbow motes, and found that the base station directly received less than 50% of temperature measurements stored in the mote buffer. Nadimi et al., who also monitored cows with this type of motes, showed packet loss rates of about 25% for wireless sensor data from cows in a pasture even the distance to the receiver (gateway) was less than 12.5 m away.

Radio propagation can be influenced by two main factors: the properties of propagation media and the heterogeneous properties of devices. In a commercial shipment, if the motes are embedded within the cargo, a significant portion of the Fresnel zone is obstructed. This is a big challenge in our application. Changing the motes? location, for example the one at the bottom of the pallets (mote 1, at the front of the semitrailer) or the one in the middle of the compartment (mote 2), might have yielded in better data reception rates but would have resulted in a loss of spatial information near the floor or at mid-height. The sensors should be as close as possible to the products transported; otherwise the measurements would not give precise information. Thus, one solution, if the same motes are to be used, could be to include

intermediates motes that allow peer to peer communication to the base station. Another solution could be to use lower frequencies; however this is not possible using ZigBee, because the only radio frequency band available for ZigBee worldwide is the 2.4 GHz one. The other ISM (Industrial, Scientific and Medical) bands (868 MHz and 915 MHz) differ from USA to Europe. Other options include developing motes with more RF power that can achieve longer radio ranges. The transmission could also be improved by optimizing antenna orientation, shape and configuration. The standard antenna mounted in the Micaz is a 3 cm long 1/2 wavelength dipole antenna. The communications could be enhanced using ceramic collinear antennas or with use of a simple reflecting screen to supplement a primary antenna, which can provide a 9dB improvement. Link asymmetry and an irregular radio range can be caused by the antenna position. In a real environment, the pattern of radio transmitted at the antenna is neither a circular nor a spherical shape. Radio irregularity affects the motes performance and degrades their ability to maintain connection to other nodes in the network. However, in our experiment Micaz motes were deployed in its best position according to a recent study. Another issue is the received signal strength indicator (RSSI), it should be recorded in further experiments in order to detect network problems and estimate the radio link quality. RSSI is a way for the radio to report the strength of the radio signal that it is receiving from the transmitting unit.

Sample rates configured in the motes were very short in order to get the maximum amount of data about the ambient conditions. In practice, a reduction in the sampling frequency of recording and transmission should be configured in order to extend battery life. According to Thiemjarus and Yang this also provides opportunities for data reduction at the mote level. It is expected that future wireless sensor motes will have on-board features to analyze recorded data and detect certain deviations. The level of a deviation determines whether the recording or transmitting frequency should be adapted .

One important feature in the motes came from the miniaturized sensors mounted on the motes that allow, in a small space (2.5 ×5 ×5 cm), to provide data not just about temperature, but also RH, acceleration and light, according to the proposal of Wang and Li. Those variables were also measured and analyzed.

3.2. Transport Conditions

For the analysis of T conditions, the average value of the two sensors mounted in each mote is considered. The set-point of the transport trailer?s cooling system was 0 °C, but the average temperature registered during the shipment was 5.33 °C, with a maximum of 8.52 °C and a minimum of ?3.0 °C. On average, 98% of the time the temperature was outside of the industry recommended range (set-point ± 0.5 °C).

Figure 2 shows the temperature fluctuations registered during the shipment, where four different markers are used corresponding to two T sensors per mote. There are large differences between the temperatures recorded with each sensor on the same mote even thought individual calibration curves were used. The SHT11 measures consistently higher temperatures than the Intersema. This behaviour could be due to the closer location of the SHT11 to the microcontroller, causing sensor self-heating effects.

In other studies, like for example Tanner and Amos, it was observed that the cargo was within the industry recommended T interval for approximately 58% of the shipment duration. Rodriguez-Bermejo et al. compared two different cooling modes in a 20? reefer container. For modulated cooling the percentage of time within the recommendation ranged between 44% and 52% of the shipment duration, whereas for off/on control cooling it ranged between 9.6% and 0%. In those experiments, lower percentages of time within industry recommended intervals are found for high T set points.

