Structure of a wireless sensor node :

A sensor node is made up of four basic components such as sensing unit, processing unit, transceiver unit and a power unit which is shown in Fig. 5. It also has application dependent additional components such as a location finding system, a power generator and a mobilizer. Sensing units are usually composed of two subunits: sensors and analogue to digital converters (ADCs) (Akyildiz et al., 2002). The analogue signals produced by the sensors are converted to digital signals by the ADC, and then fed into the processing unit. The processing unit is generally associated with a small storage unit and it can manage the procedures that make the sensor node collaborate with the other nodes to carry out the assigned sensing tasks. A transceiver unit connects the node to the network. One of the most important components of a sensor node is the power unit. Power units can be supported by a power scavenging unit such as solar cells. The other subunits, of the node are application dependent.

A functional block diagram of a versatile wireless sensing node is provided in Fig. 6. Modular design approach provides a flexible and versatile platform to address the needs of a wide variety of applications. For example, depending on the sensors to be deployed, the signal conditioning block can be re-programmed or replaced. This allows for a wide variety of different sensors to be used with the wireless sensing node. Similarly, the radio link may be swapped out as required for a given applications’ wireless range requirement and the need for bidirectional communications.

Figure 5. The components of a sensor node

Figure 6.Functional block diagram of a sensor node

Using flash memory, the remote nodes acquire data on command from a base station, or by an event sensed by one or more inputs to the node. Moreover, the embedded firmware can be upgraded through the wireless network in the field.

The microprocessor has a number of functions including:

·         Managing data collection from the sensors

·          performing power management functions

·          interfacing the sensor data to the physical radio layer

·         managing the radio network protocol

A key aspect of any wireless sensing node is to minimize the power consumed by the system. Usually, the radio subsystem requires the largest amount of power. Therefore, data is sent over the radio network only when it is required. An algorithm is to be loaded into the node to determine when to send data based on the sensed event. Furthermore, it is important to minimize the power consumed by the sensor itself. Therefore, the hardware should be designed to allow the microprocessor to judiciously control power to the radio, sensor, and sensor signal conditioner (Akyildiz et al., 2002).

Communication structure of a wireless sensor network

The sensor nodes are usually scattered in a sensor field as shown in Fig. 1. Each of these scattered sensor nodes has the capabilities to collect data and route data back to the sink and the end users. Data are routed back to the end user by a multi-hop infrastructure-less architecture through the sink as shown in Fig. 1. The sink may communicate with the task manager node via Internet or Satellite.

Figure 7.Wireless Sensor Network protocol stack

The protocol stack used by the sink and the sensor nodes is given in Fig. 7. This protocol stack combines power and routing awareness, integrates data with networking protocols, communicates power efficiently through the wireless medium and promotes cooperative efforts of sensor nodes. The protocol stack consists of the application layer, transport layer, network layer, data link layer, physical layer, power management plane, mobility management plane, and task management plane (Akyildiz et al., 2002). Different types of application software can be built and used on the application layer depending on the sensing tasks. This layer makes hardware and software of the lowest layer transparent to the end-user. The transport layer helps to maintain the flow of data if the sensor networks application requires it. The network layer takes care of routing the data supplied by the transport layer, specific multi-hop wireless routing protocols between sensor nodes and sink. The data link layer is responsible for multiplexing of data streams, frame detection, Media Access Control (MAC) and error control. Since the environment is noisy and sensor nodes can be mobile, the MAC protocol must be power aware and able to minimize collision with neighbours’ broadcast. The physical layer addresses the needs of a simple but robust modulation, frequency selection, data encryption, transmission and receiving techniques.

In addition, the power, mobility, and task management planes monitor the power, movement, and task distribution among the sensor nodes. These planes help the sensor nodes coordinate the sensing task and lower the overall energy consumption.

Literature survey

Air Quality Monitoring SystemN. Kularatna and B. H. Sudantha[10]

In general, commercial sensors have application in a variety of fields such as environmental engineering, indoor climate control and ventilation control, medicine diagnostics and breath analysis in medicine, structural monitoring, surveillance, disaster management, emergency response, gasoline vapour detection in automobiles, leak detection and fire detection in safety, food process control and fermentation control in food and other industrial productions. Sensors integrated with Wireless Sensor Networks (WSN) facilitate monitoring and controlling of physical environments from remote locations with better accuracy.

Duk-Dong Lee and Dae-Sik Lee (2001) state that natural atmospheric environment has become polluted and is rapidly deteriorating due to the dramatic growth in industrial development and urbanisation. Thus, monitoring and control of such pollutants is imperative for prevention of environmental disasters. Use of conventional analytical instruments for monitoring purpose is time consuming, expensive and seldom used in real-time in the field. An effective alternative is use of solid state gas sensors that are compact, robust with versatile applications and low cost. They have also presented comparison between analytical instruments and briefed about the various solid state gas sensors namely semiconducting type, capacitor type and electrolyte types sensors.

