Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close proximity.The edge servers are not allocated efficiently according to the dynamic nature of the network.It leads to processing delay,and the tasks are dropped due to time limitations.The researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes.The challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized manner.This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge servers.Initially,a task-offloading problem needs to be formulated based on the communication and pro-cessing.Then offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge services.The significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed idea.The processing time of the anticipated model is 6.6 s.The following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning approaches.The proposed model shows a better trade-off compared to existing approaches.

E-MOGWO Algorithm for Computation Offloading in Fog Computing

Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resource-con-strained devices is called fog computing.End devices can offload the task to close-by fog nodes to improve the quality of service and experience.Since com-putation offloading is a multiobjective problem,we need to consider many factors before taking offloading decisions,such as task length,remaining battery power,latency,communication cost,etc.This study uses the multiobjective grey wolf optimization(MOGWO)technique for optimizing offloading decisions.This is thefirst time MOGWO has been applied for computation offloading in fog com-puting.A gravity reference point method is also integrated with MOGWO to pro-pose an enhanced multiobjective grey wolf optimization(E-MOGWO)algorithm.Itfinds the optimal offloading target by taking into account two parameters,i.e.,energy consumption and computational time in a heterogeneous,scalable,multi-fog,multi-user environment.The proposed E-MOGWO is compared with MOG-WO,non-dominated sorting genetic algorithm(NSGA-II)and accelerated particle swarm optimization(APSO).The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption,computational time and the number of tasks successfully executed.

5G Data Offloading Using Fuzzification with Grasshopper Optimization Technique

Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows towards 5G communication architecture,identifying a solution for QoS in 5G through energy-efficient computing is important.In this proposed model,we perform data offloading at 5G using the fuzzification concept.Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption.Two base stations,small(SB)and macro(MB)stations,are initialized and thefirst tasks randomly computed.Then,the tasks are pro-cessed using a fuzzification algorithm to select SB or MB in the central server.The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network.The result is compared with existing algorithms and indi-cates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.

Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

Smart cities make use of a variety of smart technology to improve societies in better ways.Such intelligent technologies,on the other hand,pose sig-nificant concerns in terms of power usage and emission of carbons.The suggested study is focused on technological networks for big data-driven systems.With the support of software-defined technologies,a transportation-aided multicast routing system is suggested.By using public transportation as another communication platform in a smart city,network communication is enhanced.The primary objec-tive is to use as little energy as possible while delivering as much data as possible.The Attribute Decision Making with Capacitated Vehicle(CV)Routing Problem(RP)and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research.For the optimum network selection,a Multi-Attribute Decision Making(MADM)method is utilized.For the sake of reducing energy usage,the Capacitated Vehicle Routing Problem(CVRP)is employed.To reduce the transportation cost and risk,Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used.Moreover,a mixed-integer programming approach is used to deal with the problem.To produce Pareto optimal solutions,an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is cre-ated.A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer.Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30%of less energy than the other systems.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.