Efficient energy consumption is a critical factor for the deployment and operation of wireless sensor networks (WSNs). In general, WSNs perform clustering and routing using localized neighbor information only. Therefore, some studies have used self-organized systems and smart mechanisms as research methods. In this paper, we propose a self-organized and smart-adaptive clustering (SOSAC) and routing method, which performs clustering in WSNs, operates the formed clusters in a smart-adaptive way, and performs cluster-based routing. SOSAC is comprised of three mechanisms, which are used to change the fitness value over time, to back up routing information in preparation for any potential breakdown in WSNs, and to adapt to the changes of the number of sensor nodes for a WSN. We compared the performance of the proposed SOSAC with that of a well-known clustering and routing protocol for WSNs. Our computational experiments demonstrate that the network lifetime, energy consumption, and scalability of SOSAC are better than those of the compared method. International Journal of Distributed Sensor Networks for sensor nodes is not rechargeable or replaceable, it is also important to consider energy efficiency in order to maximize the network lifetime of the sensor nodes by minimizing the energy consumption [4]. Self-organization, which is a further development of distributed processing, is a system that uses local information only through a distributed and peer-to-peer method. Many studies on self-organized systems are being conducted using only local information and simple rules because of the associated advantages such as reduction of overheads in communication traffic, scalability, and robustness. Also, selforganization has been applied to many areas in the wireless network field such as ad-hoc networks, WSNs, wireless LAN, in routing, forming clusters, MAC protocol design, and radio resources management. Some studies have been conducted on self-organization with the features of WSNs [5] . Recently "smart" characteristics of routing have also been suggested in telecommunication networking [6] [7] [8] . Here smart means the capability to describe and analyze a situation, taking decisions based on the available data in a predictive or adaptive manner, and thereby performing smart actions. In this paper, we propose a self-organized and smartadaptive clustering (SOSAC) and routing method comprised of three mechanisms for a WSN. First, in order to select an appropriate CH, which is the most important factor influencing the clustering performance, two types of performance measure are used. The proposed mechanism adjusts the weights of these two performance measures automatically to reflect the changes in the WSN. Second, the broadcasting range, which is another important factor influencing the performance of clustering in SOSAC, is predetermined as a function of the number of sensor nodes. Third, to overcome problems caused by possible breakdown, damage, or failure of sensor nodes in WSNs, a smart backup mechanism is established to monitor the state of the CH without heavy overhead and to restore the system automatically in the case of such problems. This paper is organized as follows. Section 2 reviews previous studies on clustering, self-organization, and smart telecommunication networks and their implications for this research. Section 3 presents the radio model that is used and the basic assumptions used in this study. Section 4 explains the process of forming self-organized clusters and routing. Section 5 describes how to operate clustering in a smart-adaptive way by using three proposed mechanisms. In this section, we also compare the performance of the proposed SOSAC with that of another well-known model. The measures for comparison are network lifetime, residual energy after a certain time, and scalability of the model. The last section presents the conclusions of this study and proposes future research directions.
Authors in REF propose self-organized and smart adaptive clustering (SOSAC) protocol for WSNs.
A Self-Organized and Smart-Adaptive Clustering and Routing Approach for Wireless Sensor Networks
International Journal of Distributed Sensor Networks
Computer Science