Wen-Hwa Liao

Advances in wireless charging technology give great new opportunities for extending the lifetime of a wireless sensor network (WSN) which is an important infrastructure of IoTs. However, the existing greedy algorithms lacked learning from the experiences of energy dissipation trends. Unlike the existing studies, this paper proposes a reinforcement learning approach, called RLR, for mobile charger to learn the trends of WSNs, including the energy consumption of the sensors, the recharging cost as well as the coverage benefit, aiming to maximize the coverage contribution of the recharged WSN. The proposed RLR mainly consists of three modules, including Sensor Energy Management, Charger Location Update and Charger Reinforcement Learning modules. In the Sensor Energy Management module, each sensor manages its energy and calculates its threshold for the recharging request in a distributed manner. The Charger Location Update module adopts the quorum system to ensure effective communication between sensors and the mobile charger. Meanwhile, the Charger Reinforcement Learning module employs attraction rewards to reflect the coverage benefit and penalties of waiting time raised due to charger movement and recharging other sensors. As a result, the charger accumulates the learning experiences from the Q-Table such that it is able to execute the appropriate actions of charging or moving in a manner of state management. Performance results show that the proposed RLR outperforms the existing recharging mechanisms in terms of charging waiting time of sensors, the energy usage efficiency of the mobile charger, as well as the coverage contribution of the given sensor network.

Wireless sensor networks have been widely applied in a large number of applications. However, the sensors are battery powered. To solve the problem of limited network lifetime, many scholars have studied the recharging issue. Some sensor recharging algorithms only considered the energy consumption of the sensors, while the others only considered the recharging path of the mobile charger. None of them considered the monitoring quality when developing the recharging algorithms. This paper considers mobile wireless sensor networks where one mobile charger and a number of mobile sensors are deployed. The developed mechanism considers both the recharging efficiency and the monitoring quality. In addition, the cooperation between mobile charger and mobile sensors are explored to cope with the problem that the mobile charger needs to visit each sensor for recharging. The experimental results show that the performance of our algorithm outperforms the other compared algorithms, in terms of monitoring quality.

In wireless sensor networks (WSNs), area coverage is an important issue that aims to schedule a minimal number of sensors to cover the given monitoring area without any hole. Many of the existing strategies investigated that sensors are battery powered and considered Boolean Sensing Model (BSM). However, BSM affects the actual sensing quality since it did not consider the physical characteristics of the sensor. This study proposed an area coverage algorithm called $\textit {MSQAC}$ , which considers the rechargeable sensors and utilizes solar-powered energy. The proposed mechanism aims to maximize the surveillance quality for a given monitoring area by applying the Probabilistic Sensing Model (PSM). Two issues have been investigated while designing the $\textit {MSQAC}$ algorithm. The first one is the energy discharging rate of each sensor is larger than the energy recharging rate. Thus, how to better utilize the recharged energy by scheduling the right sensor in the right working time is a big challenge. The second challenge is the contribution of each sensor with or without cooperation is different by considering the PSM. If the neighboring sensors are scheduled at work in the same time slot, they can be treated as the combination set of neighboring sensors. It is another big challenge to find the best combination set of sensors for each space point at each time slot such that the minimal monitoring quality can be maximized. Through extensive simulations, it is shown that $\textit {MSQAC}$ yields the best performance in terms of monitoring quality ( $\textit {QoM}$ ), coverage ratio and fairness index.

Data collection using mobile sink has received much attention in the past years. In the literature, most studies assumed that the mobile sink was controllable and the proposed algorithm made decisions to control the path, speed, and stop locations. However, there still exists some challenges in using the controllable mobile sink, including limited energy and the adaptive capability for overcoming the obstacles. This article considers the bus as the uncontrollable mobile sink to collect data from a given set of deployed static sensors. A distributed data collection mechanism, called energy balanced multihop data collection (EBMDC), is proposed that aims at maximizing the transmitted data volume from sensors to the bus while prolonging the network lifetime of the wireless sensor networks (WSNs). To prolong the network lifetime, each sensor node distributes its data to its multiple parents in trees according to their remaining energies. Then, each anchor node locally schedules its transmission slots based on its and its neighbor's data volumes and remaining energies such that all data can be forwarded to the bus without collision and the lifetime of each anchor can be balanced. Experimental studies reveal that the proposed EBMDC algorithm outperforms related studies in terms of throughput, network lifetime, slot utilization, data loss ratio, and fairness.

Numerous differences exist between the healthcare industry and other industries. Difficulties in the business operation of the healthcare industry have continually increased because of the volatility and importance of health care, changes to and requirements of health insurance policies, and the statuses of healthcare providers, which are typically considered not-for-profit organizations. Moreover, because of the financial risks associated with constant changes in healthcare payment methods and constantly evolving information technology, healthcare organizations must continually adjust their business operation objectives; therefore, cloud computing presents both a challenge and an opportunity. As a response to aging populations and the prevalence of the Internet in fast-paced contemporary societies, cloud computing can be used to facilitate the task of balancing the quality and costs of health care. To evaluate cloud computing service systems for use in health care, providing decision makers with a comprehensive assessment method for prioritizing decision-making factors is highly beneficial. Hence, this study applied the analytic hierarchy process, compared items related to cloud computing and health care, executed a questionnaire survey, and then classified the critical factors influencing healthcare cloud computing service systems on the basis of statistical analyses of the questionnaire results. The results indicate that the primary factor affecting the design or implementation of optimal cloud computing healthcare service systems is cost effectiveness, with the secondary factors being practical considerations such as software design and system architecture.