A Wireless Sensor Network (WSN) can be defined as a group of independent nodes, communicating wirelessly over limited frequency and bandwidth [1]. The novelty of WSNs in comparison to traditional sensor network is that they depend on dense deployment and coordination to execute their task successfully. WSN  consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, humidity, motion or pollutants and to cooperatively pass their data through the network to a main location[2]. The more modern networks are bi-directional, also enabling control of sensor activity. Generally, a WSN is composed of a large number of sensor nodes that are deployed outdoor at random (e.g., by dropping them from an aircraft) in a field [3]. Reports transmitted by these sensors are collected by base stations. It is essential to know the node location in many applications of WSN such as target tracking, event location reporting, geographic routing and directional querying. Determining the physical locations of sensor nodes after they have been deployedis known as the problem of localization [5].

1.1 Characteristics of wireless sensor network

The main characteristics of a WSN include

Power consumption constrains for nodes using batteries or energy harvesting

Ability to cope with node failures

Mobility of nodes

Communication failures

Heterogeneity of nodes

Scalability to large scale of deployment

Ability to withstand harsh environmental conditions

Ease of use

Power consumption

The basis of this research is location of node of sensors in a wireless sensor network.The location refers to coordinates which describe each node in the network as shown in figure 1.

Figure 1: Node in a wireless sensor network

These location coordinates to be estimated may be either physical location or data location:

• Physical Location: The sensor’s physical coordinates, that is, where it exists in space, are estimated.

• Data Location: Non-physical coordinates for the sensor’s measured data are estimated.

These coordinates describes where, in a particular space, the measured data vector lies.

Estimating a sensor’s physical coordinates is intuitively important: when sensor data is reported, it should be accompanied with an indication of where in space that data was recorded. Data location coordinates are made necessary by the bandwidth and energy limitations of a network. In such sensor networks, large quantities of sensor data are recorded and are available, but typically are not sent through the network, due to the communication constraints. Instead, summary statistics may be typically communicated, and occasionally when the situation requires it, full data is sent. Data coordinates are a summary statistic, a lower-dimensional representation of the full data available, which preserve information about the relationships between sensors’ data. Automatic estimation of physical location of the sensors in these wireless networks is a key enabling technology. The overwhelming reason is that a sensor’s location must be known for its data to be meaningful. If a system is set up to respond locally to changes in sensor data, then it must know where those changes are occurring. In many cases, location itself is the data that needs to be sensed. Localization can be the driving need for wireless sensor networks in applications such as warehousing and manufacturing logistics, in which radio tagged parts and equipment must be able to be accurately located at all times. Also, sensor location information, if it is accurate enough, can be extremely useful for scalable, ‘geographic’ routing algorithms.

To make these applications viable with possibly vast numbers of sensors, device costs will need to be low, sensors will need to last for years or even decades without battery replacement, and the network will need to organize without significant human moderation. Traditional physical localization techniques such as Global Positioning System (GPS) and Local Positioning System (LPS) are not well suited for this requirement, GPS is the most popular localization system with relatively high accuracy, but it may not be realistic to equip each node in a WSN with GPS due to cost, form factor, energy consumption, and some other restrictive conditions [6]. So, several other localization schemes, classified into two categories, which are:range-based and range-free

GPS on each device is cost and energy prohibitive for many applications, not sufficiently robust to jamming for military applications, and limited to outdoor applications. Local positioning systems (LPS) rely on high-capability base stations being deployed in each coverage area, an expensive burden for most low configuration wireless sensor networks.

In this research, I assumed that the monitored area is planar, i.e. the elevation difference among the nodes can be ignored thus the nodes distance from the control node is measured in two dimensions (2D). The technique of localization used in this research for node location computation technique is called the Modified Linear Intersection (MLI) for quick localization in a localization scheme based on local distance measurement. MLI is a routine method used for control point densification in surveying engineering. It is applied in localization for WSN and some experiments to estimate its usability are given.


Some of the exciting characteristics of WSN are given as below:

1.2.1 Area monitoring

Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. A military example is the use of sensors to detect enemy intrusion; a civilian example is the geo-fencing of gas or oil pipelines.

When the sensors detect the event being monitored (heat, pressure), the event is reported to one of the base stations, which then takes appropriate action (e.g., send a message on the internet or to a satellite). Similarly, wireless sensor networks can use a range of sensors to detect the presence of vehicles ranging from motorcycles to train cars.

