An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization

An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of transmitted data by sensor nodes is repetitious and doing processes on them in many cases leads to increased power consumption and reduced network lifetime. Hence, sensor nodes should use such a pattern for data transmission which minimizes duplicate data. However, in cluster based WSN, cluster heads (CHs) consume more energy due to aggregating the data from cluster member nodes and transmitting the aggregated data to the sink. Therefore, the proper selection of CHs plays vital role for prolonging the lifetime of WSNs. In WSNs, cluster head selection is an optimization problem which is NP-hard. In this paper, using firefly algorithm, we proposed a method for aggregating data in WSNs. In the proposed method, sensor nodes are divided into several areas by using clustering. In each cluster, nodes are periodically active and inactive. Criteria such as energy and distance are taken into consideration for selecting active nodes. In this way, nodes with more remaining energy and more distance will be selected as active nodes. Simulation results, conducted in MATLAB 2016a, revealed that the proposed method was able to enhance quality of service parameters more than low energy adaptive clustering hierarchy and shuffled frog algorithm methods.

Data Aggregation in Wireless Sensor Networks Using Firefly Algorithm

This article presents a review of a novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics of river systems and the behavior of water drops when they are moving in a river system such as the variation of velocity, the change of sediment/soil in the river bed, the change of direction of the flow, and so on. Recent improved, modified, and adaptive WDAs are examined in this paper. Various applications of WDA are described briefly including the vehicle routing problem, economic load dispatch problem, economic and emission dispatch problem, optimal data aggregation tree in wireless networks, reactive power dispatch problem service selection, and reservoir operation.

Water Drop Algorithms

Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.

D. Chinh Hoang

Papers

Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm