Packet Size Optimization for Multiple Input Multiple Output Cognitive Radio Sensor Networks aided Internet of Things

The determination of Optimal Packet Size (OPS) for Cognitive Radio assisted Sensor Networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible optimal packet size of data transmission under the simple point-to-point CRSN network topology. This is contributed primarily due to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a Multiple Input Multiple Output based Cognitive Radio Sensor Networks (MIMO-CRSNs) architecture for futuristic technologies like Internet of Things (IoT) and machine-to-machine (M2M) communications. A joint optimization problem is formulated taking into account network constraints like the overall end to end latency, interference duration caused to the non-cognitive users, average BER and transmit power.We propose our Algorithm-1 based on generic exhaustive search technique blue to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm-2 based on solving classical Karush-Kuhn-Tucker (KKT) conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are Time Slotted Distributed Cognitive Medium Access Control denoted as MIMO-DTS-CMAC and CSMA/CA assisted Centralized Common Control Channel based Cognitive Medium Access Control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO based CRSN network outperforms the conventional point-to-point CRSN network in terms of overall energy consumption. Moreover, the proposed Algorithm-1 and Algorithm2 shows perfect match and the implementation complexity of Algorithm-2 is much lesser than Algorithm-1. Algorithm-1 takes almost 680 ms to execute and provides OPS value for a given number of users while Algorithm- 2 takes 4 to 5 ms on an average to find the optimal packet size for the proposed MIMO-CRSN framework

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive Radio Sensor Networks (CRSN) is state of the art communication paradigm for power constrained short range data communication. It is one of the potential technology adopted for Internet of Things (IoT) and other futuristic Machine to Machine (M2M) based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy-efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper we propose a heuristic exhaustive search based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn- Tucker (KKT) condition based Algorithm-2 to determine the optimal packet size in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on Distributed Time Slotted-Cognitive Medium Access Control (DTS-CMAC) and Centralized Common Control Channel based Cognitive Medium Access Control (CC-CMAC) and their performances are compared. The simulation results reveals that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 seconds while for KKT based Algorithm-2 it is of the order of 5 to 10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme

The Achievable Rate of Interweave Cognitive Radio in the Face of Sensing Errors

Cognitive radio (CR) systems are potentially capable of mitigating the spectrum shortage of contemporary wireless systems. In this paper, we provide a brief overview of CR systems and the important research milestones of their evolution, along with their standardization activities, as a result of their research. This is followed by the detailed analysis of the interweave policy-based CR network (CRN) and by a detailed comparison with the family of underlay-based CRNs. In the interweave-based CRN, sensing of the primary user's (PU) spectrum by the secondary user's (SU) has remained a challenge, because the sensing errors prevent us from fulfilling the significant throughput gains that the concept of CR promises. Since missed detection and false alarm errors in real-time spectrum sensing cannot be avoided, based on a new approach, we quantify the achievable rates of the interweave CR by explicitly incorporating the effect of sensing errors. The link between the PU transmitter and the SU transmitter is assumed to be fast fading. Explicitly, the achievable rate degradation imposed by the sensing errors is analyzed for two spectrum sensing techniques, namely, for energy detection and for magnitude squared coherence-based detection. It is demonstrated that when the channel is sparsely occupied by the PU, the reusing techniques that are capable of simultaneously providing low missed detection and false alarm probabilities cause only a minor degradation to the achievable rates. Furthermore, based on the achievable rates derived for underlay CRNs, we compare the interweave CR and the underlay CR paradigms from the perspective of their resilience against spectrum sensing errors. Interestingly, in many practical regimes, the interweave CR paradigm outperforms the underlay CR paradigm in the presence of sensing errors, especially when the SNR at the SU is below 10 dB and when the SNR at the PU is in the range of 10-40 dB. Furthermore, we also provide rules of thumb that identify regimes, where the interweave CR outperforms the underlay CR

New approaches to predicting the risk of sudden death.

In this review article, we will explore some of the contemporary methods for predicting sudden cardiac death (SCD). These include experimental methods yet to be adopted in the clinical setting, and methods that have been extrapolated from observational data in those with a history of SCD. We will discuss how these relate to the different aetiologies and disease processes. We will also explore how these may be used in the clinical setting to decide on management

Packet Size Optimization for Topology Aware Cognitive Radio Sensor Networks

In this paper, we propose a framework to optimize the packet length and modulation level to determine the optimal packet size (OPS) for topology aware cognitive radio sensor networks (CRSNs) using a variable rate modulation scheme. A generalized network topology with specific node density of the Primary Users (PUs) is accounted to estimate the OPS. Based on stochastic geometry and non-linear optimization techniques, a joint multivariate optimization problem is formulated to determine the OPS for the topology dependent CRSNs

Pcdp1 is a central apparatus protein that binds Ca2+-calmodulin and regulates ciliary motility

A complex that localizes to the C1d central pair projection of cilia controls flagellar waveform and beat frequency in response to calcium

Learning Curve of MIS-TLIF using 22 mm-tubular Retractor in Degenerative Spondylolisthesis (Grade 1-2) - A Review over 100 Cases

Objective To evaluate the learning curve of MIS-TLIF in degenerative spondylolisthesis with understanding of problems and challenges faced during initial cases. Methods After taken approval from institutional review board, first 109 patients who underwent MIS-TLIF for singlelevel low-grade degenerative spondylolisthesis between 2010 to 2015 were evaluated. First 100 cases that formed the study cohort at final follow-up were arranged sequentially in order of date of operation and then grouped in four quartiles. Comprehensive data which included demographics, clinical parameters, surgical parameters, peri-operative incidents (dural tear, technical issues like guide-wire migration, tube docking problems) and complications were assessed. Results Median operative time, median blood loss and median radiation exposure gradually decreased as the series progressed, however, showed statistically significant difference between Q1 and Q2 with no significant difference between later quartiles. There was a significant decline in postoperative VAS and ODI scores in all quartiles, however, there was no statistically significant difference in their values on comparison between quartiles. Guide-wire migration, dural tear and tube docking related problems, pedicle screw perforation significantly reduced after 1st quartile. Conclusion MIS-TLIF is safe and effective means of treating lumbar spondylolisthesis. The learning curve is achieved between 1st and 2nd quartile (25th to 50th cases). Familiarity with instrumentation, preoperative anatomical planning, better coordination with surgical team and hands-on tissue-training are keys to reduce the learning curve