The study highlighted the importance of maintaining good nutritional status and vitamin A status for optimal cognitive function. The presence of APOEε4 allele has a prominent role in affecting vitamin E levels, particularly among cognitively healthy elderly in our unique population.

The reliability of a wireless sensor network (WSN) is often assessed on node-to-node communication performance through link characterization. Long-term routing stability is an aspect of a WSN that is often overlooked in routing protocol implementations. In this paper, we investigate the routing stability of ZigBee PRO implemented WSN nodes that are deployed in a real-world environment. Frequent changes in next hops along routing paths between source and destination nodes can result in an increase in undesired energy consumption of the WSN. Hence, the relative routing path usage count, usage rate of unique next hop and switching frequency count are proposed as routing stability indicators. Our findings show that routing stability is subjected to not only the quality of a link but also to the implemented routing protocols, deployed environment and routing options available. More importantly, next hops with low usage rates are shown to experience a higher probability of disconnection from the Neighbor Table of respective source nodes, causing them to be short-lived. The need to avoid these links shows the importance of evaluating routing stability and identifying network bottlenecks. INDEX TERMS Wireless sensor networks, ZigBee, AODV, routing protocol, indoor radio communication, quality of service.

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models.

Abstract-Recent

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