scholarly journals A novel cooperative ant-based clustering approach for wireless multimedia sensor networks

2015 ◽  
Vol 2 (1) ◽  
pp. 7
Author(s):  
Luis Cobo
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
Cluster Head ◽  
Virtual Backbone ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Cluster Heads ◽  
Main Requirement ◽  
Cohesive Group ◽  
Clustering Approach

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAThis Cluster has been well received as one of the most effective solutions to enhance energy efficiency and scalability of large-scale wireless sensor networks. However, it is rather challenging to conforma cohesive group of Clusters having permanent communication  among Cluster heads. In this paper, a unique look at the Cluster head election problem is taken into account, specifically concentrating on applications in which the formation and maintenance  of a virtual backbone among Cluster heads is the main requirement.  Our  approach  for  Cluster-based  network  organization  is based on the use of collective social agents in order to lead the formation of these Clusters. Our algorithm uses ants to choose good Cluster head candidates and to create a virtual backbone among these router nodes.  

scholarly journals An Optimization Scheme for M2M-Based Patient Monitoring in Ubiquitous Healthcare Domain

2012 ◽  
Vol 8 (4) ◽  
pp. 708762 ◽  
Author(s):  
Sungmo Jung ◽  
Jae Young Ahn ◽  
Dae-Joon Hwang ◽  
Seoksoo Kim
Keyword(s):  
Sensor Networks ◽  
Continuous Monitoring ◽  
Large Scale ◽  
Patient Monitoring ◽  
Human Interaction ◽  
Sensor Data ◽  
Wireless Multimedia ◽  
Optimization Scheme ◽  
Ubiquitous Healthcare ◽  
Multimedia Sensor Networks

In ubiquitous healthcare systems, machine-to-machine (M2M) communication promises large opportunities as it utilizes rapidly developing technologies of large-scale networking of devices for patient monitoring without dependence on human interaction. With the emergence of wireless multimedia sensor networks (WMSNs), M2M communications improve continuous monitoring and transmission and retrieval of multimedia content such as video and audio streams, images, and sensor data from the patient being monitored. This research deploys WMSN for continuous monitoring of target patients and reports tracking for preventive ubiquitous healthcare. This study performs optimization scheme movement coordination technique and data routing within the monitored area. A movement tracking algorithm is proposed for better patient tracking techniques and aids in optimal deployment of wireless sensor networks. Results show that our optimization scheme is capable of providing scalable and reliable patient monitoring results.

ANFIS-Based Resource Mapping for Query Processing in Wireless Multimedia Sensor Networks

2017 ◽  
Vol 26 (3) ◽  
pp. 505-522
Author(s):  
Nagesha Shivappa ◽  
Sunilkumar S. Manvi
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Sink Node ◽  
Wireless Multimedia ◽  
Resource Mapping ◽  
Multimedia Sensor Networks ◽  
Node Energy

AbstractWireless multimedia sensor networks (WMSNs) are usually resource constrained, and where the sensor nodes have limited bandwidth, energy, processing power, and memory. Hence, resource mapping is required in a WMSN, which is based on user linguistic quality of service (QoS) requirements and available resources to offer better communication services. This paper proposes an adaptive neuro fuzzy inference system (ANFIS)-based resource mapping for video communications in WMSNs. Each sensor node is equipped with ANFIS, which employs three inputs (user QoS request, available node energy, and available node bandwidth) to predict the quality of the video output in terms of varying number of frames/second with either fixed or varying resolution. The sensor nodes periodically measure the available node energy and also the bandwidth. The spatial query processing in the proposed resource mapping works as follows. (i) The sink node receives the user query for some event. (ii) The sink node sends the query through an intermediate sensor node(s) and cluster head(s) in the path to an event node. A cluster head-based tree routing algorithm is used for routing. (iii) The query passes through ANFIS of intermediate sensor nodes and cluster heads, where each node predicts the quality of the video output. (iv) The event node chooses the minimum quality among all cluster heads and intermediate nodes in the path and transmits the video output. The work is simulated in different network scenarios to test the performance in terms of predicted frames/second and frame format. To the best of our knowledge, the proposed resource mapping is the first work in the area of sensor networks. The trained ANFIS predicts the output video quality in terms of number of frames/second (or H.264 video format) accurately for the given input.

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