The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms.

Due to the constantly growing geriatric population and the projected increase of the prevalence of chronic diseases that are refractory to drugs, implantable medical devices (IMDs) such as neurostimulators, endoscopic capsules, artificial retinal prostheses, and brain-machine interfaces are being developed. According to many business forecast firms, the IMD market is expected to grow and they are subject to much research aiming to overcome the numerous challenges of their development. One of these challenges consists of designing a wireless power and data transmission system that has high power efficiency, high data rates, low power consumption, and high robustness against noise. This is in addition to minimal design and implementation complexity. This manuscript concerns a comprehensive survey of the latest techniques used to power up and communicate between an external base station and an IMD. Patient safety considerations related to biological, physical, electromagnetic, and electromagnetic interference concerns for wireless IMDs are also explored. The simultaneous powering and data communication techniques using a single inductive link for both power transfer and bidirectional data communication, including the various data modulation/demodulation techniques, are also reviewed. This review will hopefully contribute to the persistent efforts to implement compact reliable IMDs while lowering their cost and upsurging their benefits.

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks' delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks.

This paper presents a wireless power and downlink data transfer system for medical implants operating over a single 10 MHz inductive link. The system is based on a Carrier Width Modulation (CWM) scheme for high-speed communication and efficient power delivery using a novel modulator circuit design. Unlike conventional modulation techniques, the data rate of the proposed CWM is not limited by the quality factors of the primary and secondary coils. The functionality of the new modulation method is proven using a hybrid implementation comprising a custom-integrated demodulator circuit and board-level discrete components. The proposed Wireless Power and Data Transfer (WPDT) system is also capable of operating under a wide range of data rates. It allows a maximum data rate of 3.33 Mb/s for a maximum power delivery of 6.1 mW at 1 cm coils separation distance. The system can recover more power, reaching 55 mW at 100 kb/s. Due to the system genericity, an operator can select the best compromise between power and data rates in accordance to application or current need, without reconfiguring the receiver. Another advantage of this modulation technique is the simple implementation and the ultra-low power consumption of the CWM demodulator despite its high-speed demodulation.

As an increasingly mature business model, crowdsourcing, especially spatial crowdsourcing, has played an important role in data collection, disaster response, urban planning and other fields. However, the rapid growth of user scale and massive data collected inevitably brings serious challenges to computing and storage resources. The emergence of cloud computing provides an opportunity to handle such challenges. Its nearly unlimited resource provision capability can provide reliable services for different crowdsourcing applications. Nevertheless, considering the risks of privacy leakage and vendor lock-in using only a single cloud, as well as the additional restrictions caused by the wide geographical distribution of data and associations among workers, the use of multi-cloud seems to be a better choice. In this paper, we define a problem to find an effective data placement scheme for spatial crowdsourcing in multi-cloud environment to achieve the cost-effectiveness and minimal latency. We take full account of the interval pricing strategy. Then we analyze the geographical distribution characteristics of data centers through a clustering algorithm, and propose an effective data initialization strategy. Finally, we use a genetic algorithm to further optimize the results. Through experiments on real-world data from cloud providers, the efficiency and effectiveness of our proposed method is verified.