Adaptive Auction Framework for Spectrum Market in Cognitive Radio Networks

For the recent decade, cognitive radio networks have received much attention as an alternative to the traditional static spectrum allocation policy since the licensed spectrum channels are not being used efficiently. The most critical issue of the cognitive radio networks is how to distribute the idle spectrum channels to the secondary users opportunistically. The auction-based market is desirable for the trade of idle spectrum channels since the secondary users can purchase a channel in timely manner and the licensed primary users can earn the additional profit while not using the channels. Among the auction algorithms proposed for the spectrum market, we focus on the TASG framework, which consists of two nested auction algorithms, because it enables the group-buying of spectrum channels for the secondary users with limited budgets, and possesses many positive properties such as budget-balance, individual rationality and truthfulness. However, the TASG framework is not very attractive to the market participants since the seller earns the small revenue and the buyer has the low utility. In this paper, we propose a new auction framework for the spectrum markets, called aDaptive and Economically robust Auction-based Leasing (DEAL), that keeps all the benefits of TASG while improving the utility (or revenue) of the participants. To this end, we develop an enhanced inner-auction algorithm, called the Global Auction algorithm in our DEAL framework, and adapt the involved parameters dynamically based on the previous bids from the potential buyers. Simulation results demonstrate that our framework significantly outperforms the previous TASG.     

Effort-aware cross-project just-in-time defect prediction framework for mobile apps

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent updates involving in many code commits to meet new requirements. Just-in-Time (JIT) defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers, which is more suitable to mobile apps. As the within-app defect prediction needs sufficient historical data to label the commit instances, which is inadequate in practice, one alternative method is to use the cross-project model. In this work, we propose a novel method, called KAL, for cross-project JIT defect prediction task in the context of Android mobile apps. More specifically, KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative features. Then, the adversarial learning technique is used to extract the common feature embedding for the model building. We conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance evaluation. The results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.     

On accounting for screen resolution in adaptive video streaming: QoE-driven bandwidth sharing framework

Screen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)-driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look-alike strategy implementing max-min fairness.     

3D Printed Enzyme-Functionalized Scaffold Facilitates Diabetic Bone Regeneration

Patients with diabetes mellitus (DM) suffer from a high risk of fractures and poor bone healing ability. Surprisingly, no effective therapy is available to treat diabetic bone defect in clinic. Here, a 3D printed enzyme-functionalized scaffold with multiple bioactivities including osteogenesis, angiogenesis, and anti-inflammation in diabetic conditions is proposed. The as-prepared multifunctional scaffold is constituted with alginate, glucose oxidase (GOx), and catalase-assisted biomineralized calcium phosphate nanosheets (CaP@CAT NSs). The GOx inside scaffolds can alleviate the hyperglycemia environment by catalyzing glucose and oxygen into gluconic acid and hydrogen peroxide (H₂O₂). Both the generated H₂O₂ as well as the overproduced H₂O₂ in DM can be scavenged by CaP@CAT NSs, while the initiated hypoxic microenvironment stimulates neovascularization. Moreover, the incorporation of CaP@CAT NSs not only enhance the mechanical property of the scaffolds, but also facilitate bone regeneration by the degraded Ca²⁺ and PO₄³⁻ ions. The remarkable in vitro and in vivo outcomes demonstrate that enzymes functionalized scaffolds can be an effective strategy for enhancing bone tissue regeneration in diabetic conditions, underpinning the potential of multifunctional scaffolds for diabetic bone regeneration.     

Femtojoule-Power-Consuming Synaptic Memtransistor Based on Mott Transition of Multiphasic Vanadium Oxides

To realize the potential of Mott transition of multiphasic vanadium oxides (VO_x) for memory applications, the development of VO_x memtransistors on SiO₂ wafer is introduced. Through electrical characterizations, the volatile memory behaviors of the VO_x memtransistors are observed in both two- and three-terminal measurements. Their capacitive memory and resistive switching mechanisms are strongly related to the mixed VO_x/SiO₂ interface (called VSiO_x). VSiO_x supports the Mott transition in VO_x at low bias voltages (<0.5 V), leading to the low power consumption of the memtransistor. Moreover, the fast switching time (≈35 ns) and tunable memory retention with the synaptic functions (potentiation and depression) of the memtransistors (by using the gate and drain biases) are demonstrated. Overall, the findings open up major opportunities for constructing ultrafast and femto-joule power-consuming neuromorphic devices.     

A Robust Wearable Point-of-Care CNT-Based Strain Sensor for Wirelessly Monitoring Throat-Related Illnesses

Point-of-care testing (POC) has the ability to detect chronic and infectious diseases early or at the time of occurrence and provide a state-of-the-art personalized healthcare system. Recently, wearable and flexible sensors have been employed to analyze sweat, glucose, blood, and human skin conditions. However, a flexible sensing system that allows for the real-time monitoring of throat-related illnesses, such as salivary parotid gland swelling caused by flu and mumps, is necessary. Here, for the first time, a wearable, highly flexible, and stretchable piezoresistive sensing patch based on carbon nanotubes (CNTs) is reported, which can record muscle expansion or relaxation in real-time, and thus act as a next-generation POC sensor. The patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. The actual extent of muscle expansion is calculated and the gauge factor for applications entailing volumetric deformations is redefined. Additionally, a bluetooth-low-energy system that tracks muscle activity in real-time and transmits the output signals wirelessly to a smartphone app is utilized. Numerical calculations verify that the low stress and strain lead to excellent mechanical reliability and repeatability. Finally, a dummy muscle is inflated using a pneumatic-based actuator to demonstrate the application of the affixed wearable next-generation POC sensor.     

A closed-loop healthcare processing approach based on deep reinforcement learning

Multimedia Tools and Applications - In healthcare, the human body is a controlled input-output system, which generates different observations with the variations of external interventions. The...     

Multiple RGB images encryption algorithm based on elliptic curve,improved Diffie Hellman protocol

Multimedia Tools and Applications - For the asymmetric key, the Diffie Hellman Key (DHK) protocol is very efficient, but sometimes it is vulnerable against brute force attacks if the parameters are...     

Emulating Software Defined Network using Mininet-ns3-WIFI Integration for Wireless Networks

SDN enables a new networking paradigm probable to improve system efficiency where complex networks are easily managed and controlled. SDN allows network virtualization and advance programmability for customizing the behaviour of networking devices with user defined features even at run time. SDN separates network control and data planes. Intelligently controlled network management and operation, such that routing is eliminated from forwarding elements (switches) while shifting the routing logic in a centralized module named SDN Controller. Mininet is Linux based network emulator which is cost effective for implementing SDN having in built support of OpenFlow switches. This paper presents practical implementation of Mininet with ns-3 using Wi-Fi. Previous results reported in literature were limited upto 512 nodes in Mininet. Tests are conducted in Mininet by varying number of nodes in two distinct scenarios based on scalability and resource capabilities of the host system. We presented a low cost and reliable method allowing scalability with authenticity of results in real time environment. Simulation results show a marked improvement in time required for creating a topology designed for 3 nodes with powerful resources i.e. only 0.077 sec and 4.512 sec with limited resources, however with 2047 nodes required time is 1623.547 sec for powerful resources and 4615.115 sec with less capable resources respectively.

     

Sustainable Green Fog Computing for Smart Agriculture

Wireless Personal Communications - Medical Body Area Networks or MBANs are gaining popularity in healthcare circles because of the convenience they provide to patients and caregivers and assist in...