Agent Based Context Aware Data Aggregation and Dissemination in Distributed Multimedia Sensor Networks

Being equipped with appropriate multimedia sensor nodes, DMSNs can enable detection of object, temperature and identification of the location of fire attack in the forest. Sensor nodes deployed in forest environment enables to gather context information such as air pressure, temperature, object awareness, location of fire, fire condition (emergency level or non emergency level), and energy awareness about each node. Data aggregation plays an important role to conserve the network life of DMSN. Hence, in this paper we propose an software agent based energy efficient context aware data aggregation and dissemination in DMSN for the targeted area. The proposed model considers the context information such as temperature, air-pressure, energy, object awareness and helps in identifying the location of fire attack in the forest. Static and mobile software agents are used along with context awareness to improve the performance of the proposed scheme. To test the operation, proposed scheme is simulated using NS2. The performance of the proposed scheme is evaluated by considering some of the parameters such as energy consumption, routing overhead, rate of redundancy of data, aggregation time and rate of dissemination of data. © 2017 IEEE

MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification

Global increase in obesity has led to alarming rise in co-morbidities leading to deteriorated quality of life. Obesity phenotyping benefits profiling and management of the condition but warrants accurate quantification of fat compartments. Manual quantification MR scans are time consuming and laborious. Hence, many studies rely on semi/automatic methods for quantification of abdominal fat compartments. We propose a MultiRes-Attention U-Net with hybrid loss function for segmentation of different abdominal fata compartments namely (i) Superficial subcutaneous adipose tissue (SSAT), (ii) Deep subcutaneous adipose tissue (DSAT), and (iii) Visceral adipose tissue (VAT) using abdominal MR scans. MultiRes block, ResAtt-Path, and attention gates can handle shape, scale, and heterogeneity in the data. Dataset involved MR scans from 190 community-dwelling older adults (mainly Chinese, 69.5% females) with mean age—67.85 ± 7.90 years), BMI 23.75 ± 3.65 kg/m2. Twenty-six datasets were manually segmented to generate the ground truth. Data augmentations were performed using MR data acquisition variations. Training and validation were performed on 105 datasets, while testing was conducted on 25 datasets. Median Dice scores were 0.97 for SSAT & DSAT and 0.96 for VAT, and mean Hausdorff distance was <5 mm for all the three fat compartments. Further, MultiRes-Attention U-Net was tested on a new 190 datasets (unseen during training; upper & lower abdomen scans with different resolution), which yielded accurate results. MultiRes-Attention U-Net significantly improved the performance over MultiResUNet, showed excellent generalization and holds promise for body-profiling in large cohort studies

Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSN) can be used in a wide range of applications such as monitoring agriculture, infrastructures, military operations, disaster management and so on. Energy conservation is a major concern in WMSN applications. This paper proposes a multi-agent based context-aware information gathering using WMSN for monitoring agriculture. Three kinds of contexts are considered in this paper such as detecting an emergency, temporal and computational contexts for detection of diseased plants, weeds, fire and interpret the soil fertility based on the soil parameters. This work considers contexts driven by a sensor node. Whenever the context is detected the information will be sent to the sink node. The proposed scheme works as follows: Every sensor senses the information and updates the node knowledge base. Based on the sensed information node interprets the context such as disease affected plants, soil fertility, fire, and growth of weeds. The sensor nodes begin to transmit the stored information to the cluster heads with the help of Path Finding Agent (PFA). Cluster heads aggregate the information received by the sensor nodes in the field before sending this information with the help of Querry Agent (QA) to the sink node. At the sink node all the information will be sent to the end-user, but in case of the fire detection, the immediate action will be taken by the sink node itself to turn on the sprinklers. Once the sensor finishes the assigned task (sensing, communicating) then automatically it goes into sleep mode. To detect plant disease and weeds, content-based image retrieval is used to compare with the healthy or useful plant images respectively. For performance analysis, the proposed scheme is simulated using NS2. Some of the performance parameters considered in this work are context detection time, delay, fusion time and energy consumption. Keywords: Wireless multimedia sensor networks, Context-aware computing, Agent technology, Content-based image retrieva

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