Delay sensitive and power-aware SMDP-based connection admission control mechanism in cognitive radio sensor networks

© 2017 Elsevier B.V. Due to the opportunistically resource usage of users in cognitive radio sensor networks (CRSNs), the availability of network resources is highly variable. Therefore, admission control is an essential mechanism to manage the traffic of cognitive radio users in order to satisfy the quality of service (QoS) requirements of applications. In this study, a connection admission control (CAC) mechanism is introduced to satisfy the requirements of delay sensitivity and power consumption awareness. This proposed mechanism is modeled through a semi Markov decision process (SMDP) and a linear programming problem is derived with the aim of obtaining the optimal policy to control the traffic of CRSNs and achieving maximum reward. The number of required channels at each network state is estimated through a graph coloring approach. An end to end delay constraint is defined for the optimization problem which is inspired from Kleinrock independence approximation. Furthermore, a power-aware weighting method is proposed for this mechanism. We conduct different simulation-based scenarios to investigate the performance of the proposed mechanism. The experimental results demonstrate the efficiency of this SMDP-based mechanism in comparison to the last CAC mechanism in CRSNs

Evaluating metallic artefact of biodegradable magnesium-based implants in magnetic resonance imaging

Magnesium (Mg) implants have shown to cause image artefacts or distortions in magnetic resonance imaging (MRI). Yet, there is a lack of information on how the degradation of Mg-based implants influences the image quality of MRI examinations. In this study, Mg-based implants are analysed in vitro, ex vivo, and in the clinical setting for various magnetic field strengths with the aim to quantify metallic artefact behaviour. In vitro corroded Mg-based screws and a titanium (Ti) equivalent were imaged according to the ASTM F2119. Mg-based and Ti pins were also implanted into rat femurs for different time points and scanned to provide insights on the influence of soft and hard tissue on metallic artefact. Additionally, MRI data of patients with scaphoid fractures treated with CE-approved Mg-based compression screws (MAGNEZIX(®)) were analysed at various time points post-surgery. The artefact production of the Mg-based material decreased as implant material degraded in all settings. The worst-case imaging scenario was determined to be when the imaging plane was selected to be perpendicular to the implant axis. Moreover, the Mg-based implant outperformed the Ti equivalent in all experiments by producing lower metallic artefact (p < 0.05). This investigation demonstrates that Mg-based implants generate significantly lower metallic distortion in MRI when compared to Ti. Our positive findings suggest and support further research into the application of Mg-based implants including post-operative care facilitated by MRI monitoring of degradation kinetics and bone/tissue healing processes

Radiofrequency induced heating of biodegradable orthopaedic screw implants during magnetic resonance imaging

Magnesium (Mg)-based implants have re-emerged in orthopaedic surgery as an alternative to permanent implants. Literature reveals little information on how the degradation of biodegradable implants may introduce safety implications for patient follow-up using medical imaging. Magnetic resonance imaging (MRI) benefits post-surgery monitoring of bone healing and implantation sites. Previous studies demonstrated radiofrequency (RF) heating of permanent implants caused by electromagnetic fields used in MRI. Our investigation is the first to report the effect of the degradation layer on RF-induced heating of biodegradable orthopaedic implants. WE43 orthopaedic compression screws underwent in vitro degradation. Imaging techniques were applied to assess the corrosion process and the material composition of the degraded screws. Temperature measurements were performed to quantify implant heating with respect to the degradation layer. For comparison, a commercial titanium implant screw was used. Strongest RF induced heating was observed for non-degraded WE43 screw samples. Implant heating had shown to decrease with the formation of the degradation layer. No statistical differences were observed for heating of the non-degraded WE43 material and the titanium equivalent. The highest risk of implant RF heating is most pronounced for Mg-based screws prior to degradation. Amendment to industry standards for MRI safety assessment is warranted to include biodegradable materials

Stochastic backlog and delay bounds of generic rate-based AIMD congestion control scheme in cognitive radio sensor networks

Performance guarantees for congestion control schemes in cognitive radio sensor networks (CRSNs) can be helpful in order to satisfy the quality of service (QoS) in different applications. Because of the high dynamicity of available bandwidth and network resources in CRSNs, it is more effective to use the stochastic guarantees. In this paper, the stochastic backlog and delay bounds of generic rate-based additive increase and multiplicative decrease (AIMD) congestion control scheme are modeled based on stochastic network calculus (SNC). Particularly, the probabilistic bounds are modeled through moment generating function (MGF)-based SNC with regard to the sending rate distribution of CR source sensors. The proposed stochastic bounds are verified through NS2-based simulations

