One of the main challenges in wireless sensor networks is the energy constraints of sensor nodes which must be considered precisely when designing algorithms for such networks. Clustering is known as one of the approaches which can be used for addressing this challenge. In this paper, an efficient method for clustering wireless sensor networks by means of cellular learning automata has been presented (LaClustering). Proposed method selects cluster head (CHs) through several stages; each considers one parameter affecting the overall performance of the clustering. Parameters considered in different stages of the proposed algorithm are energy levels of the sensor nodes, number of neighbors of each node, network connectivity, and formation of balanced clusters. To evaluate the performance of the proposed method, several experiments have been conducted using the J-sim simulator and the proposed method has been compared with some of the best clustering algorithms reported in literature. The simulation results have shown that the proposed algorithm can provide clustering infrastructure with higher overall quality than the existing algorithms, especially in balancing the number of sensor nodes in different clusters and selecting CHs with higher energy levels.
Sensor networks are established of many inexpensive sensors with limited energy and computational resources and memory. Each node can sense special information, such as the temperature, humidity, pressure and so on and then send them to the central station. One of the major challenges in these networks, is limit energy consumption and one of the ways for reducing energy consumption in wireless sensor networks, is reducing the number of packets that are transmitted in the network. Data Aggregation technique that combines related data together and prevents sending additional packets on the network can be effective in reducing the number of packets sent over the network. In this paper a Data Aggregation method based on learning automata is presented and with identifying sensors that are in the similar area, and produce the same data and enable the sensor nodes periodically avoid sending additional packets on the network, and significantly saves energy and increases the lifetime of the network. Simulation results show the optimal performance of the proposed method.
In a wireless sensor network, with numerous sensor nodes, a huge volume of data is produced. For the massive data generated by sensor networks to be understood by the machine, semantic web technologies such as ontology need to be applied. On the other hand, since the end users intend to access the high-level physical entities information monitored by the sensor network, the applicability of sensor networks can be enhanced through proposing a strategy to extract the data based on entities instead of extracting the raw sensor data. Hence, in this paper firstly, ontology will be presented for physical entities semantic modelling (PESM). Secondly, an appropriate strategy will be offered to collect and aggregate data on physical entities through software agents. The result of modelling and simulation of the proposed method represents its desirable performance to other previous strategies.
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