Simon et al (2001); Semancik et al (2001) have reviewed gas sensors and summarized that semiconductor gas sensors known also as chemo-resistive gas sensors are typically based on metal oxides (e.g. SnO2, TiO2, In2O3, WO3, NiO, etc.). They conclude that the applied studies of recent findings and products have shown some significant trends on nanotechnologies and gas28 sensing layers to be employed. One of these trends aims to implement low cost, low-power consumption, reliable, smart and miniaturized sensing devices and it shows the decisive advantage of using micro-machined silicon platform as substrates for the sensitive layers. According to them, the gas sensors can be improved in different ways by use of filters (Park et al 2002), catalysts and promoters or more specific surface additives (Vlachos et al 1997), selection of the material for the sensing layer (Moseley 1992) and its physical preparation, analysis of the transient sensor response (Distante et al 2002), selection of a fixed temperature to maximize sensitivity to a particular analyte gas (Capone2001) or by use of temperature modulated operation mode (Andrew 1999). Caponeet al (2003) are of the opinion that the demand for gas detection and monitoring has grown following awareness about the need to protect the environment. According to them, the solid state gas sensors based on a variety of principles and materials, are the best choice for this purpose. They also say that the great interest shown by industrial and scientific world on solid state gas sensors is due their numerous advantages, like small sizes, high sensitiveness in detecting very low concentrations (at level of ppm or even ppb) of a wide range of gaseous chemical compounds, possibility of on-line operation and due to possible low cost bench production.

Kawasaki et al (2004); West et al (2005) have classified sensors into semiconducting type, solid electrolyte type, electrochemical type and catalytic combustion type. According to them, the sensors have the advantages of rapid reactivity, high efficiency, and gas selectivity when suitable additives are applied to it. Ceramics are most commonly used for making sensors, as they are the most reliable materials in very severe conditions like high temperature, reactive or corrosive atmosphere and high humidity. The gas-sensing materials for semiconductor type are SnO2, WO3, In2O3, perovskite-structure oxides, etc., and the electrolyte for solid electrolyte-type gas sensor is Na3Zr2Si2PO12, Sensing properties (mainly sensitivity and selectivity) as well as stability over time of the oxide layer can be improved by reducing the metal oxide grain size down to nanometre scale (Xu et al 1991; Gurlo et al 1998). Nanocrystalline semiconducting metal oxides with controlled composition are indeed of increasing interest in gas sensing and constitute also a new and exciting subject of fundamental research (Barsan et al 1999).

Korotcenkov (2007) has focused on the conduct metric semiconducting metal oxide gas sensors (especially surface conductive metal oxide). According to the author, they constitute currently one of the most investigated groups of gas sensors. They have attracted much attention in the field of gas sensing under atmospheric conditions due to their low cost and flexibility in production, simplicity of their use and possibility of many application fields and the large number of detectable gases. In addition to the conductivity change of gas-sensing material, the detection of this reaction can be done by measuring the change of capacitance, work function, mass, optical characteristics or reaction energy released by the gas/solid interaction. As per the author’s review, there are numerous researchers who have shown that the reversible interaction of the gas with the surface of the material is a characteristic of conductometric semiconducting metal oxide gas sensors.

Chengxiang et al (2010) have reviewed sensitivity and influencing factors of Metal Oxide Gas Sensors. They have come to the conclusion that the sensitivity of the metal oxide based materials changes with the factors influencing the surface reactions, such as (i) chemical components, (ii) surface modification, (iii) microstructures of sensing layers, (iv) temperature and (v) humidity. In their brief review, the study is focused on changes of sensitivity of conductometric semiconducting metal oxide gas sensors due to the five factors mentioned above. As the authors brief further, the surface reactions can be influenced by many factors, including internal and external causes, such as natural properties of base materials, surface areas and microstructure of sensing layers, surface additives, temperature and humidity, etc. One of the important parameters of gas sensors is sensitivity that has been attracting increasing attention and much effort has been made to enhance the sensitivity of gas sensors. There is no uniform definition of gas sensor sensitivity as of now. Usually, sensitivity (S) can be defined as Ra/Rg for reducing gases or Rg/Ra for oxidizing gases, where Ra stands for the resistance of gas sensors in the reference gas (usually the air) and Rgstands for the resistance in the reference gas containing target gases. Both Ra and Rg have a significant relationship with the surface reaction(s) taking place.

Kwang (2011) says that air pollution caused by exhaust gases from automobiles has become a critical issue. The principal gases that cause air pollution from automobiles are nitrogen oxides, NOx (NO and NO2) and carbon monoxide (CO). He has defined gas sensor as a device that can substitute for human olfaction, and that converts a physical phenomenon into an electrical signal. According to him many researches are being conducted to monitor air pollution by using these gas sensors, the first decade of the 21st century has been labelled by some as the “Sensor Decade.” Sensors can be interfaced between the physical world and the world of electrical devices, such as computers.

Emily et al (2013) observe that historical approaches for monitoring air pollution generally use expensive, complex, stationary equipments (Chow1995; Fehsenfeld et al 2004) that work based on the techniques MS, GC, FTIR, etc limit data collection and access to the data. This paradigm is changing with the materialization of lower-cost, easy-to-use and portable air pollution monitors (sensors) that provide high-time resolution data in near real-time. These attributes provide opportunities for enhancement of the range of existing air pollution monitoring capabilities and perhaps provide avenues to new air monitoring applications. Sensors associated with to advances in computing and communication also provide enhanced availability and accessibility of air monitoring data. Sensor devices are currently available for monitoring a range of air pollutants and new devices are continually being introduced (White 2012). Meanwhile, the emergence of information on the high spatial variability of primary air pollutants (Seinfeld et al 1998; Solomon et al 2008; Baldauf et al 2008;