Wireless Sensor Networks[/
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Wireless Sensor Networks, with the characteristics of low energy consumption, low cost, distributed and self organization, have brought a revolution to the information perception.
The wireless sensor network is composed of hundreds of thousands of the sensor nodes that can sense conditions of surrounding environment such as illumination, humidity, and temperature. Each sensor node collects data such as illumination, humidity, and temperature of the area. Each sensor node is deployed and transmits data to base station (BS). The wireless sensor network can be applied to variable fields. For example, the wireless sensor network can be used to monitor at the hostile environments for the use of military applications, to detect forest fires for prevention of disasters, or to study the phenomenon of the typhoon for a variety of academic purposes. These sensor nodes can self organize to form a network and can communicate with each other using their wireless interfaces. Energy efficient self organization and initialization protocols are developed in, . Each node has transmitted power control and an Omni directional antenna, and therefore can adjust the area of coverage with its wireless transmission. Typically, sensor nodes collect audio, seismic, and other types of data and collaborate to perform a high-level task in a sensor web. For example, a sensor network can be used for detecting the presence of potential threats in a military conflict. Most of battery energy is consumed by receiving and transmitting data. If all sensor nodes transmit data directly to the BS, the furthest node from BS will die early. On the other hand, among sensor nodes transmitting data through multiple hops, node closest to the BS tends to die early, leaving some network areas completely unmonitored and causing network partition. In order to maximize the lifetime of WSN, it is necessary for communication protocols to prolong sensor nodes’ lifetime by minimizing transmission energy consumption, sending data via paths that can avoid sensor nodes with low energy and minimizing the total transmission power.
Architecture of Wireless Sensor Network:
Figure 1.2 shows a typical schematic of a wireless sensor network (WSN). After the initial deployment (typically ad hoc), sensor nodes are responsible for self-organizing an appropriate network infrastructure, often with multi-hop connections between sensor nodes . The onboard sensors then start collecting acoustic, seismic, infrared or magnetic information about the environment, using either continuous or event driven working modes. Location and positioning information can also be obtained through the global positioning system (GPS) or local positioning algorithms. This information can be gathered from across the network and appropriately processed to construct a global view of the monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission.
The differences between WSNs and traditional networks
Wireless sensor networks, on the one hand, share the similarity of self-configuration without manual management with Mobile ad-hoc networks; on the other hand, they are different from traditional networks in many aspects due to their strict energy constraints and application specific characteristics.
NO one-size-fits-all solution: A WSN is organized as a collection of sensor nodes which co-ordinate with each other to fulfil a certain task. The entire network infrastructure depends directly on the specific application scenario. It is unlikely that a one-size-fits-all solution exists for all these different applications. The old fixed protocol stack which applied successfully to traditional networks is no longer suitable for WSNs. Many new communication algorithms have been developed for different applications. As one example, WSNs are deployed with very different network densities, from sparse to dense deployments. Each case requires unique network configuration.
WSNs distinguish themselves from traditional networks due to their application specific and energy constraints. Their structure and characteristics depend on their electronic, mechanical and communication limitations but also on application specific requirements.
One of the major and probably most important challenges in the design of WSNs is their application specific characteristic. A sensor network is set up to fulfil a specific task and the data collected from the network may be of different types due to various application scenarios. Respectively, different types of applications have their own specific requirements. These requirements are turned into specific design properties of a WSN. In other words, a WSN's architecture directly depends on the assigned application scenarios. For the acceptable performance of a given task, the optimal WSN infrastructure should be selected out of the hundreds of network solutions before the practical deployment.
Equally, an issue that has been frequently emphasized in the research literature is the fact that energy resources are significantly limited. Recharging or replacing the battery of sensor nodes may be difficult or impossible. Hence, power efficiency often turns out to be the major performance metric, directly influencing the network lifetime. Power consumption according to the functioning of a sensor node can be divided into three domains: sensing, communication, and data processing. There has been research effort in hardware improvements to optimize the energy consumed by sensing and data processing. Several studies of energy efficiency of WSNs have been discussed and several algorithms that lead to optimal connectivity topologies for power conservation have been proposed .
The work reported herein investigates chaining mechanism in PEGASIS using evolutionary algorithms like Ant Colony optimisation and Genetic algorithms and lifetime enhancement by chain leader selection criteria and maintenance of priority queue at each node if the next node fails. Lifetime measurement of WSN using various types of PEGASIS variants for both Homogenous and heterogeneous has been evaluated.
