MEC-IoT-Healthcare: Analysis and Prospects

Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology,the spectrum resources are increasingly strained which needs optimal solutions.Cognitive radio(CR)is one of the key technologies to solve this problem.Spectrum sensing not only includes the precise detection of the communication signal of the primary user(PU),but also the precise identification of its modulation type,which can then determine the a priori information such as the PU’service category,so as to use this information to make the cognitive user(CU)aware to discover and use the idle spectrum more effectively,and improve the spectrum utilization.Spectrum sensing is the primary feature and core part of CR.Classical sensing algorithms includes energy detection,cyclostationary feature detection,matched filter detection,and so on.The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal,but it is easily affected by noise and the threshold is not easy to determine.The combination of multiple-input multiple-output(MIMO)with CR improves the spectral efficiency and multipath fading utilization.To best utilize the PU spectrum while minimizing the overall transmit power,an iterative technique based on semidefinite programming(SDP)and minimum mean squared error(MMSE)is proposed.Also,this article proposed a new method for max-min fairness beamforming.When compared to existing algorithms,the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio(SINR).Furthermore,the proposed algorithm effectively improved the system performance in terms of number of iterations,interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

An Optimal Framework for SDN Based on Deep Neural Network

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network programming.However,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in SDN.To address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is proposed.This approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection module.The initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on DNN.DDoS assaults were found when suspected irregular traffic was validated.Experiments reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy rate.The false alarm rate(FAR)is much lower than that of the information entropy-based detection method.Simultaneously,the proposed framework can shorten the detection time and improve the resource utilization efficiency.