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

High-Resolution Melting System to Perform Multilocus Sequence Typing of Campylobacter jejuni

Multi-locus sequence typing (MLST) has emerged as the state-of-the-art method for resolving bacterial population genetics but it is expensive and time consuming. We evaluated the potential of high resolution melting (HRM) to identify known MLST alleles of Campylobacter jejuni at reduced cost and time. Each MLST locus was amplified in two or three sub fragments, which were analyzed by HRM. The approach was investigated using 47 C. jejuni isolates, previously characterized by classical MLST, representing isolates from diverse environmental, animal and clinical sources and including the six most prevalent sequence types (ST) and the most frequent alleles. HRM was then applied to a validation set of 84 additional C. jejuni isolates from chickens; 92% of the alleles were resolved in 35 hours of laboratory time and the cost of reagents per isolate was $20 compared with$100 for sequence-based typing. HRM has the potential to complement sequence-based methods for resolving SNPs and to facilitate a wide range of genotyping studies

Minim Typing – A Rapid and Low Cost MLST Based Typing Tool for Klebsiella pneumoniae

Here we report a single nucleotide polymorphism (SNP) based genotyping method for Klebsiella pneumoniae utilising high-resolution melting (HRM) analysis of fragments within the multilocus sequence typing (MLST) loci. The approach is termed mini-MLST or Minim typing and it has previously been applied to Streptococcus pyogenes, Staphylococcus aureus and Enterococcus faecium. Six SNPs were derived from concatenated MLST sequences on the basis of maximisation of the Simpsons Index of Diversity (D). DNA fragments incorporating these SNPs and predicted to be suitable for HRM analysis were designed. Using the assumption that HRM alleles are defined by G+C content, Minim typing using six fragments was predicted to provide a D = 0.979 against known STs. The method was tested against 202 K. pneumoniae using a blinded approach in which the MLST analyses were performed after the HRM analyses. The HRM-based alleles were indeed in accordance with G+C content, and the Minim typing identified known STs and flagged new STs. The tonB MLST locus was determined to be very diverse, and the two Minim fragments located herein contribute greatly to the resolving power. However these fragments are refractory to amplification in a minority of isolates. Therefore, we assessed the performance of two additional formats: one using only the four fragments located outside the tonB gene (D = 0.929), and the other using HRM data from these four fragments in conjunction with sequencing of the tonB MLST fragment (D = 0.995). The HRM assays were developed on the Rotorgene 6000, and the method was shown to also be robust on the LightCycler 480, allowing a 384-well high through-put format. The assay provides rapid, robust and low-cost typing with fully portable results that can directly be related to current MLST data. Minim typing in combination with molecular screening for antibiotic resistance markers can be a powerful surveillance tool kit

What Pinnipeds Have to Say about Human Speech, Music, and the Evolution of Rhythm

Research on the evolution of human speech and music benefits from hypotheses and data generated in a number of disciplines. The purpose of this article is to illustrate the high relevance of pinniped research for the study of speech, musical rhythm, and their origins, bridging and complementing current research on primates and birds. We briefly discuss speech, vocal learning, and rhythm from an evolutionary and comparative perspective. We review the current state of the art on pinniped communication and behavior relevant to the evolution of human speech and music, showing interesting parallels to hypotheses on rhythmic behavior in early hominids. We suggest future research directions in terms of species to test and empirical data needed