Achievable Single-Valued Neutrosophic Graphs in Wireless Sensor Networks

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Neutrosophic set (NS) is an extensively used framework whenever the imprecision and uncertainty of an event is described based on three possible aspects. The association, neutral, and nonassociation degrees are the three unique aspects of an NS. More importantly, these degrees are independent which is a great plus point. On the contrary, neutrosophic graphs (NGs) and single-valued NGs (SVNGs) are applicable to deal with events that contain bulks of information. However, the concept of degrees in NGs is a handful tool for solving the problems of decision-making (DM), pattern recognition, social network, and communication network. This manuscript develops various forms of edge irregular SVNG (EISVNG), highly edge irregular SVNG (HEISVNG), strongly (EISVNG), strongly (ETISVNG), and edge irregularity on a cycle and a path in SVNGs. All these novel notions are supported by definitions, theorems, mathematical proofs, and illustrative examples. Moreover, two types of DM problems are modelled using the proposed framework. Furthermore, the computational processes are used to confirm the validity of the proposed graphs. Furthermore, the results approve that the decision-making problems can be addressed by the edge irregular neutrosophic graphical structures. In addition, the comparison between proposed and the existing methodologies is carried out.

Based on the principles of wireless mesh network (WMN), clustering of wireless sensor network (WSN) was reconstructed by combination of WMN and WSN into a topology named wireless mesh sensor network (WMSN). In terms of Voronoi diagram and Delaunay triangulations of computational geometry, central degree theory of complex network and heuristic algorithm based on mean weight of communication energy, a optimal deployment algorithm named WHD algorithm which can figure out large scale WMSN was designed. The study results show that the combination of WSN and WMN can achieve mesh wide hybrid network topology; WHD algorithm can be an effective solution to optimal deployment of large-scale WMSN. The experimental results show that WHD algorithm not only ensures optimal deployment of large-scale WMSN but also has the energy saved features, and then extends the network life-cycle efficiently.

By taking the data-receiving energy consumption into account, we propose an extended analytical model to derive the optimal parameters ? the number of clusters for wireless sensor networks. Our analyses show that the previous existing model underestimates the optimal number of clusters within a sensor network. The simulation results verify our analyses and demonstrate the modified model is more accurate in deriving the optimal number of clusters to maximize the network lifetime of the wireless sensor networks. Using our extended model, we also develop the energy-threshold-driven based clustering reconfiguration schemes with the optimal number of clusters to further improve the wireless sensor-network lifetime. The simulation experiments indicate that our clustering reconfiguration schemes outperform the existing ones in terms of sensor-network lifetime while imposing low overhead due to clustering reconfigurations.

Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. The main goal of clustering protocols is to minimize the energy consumption and enhance the network lifetime of wireless sensor networks. We propose DEBC protocol, which is distributed energy balanced clustering for wireless sensor networks. DEBC forms clusters, selects cluster heads, selects cluster senders and determines appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. DEBC compared to well known protocol with two metrics network lifetime and energy dissipation. The simulation results show that DEBC minimizes energy consumption of sensor nodes and enhance the network lifetime.

with the analysis of wireless sensor network clustering route mechanism, Distributed Energy-surplus and Centralism Distance clustering protocol (DECDC) has been proposed for dynamic optimization problem of wireless sensor network routing technology cluster head choice and clustering and inter-cluster route, in witch a node, according to its residual energy and the position and distance from sink node, independently makes its decision compete for becoming a cluster head. Moreover this protocol proposes a communication method, which based on the minimum way tree, causing cluster head to carry on according to the minimum gathering by tree. Experiment results have shown that compared with LEACH, in the same case, can lead to the increase of sensor network lifetime by 800% respectively,the energy consumption is more balance. So the reliability of sensing information in DECDC is higher than the other protocol.

Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network

Energy efficiency is a critical factor for operation of a wireless sensor network. In general, wireless sensor networks perform clustering and routing using localized neighbor information and simple rules only. In this paper, we propose an energy efficient clustering and routing method for wireless sensor networks by exploiting energy state information of neighbor sensor nodes for selection of cluster heads. We compared the performance of the proposed method with that of a well known clustering and routing protocol. Our computational experiments demonstrate that the lifetime and distribution of energy consumptions of the proposed method are better than those of the compared method.

Wireless sensor networks is a key technology for new ways of interaction between computers and the physical environment. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. Since sensor nodes are densely deployed, redundant data may occur. While cluster-based data gathering is efficient at energy and bandwidth, it's difficult to cluster network efficiently. In this work, a new distributed clustering algorithm for ubiquitous sensor network is presented. Clustering is based on the distance between nodes and the number in a cluster for wireless sensor networks. Simulation results show that the proposed algorithm balances the energy dissipation over the whole network thus increase the amount of data delivery to the sink

Study of impacts of duty-cycle on overlapping multi-hop clustering in wireless sensor networks

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases[17]. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytical solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.

In recent years there are many extensive applications of wireless sensor networks such as environment monitoring, surveillance, enemy tracking, etc. Since the available energy of sensor nodes are limited and hard to renew, energy supervision is critical for nodes in wireless sensor networks. In this paper, we propose a dynamic multi hop routing technique using residual energy based clustering algorithm to prolong node as well as network lifetime. Here, clusters are constructed using certain suitable parameters such as remaining energy of the nodes, its centrality, energy efficiency and cluster heads selection frequency Furthermore, after collecting all data within a cluster, the cluster heads gradually enhance their transmission range till finding a high residual energy based adjacent cluster heads to forward the data to the base station. In this way the proposed method generates an energy efficient routing path from each sensor to base station to send the data. Simulation results show that our approach effectively conserves energy for cluster heads as well as cluster members and prolong their life time effectively. This proposed method also reduces number of clusters and thus improve the nodes life time significantly.

A high-density wireless sensor network can be deployed for specific information-gathering. In such a network, sensors need to route their sensed data to a base station, consuming highly-limited and unreplenishable energy resource. Hence, one of the most critical issues in designing sensor data gathering algorithms is to minimize the energy consumption for network longevity while meeting certain requirements given, such as delay constraints, which may vary depending on specific applications or environmental situations. We propose a novel two-phase clustering (TPC) scheme for energy-saving and delay-adaptive data gathering in wireless sensor networks. The proposed scheme partitions the network into clusters in phase I, each with a cluster head, forming a direct link between cluster member and cluster head. In phase II, each cluster member searches for a neighbor closer than the cluster head within the cluster to set up an energy-saving data relay link. The sensors use either the direct link or the data relay link for their sensed data forwarding depending on the requirements specified by the users or applications. Simulation results show that the TPC reduces average transmission distance by up to 75% with the help of data relay links when there is no delay constraint, implying that there is a significant amount of energy-savings in collecting sensed data.