The analysis of variance of the T data shows that the variability in temperature depended both in the type of sensor and on the mote used. The interaction between these two factors also has an impact on the T measurements. The critical value of F at 95% probability level is much lower than the observed values of F, which means that the null hypothesis is false. The mote is the factor that has most influence on the variability of the measurements (highest Fishers?s F); this fact seems to be due to the location of the node. Mote 4 is closer to the cooling equipment which results in lower temperature measurements.

The node is a very significant factor in the measurements registered. In the case of RH, pressure, light and acceleration, the node location has great influence in data variability . However, node location has more impact on the measured RH than on the other variables.

Inside the semi-trailer RH ranged from 55 to 95% (see Figure 3). The optimal RH for

lettuce is 95%. Humidity was always higher at mote 4 (at the top middle of the semi-trailer; average RH 74.9%) than at mote 3 (located at the rear; average RH 62.1%).

摘要

生产商、供应商、运输决策者和消费者越来越关心易腐货物在运输和交付服务中对质量的掌握和把控。最大的挑战是确保从生产商到消费者持续的“冷链”以保证货物。在这个框架内,几位作者提出用于监测冷藏运输ZigBee协议的适用性。然而,到目前为止没有任何真实条件下进行的实验工作。因此,我们实验的主要目的是测试基于ZigBee / IEEE 802.15.4协议的无线传感器在实际装运中的应用。本次实验是一辆穿过两个国家(法国和西班牙)的冷藏车的一个1051公里的旅程。本文阐述了这种类型的传感器的巨大潜力,提供信息的几个参数,如温度,相对湿度,门洞口停车等。焓湿图也被开发,提高对产品在运输和冷凝水损失知识的了解。

关键词:ZigBee;无线传感;冷链物流;食品

1 介绍

蔬菜,水果,肉类或鱼这些易腐蚀食品需要冷藏运输。对于这些产品延长保质期,以确保冷链保持足够长的温度(T)是必不可少的。然而,局部温度偏差会出现在大部分的运输情况中。从文献报告显示梯度为5°C以上时,只要温度存在几度的偏差,就可能会导致商品的腐蚀和数千欧元的赔偿。最近的一项研究表明,从供应商到配送中心的车次冷藏运输的温度会上升30%以,而从配送中心到门店的则上升15%。Roy et al分析了,在日本供应新鲜的西红柿在运输和分配时会给产品带来5%亏损,对远洋运输的温度变化也进行了研究。结果表明,整个容器的宽度随着时间而逐渐上升,并超过标准时间的30%,所以存在显着的温度变化。在这些实验中,在一个容器里安装了数百个监测有线传感器的装置,这样的系统架构是不可能实现的。

在冷藏公路车辆和集装箱运输中会配备嵌入式冷却系统,在这样的环境中,如果冷藏系统发生故障会导致温度上升的速度非常快。商业系统主要应用于检测集装箱和卡车中,由于它们仅仅测量一个点的温度,所以他们给不出货物的完整信息。

除了温度的影响,水分的流失也是导致食物易腐烂的主要原因之一。蒸腾作用使水分子大量的流失,这个过程被称为水果减肥。相对湿度、大气周围的温度和空气的流速都会影响食品水分的流失。通过冷凝方式减少水分的流失,这样不仅促进了微生物的生长,同时也减少了材料的包装。

参与各方需要严格的保证产品质量,以满足客户的需求。成功的运输需要食品在运输过程中具有有效的监测和控制出货量的能力。其挑战是从生产者到消费者保持持续的冷链状态,从而保证货物的质量。

青山等人提出在冷藏车使用无线传感器这种新的监控方式。专业WSN监控设备改变了易腐蚀产品,给供应商和分销商在整个分销过程中提供了连续且又准确读数范围的运输和装载能力。由于ZigBee技术其能耗低和先进的网络功能的特点,已成为现在非常有前途的无线传感器网络协议。其冷链监测能力虽然有效,但没有进行的实验,出于这个原因,我们工作的目的是探索这项技术的优势。

这个项目的主要目的发掘ZigBee / IEEE 802.15.4无线技术,为他们在商业冷藏运输公路上的应用。第二个目标是改善其条件,通过学习相关的参数,如温度,相对湿度,光照等性能,了解易腐食品在运输过程中的知识。

