Solving hidden terminal problem in MU-MIMO WLANs with fairness and throughput-aware precoding and a degrees-of-freedom-based MAC design

© 2016, Shrestha et al. We generally emphasize that the zeroforcing (ZF) technique backed by an appropriate medium access control (MAC) protocol can be used to address the inevitable hidden terminal (HT) problem in multi-user multiple input multiple output (MU-MIMO) wireless local area network (WLAN) settings. However, to address the implementation-specific requirements of MU-MIMO WLANs, such as fairness in client access and throughput of the network, we propose a fairness and a throughput-aware ZF precoding in our design at the physical layer (PHY). This precoding scheme not only solves the HT problem but also meets the fairness and the throughput requirements of MU-MIMO WLANs. Besides, we design a MAC layer protocol, supportive to PHY, which decides transmission opportunities (TXOPs) among access points (APs) based on the available degrees of freedom (DoF). We make a mandatory provision in our design that APs should have a sufficient DoF. This can ensure collision-free transmission whenever APs/transmitters transmit in the HT scenario. Additionally, we design an improved channel sounding process for MU-MIMO WLANs with a less signaling overhead than IEEE802.11ac. We demonstrate the feasibility of our PHY in a USRP2/GNU Radio testbed prototype in the lab settings. It is found that our PHY improves the SNR and effective SNR of the received signal from about 5 to 11 dB in the HT scenario. The performance of our MAC design is checked with simulation studies in a typical six-antenna AP and clients scenario. We observe that our MAC protocol has a slightly higher signaling overhead than traditional ready to send/clear to send (RTS/CTS) due to design constraints; however, the signaling time overheads are reduced by 98.67 μs compared to IEEE802.11ac. Another interesting aspect to highlight is the constant Throughput gain of four to five times that of the traditional RTS/CTS. Our MAC protocol obtains this gain as early as 98.67 μs compared to IEEE802.11ac

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model

Redsharc: A Programming Model and On-Chip Network for Multi-Core Systems on a Programmable Chip

The reconfigurable data-stream hardware software architecture (Redsharc) is a programming model and network-on-a-chip solution designed to scale to meet the performance needs of multi-core Systems on a programmable chip (MCSoPC). Redsharc uses an abstract API that allows programmers to develop systems of simultaneously executing kernels, in software and/or hardware, that communicate over a seamless interface. Redsharc incorporates two on-chip networks that directly implement the API to support high-performance systems with numerous hardware kernels. This paper documents the API, describes the common infrastructure, and quantifies the performance of a complete implementation. Furthermore, the overhead, in terms of resource utilization, is reported along with the ability to integrate hard and soft processor cores with purely hardware kernels being demonstrated

Living FIFO: the experiences and psychosocial wellbeing of Western Australian fly-in/fly-out employees and partners

Using a concurrent multi-methods design employing both quantitative and qualitative methodologies this study investigated the psychosocial wellbeing Western Australian fly-in/fly-out (FIFO) mining employees and their partners. The quantitative phase of the study assessed the psychological wellbeing, relationship satisfaction and perceptions of family function of 90 FIFO mining employees and 32 partners of FIFO employees using the General Health Questionnaire 12, the Dyadic Adjustment Scale and the Family Assessment Device. Analyses revealed that both FIFO employees and their partners are within the norms for healthy functioning on the scales and sub-scales of the measures of psychological wellbeing, relationship satisfaction and perceptions of family function, and that there were no statistically significant differences between the scores of the two groups on any of these measures. Further, there were no significant differences when data were analysed according to family type or profile of absence. Thus, despite perceptions that regular FIFO employment related absence would have adverse impacts on various aspects of wellbeing, the group of FIFO employees and partners in this study report similar levels of psychological wellbeing, relationship satisfaction and perceptions of family function to those of the general Australian population. The qualitative phase used constructivist grounded theory methodology to explore the experiences of FIFO employees and partners of FIFO employees in order to develop an understanding and theoretical scheme of the role of contextual factors in their adaptation to the FIFO lifestyle. In-depth interviews were conducted with a medium sized sample of 16 FIFO employees and 12 partners of FIFO employees. The findings from the qualitative phase are discussed in light of existing literature and the findings from the quantitative phase. The data revealed a number of individual, family, community and workplace factors that impact on individual experiences of and adaptation to the FIFO lifestyle. Informants generally made purposeful and informed choices to undertake FIFO employment based on the notion that “the benefits outweigh the costs”, that the lifestyle associated with FIFO employment would considerably increase individual and family access to financial and psychosocial resources, and that the net gains in personal and family resources would outweigh any losses. These findings challenge earlier presumptions that the regular absences associated with FIFO employment would result in a loss of individual and family resources and would impact negatively on the psychosocial wellbeing of FIFO employees and their partners. The strengths and limitations of the study are outlined as are suggestions for future research. Implications of the findings at the individual, community, corporate and government levels are presented together with recommendations for future actions

Design and Verification of a Distributed Communication Protocol

The safety of remotely operated vehicles depends on the correctness of the distributed protocol that facilitates the communication between the vehicle and the operator. A failure in this communication can result in catastrophic loss of the vehicle. To complicate matters, the communication system may be required to satisfy several, possibly conflicting, requirements. The design of protocols is typically an informal process based on successive iterations of a prototype implementation. Yet distributed protocols are notoriously difficult to get correct using such informal techniques. We present a formal specification of the design of a distributed protocol intended for use in a remotely operated vehicle, which is built from the composition of several simpler protocols. We demonstrate proof strategies that allow us to prove properties of each component protocol individually while ensuring that the property is preserved in the composition forming the entire system. Given that designs are likely to evolve as additional requirements emerge, we show how we have automated most of the repetitive proof steps to enable verification of rapidly changing designs

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load