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Routing protocols in wireless sensor networks (WSN) are a crucial challenge for which the goal is maximizing the system lifetime. Since the sensor nodes are with limited capabilities, these routing protocols should be simple, scalable, energy‐efficient, and robust to deal with a very large number of nodes, and also self‐configurable to node failures and changes of the network topology dynamically. The purpose of this paper is to present a new algorithm for cluster forming in WSN based on the node energy required to transmit to the base station.

Rotation selection of cluster‐head considering the remoteness of the nodes to the sink, and the network node residual energy.

The simulation results show that this algorithm allows network stability extension compared to the most known clustering algorithm.

Giving a probability to become cluster‐head based on the remoteness of the node to the sink.

Geographical query formulation is one of the key difficulties for users in search engines. The purpose of this study is to improve geographical search by proposing a novel geographical query reformulation (GQR) technique using a geographical taxonomy and word senses.

This work introduces an approach for GQR, which combines a method of query components separation that uses GeoNames, a technique for reformulating these components using WordNet and a geographic taxonomy constructed using the latent semantic analysis method.

The proposed approach was compared to two methods from the literature, using the mean average precision (MAP) and the precision at 20 documents (P@20). The experimental results show that it outperforms the other techniques by 15.73% to 31.21% in terms of P@20 and by 17.81% to 35.52% in terms of MAP.

According to the experimental results, the best created taxonomy using the geographical adjacency taxonomy builder contains 7.67% of incorrect links. This paper believes that using a very big amount of data for taxonomy building can give better results. Thus, in future work, this paper intends to apply the approach in a big data context.

Despite this, the reformulation of geographical queries using the new proposed approach considerably improves the precision of queries and retrieves relevant documents that were not retrieved using the original queries. The strengths of the technique lie in the facts of reformulating both thematic and spatial entities and replacing the spatial entity of the query with terms that explain the intent of the query more precisely using a geographical taxonomy.