Towards a Transmission Power Self-Optimization in Reliable Wireless Sensor Networks F. Lavratti 1 , A. R. Pinto 2,3 , D. Prestes 1 , L. Bolzani 1 , F. Vargas 1 , C. Montez 2,3 1 Catholic University of Rio Grande do Sul, PUCRS Porto Alegre, Brazil vargas@computer.org 2 PGEEL, 3 PGEAS, Universidade Federal de Santa Catarina, UFSC Florianópolis, Brazil {arpinto, montez}@das.ufsc.br Abstract — Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. The WSN is composed of several nodes each provided with its separated power supply, e.g. battery. Working in hardly accessible places it is preferable to assure the adoption of the minimum transmission power in order to prolong as much as possible the WSN’s lifetime. Though, we have to keep in mind that the reliability of the data transmitted represents a crucial requirement. Therefore, power optimization and reliability have become the most important concerns when dealing with modern systems based on WSN. In this context, we propose a new algorithm able to guarantee an equally high Quality of Service (QoS), concentrating on the WSN’s Efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind our approach is to reach a trade-off between Ef and energy consumption in an environment with inherent noise. Keywords-WSN; QoS; energy optimization. I. INTRODUCTION Recent advantages in wireless communication and electronic technology have made possible the development of small, low-cost, low-power and multifunctional sensor nodes [5][6]. Wireless Sensor Networks (WSNs) are composed of small communication nodes, which contain sensing, data processing and communication components as well as power supply, typically a battery. In more detail, these nodes are able to collect different types of data and to communicate with each other. Nowadays, WSNs have been increasingly deployed for both civil and military applications typically working in harsh environments. Considering sensor nodes, resources like processor, memory and battery are generally restricted, since their replacement is considered prohibitive due to hazardous and inaccessible places where they are supposed to operate [1]. In this context, fault tolerance or WSN’s Efficiency (Ef), and power optimization to prolong the network’s lifetime have become very important issues [7]. Usually, WSNs are required to perform timely detection, processing and delivery of information interacting with their environment. Due to the real-time constraints, the high degree of faults, the noise and non-determinism caused by the uncontrolled aspects of the environment, it does not surprise that WSNs frequently show faulty behavior or in other words demonstrate poor Quality of Service (QoS) [3]. One strategy to cope with the QoS requirements is to adopt data fusion techniques. In dense networks, they are used in order to increase the sensor’s reading dependability, to achieve a more accurate estimation of monitored environment and finally to assure longer network lifetime [1]. In these approaches, sensed scalars are sent to base stations that fuse the data with the objective to extract useful information from a set of readings. Moreover, the increasing number of nodes that compose WSNs leads to a high complexity of the system and the impossibility of human administration. Facing this problem, the development of computing systems that do not need human intervention to operate correctly has emerged. Thus, systems with so called self-management characteristics, computer systems that are able to manage themselves based on high-level objectives given by the administrators, have been developed to cope with the increasing complexity [4]. In this paper, we propose a self-optimization technique able to adjust the transmission power guaranteeing a specific targeted WSN’s Ef. The main idea behind our approach is to guarantee the trade-off between QoS and energy consumption, consequently achieving a prolonged sensor node lifetime. The effectiveness of the proposed technique has been preliminarily evaluated using a case study composed of two sensor nodes working in a noisy environment. The obtained results demonstrate that the proposed algorithm significantly reduces the energy consumption with respect to the data transmission. This paper has been organized as follows: in Section II we present the self-optimization technique detailing the communication model, the proposed algorithm as well as the case study adopted. Section III summarizes the experimental results and finally in Section IV we present our final considerations. II. THE PROPOSED TECHNIQUE The algorithm presented in this paper deals with the trade- off between Ef and energy consumption. In other words, the proposed algorithm automatically adjusts the transmission   ,((( Authorized licensed use limited to: Pontificia Universidade Catolica do Rio Grande do Sul (PUC/RS). Downloaded on January 26,2021 at 19:44:44 UTC from IEEE Xplore. Restrictions apply.