The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each.

Combining the Internet-of-Things technology with cloud computing is a significant alternative for powering the utilization of computing resources in a connected environment. A grand challenge in communications is raised by the emergence of big data, due to the large-sized data transmissions and frequent data exchanges. Applying fog computing is considered an option for resolving the communication challenge. However, a high extent of available heterogeneous computing attached to fog computing servers leads to a restriction of the resource management. This paper addresses the resource management issue by proposing a novel approach - named Energy-aware Fog Resource Optimization model- to optimizing the utilization of connected devices in fog computing. We develop a heuristic algorithm minimizing both energy cost and time consumption in a holistic way. A salient feature of EFRO lies in the integration of the standardization and smart shift operations fueled by a hill-climbing mechanism to produce near-optimal resource allocation solutions. Experimental results demonstrate that our EFRO is adroit at making near-optimal decisions in managing resources in fog computing environments. In particular, EFRO boosts the energy efficiency of the existing MESF and RR schemes by 54.83% and 71.28%, respectively. EFRO shortens DECM's allocation-generation time by up to a factor of 507.

The Internet of Things (IoT) employs sensors and the Internet for information exchange, enabling intelligent identification, monitoring, and management, which has deeply impacted various sectors such as power, medical care, and security, transforming social activities and lifestyles. Regrettably, IoT systems suffer from two main challenges, namely sustainability and security. Hence, pondering how to enhance sustainable and energy-efficient practices for IoT systems to mitigate risks becomes a worthwhile endeavor. To address this issue, we conduct a survey of energy-aware security mechanisms in the Internet of Things. Specifically, we examine the challenges that IoT is facing in terms of energy efficiency and security, and we inspect current energy-saving and privacy-preserving technologies for IoT systems. Moreover, we delineate a vision for the future of IoT, emphasizing energy-aware security mechanisms. Finally, we outline the challenges encountered in achieving energy-aware security mechanisms, as well as the direction of future research. Motivated by this study, we envision advancements in the IoT that not only harness the benefits of science and technology but also enhance the security and safety of our data.

When it comes to NAND Flash-based solid-state disks (SSDs), cache can narrow the performance gap between user-level I/Os and flash memory. Cache management schemes impose relentless impacts on the endurance and performance of flash memory. A vast majority of existing cache management techniques adopt a passive data-update style (e.g., GCaR, LCR), thereby undermining response times in burst I/O requests-based applications ¹ 1.Burst I/O requests must be served in a real-time manner. This type of I/O access pattern is prevalent in data-intensive workloads.. To address this issue, we propose a collaborative active write-back cache management scheme, called Co-Active, customized for I/O access patterns and the usage status of a flash chip. We design a hot/cold separation module to determine whether data is cold or hot in workload. When a flash chip is idle, cold and dirty data in the cache is flushed into the idle flash chip to produce clean data. To curtail cache replacement cost, clean data are preferentially evicted amid the procedure of cache replacement. A maximum write-back threshold is configured according to the level of burst I/O requests in workload. This threshold is intended to avert redundant write I/Os flushing into flash memory, thereby boosting the endurance of flash memory. The experiments are conducted to validate the advantages of Co-Active in terms of average response time, write amplification, and erase count. The findings unveil that compared with the six popular cache management schemes (LRU, CFLRU, GCaR_CFLRU, LCR, and MQSim), Co-Active (1) slashes the average response time by up to 83.89 percent with an average of 32.7 percent; (2) drives up the performance cliff degree by up to 76.4 percent with an average of 42.3 percent; and (3) improves write amplification rate by up to 60.5 percent with an average of 5.4 percent.