Examining suitable solutions to optimize energy consumption in wireless sensor networks using memetic algorithm

Dissertation for master's degree

Trend: Power Electronics

Abstract

One of the important design challenges in wireless sensor networks (WSN) is to prolong the lifetime of the system (node), while achieving an acceptable quality of service for the applications. Extending the life of the node is directly related to reducing energy consumption. In WSN, each sensor node works with battery power, and in most cases, especially in remote and hostile environments, it is not easy to recharge and replace. Due to the limited capabilities of sensor nodes, it is usually desirable that the WSN should be expanded with high density and therefore redundancy can be used to increase and optimize the network lifetime. In this thesis, we first examine the researched patterns with the aim of increasing the lifetime of wireless sensor networks, then we present an effective algorithm for lifetime optimization and self-stabilization to enhance the lifetime of wireless sensor networks. Our algorithm seeks to create resistance by maintaining the necessary set of operating nodes and replacing damaged nodes if needed. We present some theoretical and simulation results that fully demonstrate the usefulness of the proposed algorithm. Keywords: lifetime optimization, sensor networks, self-stabilizing reliability algorithm, Weibel distribution A wireless sensor network [1] (WSN) consists of a large number of sensor nodes that are densely distributed around the desired physical phenomenon. These networks provide at least the computational possibility and some sensory tools to sense parameters such as temperature, light, vibration, sound, radiation, humidity, etc. have  The evaluation parameters of wireless sensor networks (WSN) are:

Lifetime

Coverage range

Cost and ease of use

Response time: real-time applications

Accuracy: environmental applications

Security

Effective sampling rate

Despite the numerous capabilities these nodes have, but because their energy is powered by batteries. provided with limited power, their lifespan will be limited. In fact, the energy limitation of the nodes and the lifetime of the network is one of the important challenges on the way of these networks. Among the various factors of energy consumption, data routing is considered one of the most important factors. In this thesis, relying on the topic of reducing energy consumption and increasing the lifespan of wireless sensor networks, we explain and examine these challenges.

1-2- Statement of the problem

In recent years, the advancement of telecommunication technology and the industry of small electrical and electronic components has led to the construction of small and relatively cheap sensors that are connected to each other through a wireless network. These networks, which are called wireless sensor networks, have become a suitable tool for extracting data from the surrounding environment and monitoring environmental events, and their applications in domestic, industrial and military fields are increasing day by day. Wireless sensor networks are a collection of small sensor nodes that have the ability to monitor and sense their surroundings and send the sensed data to a main station.  The limited energy available in the nodes is the basic challenge of sensor networks, which affects the survival of the network. On the other hand, due to the presence of a large number of sensors in the network or the impossibility of accessing them, replacing or charging the sensors' batteries is not practical. For this reason, providing methods for optimal energy consumption that will ultimately increase the life of the network is strongly felt. Previous researches have shown that by organizing network nodes in clusters, it is possible to increase the energy efficiency and increase the life of the network. In most researches, the time elapsed until the death of the first or last node of the network is called network life. 1-3- Importance and necessity of research Wireless sensor networks, which usually contain hundreds or even thousands of inexpensive sensor nodes, are an ideal solution for various monitoring and surveillance applications, including traffic control, health care, environmental monitoring, battlefield surveillance, etc.  Considering the limitations of sensor nodes in terms of energy resources, the unattended expansion of sensor nodes in the operational environment, as well as the nature of applications of wireless sensor networks, establishing security in these networks is an important and at the same time difficult issue. Because the maintenance of sensor nodes is closely related to their lifetime and the lifetime of sensor networks is usually short, and the reason for this is the limitation.Since the care of sensor nodes has a close relationship with their lifespan and the lifespan of sensor networks is usually short and the reason is the limitation of power supply energy, providing suitable structural models and providing management methods and power-aware algorithms with the aim of increasing the lifespan of sensor networks are among the important research topics.  In this thesis, we will examine these solutions and appropriate algorithms. 1-4- Research goals This research was formed in order to achieve the following goals: 1-4-1- Main goal: Identifying and reviewing effective solutions to optimize energy consumption using the memetic algorithm. 1-4-2- Sub-goals: 1- Identifying the solution effective ways to increase the network lifetime as a result of balancing energy consumption. 2- Examining the importance of optimal energy consumption in wireless sensor networks. 1-5 research questions 1-5-1- Main question How is it possible to optimize energy consumption using the memetic algorithm? 1-5-2 Sub-questions 1- The most effective result of balancing energy consumption What is in a wireless network?

2- How does congestion control increase the lifespan of a wireless sensor network?

1-6- Research assumptions

1-6-1- The main hypothesis

Congestion control in the network will result in a balance in energy consumption, in which case we will see an increase in network lifetime and optimization of energy consumption.

1-6-2- Assumptions Secondary

1- Creating a balance in energy consumption leads to longer network life.

2- Congestion control in wireless sensor networks by reducing energy consumption increases the life of wireless sensor networks.

1-7- Definitions of terms

1-7-1- Definition of network life

The time elapsed until the first death or the last node of the network is called network life. .

1-7-2-Definition of memetic algorithm

Memetic algorithm is a search strategy among a set of optimizing factors that are competitively or cooperatively placed together. In memetic algorithm, we have a set of solutions to solve the problem. In computer networks, when there are many packets in a part of the subnet, the performance decreases. This situation is called crowding. 1-8- Research plan and data analysis method Considering that the purpose of the research is to investigate effective methods to optimize energy consumption using the memetic algorithm, it is a field study and a review of related articles in this field. For this purpose, various sites such as Elsevier and Springer are searched. Since this research is based on the issue of minimizing energy consumption for data analysis The software of this article has been used.

The history of wireless sensor networks

The beginning of research in this field goes back to around 1980 when the Defense Advanced Research Projects Agency (DARPA) [2] was working on the Distributed Wireless Networks (DSN) program [3], that is, when ARPANET (a network before the Internet) with an area of ??about 200 hosts was working in universities and scientific centers. DSNs are sensor nodes that are geographically dispersed but interact and cooperate with each other. In addition, each one is autonomous and self-governing, and these networks route information to the node that has the best ability to use this information. Generally, the components used in a DSN are sensors (audio, etc.), communication, processing techniques, and distributed algorithms and software. Melon University researchers developed a communication-oriented operating system called Accent. presented that with flexibility and transparent access to distributed resources, a DSN with error florence capability could be prepared. One of the implemented applications of DSN developed by the Massachusetts Institute of Technology was a helicopter tracking system that used a number of acoustic microphones followed by signal matching and interception techniques. But in line with the initial research on sensor networks, especially DSNs, the technology was not yet fully ready, for example, the sensors were relatively large (for example, a shoe box or larger), which reduced the application range of these networks.