No abstract

Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

Drones can be used in agriculture applications to monitor crop yield and climate conditions and to extend the communication range of wireless sensor networks in monitoring areas. However, monitoring the climate conditions in agriculture applications faces challenges and limitations, such as drone flight time, power consumption, and communication distance, which are addressed in this study. Wireless power transfer (WPT) can be used to charge drone batteries. WPT using a magnetic resonant coupling (MRC) technique was considered in this study because it allows high transfer power and efficiency with tens of centimeters, power transfers can be achieved in misalignment situations, charging several devices simultaneously, and unaffected by weather conditions. WPT was practically implemented based on a solar cell using a proposed flat spiral coil (FSC) in the transmitter circuit and multiturn coil (MTC) in a receiver circuit (drone) for the alignment and misalignment of two coils at different distances. FSC and MTC improved power transfer and efficiency to 20.46 W and 85.25%, respectively, at 0 cm with the loaded system under alignment condition. In addition, the two coils achieved appropriate transfer efficiencies and power for charging the drone battery under misaligned conditions. The maximum power transfer and efficiency were 17.1 W and 71% for the misalignment condition, at an air gap of 1 cm between two coils when the system was loaded with the drone battery. Moreover, the battery life of the drone was extended to 851 minutes based on the proposed sleep/active strategy relative to the traditional operation (i.e., 25.84 minutes). Consequently, a 96.9% battery power saving was achieved based on this strategy. Comparison results showed that the proposed system outperformed some present techniques in terms of the transfer power, transfer efficiency, and drone battery life. The proposed WPT technique developed in this study has been proven to solve the misalignment issue. Thus it offers a great opportunity as a key deployment component for the automation of farming practices toward the Internet of Farming applications.INDEX TERMS Battery life, drone, energy efficiency, farming, flat spiral coil, flight time, multiturn coil, power consumption, wireless sensor network, solar panel. I. INTRODUCTIONDrones can be used in the agriculture field [1]-[3] to monitor crop yield and climate conditions and to extend the The associate editor coordinating the review of this manuscript and approving it for publication was el-Hadi M. Aggoune. communication range of monitoring areas. Drones can be equipped with several payloads, such as sensors, highresolution and infrared cameras, tracking, and a global positioning system (GPS), as a delivery vehicle [4]. These drones generally run on batteries powered with high energy, such as lithium batteries, to enable flight times of

The use of wireless sensor networks (WSNs) in modern precision agriculture to monitor climate conditions and to provide agriculturalists with a considerable amount of useful information is currently being widely considered. However, WSNs exhibit several limitations when deployed in real-world applications. One of the challenges faced by WSNs is prolonging the life of sensor nodes. This challenge is the primary motivation for this work, in which we aim to further minimize the energy consumption of a wireless agriculture system (WAS), which includes air temperature, air humidity, and soil moisture. Two power reduction schemes are proposed to decrease the power consumption of the sensor and router nodes. First, a sleep/wake scheme based on duty cycling is presented. Second, the sleep/wake scheme is merged with redundant data about soil moisture, thereby resulting in a new algorithm called sleep/wake on redundant data (SWORD). SWORD can minimize the power consumption and data communication of the sensor node. A 12 V/5 W solar cell is embedded into the WAS to sustain its operation. Results show that the power consumption of the sensor and router nodes is minimized and power savings are improved by the sleep/wake scheme. The power consumption of the sensor and router nodes is improved by 99.48% relative to that in traditional operation when the SWORD algorithm is applied. In addition, data communication in the SWORD algorithm is minimized by 86.45% relative to that in the sleep/wake scheme. The comparison results indicate that the proposed algorithms outperform power reduction techniques proposed in other studies. The average current consumptions of the sensor nodes in the sleep/wake scheme and the SWORD algorithm are 0.731 mA and 0.1 mA, respectively.

Single-tube loop coil (STLC) and multi-turn copper wire coil (MTCWC) wireless power transfer (WPT) methods are proposed in this study to overcome the challenges of battery life during low-power home appliance operations. Transfer power, efficiency, and distance are investigated for charging mobile devices on the basis of the two proposed systems. The transfer distances of 1–15 cm are considered because the practicality of this range has been proven to be reliable in the current work on mobile device battery charging. For STLC, the Li-ion battery is charged with total system efficiencies of 86.45%, 77.08%, and 52.08%, without a load, at distances of 2, 6, and 15 cm, respectively. When the system is loaded with 100 Ω at the corresponding distances, the transfer efficiencies are reduced to 80.66%, 66.66%, and 47.04%. For MTCWC, the battery is charged with total system efficiencies of 88.54%, 75%, and 52.08%, without a load, at the same distances of 2, 6, and 15 cm. When the system is loaded with 100 Ω at the corresponding distances, the transfer efficiencies are drastically reduced to 39.52%, 33.6%, and 15.13%. The contrasting results, between the STLC and MTCWC methods, are produced because of the misalignment between their transmitters and receiver coils. In addition, the diameter of the MTCWC is smaller than that of the STLC. The output power of the proposed system can charge the latest smartphone in the market, with generated output powers of 5 W (STLC) and 2 W (MTCWC). The above WPT methods are compared with other WPT methods in the literature.