Investigation, simulation and improvement of energy consumption reduction algorithms in wireless sensor networks

Master's Thesis

Electronics Orientation

Abstract

Today, according to the advantages of wireless sensor networks, which are simple and cheap implementation, low power consumption and high scalability, they are used in many applications. Designing sustainable wireless sensor networks is a very challenging issue. Sensors with limited energy are expected to operate automatically for a long time. Meanwhile, replacement of dead batteries may be expensive or even impossible in harsh environments. On the other hand, unlike other networks, wireless sensor networks are designed for specific small-scale applications such as medical monitoring systems and large-scale applications such as environmental monitoring. In this context, a lot of research work has been done in order to propose a wide range of solutions for the problem of energy saving. In this thesis, a routing algorithm is designed to generate the best route between the sensor nodes and the local aggregator node, with the aim of achieving appropriate traffic distribution and, as a result, balancing the energy consumption of intermediate nodes. Creating such a balance helps to increase the lifetime of the network and will lead to the improvement of the energy consumption pattern in wireless sensor networks with limited energy resources. On the other hand, by using the possibility of changing the range of nodes, it is tried to increase the possibility of load distribution in low density points of the network. The results of the simulations show a 20% improvement in the lifetime of the network using the proposed algorithm compared to some energy-sensitive routing algorithms proposed in recent years. Keywords: wireless sensor networks, energy-sensitive routing, energy consumption rate balancing, scalability, coverage, delay time, quality of service, security, and mobility are among the main needs of wireless sensor networks. It is used in various applications, including environmental monitoring, public security, medical care, and military and industrial applications. In these applications, the sensor is expected to operate automatically for long periods of time, be it weeks or months. However, due to the limited battery resources available in the sensors, these networks suffer from the limitation of the network lifetime.

     In the last few years, several methods have been proposed to save energy in wireless sensor networks, and many researches are still being conducted on how to optimize energy consumption for wireless sensor networks with limited energy resources. In the next section, we will state the existing standards for increasing the lifetime of wireless sensor networks with regard to energy storage. 1.1 Energy storage mechanisms in wireless sensor networks In this section, we will review the main methods available to solve the problem of energy consumption in wireless sensor networks presented in the article [1]. A classification of the proposed energy storage mechanisms is summarized in Figure 1-1. Figure 1-1 Classification of Energy Storage Mechanisms 1.1.1 Radio Optimization Radio transactions in sensor nodes play the most important role in draining battery energy. Considering the nature of wireless communication, researchers have proposed methods based on optimization of radio parameters such as modulation and coding schemes, transmission power control and directional antennas to reduce energy loss in wireless sensor networks. A) Modulation optimization[1]: Modulation optimization aims to find optimal modulation parameters to minimize radio energy consumption. Existing research tries to find an optimal balance between the set size (the number of characters used), the rate of information sent (the number of bits of information per symbol), communication time, the distance between nodes and noise [3,2].

b) Collaborative communication schemes [2] (multiple hubs): In order to improve the quality of the received signal by using the cooperation of several antennas, which lead to the creation of a multi-antenna virtual transmitter [3], it has been presented. The idea of ??this design is derived from the fact that the information is usually heard by the neighbors of a node due to the nature of the broadcast from the channel.Many researches have been done in the field of comparing the energy consumption of SISO networks (one input and one output[4]) and virtual MIMO networks (multiple inputs and multiple outputs[5]). The results of these researches show better energy saving and lower end-to-end delay at greater distances from the transmission range of nodes in MIMO systems, even with the additional overhead energy required to run these algorithms [5,4].

P) Transmission Power Control [6]: Transmission Power Control (TPC) has been investigated in order to increase energy efficiency in the physical layer by adjusting the power of radio transmitters. Therefore, a node with a higher residual energy can increase its transmission power, which activates the transmission power reduction algorithm in other nodes, ultimately saving energy. The TPC strategy not only reduces energy but also reduces the risk of interference by reducing transmission power. In addition, fewer nodes are located in the listening area of ??a node. On the other hand, increasing the transmission power can lead to the development of the telecommunication range of the node and increase the number of its neighbors. This can increase the ability to distribute traffic in nodes that have a small number of neighbors and lead to the improvement of the network lifetime [7,6].

i) Directional antennas [7]: Directional antennas have the ability to receive signals sent at the same time and in one direction. This improves the transmission range and energy consumption. These antennas may require local techniques, but they are capable of receiving multiple communications simultaneously. In this way, more bandwidth is used. Therefore, this algorithm can improve the network capacity and lifetime while it can also affect the delay and connection. However, some problems that are specific to directional antennas, such as signal interference, antenna settings, and deafness problems [8] should be considered when using these antennas [8, 9].

D) Cognitive radio with energy efficiency [9]: Cognitive radio (CR) is an intelligent radio that can dynamically choose its communication channel in the wireless spectrum and adapt itself to the transmission and reception parameters. Based on software-defined radio technology, it is expected to fully automatically reconfigure its transceiver according to the requested network parameters, which improves network awareness. However, the most important radio cognitive requirement is the energy consumption of the node due to the complexity of the antenna and new features compared to conventional devices. In this regard, the design of cognitive radio sensor networks is the key to solving the challenge of intelligent use of battery energy. Recent cognitive radio studies are in the fields of transmitter power control, channel allocation based on residual energy, and the combination of network coding and cognitive radio [11, 10]. Two methods of limiting unnecessary samples and limiting the measurement tasks of each node can be used jointly. Because obtaining and sending information are expensive in terms of energy consumption.

A) Aggregation[10]: In the information aggregation scheme, nodes along a route towards the aggregator node perform the aggregation of information to reduce the amount of information sent. In addition, the accumulation of information by reducing the traffic can also reduce the delay. However, the data collection methods may reduce the accuracy of the data collected. In fact, after the aggregation operation, the information cannot be recovered by the aggregator, and as a result, the accuracy of the information is lost [12]. Consensus sampling techniques adjust the sampling rate in each sensor according to the needs of applications such as coverage and obtaining accurate information [13].

P) Network Coding [12] (NC): Network Coding (NC) is used to reduce traffic in broadcast scenarios by sending a linear combination of several packets instead of a copy of each packet. Receiving nodes can also use linear equations to decode packets [14].