2 材料和方式

2.1 ZigBee技术

应用四个ZigBee / IEEE 802.15.4微尘(发射器)和一个基站(接收器)。微尘由一个微控制器板(Micaz)和一个具有52针连接器独立连接的传感器板(MTS400)组成。Micaz微尘主板是爱特梅尔ATMEGA103/128L CPU处理器,使执行程序使用的是TinyOS微型操作系统开发的nesC语言。Micaz是Chipcon公司的IEEE 802.15.4无线电设备具有CC2420 2.4 GHz和250 Kbps其电源由两节AA锂电池构成。

传感器主板承载了各种传感器:温度和相对湿度(SHT11 Sensirion的),T和气压(MS5534B INTERSEMA)的,光强度(TAOS TSL2550D)的两轴加速度计(ADXL202JE)。一台笔记本电脑被用作接收器,MIB520的ZigBee / USB网关板安装在Micaz上并与各个节点进行通信。

每个经过SENSIRION SHT11单独校准精确的湿度室。在测量过程中,在内部使用校准系数来校准来自传感器的信号。温度和相对湿度的精度为±0.5°C(25°C)和±3.5%。

INTERSEMA MS5534B是SMD的一个特殊装置,其包括一个电阻压力传感器和一个ADC,IC接口。它提供了一个16位数据字。此外,该模块包含了一个六位可读系数的高度精确的软件传感器。

TSL2550是一个具有两线SMBus串行接口的数字输出光传感器。它结合了两个光电二极管和一个模拟到数字转换器(ADC),在一个单一的CMOS集成电路中提供一个12位的动态范围的光测量。ADXL202E可以测量动态加速度(如振动)和静态加速度(如重力)其测量加速度的满程范围为±2克。

2.2实验装置

实验进行23小时41分钟21秒冷藏卡车行驶从穆尔西亚(西班牙)到阿维尼(法国)1,051公里的距离。卡车运送约14,000公斤生菜V AR。28托盘1,000×1,200毫米的小宝石。生菜用开口的纸板箱包装以便空气流通。

拖车的长度为15米,载有其1800的制冷单元,其被安装到拖车的前部。对于这批货物把温度集中控制到0℃。

在拖车中配备了无线系统,在拖车的前部配备了冷却设备,具有不同的高度和长度。四微尘搭载货物(参见图1):1指的半拖车的前侧且的底部,2在半拖车的中间,3是在后方的托板上,4在半拖车顶部大约占拖车的前部和后部之间三分之一的距离。

1,2和3里面的箱子里装有生菜。该程序安装在一个固定的采样率(7.2)传感器中,每次传输被称为一个“数据包”。Micaz的RF功率可以设置从-24 dBm至0 dBm。在实验过程中,RF功率被设置到最大,为0dBm(1mW的约数)。

图1 实验装置侧面图和俯视图

2.4方差分析

方差分析(ANOV A)时,以评估不同的传感器的的测量,包括T(由装置Sensirion 的INTERSEMA的),相对湿度,气压,光强度和加速模块的效果。方差分析允许所观察划分成组件。STA TISTICA软件(StatSoft推出,公司)被用于此项研究。渔民“F比比较样本组内变异(”内在差异“)组(因素)之间的差异。我们知道哪些因素具有更大的影响力对此进行统计。

2.5湿度分析

湿度测定法研究了湿空气的热力学性质,这些性质的使用的分析条件和过程涉及的潮湿的空气。焓湿图显示之间的关系的图形表示,相对湿度和水蒸汽压在潮湿的空

气中。它们可用于在产品水分的损失和凝结的检测。

第三章结果与讨论

3.1 传输的可靠性

通过接收生菜信号的无线电信号,发现在射频信号强度和接收机的链路质量有很大的衰减。在实验过程中,只有3号和4号传感器能够传递给协调器。在盘的底部遥控接收的1号和在托盘中间的2号没有信号。传感器3比4更靠近协调器,但传感器3的射频信号被生菜块包围。然而传感器4和协调器有自由传输空间。因此丢失的数据包的最大比值在100%和误差在4.5%±0.1%。

