Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks(MANET)usage across the globe is increas-ing by the day.Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes.The trust values are estimated based on the reputation values of each node in the network by using different mechanisms.However,these mechanisms have various challenging issues which degrade the network performance.Hence,a novel Quality of Service(QoS)Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work.Initially,the QoS-based trust estimation is proposed by using a Fuzzy logic scheme.The trust value of each node is estimated by using each node’s reputation values which are deter-mined based on the fuzzy membership function values and utilizing QoS para-meters such as residual energy,bandwidth,node mobility,and reliability.This mechanism prevents only the black hole attack in the network during transmis-sion.But,the gray hole attacks are not identified which in turn increases the pack-et drop rate significantly.Hence,the gray hole attack is also detected based on the Kullback-Leibler(KL)divergence method used for estimating the statistical mea-sures.Additional QoS metrics are considered to prevent the gray hole attack,such as packet loss,packet delivery ratio,and delay for each node.Thus,the proposed mechanism prevents both black hole and gray hole attacks simultaneously.Final-ly,the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Opportunistic Networks(OppNets)is gaining popularity day-by-day due to their various applications in the real-life world.The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication.Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks(Manets)and Wireless Sensor Networks(WSNs),therefore,it inherits most of the traits from both mentioned networking techniques.Apart from its popularity,Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network.These issues lower the overall performance of the network.Keeping this thing in mind,ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz.pattern prediction,decision tree prediction,adamic-adar method for complex networks.Apart from this,ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background.Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service(QoS)parameters.

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Minimum QOS Parameter Setin Transport Layer

QOS (Quality of Service) parameter definitions are the basis of further QOS control.But QOS parameters defined by orgallizations such as ISO and ITU are incoherent and incompatible. It leads to the inefficiency of QOS controls. Based on the analysis of QOS parameters defined by ISO and ITU, this paper first promotes Minimum QOS Parameter Set in transport layer. It demonstrates that the parameters defined by ISO and ITU can be represented by parameters or a combination of parameters of the Set. The paper also expounds that the Set is open and manageable and it can be the potential unified base for QOS parameters.