MR-DFM: A multi-path routing algorithm based on data fusion mechanism in sensor networks

Abstract

Sensor networks will always suffer from load imbalance, which causes bottlenecks to the communication links. In order to address this problem, a multi-path routing algorithm based on data-fusion-mechanism (MR-DFM) is proposed in this work. In this algorithm, the Mobile Sink controls the clustered energy consumption of the nodes in the event domain according to the delay messages relayed by the neighbor nodes. Meanwhile, the optimal neighbor node in the candidate set is obtained according to the data stream of the neighbor nodes to perform the relay of the data packets. It is shown via simulation results that the proposed MR-DFM algorithm shows obvious improvement according to the energy consumption of the network throughput of the sensing data and the throughput of the sensing data and each hop of the neighbor nodes. Therefore, it is verified that the proposed MR-DFM algorithm shows remarkable data fusion effects and optimizes the network resources.

SCR-CC: A Novel Sensing Clustering Routing Algorithm Based on Collaborative Computing in Heterogeneous Sensor Networks

“…e data will be sent to the sink node by the above optimal path. Literature [30] proposed an optimized network routing control protocol based on the ant colony algorithm. e protocol uses the ant colony algorithm to prove that most ants sending pheromone to each other can find the shortest path from the source node to the destination node.…”

Extraction of mosaic regions through projection and filtering of features from image big data

“…X diff , the difference among the adjacent x coordinates in group X accum , and Y diff , the difference among the adjacent y coordinates in group X accum are calculated as shown in (5) and (6), respectively. Then, of X diff and Y diff , the values having the highest frequency and the value having the lowest frequency are selected and determined as N and M of the mosaic blocks having a size equivalent to the NM pixel size.…”

“…Recently, with the rapid development of mobile sensing, computer networks, highdefinition cameras and artificial intelligence technologies, the amount of social multimedia data available is increasing explosively [1][2][3][4][5]. In particular, as smart mobile devices and location-based services are expanded, real-time location-based video data are actively used, and such digital image data are creating large-scale big data [6][7][8][9][10].…”

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

“…Whether it is a task scheduling strategy or a virtualization-based energy consumption optimization strategy, the key issue is to understand the characteristics of energy consumption when different loads are running in a cluster environment. To this end, the paper [37] established a linear regression model of system energy consumption based on data collected by hardware counters inside the system. The paper [38] established a virtual machine energy consumption prediction model in order to optimize the energy utilization rate during virtual machine aggregation.…”