Distributed energy-efficient and secure offloading in air-to-ground MEC networks

This paper mainly investigates the energy-efficient and secure offloading problem in air-to-ground Mobile Edge Computing (MEC) networks. The proposed efficient offloading mechanism is as per the requirements of the heterogeneous tasks of ground users. Further, the optimizing offloading rate, offloading object, and channel access jointly formulate system energy consumption and eavesdropping rate minimization. A distributed two-stage offloading scheme is proposed for achieving the sub-optimal solution for the Mixed-integer Nonlinear Programming (MINLP) problem. Finally, simulation results demonstrate that the proposed scheme is superior to several benchmark schemes.

Joint admission control and power allocation in hospital networks based on cognitive radios.

In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

Optimization of a two-echelon supply chain with random demand and random defect rate under strict carbon cap policy

PurposeThe yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this paper, a constrained optimization model is developed with consideration of the yield of defective items and strict carbon cap policy simultaneously and then optimized. Further, sensitivity analyses have been carried out to draw different managerial insights. Precisely, we have tried to address the following research questions: (1) how to optimize the cost for a two-echelon supply chain considering yield of defective items and strict carbon cap policy, (2) how the total expected cost and total expected emissions act with changing parameters.Design/methodology/approachThe mathematical modeling approach has been adopted to develop a model and further optimized it with optimization software. Costs and emissions from different areas of a supply chain have been derived and then the total cost and total emissions have been formulated mathematically. One constrained mixed-integer nonlinear programming (MINLP) problem has been formulated and solved considering emissions-related, velocity and production related-constraints. Further, different sensitivity analyses have been derived to draw some managerial insights.FindingsIn this paper, many decision variables have been calculated with a set of basic values of other parameters. It has been found that both cost and emissions can be controlled by controlling different parameters. It has been also found that some parameters have very little or no influence either on cost or emissions. In most cases, originations may exhaust the given limit of carbon cap to optimize their costs.Originality/valueIn spite of my sincere efforts, no paper has been found that has considered the yield of defective items and strict carbon cap policy simultaneously. In this paper, it is assumed that both demand and defect rates are random in nature. The model, presented in this paper may give insights to develop different supply chain models with consideration of both defective items and strict carbon cap policy. Sensitivity analyses, drawn in this paper may give deep insights to managers and carbon regulatory bodies.

Joint admission control and power allocation in hospital networks based on cognitive radios.

In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.

Distributed Energy-Efficient and Secure Offloading in Air-to-Ground MEC Networks

This paper mainly investigates the energy-efficient and secure offloading problem in air-to-ground MEC networks. The proposed efficient offloading mechanism is as per the requirements of the heterogeneous tasks of ground users. Further, the optimizing offloading rate, offloading object, and channel access jointly formulate system energy consumption and eavesdropping rate minimization. A distributed two-stage offloading scheme is proposed for achieving the sub-optimal solution for the Mixed-integer Nonlinear Programming (MINLP) problem. Finally, simulation results demonstrate that the proposed scheme is superior to several benchmark schemes.

A Hybrid Framework for Simultaneous Process and Solvent Optimization of Continuous Anti-Solvent Crystallization with Distillation for Solvent Recycling

Anti-solvent crystallization is frequently applied in pharmaceutical processes for the separation and purification of intermediate compounds and active ingredients. The selection of optimal solvent types is important to improve the economic performance and sustainability of the process, but is challenged by the discrete nature and large number of possible solvent combinations and the inherent relations between solvent selection and optimal process design. A computational framework is presented for the simultaneous solvent selection and optimization for a continuous process involving crystallization and distillation for recycling of the anti-solvent. The method is based on the perturbed-chain statistical associated fluid theory (PC-SAFT) equation of state to predict relevant thermodynamic properties of mixtures within the process. Alternative process configurations were represented by a superstructure. Due to the high nonlinearity of the thermodynamic models and rigorous models for distillation, the resulting mixed-integer nonlinear programming (MINLP) problem is difficult to solve by state-of-the-art solvers. Therefore, a continuous mapping method was adopted to relax the integer variables related to solvent selection, which makes the scale of the problem formulation independent of the number of solvents under consideration. Furthermore, a genetic algorithm was used to optimize the integer variables related to the superstructure. The hybrid stochastic and deterministic optimization framework converts the original MINLP problem into a nonlinear programming (NLP) problem, which is computationally more tractable. The successful application of the proposed method was demonstrated by a case study on the continuous anti-solvent crystallization of paracetamol.

Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things

Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.

Energy Efficient D2D based Multicast Communication through Channel Allocation Scheme

In the earlier transmission schemes the base station is responsible for continuous transmission of packets to the receivers when they are not received properly.in these type of re-transmission the BS experiences a great traffic. This paper considers the concept of channel allocation scheme with partial information of device locations. To implement the users in the geographical area is divided into sub clusters, each sub cluster contains one ACK device and few number of NACK devices. The transmission can be done in the TDMA mode. Then the next step is the joint optimization of NACK-devices association and transmission powers of re-transmitters as an MINLP problem, minimizing the total energy consumption of re-transmitters. Finally solving the problem by using an efficient algorithm for this problem to find good association pattern and transmission power.

Optimization of Distillation Sequences with Nonsharp Separation Columns

Nonsharp distillation sequences are widely used in industrial separation processes; however, most current research has not discussed this topic, except in sequences with heat integration under special operating conditions, including complex columns. The sequence with nonsharp separation has the features of general distillation sequences, which are usually optimized by adjusting the separation sequence and the design/operation parameters of each column in the sequence, making the optimization a mixed integer nonlinear programming (MINLP) problem, which is usually hard to solve. With inclusion of nonsharp separation columns, the sequence optimization becomes even more complicated and computationally intensive. This work aimed to optimize the distillation sequence, including nonsharp distillation alongside simple columns and dividing wall columns. Inspired by the dynamic programing method for sharp distillation sequence, a framework for automatic optimization is proposed to decompose the MINLP problem into integer programming (IP) and nonlinear programming (NLP) problems. The optimization processes of sharp and nonsharp distillation sequences are compared and the solution space in terms of the possible number of distillation sequences with nonsharp separation is discussed. Two optimization cases, including an industrial one, are included to validate the proposed framework.

Cost-Optimal Heat Exchanger Network Synthesis Based on a Flexible Cost Functions Framework

In this article an approach to incorporate a flexible cost functions framework into the cost-optimal design of heat exchanger networks (HENs) is presented. This framework allows the definition of different cost functions for each connection of heat source and sink independent of process stream or utility stream. Therefore, it is possible to use match-based individual factors to account for different fluid properties and resulting engineering costs. Layout-based factors for piping and pumping costs play an important role here as cost driver. The optimization of the resulting complex mixed integer nonlinear programming (MINLP) problem is solved with a genetic algorithm coupled with deterministic local optimization techniques. In order to show the functionality of the chosen approach one well studied HEN synthesis example from literature for direct heat integration is studied with standard cost functions and also considering additional piping costs. Another example is presented which incorporates indirect heat integration and related pumping and piping costs. The versatile applicability of the chosen approach is shown. The results represent designs with lower total annual costs (TAC) compared to literature.