1.2.2 Forest fire detection

A network of Sensor Nodes can be installed in a forest to detect when a fire has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fire in the trees or vegetation. The early detection is crucial for a successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know when a fire is started and how it is spreading.

1.2.3 Air pollution monitoring

Wireless sensor networks have been deployed in several cities (Stockholm, London or Brisbane) to monitor the concentration of dangerous gases for citizens. These can take advantage of the ad-hoc wireless links rather than wired installations, which also make them more mobile for testing readings in different areas. There are various architectures that can be used for such applications as well as different kinds of data analysis and data mining that can be conducted.(8)

1.2.4 Landslide detection

A landslide detection system makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. And through the data gathered it may be possible to know the occurrence of landslides long before it actually happens. This can help in avoiding the disasters caused by earthquakes, hurricanes, etc.

1.2.5 Industrial monitoring

In this section, some of the applications of WSN to the industries are outlined. Machine health monitoring

Wireless sensor networks have been developed for machinery condition-based maintenance (CBM) as they offer significant cost savings and enable new functionalities. In wired systems, the installation of enough sensors is often limited by the cost of wiring. Previously inaccessible locations, rotating machinery, hazardous or restricted areas, and mobile assets can now be reached with wireless sensors. Data Logging

Wireless sensor networks are also used for the collection of data for monitoring of environmental information this can be as simple as the monitoring of the temperature in a fridge to the level of water in overflow tanks in nuclear power plants. The statistical information can then be used to show how systems have been working. The advantage of WSNs over conventional loggers is the “live” data feed that is possible. Industrial sense and control applications

In recent research a vast number of wireless sensor network communication protocols have been developed. While previous research was primarily focused on power awareness, more recent research have begun to consider a wider range of aspects, such as wireless link reliability, real-time capabilities, or quality-of-service. These new aspects are considered as an enabler for future applications in industrial and related wireless sense and control applications, and partially replacing or enhancing conventional wire-based networks by WSN techniques [12]. Water/wastewater monitoring

There are many opportunities for using wireless sensor networks within the water/wastewater industries. Facilities not wired for power or data transmission can be monitored using industrial wireless I/O devices and sensors powered using solar panels or battery packs and also used in pollution control board. Agriculture

Using wireless sensor networks within the agricultural industry is increasingly common; using a wireless network frees the farmer from the maintenance of wiring in a difficult environment. Gravity feed water systems can be monitored using pressure transmitters to monitor water tank levels, pumps can be controlled using wireless I/O devices and water use can be measured and wirelessly transmitted back to a central control center for billing. Irrigation automation enables more efficient water use and reduces waste [9]. Greenhouse monitoring

Wireless sensor networks are also used to control the temperature and humidity levels inside commercial greenhouses. When the temperature and humidity drops below specific levels, the greenhouse manager must be notified via e-mail or cell phone text message, or host systems can trigger misting systems, open vents, turn on fans, or control a wide variety of system responses(Greene, 2007). Structural monitoring

Wireless sensors can be used to monitor the movement within buildings and infrastructure such as bridges, flyovers, embankments, tunnels etc… enabling Engineering practices to monitor assets remotely without the need for costly site visits, as well as having the advantage of daily data, whereas traditionally this data was collected weekly or monthly, using physical site visits, involving either road or rail closure in some cases. It is also far more accurate than any visual inspection that would be carried out. Passive localization and tracking

The application of WSN to the passive localization and tracking of non-cooperative targets (i.e., people not wearing any tag) has been proposed by exploiting the pervasive and low-cost nature of such technology and the properties of the wireless links which are established in a meshed WSN infrastructure.

1.2.6 Military application

One of the main drivers for investigating wireless sensor networks is their use in military applications. The military use-cases for wireless sensor networks are diverse. They encompass applications such as:

Monitoring militant activity in remote areas of specific interest (e.g., key roads, villages);

Force protection (e.g., ensuring that buildings which have been cleared remain clear from infiltration by an adversary).

One prominent use-case which has received a great deal of interest from military personnel recently is base protection (or force protection in general). A possible set-up is depicted in Figure 2. Having deployed a headquarters in an area of active engagement it is essential to prevent the base from being attacked. The surrounding terrain may be undulating or mountainous and potentially could be obscured in trees and vegetation. Attack could come in the form of militant groups on foot or with motor vehicles. In order to facilitate an early detection, the perimeter protection in Figure 2 would cover a belt around the camp of up to 4 km, while in practice ranges of up to 10 km might be a requirement. Detection may be needed throughout the whole of this range whilst identification may only be required within a belt of around one to 1–2 km around the base.



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