类似的比率是由几位作者进行真实条件下的无线传感器网络实验报告中出现的,例如在监测葡萄园。就像Baggio和Haneveld的实验,在一年内将在868 / 916MHZ 波段操作的传感器在土豆田里使用,依报告显示数据包丢失了98%。然而,在第二年的总采集数据量为51%,这是一个明显的改进。Ipema等也是如此。监测奶牛中传感器,并发现基站直接得到小于温度测量,存储在缓冲区50%粒。Nadimi等也是这样。谁还监测奶牛这种类型的传感器,显示关于无线传感器数据从奶牛在牧场甚至到接收机的距离25%的数据包丢失率即网关小于12.5米的距离。

无线电波的传播可以通过两个主要因素的影响:传播媒体的特性和设备的异构特性。在一个商业装运,如果微粒嵌入在货物,菲涅耳带的很大一部分被阻塞。这是在我国应用的一大挑战。改变微尘?位置,例如在托盘底部的一个(节点1,在半挂车前)或一个在车厢的中部(节点2),可能会产生更好的数据接收速率,但会导致的损失的空间信息在地板上或在中等身高。传感器应尽量接近成品运输;否则,将不提供精确的信息。因此,一个解决方案,如果同样的尘埃被使用,可能包括中间体微尘,允许对等通信的基站。另一个解决方案是使用频率较低;然而这是不可能使用ZigBee ZigBee,因为用于全球唯一的广播频率是2.4 GHz的一个。其他的ISM(工业,科学和医疗)频段(868兆赫和915兆赫)不同,从美国到欧洲。其他选项包括发展更多的射频功率,可以实现更长的无线电范围内的尘埃。传输也可以通过优化天线的方向改进,形状和结构。安装在MicaZ标准天线是一个3厘米长的1/2波长的偶极子天线。通讯可以采用陶瓷共线天线或使用的增强简单的反射屏补充主天线,它可以提供一个9dB的改进。链路的不对称性和不规则的无线电范围可以由天线位置引起的。在实际环境中,无线发送天线模式既不圆也不是一个球形的形状。无线电不规则影响的微尘的性能和降低他们维持到网络中的其他节点的连接能力。然而,根据最近的一项研究在我们的实验MicaZ节点被部署在最好的位置。另一个问题是所接收的信号强度指示器(RSSI),它应该被记录下来,在进一步的实验来检测网络的问题和估计的无

线链路质量。RSSI的无线电报告的无线电信号的强度,它接收从发送单元的方法。

采样率配置在尘埃非常短,为了获得对环境条件的最大数据量。在实践中,在记录和传输的采样频率减少的配置应以延长电池寿命。根据Thiemjarus这也提供了在尘埃层数据的减排机会。预计未来无线传感器微尘会上的特征来分析记录的数据和检测到一定的偏差。一个偏差水平决定的记录或传输频率。

在Motes的一个重要特征是从小型化的传感器安装在微尘,允许,在一个小空间(2.5×5×5厘米),提供的数据不只是温度,而且RH,加速和光,根据王、李[ 30 ]的建议。进行测量和分析这些变量。

3.2 运输条件

对条件的分析,对两个传感器安装在每个节点的平均值是。对运输拖车?冷却系统的设定点为0°C,但在装运注册的平均温度是5.33°C,最多的有8.52°C和最小的?3°C.平均温度的,是业内外的时间98%推荐的范围(设定点±0.5°C)。

图2显示了在装运中注册的温度波动,在四个不同的标记是用对应于每两个传感器微尘。有记录的每个传感器在相同的尘埃温度之间的大的差异,甚至认为是个人的校准曲线。SHT11措施比intersema始终保持较高的温度。这种行为可能是由于该SHT11的接近的位置控制器,使传感器的自加热效应。

在其他的研究中,例如Tanner和阿摩司,发现货物在行业推荐的大约58%的出货时间 T间期。罗德里格兹贝尔梅霍等。比较了两种不同的冷却方式在20?冷藏集装箱。调制在推荐的冷却时间的百分比范围的装运时间44%和52%之间,而打开/关闭的控制冷却,在9.6%和0%之间。在这些实验中,较低的百分比的时间间隔内的行业推荐发现高T设定点。

对数据的方差分析表明,在温度的变化取决于在传感器的类型和使用的微粒。这两个因素之间的相互作用还对T测量的影响。在95%的概率水平下的临界值比F观测值低得多,这意味着零假设是错误的。这本书已在测量的变异最影响因素(最高的渔民?S F);这一事实似乎是由于节点的位置。4是在较低的温度测量更接近冷却设备,结果。

