The Overview Of Wireless Sensor Networks Computer Science Essay

Published Date: 02 Nov 2017

CHAPTER 1

During the past few years, there has been a lot of research on developing techniques for position estimation and distance measurements in Wireless Sensor Networks. The networks of two or more mobile devices connected to each other without the help of intervening infrastructure is called as an Ad-hoc or Short-live network. In remote geographical locations, a fixed wireless network and an ad-hoc network can be deployed, but it requires minimum set up and administration costs. Also the combination of an ad-hoc network with a bigger network such as the Internet or a wireless infrastructure network increases the coverage area and application domain of the ad-hoc network. But the fact remains that communication in an ad-hoc network between different hosts that are not directly linked is an issue not only for search and rescue operations, but also for educational and business purposes.

An ad-hoc network can be mainly classified into two types: mobile ad-hoc network and mobile ad-hoc sensor network [1]. Sensor networks communicate directly with the centralized controller, where as a mobile ad-hoc sensor network follows broader sequence of operational scenarios, thus demanding a less complex procedure [2].

A mobile ad-hoc sensor or hybrid ad-hoc network comprises a number of sensors spreading over a geographical area [3]. Sensor positioning is a fundamental and essential issue for sensor network operation and management. In some situations, where most existing sensor positioning methods tend to fail to perform well, an example being when the topology of a sensor network is anisotropic [1].

The paradigm of context-aware computing [4-6] has become increasingly interesting to researchers lately. Context-aware computing systems aim to autonomously change their function based on their observation of the environment around them. By determining the context or environment, the computing devices are able to adjust themselves to the current computing demands, customize their behavior according to their location, or even actively react to their surroundings. The paradigm of context-aware computing represents a significant step towards the vision of ubiquitous computing [7-9]. Location-aware computing [10] is an important and practical subset of the context aware computing paradigm. Fundamental to the computing in these systems is location awareness. By detecting and tracking the locations of objects, it is feasible to derive other useful location related information, such as the objects’ orientation and mobility. The behavior of location-aware computing systems depends heavily on location information. Since, these location-aware computing systems are usually embedded into the physical world, where they are deployed , which means that the sensor nods of these systems often need to determine their locations or spatial relationship with the particular objects is indispensable for the management and operation of sensor networks.

In this research work it is proposed to implement Distribution of nodes on square area, Hexagonal area and find out the distance between source and destinations using shortest path method. The need to monitor and measure various physical phenomena (e.g. temperature, fluid levels, vibration, strain, humidity, acidity, pumps, generators to manufacturing lines, aviation, building maintenance and so forth) is common to many areas including structural engineering, agriculture and forestry, healthcare, logistics and transportation, and military applications [4].

Fig.1. 1: Wireless Sensor Networks Overview

MOTIVATION

Based on the above study Multidimensional scaling (MDS) and Iterative Routing (IR) algorithms are better suited for Position estimation and distance measurements. The various methods indicate that multidimensional scaling algorithm and Iterative Routing algorithm are better choice of position estimation and distance measurements in wireless sensor networks.

The main objective of wireless sensor networks is to provide maximum coverage area and low computational cost. Distribution of nodes on square method is developed for 2 dimensional and 3 dimensional open and closed surfaces in geographical area. Shortest path position estimation method is deployed for connectivity of information with low cost. 2D iterative routing algorithm is used to distance measurement with low cost and reducing range error. Hexagonal Method is used to deploy more number moving nodes in the geographical area. It reduces range error, computational cost and increases coverage area.

For the purposes of animal behavior and biological research, it is very useful to track animals over time and very wide spaces [12]. The animal behavior and interactions within their own and with other species can be tracked. Using current practices, tracking is a very difficult, expensive process, and requires bulky tags that rapidly run out of energy [12]. A typical practice is to attach VHF transmitter collars to animals to be tracked, and then triangulate their location by driving (or flying) to various locations with a directional antenna [13]. Alternatively, GPS-based collars can be used, but are limited by cost concerns, and offer only a short lifetime due to high energy consumption [2]. Using wireless sensor networks can dramatically improve the abilities of biological researchers as demonstrated by ‘Zebra Net’ [13].The multi-hop routing of position estimation data through the sensor network enables low powers transmit from the animal tags. Furthermore, inter-animal distances, which are of particular interest to animal behaviorists, can be estimated using distance measurements and position estimation methods, without restoring to GPS.

