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The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.

To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.

Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.

The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.

The purpose of this study is to verify that if applications categories are segmented and resources are allocated based on their specific category, how effective scheduling can be done?.

This paper proposes a scheduling framework for IoT application jobs, based upon the Quality of Service (QoS) parameters, which works at coarse grained level to select a fog environment and at fine grained level to select a fog node. Fog environment is chosen considering availability, physical distance, latency and throughput. At fine grained (node selection) level, a probability triad (C, M, G) is anticipated using Naïve Bayes algorithm which provides probability of newly submitted application job to fall in either of the categories Compute (C) intensive, Memory (M) intensive and GPU (G) intensive.

Experiment results showed that the proposed framework performed better than traditional cloud and fog computing paradigms.

The proposed framework combines types of applications and computation capabilities of Fog computing environment, which is not carried out to the best of knowledge of authors.

While high-stakes mobile assessment is increasing, researchers have done little to adapt traditional assessments to this new medium. The present study developed and tested a new response method for personality assessment using a mobile-first gamification design paradigm.

Participants used smartphones to “swipe” right or left to indicate agreement or disagreement with Goldberg's (1992) Big Five adjective indicators. These scores were correlated with responses to a Likert-type measure and participants provided reactions to both measures.

Each of the swipe-based measures was found to be a reliable and valid predictor of the corresponding dimensions measured using the Likert-type scale. Reactions to the swipe measure were mixed when compared to a traditional Likert-type measure. Response latencies of swipes were used as an indicator of self-schema beliefs. Transformed latency scores contributed incremental variance to the prediction of Likert responses beyond the dichotomous responses alone for some personality dimensions.

Convergent validity between the two measures was likely attenuated due to differences in scales, response methods, devices, connection speeds, and social desirability effects indicating that the present results may constitute a lower-bound estimate of convergent validity between the two measurement styles.

Designing assessments for mobile administration requires balancing trade-offs in speed, ease of use, and number of items relative to the reliability and validity of the measures.

Mobile-first designs such as swipe-based responses show potential to enhance future mobile assessment practices with further development.

End-to-end latency in network affects the overall performance in number of ways. It is one of the major tasks to minimize the end-to-end latency in cognitive radio ad hoc networks (CRAHN), as the transmission of packets passes through every hop of the routing path. This paper aims to propose a new reactive multicast routing protocol, namely, improved frog leap inspired protocol (IFLIP) to reduce the overall end-to-end latency in CRAHN.

It is difficult to solve the problems that emerge in optimization. Routing is the procedure for choosing the best network path. This paper proposes a novel algorithm by improving the FLIP to use an ideal route, which progressively reduces the congestion level on various routing path by considering the spectrum accessibility and the service rate of each hop in CRAHN.

Result of this research work concludes that IFLIP significantly outperforms other baseline schemes (namely, TIGHT and Greedy TIGHT) in minimizing the end-to-end latency in CRAHN.

It is proved that IFLIP gives a better ratio of packet delivery under varying primary users and secondary users. IFLIP results in increased packet deliver ratio, reduced end-to-end latency and better throughput.

The purpose of this study is to introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases.

This study presents comprehensive approaches for deriving these metrics. More specifically, the study utilizes concrete SPARQL queries to determine corresponding values. Furthermore, it validates and discusses the introduced metrics through extensive evaluation on real-world SPARQL endpoints.

The evaluation determined that endpoints exhibit different characteristics. While it comes as no surprise that latency and throughput are influenced by the network infrastructure, the costs for join operations depend on a number of factors that are not obvious to a data consumer. Moreover, as the author discusses mean, median and upper quartile values, it was found both endpoints behaving consistently as well as repositories offering varying levels of performance.

On the one hand, the contribution of the authors work lies in assisting data consumers in evaluation of the quality of service of publicly available SPARQL endpoints. On the other hand, the performance metrics introduced in this study can also be considered as additional input features for distributed query processing frameworks. Moreover, the author provides a universal means for discerning characteristics of different SPARQL endpoints without the need of (synthetic or real-world) query workloads.

This study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method.

The unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model.