节点注册测量的一个重要因素。在RH,压力的情况下,光和加速度,节点的位置在数据变异性很大的影响。然而,节点位置对测量相对湿度的影响较其他变量。

在半挂车RH的范围从55到95%。生菜最佳相对湿度95%。湿度总是高于在点4(在半挂车;顶部中间的平均相对湿度74.9%)比在点3(位于后部;平均相对湿度

62.1%)。

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摘要:编程节点和活跃网络的概念将可编程性引入到通信网络中,并且代码和数据可以在发送过程中进行修改。最近,多个研究小组已经设计和实现了自己的设计平台。每个设计都有其自己的优点和缺点,但是在不同平台之间都存在着互操作性问题。因此,我们引入一个类似网络socket编程的概念。我们建立一组针对应用程序进行编程的简单接口,这组被称为活跃网络Socket编程(ANSP)的接口,将在所有执行环境下工作。因此,ANSP 提供一个类似于“一次性编写,无限制运行”的开放编程模型,它可以工作在所有的可执行环境下。它解决了活跃网络中的异构性,当应用程序需要访问异构网络内的所有地区,在临界点部署特殊服务或监视整个网络的性能时显得相当重要。我们的方案是在现有的环境中,所有应用程序可以很容易地安装上一个薄薄的透明层而不是引入一个新的平台。 关键词:活跃网络;应用程序编程接口;活跃网络socket编程

1 导言 1990年,为了在互联网上引入新的网络协议,克拉克和藤农豪斯[1]提出了一种新的设 计框架。自公布这一标志性文件,活跃网络设计框架[2,3,10]已经慢慢在20世纪90 年代末成形。活跃网络允许程序代码和数据可以同时在互联网上提供积极的网络范式,此外,他们可以在传送到目的地的过程中得到执行和修改。ABone作为一个全球性的骨干网络,开 始进行活跃网络实验。除执行平台的不成熟,商业上活跃网络在互联网上的部署也成为主要障碍。例如,一个供应商可能不乐意让网络路由器运行一些可能影响其预期路由性能的未知程序,。因此,作为替代提出了允许活跃网络在互联网上运作的概念,如欧洲研究课题组提出的应用层活跃网络(ALAN)项目[4]。 在ALAN项目中,活跃服务器系统位于网络的不同地址,并且这些应用程序都可以运行在活跃系统的网络应用层上。另一个潜在的方法是网络服务提供商提供更优质的活跃网络服务类。这个服务类应该提供最优质的服务质量(QOS),并允许路由器对计算机的访问。通过这种方法,网络服务提供商可以创建一个新的收入来源。 对活跃网络的研究已取得稳步进展。由于活跃网络在互联网上推出了可编程性,相应 地应建立供应用程序工作的可执行平台。这些操作系统平台执行环境(EES),其中一些已 被创建,例如,活跃信号协议(ASP)[12]和活跃网络传输系统(ANTS)[11]。因此,不 同的应用程序可以实现对活跃网络概念的测试。 在这些EES 环境下,已经开展了一系列验证活跃网络概念的实验,例如,移动网络[5],网页代理[6],多播路由器[7]。活跃网络引进了很多在网络上兼有灵活性和可扩展性的方案。几个研究小组已经提出了各种可通过路由器进行网络计算的可执行环境。他们的成果和现有基础设施的潜在好处正在被评估[8,9]。不幸的是,他们很少关心互操作性问题,活跃网络由多个执行环境组成,例如,在ABone 中存在三个EES,专为一个EES编写的应用程序不能在其他平台上运行。这就出现了一种资源划分为不同运行环境的问题。此外,总是有一些关键的网络应用需要跨环境运行,如信息收集和关键点部署监测网络的服务。 在本文中,被称为活跃网络Socket编程(ANSP)的框架模型,可以在所有EES下运行。它提供了以下主要目标: ??通过单一编程接口编写应用程序。 由于ANSP提供的编程接口,使得EES的设计与ANSP 独立。这使得未来执行环境的发展和提高更加透明。

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