A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

Wireless sensor network (WSN) consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST) routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST). The proposed algorithm computes the distance-based Minimum Spanning Tree (MST) of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm

EMEEDP: Enhanced Multi-hop Energy Efficient Distributed Protocol for Heterogeneous Wireless Sensor Network

In WSN (Wireless Sensor Network) every sensor node sensed the data and transmit it to the CH (Cluster head) or BS (Base Station). Sensors are randomly deployed in unreachable areas, where battery replacement or battery charge is not possible. For this reason, Energy conservation is the important design goal while developing a routing and distributed protocol to increase the lifetime of WSN. In this paper, an enhanced energy efficient distributed protocol for heterogeneous WSN have been reported. EMEEDP is proposed for heterogeneous WSN to increase the lifetime of the network. An efficient algorithm is proposed in the form of flowchart and based on various clustering equation proved that the proposed work accomplishes longer lifetime with improved QOS parameters parallel to MEEP. A WSN implemented and tested using Raspberry Pi devices as a base station, temperature sensors as a node and xively.com as a cloud. Users use data for decision purpose or business purposes from xively.com using internet.Comment: 6 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1409.1412 by other author

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks

Improved LEACH Protocol based on Moth Flame Optimization Algorithm for Wireless Sensor Networks

Wireless sensor nodes are made up of small electronic devices designed for detecting, determining, and sending data under severe physical conditions. These sensor nodes rely heavily on batteries for energy, which drain at a quicker pace due to the extensive communication and processing tasks they must carry out. Managing this battery resource is the major challenge in wireless sensor networks (WSNs). This work aims at developing an improved performance and energy-efficient low-energy adaptive clustering hierarchy (IPE-LEACH) that can extend the lifespan of networks. This paper proposes a novel LEACH protocol that uses the moth flame optimization (MFO) algorithm for clustering and routing to increase the longevity of the sensor network. IPE-LEACH proved to have a better cluster-head (CH) selection technique by eliminating redundant data, thereby extending the network lifetime. IPE-LEACH was compared with four other existing algorithms, and it performed better than: original LEACH by 60%, EiP-LEACH by 45%, LEACH-GA by 58%, and LEACH-PSO by 13.8%. It can therefore be concluded that IPE-LEACH is a promising clustering algorithm that has the potential to realize high flexibility in WSNs in case the CH fails.     

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks

Effective Node Clustering and Data Dissemination In Large-Scale Wireless Sensor Networks

The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals that have been identified by researchers as necessary in order to further expand the deployment of such networks for their use in various applications. This thesis aims to provide an energy efficient and effective node clustering and data dissemination algorithm in large-scale wireless sensor networks. In the area of clustering, the proposed research prolongs the lifetime of the network by saving energy through the use of node ranking to elect cluster heads, contrary to other existing cluster-based work that selects a random node or the node with the highest energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a level of universal awareness of existing nodes in the subject area and to avoid the problem of disconnected or forgotten nodes. In the area of data dissemination, the aim of this research is to effectively manage the data collection by developing an efficient data collection scheme using a ferry node and applying a selective duty cycle strategy to the sensor nodes. Depending on the application, mobile ferries can be used for collecting data in a WSN, especially those that are large in scale, with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry-based approach thus eliminates, or minimizes, the need for the multi-hop forwarding of data, and as a result, energy consumption at the nodes will be significantly reduced. This is especially true for nodes that are near the base station as they are used by other nodes to forward data to the base station. MATLAB is used to design, simulate and evaluate the proposed work against the work that has already been done by others by using various performance criteria