The other example is to consider and deploying a sensor network in an office building, manufacturing floor, and warehouse. Sensors already play a very important role in manufacturing [22]. Monitor and control of machinery has usually been wired, but making these sensors wireless reduces the high cost of cabling and makes the manufacturing floor more dynamic. Automatic position estimation of these sensors further increases automation. Also, boxes and parts to be warehoused and factory and office equipment can all be tagged with sensors when first brought into the facility [22]. These sensors monitor storage conditions like temperature, humidity etc. Sensors on mobile equipment report their physical location when the equipment is lost or needs to be found (e.g. during inventory), and even contact security if they are about to ‘walk out’ of the building. Knowing where parts and equipment are when they are critically needed reduces the need to have duplicates as back-up, savings which could pay for the wireless sensor network itself [14].

Radio-frequency identification (RFID) tags, such as those now required by Walmart on pallets and cartons entering in its warehouses [14], represent a first step in warehouse logistics. RFID tags are only positioned when they pass within a few feet of a reader, thus remaining out of access most of their time in the warehouse. Network wireless sensors though queried and located as long as they are within range (on the order of 10 m) of the closest other wireless sensor. Costs of Commercial Logistics Solutions, Position Estimation Method are also proposed for logistics applications. For Position Estimation Method, sensors are active, using signals transmitted to or received from high-capability base stations to locate them. One issue with Position Estimation Method is the cost of deploying base stations which cover an area of interest. Suppose a company called Detection Systems, Inc. (now owned by Bosch Security Systems) deployed a Position Estimation Method for a personal security application system on a small college campus. The base stations use measured RSS to calculate and report the location of a radio tag when its ‘Alarm’ button is pushed, so that police can be dispatched to the exact position of the person request assistance [15]. It was perhaps this high cost of operation which limited the implementation of this system at other campuses. In addition, the theoretical accuracy of position estimation method increases with the density of sensors.

1.3. ORGANIZATION OF THESIS

The thesis can be structured into seven chapters as follows:

An overview of wireless sensor networks and motivation of the current work are presented in Chapter 1.

Chapter 2 deals with the necessary background information and related research for sensor localization in distributed ad-hoc sensor networks. The challenges for effective and robust sensor position estimation are also discussed.

Chapter 3 explains the idea of various methods using multidimensional scaling for position estimation [16] and simulated Distribution of nodes on square method [17] for WSNs. In this multi-dimensional scaling algorithm is used for position estimation. A distributed algorithm that produces a large number of nodes is placed in the Squaring method for an arbitrary sensor network, with no constraints on communication model. Its suitability in 2D and further extends it to senor networks developed on 3D open and closed surfaces are done.

Chapter 4 focuses on shortest path position estimation between source and destination nodes in wireless Sensor Networks with low cost [18]. In this, Shortest Path method algorithm techniques are discussed and it extends to 2D IR algorithm for position estimation [19]. Position estimation is important when there is an uncertainty of the exact position of some fixed or mobile devices. The sensing and computation nodes were considered as a part of the sensor network in the previous method. Some of the computing may be done in the network itself in detail. An Iterative Routing Algorithm is used to find the shortest path between sensor nodes [64]. The number of nodes estimated, position estimation accuracy, ranging error and computational cost is determined.

Chapter 5 describes a comparative analysis of IR Algorithm with conventional methods. A comparative analysis is made for proposed method along with existing methods with respect to computational cost, shortest path, and number of nodes, radio range and ranging error [20]. The simulation results show that, the proposed method iterative routing algorithm provides better performance over existing methods in the aspects considered.

Chapter 6 explains the technique for Nodes Distribution on Hexagonal method [21]. In some environmental conditions, randomly deployed sensor nodes could not satisfy the requirements of wireless sensor networks. So that it is necessary to use mobile sensor nodes. The simulation results for various types of position estimation and distance measurements are also presented. The proposed methods are implemented using Matlab 2011b.

Chapter 7 summarizes the contributions of this dissertation work on Position estimation algorithms for wireless sensor network systems. Then, we examine potential extension based on the proposed approaches. Finally, some directions for future work are discussed.