scholarly journals δ- Risk : Toward Context-aware Multi-objective Privacy Management in Connected Environments

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Karam Bou-Chaaya ◽  
Richard Chbeir ◽  
Mansour Naser Alraja ◽  
Philippe Arnould ◽  
Charith Perera ◽  
...  
Keyword(s):  
Low Complexity ◽  
Context Aware ◽  
Privacy Management ◽  
Data Utility ◽  
Multi Objective ◽  
Physical Environments ◽  
Protection Strategies ◽  
Quantification Model ◽  
Privacy Risks ◽  
The Cost

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Mathematical modeling for optimizing the blood supply chain network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Rahimzadeh Dehaghani ◽  
Muhammad Nawaz ◽  
Rohullah Sultanie ◽  
Tawiah Kwatekwei Quartey-Papafio
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective ◽  
Blood Supply Chain ◽  
The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

scholarly journals Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2339
Author(s):  
Mahboubeh Farid ◽  
Hampus Hallman ◽  
Mikael Palmblad ◽  
Johannes Vänngård
Keyword(s):  
Portfolio Optimization ◽  
Decision Makers ◽  
Optimization Under Uncertainty ◽  
Product Development Process ◽  
Multi Objective ◽  
Probability Of Success ◽  
Low Probability ◽  
Key Aspects ◽  
The Cost ◽  
Selection Of

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

Performance Analysis of Low Complexity Multiuser Scheduling Algorithms in MIMO System

2013 ◽  
Vol 392 ◽  
pp. 867-871
Author(s):  
Ming Xia Lv ◽  
Yan Kun Lai ◽  
Dong Tang
Keyword(s):  
Mimo System ◽  
Scheduling Algorithm ◽  
Low Complexity ◽  
Base Station ◽  
User Scheduling ◽  
Sum Capacity ◽  
Multiuser Scheduling ◽  
The Common ◽  
The Cost ◽  
Selection Algorithms

The total throughput of the communication system can be maximized by allocating the common radio resource to the user or the user group having the best channel quality at a given time and the multiuser diversity gain can be obtained when multiple users share the same channel at one time. The object to select the users is to select the users with the maximum sum capacity. As for a scheduling algorithm, exhaustive algorithm can get the largest capability of the system by multi-user scheduling. However, this algorithm is quite complex hence the cost of operation to a base station has substantial increased. We compare the multiuser performance of two fast user selection algorithms with low complexity in MIMO-MRC systems with co-channel interferences. From the simulation results, these two algorithms not only decrease the computational complexity of the scheduling algorithm but also retain large capability of the MIMO system.

scholarly journals FPGA Implementation of UFMC Based Baseband Transmitter: Case Study for LTE 10MHz Channelization

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Atif Raza Jafri ◽  
Javaria Majid ◽  
Lei Zhang ◽  
Muhammad Ali Imran ◽  
M. Najam-ul-Islam
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Low Complexity ◽  
Current Form ◽  
Overall Design ◽  
Band Emission ◽  
Operational Frequency ◽  
Orthogonal Property ◽  
Transmitter Case ◽  
The Cost

Universal filtered multicarrier (UFMC) is a low complexity promising waveform that provides quasi-orthogonal property among subcarriers. In addition, it can achieve much better out-of-band emission performance than orthogonal frequency division multiplexing (OFDM) system. Authors have proposed a hardware platform to implement a UFMC transmitter in this paper. Highly reduced complexity schemes for IFFT, filtering, and spectrum shifting are realized on actual hardware. This helps to achieve overall architecture of the transmitter at the cost of minimal FPGA resource usage. Hence, the overall design uses only 1038 slice registers, 1154 slice LUTs, and 64 multipliers of Xilinx Virtex-7 XC7VX330t device. A throughput of 773.5 Msamples/sec at an operational frequency of 364 MHz is achieved. This throughput is adequate for processing 50 Physical Resource Blocks (PRB) of LTE 10 MHz channelization in required time. The presented architecture provides a latency of only 2% of one LTE 10MHz channelization symbol due to the implementation of pipelining at different levels. Although the presented hardware design in its current form meets LTE 10MHz channelization throughput requirements, further increase in throughput is possible due to the scalable nature of the architecture. To the best of our knowledge, this work is first ever FPGA solution for UFMC transmitter presented in the literature.

scholarly journals Clustering Control of Multi-Objective Problems with Application to E-commerce

2018 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Liang Chen ◽  
Xingwei Wang ◽  
Jinwen Shi
Keyword(s):  
Ant Colony Algorithm ◽  
Time Window ◽  
Ant Colony ◽  
Distribution Area ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Multi Objective ◽  
The Cost ◽  
Vehicle Capacity ◽  
Cost Of Transportation

In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.

Integrated Flood Risk Analysis for Assessing Flood Protection Strategies

2010 ◽  
pp. 244-263
Author(s):  
J. Ernst ◽  
B.J. Dewals ◽  
S. Detrembleur ◽  
P. Archambeau ◽  
S. Erpicum ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Flood Risk ◽  
Management Strategies ◽  
Cost Effective ◽  
Flood Management ◽  
Flood Protection ◽  
River Meuse ◽  
Flood Risk Analysis ◽  
Protection Strategies ◽  
The Cost

The present chapter describes an end-to-end methodology for assessing flood protection strategies, including the whole methodological process from hydrological statistics to detailed 2D hydraulic modelling, damage calculation and flood risk evaluation. This risk-based approach serves as a component of a decision-support system (DSS) developed in Belgium for identifying cost-effective flood management strategies in the context of climate change. The DSS accounts for both hydraulic and socio-economic parameters to quantify the benefits (in terms of avoided risk) and the cost of each strategy. Besides reviewing fundamentals of flood risk assessment, including the inundation model and main concepts related to flood risk, a consistent methodology for micro-scale flood risk analysis is presented in detail, combining complementary sources of GIS information such as high resolution and high accuracy land use database as well as socio-economic datasets. Finally a case study on a main tributary of river Meuse in Belgium is described.

scholarly journals Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Madhuri Siddula ◽  
Yingshu Li ◽  
Xiuzhen Cheng ◽  
Zhi Tian ◽  
Zhipeng Cai
Keyword(s):  
Social Networking Sites ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
Context Aware ◽  
Friendship Networks ◽  
Mobile Social Network ◽  
User Privacy ◽  
The Cost ◽  
Privacy Issues

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

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