Abstract
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.
1. Introduction
WSNs are constituted of small sensors with specialized applications and limitations designed for specific purposes. The applications are divided into military, commercial, and medical applications. Among military applications are communication, command, and intelligence defense networks. Health care system for disables in remote areas, smart environment for the elderly, physicians, and medical staff communication networks, and patient surveillance systems are some of medical applications. Moreover, there is a wide range of commercial applications including security systems, fire safety systems, environment pollution monitor systems (chemical, microbial, and nuclear pollutions), vehicle tracking, supervising and controlling systems, traffic control system, and natural disasters studies (e.g., earthquake and flood) [1]. Wide range of applications has resulted in development of variety of protocols which include plenty of flexible parameters. At any rate, some parameters, due to their wide range of utilization, can be found in several applications (as common parameters) and of great importance. Wireless sensor networks use mobile energy sources and rechargeable batteries, and due to technological limitations, these batteries can supply energy for a short period of time. Thus, optimum utilization of energy in such networks is of great importance [2].
Necessity of data integrity in WNSs due to support continuous and permanent communication among the sensors has made the lifetime another important parameter in WSNs. The present study surveys some specific parameters throughout different operational stages of WSNs. In general, operational stages of classic WSNs are divided into node placement, network coverage, clustering, and data aggregation. Figure 1(a) pictures general classification of the main operational stages of WSNs.
An important stage before establishment of a WSN is “node placement.” Generally, there are several types of node distributions in WSNs including regular, random, and grid distributions. Under grid layout, the distance between each node can be estimated. An example of grid layout is pictured in Figure 1(b) and, clearly, the gap between the nodes is fixed.
The decision about type of layout depends on the expected application, so that nodes for military purposes are usually scattered by airplanes over military zones, while in case of underwater sensors, regular distribution is adopted and grid layouts are usually used for urban networks (Figure 1(b)).
Poisson’s distribution is useful for modeling different types of random phenomena; it generates an estimate of binomial probabilities. In addition to an estimator distribution, Poisson’s distribution is a useful probability model for the events that happen randomly whether in time or place. The distribution is usually used for detailed study on and simulation of wireless networks.
The next stage is to connect the sensors based on the range of service. As mentioned before, among different features of sensors, radio range and service domain are key factors. Taking into account the required area coverage, the best layout must be adopted to reach the best quantity and quality of the services. Ineffective layout means waste of energy and financial resources.
Clustering is another main operation, which plays a key role in WSN optimization. By clustering, the sensor nodes are divided into groups known as division cluster. Each cluster has a cluster head that aggregates data from the nodes in the cluster and forwards the data to the sink directly or step by step using other clusters’ heads. Therefore, the nodes may reduce their communication heading compared with the situation when data are forwarded directly to the sink. That is, clustering is an effective approach to attenuate load between sensor nodes.
Figure 1(d) illustrates nodes clustering in WSNs. Clustering is a way to save more energy and increase lifetime of the sensors in WSNs. The technique also has other advantages such as improved security, less extra data, and improved scalability of the network. To achieve better performance, different protocols can be used depending on the application.
Babamir and Norouzi proposed an efficient aggregate signcryption scheme to maximize the security of data in a kind of wireless medical network named the disconnected or unattended wireless sensor network [3]. Also in other work, they proposed another new secure scheme in which various security goals such as confidentiality, authentication and integrity. In addition, the aggregation process of their scheme reduces the space and communication overheads both for sensors and sink. The proposed technique efficiently enables the sensors and sinks to protect, verify, and recover all the related data [4].
The protocols are reliable ways to increase lifetime of the networks, although they cause more energy consumption by the cluster heads. Therefore, to increase lifetime of the network, cluster heads must be reelected during each period of cluster layout. In spite of the fact that the protocols ensure implementation of an effective clustering algorithm, they fail to guarantee adoption of the best node as cluster head.
Through optimization, the algorithms may attenuate energy consumption to a great extent and consequently improve efficiency and lifetime lifetime of the network.
Eventually, the transfer of data and queries between the main stations, information sinks, or events is another important issue in WSNs. A simple process for transfer of data is the direct transfer of data between the node and base station. The single-step oriented process is too costly; the more the distance between the node and base station, the more energy is needed and consequently the shorter the lifetime of the network.
Another process for transfer of data is multistep oriented transfer for a specific radius. This process saves considerable deal of energy and lessens collision in the network to a great deal, although, depending on the place of using routing mechanisms, they have some limitations.
Main reason that makes researchers more interested in the issue data gathering and routing stages is the considerable energy consumed at this stage. Figure 1(e) pictures required energy in every states of the WSN. Clearly, the highest energy consumption is by radio communication. Therefore, more detailed studies on this stage hold great promises to optimize WSN concerning energy consumption and lifetime of the network [2, 5, 6].
Improvement of the parameters mentioned above eventuates in an optimized WSN. There are variety of methods to this end, such as fuzzy theory, neural networks, and evolutionary algorithm and thanks to its better results for larger scale networks and the fact that it generates final formula at the end, genetic algorithm is more common. The availability of final formula makes the algorithm more useful and helpful for human users. Thus, the present study uses genetic algorithm for optimization and customization of the networks [7].
This paper is organized as follows; Section 2 gives a brief description of genetic algorithm. Sections 3, 4, 5 and 6 present our proposed fitness function in node placement, network coverage, clustering, data aggregation, and details of algorithms, respectively. Finally, Section 7 presents conclusions and suggestions for future projects.
2. Genetic Algorithm
Also known as a global heuristic algorithm, a generic algorithm estimates an optimal solution through generating different individuals [8]. Focused fitness function is one of procedures of the algorithm. Following section describes the fundamental parts of a generic algorithm. Figure 2(a) indicates the general scheme of genetic algorithm mechanism.
2.1. Initialization
The genetic algorithm starts with an elementary population comprised of random chromosomes which includes genes with a sequence of 0 s or 1 s. Afterward, the algorithm leads individuals to achieve an optimum solution by the way of repetitive processes including crossover and selection operators. There are two ways to develop a new population [9]: steady-state GA and generational GA. In the case of the former, one or two members in the population are replaced and at the same time, the generational GA replaces all the generated individuals of a generation.
2.2. Fitness
Under the genetic algorithm, the fitness function, by definition, is a process for scoring each chromosome based on their qualification. The assigned score is a trait for continuation of further reproduction. Dependence to problem by the fitness function is considerable, so that in case of some problems, it is not possible to define the problem. Naturally, individuals are permitted to go to the new generation based on their fitness score. Therefore, the score dictates the fate of individuals.
Group No: 01
A_46_Rishabh Mishra
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