Energy-efficient self -organization of wireless acoustic sensor networks for ground target tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Best Management Practices in Green Lodging Defined and Explained

Best management practices in green lodging are sustainable or “green” business strategies designed to enhance the lodging product from the perspective of owners, operators and guests. For guests, these practices should enhance their experience while for owners and operators, generate positive returns on investments. Best management practices in green lodging typically starts with a clear understanding of each lodging firm’s role in society, its impact on the environment and strategies developed to mitigate negative environmental externalities generated from the production of lodging goods and services. Negative externalities of hotel operations manifest themselves in energy and water usage, waste generation and air pollution. Hence, best management practices in green lodging are dynamic, cost effective, innovative, stakeholder driven and environmentally sound technical and behavioral solutions that attempt to ameliorate or eliminate the negative environmental externalities associated with lodging operations, while simultaneously generate positive returns on green investments. Thus, best management practices in green lodging should reduce lodging firms’ operating costs, increase guest satisfaction, reduce or eliminate the negative environmental impacts associated with hotel operations while simultaneously enhance business operations.

Summertime Influences of Tidal Energy Advection on the Surface Energy Balance in a Mangrove Forest

Mangrove forests are ecosystems susceptible to changing water levels and temperatures due to climate change as well as perturbations resulting from tropical storms. Numerical models can be used to project mangrove forest responses to regional and global environmental changes, and the reliability of these models depends on surface energy balance closure. However, for tidal ecosystems, the surface energy balance is complex because the energy transport associated with tidal activity remains poorly understood. This study aimed to quantify impacts of tidal flows on energy dynamics within a mangrove ecosystem. To address the research objective, an intensive 10-day study was conducted in a mangrove forest located along the Shark River in the Everglades National Park, FL, USA. Forest–atmosphere turbulent exchanges of energy were quantified with an eddy covariance system installed on a 30-m-tall flux tower. Energy transport associated with tidal activity was calculated based on a coupled mass and energy balance approach. The mass balance included tidal flows and accumulation of water on the forest floor. The energy balance included temporal changes in enthalpy, resulting from tidal flows and temperature changes in the water column. By serving as a net sink or a source of available energy, flood waters reduced the impact of high radiational loads on the mangrove forest. Also, the regression slope of available energy versus sink terms increased from 0.730 to 0.754 and from 0.798 to 0.857, including total enthalpy change in the water column in the surface energy balance for 30-min periods and daily daytime sums, respectively. Results indicated that tidal inundation provides an important mechanism for heat removal and that tidal exchange should be considered in surface energy budgets of coastal ecosystems. Results also demonstrated the importance of including tidal energy advection in mangrove biophysical models that are used for predicting ecosystem response to changing climate and regional freshwater management practices.

Psychological and Cognitive Factors that Influence Post-Exercise Energy Intake in Normal Weight and Overweight Sedentary Males

The primary purpose of this study was to evaluate the effects of a single bout of moderate-intensity exercise on acute (ad libitum lunch) post-exercise energy intake (PE-EI) and 12-hour energy intake in normal-weight and overweight sedentary males. Accuracy in estimating energy intake (EI) and energy expenditure (EE), solid vs. liquid carbohydrate intake, mood, and perceived hunger were also assessed. The study consisted of two conditions, exercise and rest, with each subject participating in each condition, in a counterbalanced-crossover design on two days. The participants were randomly assigned to either the exercise or resting (seated) control condition on the first day of the experiment, and then the condition was reversed on the second day. Exercise consisted of walking on a treadmill at moderate-intensity for 60 minutes. Eighty males, mean age 30+8 years were categorized into five groups according to weight status (overweight/normal-weight), dietary restraint status (high/low), and dieting status (yes/no). The main effects of condition and group, and the interaction were not significant for acute (lunch) or 12-hour PE-EI. Overall, participants estimated EE for exercise at 46% higher than actual exercise EE, and they estimated EE for rest by 45% lower than actual resting EE. Participants significantly underestimated EI at lunch on both the exercise and rest days by 43% and 44%, respectively. Participants with high restraint were significantly better at estimating EE on the exercise day, and better at estimating EI on the rest day. Mood, perceived hunger, and solid vs. liquid carbohydrate intake were not influenced by dietary restraint, weight, or dieting status. In conclusion, a single bout of moderate-intensity exercise did not influence PE-EI in sedentary males in reference to dietary restraint, weight, and dieting status. Results also suggested that among sedentary males, there is a general inability to accurately estimate calories for moderate-intensity physical activity and EI. Inaccurate estimates of EE and EI have the potential to influence how males manage their weight.

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.