Mobile Ad hoc Network (MANET) is a collection of nodes formed together to make communication without need of infrastructure. Due to restricted characteristics of MANET has to relay on wireless communication. This causes the battery power is an important factor in MANET to provide reliable communication without any power failure. Many research works has been carried out to improve the battery power to avoid packet dropping or packet loss while transmitting of packets. The battery power optimization in MANET is still not come in to the wrapping up. This research article proposed a method to improve the battery power in MANET and provide the better quality in packet transmission. This is achieved by using AODV protocol with improving the routing strategy in packet transmission. The result was implemented in Network simulator and shows better improvement comparing with the existing power optimization strategy.

Efficient energy optimization in mobile ad hoc network (MANET) using better-quality AODV protocol

Energy efficient channel aware multipath routing protocol for mobile ad‐hoc network

Enhancing the quality of service is still facing various challenges due to nature and configuration of mobile ad-hoc network (MANET) such as free movement of nodes, unpredictable changes in network topology as no infrastructure exist in the network. The large usage of mobile ad-hoc network in various applications raises the demand to enhance the performance as well as reduce loss of packets through various factors. The routing protocols play the most important aspect in MANET. There are three kinds of routing protocols that are: reactive, proactive and hybrid that are utilized to optimize the network’s functionality. Ad-hoc on-demand Distance victor (AODV) routing protocol is the popular used to achieve best output result. The most important objectives of this paper are to enhance the performance of mobile ad-hoc network by using AODV protocol. This paper gives some changes in AODV routing protocols that’s assist the performance enhancement of AODV in mobile ad-hoc network. The simulation is carried out on the network simulator NS2 and the output result show the improved AODV has excellent performance in term of packet delivery ratio, secondly throughput, third is the routing overhead and finally is end to end delay.

Enhance the performance of AODV routing protocol in mobile ad-hoc networks

Over 40 % of United States Veterans Health Administration (VHA) primary care patients have obesity. Few patients use VHA's flagship weight management program, MOVE! and there is little information on other behavioral weight management program use.The national United States cohort included over 1.5 million primary care patients with obesity, age 18-79, based on VHA administrative data. Gender stratified multivariable logistic regression identified correlates of weight management use in the year after a patient's first primary care appointment (alpha of 0.05). Weight management use was defined as MOVE! or nutrition clinic visits.The cohort included 121,235 women and 1,521,547 men with 13 % and 7 % using weight management, respectively. Point estimates for specific correlates of use were similar between women and men, and across programs. Black patients were more likely to use weight management than White patients. Several physical and mental health diagnoses were also associated with increased use, such as sleep apnea and eating disorders. Age and distance from VHA were negatively associated with weight management use.When assessing multiple types of weight management visits, weight management care in VHA appears to be used more often by some populations at higher risk for obesity. Other groups may need additional outreach, such as those living far from VHA. Future work should focus on outreach and prevention efforts to increase overall use rates. This work could also examine the benefits of tailoring care for populations in greatest need. 

Behavioral weight management use in the Veterans Health Administration: Sociodemographic and health correlates.

Almost 40% of Veterans Health Administration (VHA) users have obesity. VHA's national weight management program is associated with weight loss and improved health. However, while 94% of eligible VHA users are offered weight management programs, <8% use them. We developed EMBER – a novel, Motivational Interviewing-based, self-help tool – with the goal of Enhancing Motivation for Better Engagement and Reach for weight management. EMBER is not a weight management program; instead it engages people in existing programs by informing and guiding choices about weight management. The EMBER Trial is a randomized hybrid type 1 effectiveness implementation trial. Participants are Palo Alto or Houston VA Health Care System users with obesity who have not used a VHA weight management program in the past two years (target N = 470). Participants are randomly assigned to EMBER or an information-only control condition, after which they receive materials on paper or digitally, per their preference. The trial's primary goal is to determine whether participants randomized to EMBER are more likely to have any weight management engagement at two-month follow-up compared to those in the control condition. Secondary outcomes include 6-month retention in weight management, weight management behaviors, weight loss, quality of life, and implementation outcomes (e.g., reach, appropriateness). EMBER is the first self-directed, Motivational Interviewing-based intervention designed to increase weight management program engagement. The study takes a low-touch, population health approach that could be modified for other programs if effective. The Hybrid Type 1 design will ensure results can be scaled and sustained. 

The EMBER trial for weight management engagement: A hybrid type 1 randomized controlled trial protocol.

                                                                                                                                                                                                                                                                                                                                                 

Improved AODV Routing Protocol for Manet to Enhance The Network Performance

Weight Management Treatment Representations: A Novel Use of the Common Sense Model.

Common Sense Models of Obesity: a Qualitative Investigation of Illness Representations

This project portrays the strategies for breaking down, planning, improving and controlling the medical services the board framework. Self-sufficient Intelligent Robots can perform wanted errands in a climate without the ceaseless direction of human being. A line adherent robot is robotized gadget customized to follow a particular way. A line following bots conveys the medication to the patient at whatever point they need it dependent on the predefined way that can be either noticeable on a dark line on a white surface or the other way around It utilizes three degrees of input for arrangement, pivot balance and for keeping away from obstructions. Since the way of the wards stays same in medical clinics, so a fixed way is characterized and is stacked. In this venture the robot utilized is equipped for gathering the boundaries like temperature, pulse and of the patients. It utilizes three degrees of criticism for methodment, pivot counterbalance and for keeping away from snags. Since the way of the wards stays same in the clinics, so a fixed way is characterized and is stacked. In this venture the robot utilized is equipped for gathering the boundaries l ike temperature, pulse and life of the patients. 

Design and implementation of patient monitoring robot in hospitals

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Improved AODV Routing Protocol for Manet to Enhance The Network Performance

Weight Management Treatment Representations: A Novel Use of the Common Sense Model.

Common Sense Models of Obesity: a Qualitative Investigation of Illness Representations

Design and implementation of patient monitoring robot in hospitals