Clustering Opportunistic Ant-based Routing Protocol for Wireless Sensor Networks

The wireless Sensor Networks (WSNs) have a wide range of applications in many ereas, including many kinds of uses such as environmental monitoring and chemical detection. Due to the restriction of energy supply, the improvement of routing performance is the major motivation in WSNs. We present a Clustering Opportunistic Ant-based Routing protocol (COAR), which comprises the following main contributions to achieve high energy efficient and well load-balance: (i) in the clustering algorithm, we caculate the theoretical value of energy dissipation, which will make the number of clusters fluctuate around the expected value, (ii) define novel heuristic function and pheromone update manner, develop an improved ant-based routing algorithm, in this way, the optimal path with lower energy level and shorter link length is established, and (iii) propose the energy-based opportunistic broadcasting mechanism to reduce the routing control overhead. We implement COAR protocol in NS2 simulator and our extensive evaluation shows that COAR is superior to some seminal routing algorithms under a wide range of scenarios

Improving QoS Parameters for Clustering in MANET using Grey Wolf Optimization and Global Algorithm Technique

Manet have several nodes that are linked together wirelessly and the nodes are mobile in nature. Nodes position is flexible. The primary issue with the traditional clustering method is that it is prone to become trapped in the regional optimum path. While the nodes are being transmitted or received, energy is used. Through the use of the GWOGA clustering technique, CHs collect data from cluster participants and communicate other nodes with the cluster. While choosing the best CH to lengthen the network's lifespan is the Manet is most vital responsibility. Clustering based on the Grey Wolf Optimization and Global Algorithm (GWOGA), an attempt has been made to address this issue. GWOGA search function is employed for the best cluster centres in the supplied feature span. The cluster centres are encoded using the agent representation. When choosing the best CHs, the GWOGA dynamically balances the process of increasing and diversifying search. In addition, choosing the best CHs for the network is aided by factors like node degree, energy, distance, node centrality, Throughput, Delay, Path Loss Ratio, etc. The computational findings show that GWOGA offers improved values of excellent throughput, packet delivery ratio, end delay, energy a better lifespan segregated with the performance of normal clustering method. NS2 simulator is used for the simulation for finding QoS parameters

Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method

Self-adaptive GA, quantitative semantic similarity measures and ontology-based text clustering

As the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms which do not co-occur literally are ignored. A genetic algorithm-based clustering technique, named GA clustering, in conjunction with ontology is proposed in this article to overcome this problem. In general, the ontology measures can be partitioned into two categories: thesaurus-based methods and corpus-based methods. We take advantage of the hierarchical structure and the broad coverage taxonomy of Wordnet as the thesaurus-based ontology. However, the corpus-based method is rather complicated to handle in practical application. We propose a transformed latent semantic analysis (LSA) model as the corpus-based method in this paper. Moreover, two hybrid strategies, the combinations of the various similarity measures, are implemented in the clustering experiments. The results show that our GA clustering algorithm, in conjunction with the thesaurus-based and the LSA-based method, apparently outperforms that with other similarity measures. Moreover, the superiority of the GA clustering algorithm proposed over the commonly used k-means algorithm and the standard GA is demonstrated by the improvements of the clustering performance

Improved Purely Additive Fault-Tolerant Spanners

Let $G$ be an unweighted $n$-node undirected graph. A \emph{$\beta$-additive spanner} of $G$ is a spanning subgraph $H$ of $G$ such that distances in $H$ are stretched at most by an additive term $\beta$ w.r.t. the corresponding distances in $G$. A natural research goal related with spanners is that of designing \emph{sparse} spanners with \emph{low} stretch. In this paper, we focus on \emph{fault-tolerant} additive spanners, namely additive spanners which are able to preserve their additive stretch even when one edge fails. We are able to improve all known such spanners, in terms of either sparsity or stretch. In particular, we consider the sparsest known spanners with stretch $6$, $28$, and $38$, and reduce the stretch to $4$, $10$, and $14$, respectively (while keeping the same sparsity). Our results are based on two different constructions. On one hand, we show how to augment (by adding a \emph{small} number of edges) a fault-tolerant additive \emph{sourcewise spanner} (that approximately preserves distances only from a given set of source nodes) into one such spanner that preserves all pairwise distances. On the other hand, we show how to augment some known fault-tolerant additive spanners, based on clustering techniques. This way we decrease the additive stretch without any asymptotic increase in their size. We also obtain improved fault-tolerant additive spanners for the case of one vertex failure, and for the case of $f$ edge failures.Comment: 17 pages, 4 figures, ESA 201

Using Element Clustering to Increase the Efficiency of XML Schema Matching

Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research