A Quality of Service (Qos) Load Balanced Hybrid Simulated Annealing -Stochastic Diffusion Search (Sa-Sds) Network Backbone for Manet

The Mobile Ad hoc Networks (MANET) are those networks that do not have the infrastructure and are formed dynamically by means of an autonomous system of some mobile nodes connected through wireless links. All routers are left free to be able to randomly move and arbitrarily organize themselves. So, the wireless topology of the network can have unpredictable and rapid changes. In these types of networks, the provisioning of services based on Quality of Service (QoS) can pose to be very challenging. The work further presented a newer approach that was based on a hybrid Simulated Annealing (SA) along with a Stochastic Diffusion Search (SDS) based multi-path routing network which backbones in giving support to the enhanced QoS in the MANETs. This multipath routing had the objective of improving the dependability and the throughput along with load balancing. The SA is used for solving the problem of the Minimum Dominating Set (MDS). This SDS heuristic gives an algorithm which is simple in its structure and also provides a high exploration level along with fast convergence in comparison with the other algorithms. SA algorithms are also used for improving the diversity of agent and for avoiding it from being trapped within the local optimum. The results of the experiment proved that the SA-SDS method proposed had a better performance compared to the Connected Dominating Set (CDS)-SA.

Three Swarm Intelligence Algorithms for Wireless Sensor Network Applications: A review

: Wireless Sensor Networks (WSNs) are increasingly realizing applications in IoT, smart grids, healthcare, security, swarm robotics, etc. Swarm Intelligence is used to optimize performance parameters associated with WSNs including localization, coverage, network lifetime, energy efficiency to name a few. The scope of this paper is restricted to a survey on Stochastic Diffusion Search, Genetic Algorithm and Particle Swarm Algorithm with respect to their organization and capacity to optimize these network parameters and their current as well as potential applications in WSNs.

Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have been used to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.