Search

Opportunistic routing has increased the efficiency and reliability of Cognitive Radio Ad-Hoc Networks (CRAHN). Many researchers have developed opportunistic routing models, among them the Spectrum Map-empowered Opportunistic Routing (SMOR) model, which is considered a more efficient model in this field. However, there are certain limitations in SMOR, which require attention and resolution. The issue of delay and degradation of packet delivery ratio due to non-consideration of network bandwidth and throughput are addressed in this paper. In order to resolve these issues, a hybrid optimization algorithm comprising firefly optimization and grey wolf optimization algorithms are used in the basic SMOR routing model. Thus, developed Hybrid Firefly and Grey-Wolf Optimization-based SMOR (HFGWOSMOR) routing model improves the performance by high local as well as global search optimization. Initially, the relationship between the delay and throughput is analyzed and then the cooperative multipath communication is established. The proposed routing model also computes the energy values of the received signals within the bandwidth threshold and time; hence, the performance issues found in SMOR are resolved. To evaluate its efficiency, the proposed model is compared with SMOR and other existing opportunistic routing models, which show that the proposed HFGWOSMOR performs better than other models.

Search Engine retrieves significant and essential information from the web based on query terms given by the user. Due to the lack of background knowledge about the information required, shorter length queries posed by the user, the ambiguity of query keywords and dynamic growth of the web, irrelevant and redundant results are also retrieved by the search engine. Query recommendations is an important technique which analyze the real search intent of the user and suggests the alternative queries to be used by the user in future to satisfies their information need. The proposed method recommends and ranks the alternative queries and evaluates the ranking order of the recommendations with the help of the ranking measures Normalized Discounted Cumulative Gain (NDCG) and Coefficient of Variance (CV). These measures identify the relationship between the ranking techniques. The proposed strategies are experimentally evaluated using a real time search engine query log.