scholarly journals Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

2022 ◽  
Vol 40 (1) ◽  
pp. 65-74
Author(s):  
Nagham A. Al-Madi ◽  
Adnan A. Hnaif
Keyword(s):  
Genetic Algorithm ◽  
Smart Cities ◽  
Traffic Signals

scholarly journals GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 43
Author(s):  
Muhammad K. Shahzad ◽  
S. M. Riazul Islam ◽  
Mahmud Hossain ◽  
Mohammad Abdullah-Al-Wadud ◽  
Atif Alamri ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Smart Cities ◽  
Hot Spot ◽  
Time Instant ◽  
Wireless Sensor ◽  
Injection Attacks ◽  
Fixed Path

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

scholarly journals Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhen Yang ◽  
Wei Wang ◽  
Shuyan Chen ◽  
Haoyang Ding ◽  
Xiaowei Li
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Public Transport ◽  
Major Part ◽  
Traffic Signals ◽  
Traffic Efficiency ◽  
Road Section ◽  
Bus Travel Time ◽  
The City ◽  
Urban Arterial

Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part ofZhongshan NorthStreet in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.

scholarly journals Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2869 ◽  
Author(s):  
Kwok Tai Chui ◽  
Miltiadis D. Lytras ◽  
Anna Visvizi
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Urban Space ◽  
Performance Indicators ◽  
Smart Cities ◽  
Hybrid Genetic Algorithm ◽  
Value Added ◽  
Energy Utilization ◽  
Support Vector ◽  
Energy Sustainability

Energy sustainability is one of the key questions that drive the debate on cities’ and urban areas development. In parallel, artificial intelligence and cognitive computing have emerged as catalysts in the process aimed at designing and optimizing smart services’ supply and utilization in urban space. The latter are paramount in the domain of energy provision and consumption. This paper offers an insight into pilot systems and prototypes that showcase in which ways artificial intelligence can offer critical support in the process of attaining energy sustainability in smart cities. To this end, this paper examines smart metering and non-intrusive load monitoring (NILM) to make a case for the latter’s value added in context of profiling electric appliances’ electricity consumption. By employing the findings in context of smart cities research, the paper then adds to the debate on energy sustainability in urban space. Existing research tends to be limited by data granularity (not in high frequency) and consideration of about six kinds of appliances. In this paper, a hybrid genetic algorithm support vector machine multiple kernel learning approach (GA-SVM-MKL) is proposed for NILM, with consideration of 20 kinds of appliance. Genetic algorithm helps to solve the multi-objective optimization problem and design the optimal kernel function based on various kernel properties. The performance indicators are sensitivity (Se), specificity (Sp) and overall accuracy (OA) of the classifier. First, the performance evaluation of proposed GA-SVM-MKL achieves Se of 92.1%, Sp of 91.5% and OA of 91.8%. Second, the percentage improvement of performance indicators using proposed method is more than 21% compared with traditional kernel. Third, results reveal that by keeping different modes of electric appliance as identical class label, the performance indicators can increase to about 15%. Forth, tunable modes of GA-SVM-MKL classifier are proposed to further enhance the performance indicators up to 7%. Overall, this paper is a bold and novel contribution to the debate on energy utilization and sustainability in urban spaces as it integrates insights from artificial intelligence, IoT, and big data analytics and queries them in a context defined by energy sustainability in smart cities.

A genetic algorithm for energy efficient fog layer resource management in context-aware smart cities

2020 ◽  
Vol 63 ◽  
pp. 102428 ◽  
Author(s):  
K. Hemant Kumar Reddy ◽  
Ashish Kr. Luhach ◽  
Buddhadeb Pradhan ◽  
Jatindra Kumar Dash ◽  
Diptendu Sinha Roy
Keyword(s):  
Genetic Algorithm ◽  
Resource Management ◽  
Energy Efficient ◽  
Smart Cities ◽  
Context Aware

scholarly journals Applying a Genetic Algorithm to a m-TSP: Case Study of a Decision Support System for Optimizing a Beverage Logistics Vehicles Routing Problem

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2298
Author(s):  
David E. Gomes ◽  
Maria Inês D. Iglésias ◽  
Ana P. Proença ◽  
Tânia M. Lima ◽  
Pedro D. Gaspar
Keyword(s):  
Genetic Algorithm ◽  
Environmental Sustainability ◽  
Smart Cities ◽  
Route Optimization ◽  
Fixed Costs ◽  
Critical Approach ◽  
Routing Problem ◽  
Multiple Traveling Salesman Problem ◽  
Huge Impact ◽  
Client Relations

Route optimization has become an increasing problem in the transportation and logistics sector within the development of smart cities. This article aims to demonstrate the implementation of a genetic algorithm adapted to a Vehicle Route Problem (VRP) in a company based in the city of Covilhã (Portugal). Basing the entire approach to this problem on the characteristic assumptions of the Multiple Traveling Salesman Problem (m-TSP) approach, an optimization of the daily routes for the workers assigned to distribution, divided into three zones: North, South and Central, was performed. A critical approach to the returned routes based on the adaptation to the geography of the Zones was performed. From a comparison with the data provided by the company, it is predicted by the application of a genetic algorithm to the m-TSP, that there will be a reduction of 618 km per week of the total distance traveled. This result has a huge impact in several forms: clients are visited in time, promoting provider-client relations; reduction of the fixed costs with fuel; promotion of environmental sustainability by the reduction of logistic routes. All these improvements and optimizations can be thought of as contributions to foster smart cities.

Sign in / Sign up

Export Citation Format

Share Document