Using a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes.

The system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.

Sleep disturbances, present in more than 90% of major depressive disorder (MDD) patients, are moderated by sex in adult MDD. In particular, slow-wave electroencephalographic activity (SWA; 0.5-4 Hz) accumulation is low and dissipation impaired. This SWA abnormality in depressed adult males does not change with age, suggesting that SWA abnormality appears at early ages. The present study evaluated sex differences in SWA in adolescents with MDD compared to healthy controls. We evaluated regularized sleep-wake schedules at home for 5-7 days, followed by two consecutive nights of sleep EEG recording. The study included 104 participants, 52 symptomatic and depressed subjects (MDD: 20 males and 32 females) and 52 healthy controls (HC: 20 males and 32 females), aged 13-18 years. SWA power and dissipation, and duration and latencies to each Non-Rapid Eye Movement (NREM) sleep period were calculated for each group. Results showed that SWA accumulation in the first NREM period was lower and its dissipation across the night more irregular in MDD males compared to HC males (P<0.009). By contrast, SWA was equivalent in MDD and HC females. In conclusion, as reported in adult MDD, the accumulation and dissipation of SWA was abnormal in depressed adolescents, but only in males. SWA abnormalities in adolescent MDD may relate to different depressive symptoms in females and males. These results underscore the need to develop sex-specific therapies to enhance and restore SWA in depressed adolescents.

Traditional embedded operation systems are resource consuming multitask, thus they are not adapted for smart wireless sensors. This paper presents a super‐small distributed real‐time microkernel (SDREAM) dedicated to wireless sensors. SDREAM is a tuple‐based message‐driven real‐time kernel. It adopts a meta language: Kernel Modeling Language to define and describe the system primitives in abstract manner. The IPC and processes synchronization are based on the LINDA concept: the tuple model implemented by two light primitives (SND: OUT & RCV: IN). In SDREAM, tasks are classified into two categories: periodic and priority. The periodic task has the highest priority level and is responsible for capturing sensor signals or actuating control signals; the priority task has various priority levels and is suitable for time‐constraints applications. A two‐level task scheduling policy scheme, named priority‐based pre‐emptive scheduling, is used for task scheduling. SDREAM is simple and efficient. It has a flexible hardware abstraction capability that enables it to be rapidly ported into different WSN platforms and other tiny embedded devices. Currently, it has been ported and evaluated in several hardware platforms. The performance results show SDREAM requires tiny resource and is suitable and efficient for hard real‐time multitask WSN applications.

The small area network for data communication within routers is suffering from storage of packet, throughput, latency and power consumption. There are a lot of solutions to increase speed of commutation and optimization of power consumption; one among them is Network-on-chip (NoC). In the literature, there are several NoCs which can reconfigurable dynamically and can easily test and validate the results on FPGA. But still, NoCs have limitations which are regarding chip area, reconfigurable time and throughput.

To address these limitations, this research proposes the dynamically buffered and bufferless reconfigurable NoC (DB2R NoC) using X-Y algorithm for routing, Torus for switching and Flexible Direction Order (FDOR) for direction finding between source and destination nodes. Thus, the 3 × 3 and 4 × 4 DB2R NoCs are made free from deadlock, low power and latency and high throughput. To prove the applicability and performance analysis of DB2R NoC for 3 × 3 and 4 × 4 routers on FPGA, the 22 bits for buffered and 19 bit for bufferless designs have been successfully synthesized using Verilog HDL and implemented on Artix-7 FPGA development bond. The virtual input/output chips cope pro tool has been incorporated in the design to verify and debug the complete design on Artix-7 FPGA.

In the obtained result, it has been found that 35% improvement in throughput, 23% improvement in latency and 47% optimization in area has been made. The complete design has been tested for 28 packets of injection rate 0.01; the packets have been generated by using NLFSR.

In the obtained result, it has been found that 35% improvement in throughput, 23% improvement in latency and 47% optimization in area has been made. The complete design has been tested for 28 packets of injection rate 0.01; the packets have been generated by using NLFSR.

Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works.

The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms.

The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing.

This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English.

The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future.

Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.