Modeling of rate-based congestion control schemes in cognitive radio sensor networks

Performance evaluation of transport layer protocols in cognitive radio sensor networks (CRSNs) is useful to provide quality-of-service for real-time reliable applications. This paper develops an analytical framework to model the steady-state sending rate of collecting cognitive radio (CR) sensors in rate-based generic additive-increase multiplicative-decrease (AIMD) and additive-increase additive-decrease (AIAD) congestion control schemes. Evolution process of sending rate is modeled by a discrete time Markov chain (DTMC) in the terms of queue length. We model the queue length distribution of a CR node by a semi-Markov chain (SMC) with assuming general probability density functions (PDFs) of input rate and attainable sending rate of the node. These PDFs are derived based on the parameters of MAC and physical layers and CRSN configuration. The proposed models are verified through various simulations

Delay sensitive and power-aware SMDP-based connection admission control mechanism in cognitive radio sensor networks

© 2017 Elsevier B.V. Due to the opportunistically resource usage of users in cognitive radio sensor networks (CRSNs), the availability of network resources is highly variable. Therefore, admission control is an essential mechanism to manage the traffic of cognitive radio users in order to satisfy the quality of service (QoS) requirements of applications. In this study, a connection admission control (CAC) mechanism is introduced to satisfy the requirements of delay sensitivity and power consumption awareness. This proposed mechanism is modeled through a semi Markov decision process (SMDP) and a linear programming problem is derived with the aim of obtaining the optimal policy to control the traffic of CRSNs and achieving maximum reward. The number of required channels at each network state is estimated through a graph coloring approach. An end to end delay constraint is defined for the optimization problem which is inspired from Kleinrock independence approximation. Furthermore, a power-aware weighting method is proposed for this mechanism. We conduct different simulation-based scenarios to investigate the performance of the proposed mechanism. The experimental results demonstrate the efficiency of this SMDP-based mechanism in comparison to the last CAC mechanism in CRSNs

A correlation-based and spectrum-aware admission control mechanism for multimedia streaming in cognitive radio sensor networks

Bandwidth management and traffic control are critical issues to guarantee the quality of service in cognitive radio networks. This paper exploits a network load refinement approach to achieve the efficient resource utilization and provide the required quality of service. A connection admission control approach is introduced in cognitive radio multimedia sensor networks to provide the data transmission reliability and decrease jitter and packet end-to-end delay. In this approach, the admission of multimedia flows is controlled based on multimedia sensors' correlation information and traffic characteristics. We propose a problem, connection admission control optimization problem, to optimize the connection admission control operation. Furthermore, using a proposed weighting scheme according to the correlation of flows issued by multimedia sensors enables us to convert the connection admission control optimization problem to a binary integer-programming problem. This problem is a kind of a Knapsack problem that is solved by a branch and bound method. Simulation results verify the proposed admission control method's effectiveness and demonstrate the benefits of admission control and traffic management in cognitive radio multimedia sensor networks. Copyright © 2015 John Wiley & Sons, Ltd

Radiofrequency antenna helmet array for thermal magnetic resonance of brain tumours at 297 MHz

Thermal magnetic resonance (Thermal MR) uses an RF-applicator to add a thermal intervention dimension to a diagnostic imaging device. Optimizing the performance of RF applicator configurations can eventually improve the performance of Themal MR. Recognizing this opportunity this work examines the feasibility of multi-channel RF applicators using broadband Self-Grounded Bow-Tie (SGBT) antenna building blocks. The focus is on enhancing focal RF power deposition in a target volume by using a multi-channel helmet RF array configuration versus conventional annular RF arrays. A 10-channel helmet RF applicator was designed for Thermal MR, evaluated in EMF simulations and benchmarked against an annular RF array using the same number of RF-elements. Our phantom studies demonstrate that the helmet RF applicator affords an ~10%- 30% improvement in maximum SAR10g in the TV over the conventional annular RF array. Our preliminary findings obtained for the human head voxel model Duke show improved target coverage of high SAR10g for the helmet RF applicator