Fog Integrated Secured and Distributed Environment for Healthcare Industry with Software Defined Networking

Fog computing is a segment of cloud computing where a vast number of peripheral equipment links to the internet. The term "fog" indicates the edges of a cloud in which high performance can be achieved. Many of these devices will generate voluminous raw data as from sensors, and rather than forward all this data to cloud-based servers to be processed, the idea behind fog computing is to do as much processing as possible using computing units co-located with the data-generating devices, so that processed rather than raw data is forwarded, and bandwidth requirements are reduced. A major advantage of processing locally is that data is more often used for the same computation machine which produced the data. Also, the latency between data production and data consumption was reduced. This example is not fully original, since specially programmed hardware has long been used for signal processing. The work presents the integration of software defined networking with the association of fog environment to have the cavernous implementation patterns in the health care industry with higher degree of accuracy

EETP-MAC: energy efficient traffic prioritization for medium access control in wireless body area networks

[EN] Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient's body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient's packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption.The research is supported by Ministry of Higher Education Malaysia (MOHE) and conducted in collaboration with Research Management Center (RMC) at University Teknologi Malaysia (UTM) under VOT NUMBER: R.J130000.7828.4F859Ullah, F.; Abdullah, AH.; Kaiwartya, O.; Lloret, J.; Arshad, MM. (2020). EETP-MAC: energy efficient traffic prioritization for medium access control in wireless body area networks. Telecommunication Systems. 75(2):181-203. https://doi.org/10.1007/s11235-017-0349-518120375

Energy-efficient Static Task Scheduling on VFI based NoC-HMPSoCs for Intelligent Edge Devices in Cyber-Physical Systems

The interlinked processing units in the modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC) based multiprocessor system-on-chip (MPSoC) architecture is becoming a de-facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS therefore, Voltage Frequency Island (VFI) recently adopted for effective energy management mechanism in the large scale multiprocessor chip designs. In this paper, we investigate energy and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI based NoC-MPSoCs with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we propose a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We have introduced a notion of Energy Gradient (EG) that guides EGD in its search for islands voltage settings and minimize the total energy consumption. We conducted the experiments on 8 real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ~ 24% and ~ 30% over CA-TMES-Search and CA-TMES-Quick respectively

A survey on intelligent sensor network and its applications

[[abstract]]With advances in technology, small form-factor sensors are feasible for various kinds of applications. The improvements on communication technology further make it practical to construct a wireless sensor network (WSN). In this paper, we review the works that are related to intelligent sensor network. Because there are no precise definitions of intelligent sensor network, we group them into two categories. One is to solve WSN issues with intelligent algorithms. The other is to design an intelligent application that incorporates sensor networks as the data sources. According to the categorization, the description of WSN issues, intelligent algorithms, and intelligent application technology are also provided in this article.[[notice]]補正完