The thesis has been organised in the fallowing manner. Fallowing this chapter, chapter 2 presents extensive literature survey on routing algorithms for WSN. It mainly discusses energy efficient hierarchical routing protocols for WSN. Evolutionary algorithms are also presented in this section. Chain forming mechanism using GREEDY algorithm is presented in chapter 3. It mainly investigates the lifetime of PEGASIS protocol under various scenarios. Chapter 4 deals with Ant Colony Optimisation technique applied to PEGASIS protocol and lifetime Measurement. Chapter 5 deals with Genetic algorithm and its lifetime measurement. Chapter 6 gives the comparative study of all the algorithms proposed.
Routing Challenges and Design Issues in WSNs:
Despite plethora of applications of WSN, these networks have several restrictions, e.g., limited energy supply, limited computing power, and limited bandwidth of the wireless links connecting sensor nodes. One of the main design goals of WSN is to carry out data communication while trying to prolong the lifetime of the network and prevent connectivity degradation by employing aggressive energy management techniques. In order to design an efficient routing protocol, several challenging factors should be addressed meticulously. The following factors are discussed below:
Node deployment: Node deployment in WSN is application dependent and affects the performance of the routing protocol. The deployment can be either deterministic or randomized. In deterministic deployment, the sensors are manually placed and data is routed through pre-determined paths; but in random node deployment, the sensor nodes are scattered randomly creating an infrastructure in an ad hoc manner. Hence, random deployment raises several issues as coverage, optimal clustering etc. which need to be addressed.
Classification of Routing Protocols in WSNs:
In general, routing in WSNs can be divided into flat-based routing, hierarchical-based routing, and location-based routing depending on the network structure. In flat-based routing, all nodes are typically assigned equal roles or functionality. In hierarchical-based routing, however, nodes will play different roles in the network. In location-based routing, sensor nodes' positions are exploited to route data in the network.
A routing protocol is considered adaptive if certain system parameters can be controlled in order to adapt to the current network conditions and available energy levels. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS-based, or routing techniques depending on the protocol operation. In addition to the above, routing protocols can be classified into three categories, namely, proactive, reactive, and hybrid protocols depending on how the source sends a route to the destination. In proactive protocols, all routes are computed before they are really needed, while in reactive protocols, routes are computed on demand. Hybrid protocols use a combination of these two ideas. When sensor nodes are static, it is preferable to have table driven routing protocols rather than using reactive protocols. A significant amount of energy is used in route discovery and setup of reactive protocols. Another class of routing protocols is called the cooperative routing protocols. In cooperative routing, nodes send data to a central node where data can be aggregated and may be subject to further processing, hence reducing route cost in terms of energy usage.
PEGASIS (Power Efficient Gathering in Sensor Information Systems)
Wireless sensor nodes sense data and send it directly to the base station or they perform a clustering procedure as in LEACH. LEACH is known for cluster formation which contains cluster members sensing the data and the cluster head which gathers the data collected in a
fused manner (all the data is sent as a single packet) to the base station. This procedure has
gained in conserving a lot of energy that would otherwise be wasted. PEGASIS is an extension to LEACH; it has better ways of conserving energy which last even more than using cluster mechanism in LEACH .
When the nodes in the network which are at some distance from the base station, the easiest and the simplest way of transmitting the sensed data to the base station is to transmit it directly, which may lead to quicker depletion of energy in all the nodes. The nodes at a large distance away from the base station are depleted quicker than the nodes which are closer to the base station as they need some extra energy to reach the farthest base station. Another approach where energy is consumed in low amounts is by forming cluster heads and cluster members using the sensor nodes in the network. Cluster members perform the sensing and computing the data (Data Fusion) and the cluster heads transmit the fused data to the base station. All the nodes in the network take their chance to act as cluster heads to send the fused data to the base station; again the farthest cluster head needs some extra energy to send the data to the base station.
The research carried out for this thesis, investigates energy efficient routing algorithms related to WSNs. A new cluster head selection criteria and maintenance of priority queue at each node is proposed. This increases the life of WSNs. Ant Colony Optimisation and Genetic Algorithms are used in making the chain of PEGASIS. Lifetime of WSN under various scenarios has been investigated. These chapter summaries the work reported in this thesis, specifying the limitations of the study and provides some suggestions to future work. Following this introduction, section 7.2 lists the achievements of the research work. Section 7.3 presents some of the future research area that can be extended to this thesis.