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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.

The purpose of this paper is to present a distributed multiple mobile robot system that provides a collaborative control and simulation environment.

A CORBA‐based cooperative system is designed to implement a robotic layered cooperative mechanism. The mechanism has three layers: mission, transport and execution. In order to realize a flexible and effective communication in the cooperative mechanism, an extended robot event service (federated event service) is proposed to improve the cooperative system's real time performance.

Experimentation has proved the validity and effectiveness of the system. The federated event service's latency is approximately 9 percent less than the standard event service latency when the CPU is determined.

The robotic modularized system includes the map‐building, path‐planning, robot task‐planning, simulation and actual robot control function modules, and uses CORBA to integrate the whole system. It is easy to implement a layered cooperative mechanism for multiple mobile robots. Given the problem on multiple robots cooperation latency, a useful extended robot event service is proposed.

The paper focuses on the distributed functional modular architecture, and the multiple robots cooperative layered mechanism. In the mechanism, an extended robot event service (federated event service) is proposed to reduce the cooperative system's real time latency. The conducted experiment validates the proposed system with a good performance for multiple mobile robots' cooperation.

The purpose of this paper is to reduce parity generation latency if the input value is narrow.

Soft errors caused by cosmic particles and radiation emitted by the packaging are important problems in contemporary microprocessors. Parity bits are used to detect single bit errors that occur in the storage components. In order to implement parity logic, multiple levels of XOR gates are used and these XOR trees are known to have high delay. Many produced and consumed values inside a processor hold consecutive zeros and ones in their upper order bits. These values can be represented with less number of bits and hence are termed narrow. In this paper, a parity generator circuit design is proposed that is capable of generating parity if the input value is narrow. It is shown that the parity can be generated faster than a regular XOR tree implementation using this design for the values that can be represented using fewer bits.

The proposed technique reduces the parity generation latency of 64‐bit values by 50 percent for eight‐bit narrow values. Considering the fact that around 70 percent of the immediate values written to the immediate field of the issue queue and around 40 percent of the value written to the integer register file can be expressed with only eight bits, the coverage of the proposed scheme is quite high.

This paper shows the simulation results of fast parity generator circuit if the input value is narrow.

Fog computing 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 model such as unreliable latency, bandwidth constraints, security and mobility. This paper aims to provide detailed survey in the field of fog computing covering the current state-of-the-art in fog computing.

Cloud was developed for IT and not for Internet of Things (IoT); as a result, cloud is unable to meet the computing, storage, control and networking demands of the IoT applications. Fog is a companion for the cloud and aims to extend the cloud capabilities to the edge of the network.

Lack of survey papers in the area of fog computing was an important motivational factor for writing this paper. This paper highlights the capabilities of the fog computing and where it fits in between IoT and cloud. This paper has also presented architecture of the fog computing model and its characteristics. Finally, the challenges in the field of fog computing have been discussed in detail which need to be overcome to realize its full potential.

This paper presents the current state-of-the-art in fog computing. Lack of such papers increases the importance of this paper. It also includes challenges and opportunities in the fog computing and various possible solutions to overcome those challenges.

Purpose — The research was designed to explore people's willingness/ability to understand complex road user charges. However, the results raise issues about respondent engagement and ecological validity and so have important implications for questionnaire practice.

Methodology — Computer-based experiments administered in the United Kingdom and Germany gathered respondents' estimates of road user charges along with their response latencies, personal characteristics, acceptance of road charging, assessments of task complexity and attitudes to analytical tasks.

Findings — The results demonstrate questionnaire learning effects and show the effect of personal characteristics on the accuracy and speed of questionnaire completion. The tendency of males, younger people and students to complete the task more quickly is interesting as is the fact that fewer and smaller errors were made by participants who claimed to gain satisfaction from completing a task which has involved mental effort. Engagement was seen to vary with personal characteristics, attitudes to decision making, task complexity and acceptance of the policy being tested. A key finding is that disengagement was more evident among participants who were broadly supportive of road charging than among those who were not.

Implications — The findings have important implications for the design of data collection exercises and for the interpretation of resulting data. It is concluded that repeated choice experiments are an inappropriate source of data on responses to unfamiliar circumstances. The collection of data on response latencies and the inclusion of questions on respondents' attitudes to task completion is a strongly recommended addition to standard questionnaire practice. The extent to which disengagement in an experimental context is, or is not, indicative of real-world behaviour is an important and urgent subject for further research.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

This paper has proposed a Fog architecture-based framework, which classifies dengue patients into uninfected, infected and severely infected using a data set built in 2010. The aim of this proposed framework is to developed a latency-aware system for classifying users into different categories based on their respective symptoms using Internet of Things (IoT) sensors and audio and video files.

To achieve the aforesaid aim, a smart framework is proposed, which consist of three components, namely, IoT layer, Fog infrastructure and cloud computing. The latency of the system is reduced by using network devices located in the Fog infrastructure. Data generated by IoT layer will first be processed by Fog layer devices which are in closer proximity of the user. Raw data and data generated will later be stored on cloud infrastructure, from where it will be sent to different entities such as user, hospital, doctor and government healthcare agencies.

Experimental evaluation proved the hypothesis that using the Fog infrastructure can achieve better response time for latency sensitive applications with the least effect on accuracy of the system.

The proposed Fog-based architecture can be used with IoT to directly link it with the Fog layer.