Fuzzy logic-based emergency vehicle routing: An IoT system development for smart city applications

Rout, Rashmi Ranjan; Vemireddy, Satish; Raul, Sanjib Kumar; Somayajulu, Durvasula V. L. N.

doi:10.1016/j.compeleceng.2020.106839

Cited by 44 publications

(16 citation statements)

References 15 publications

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“…The algorithm combines particle swarm and ant colony algorithms to make it more suitable for dynamic route planning on congested roads by quantifying attributes such as urban road length, the number of lanes, and incoming and outgoing traffic flow. Rout et al propose a fuzzy logic-based decision support system for estimating congestion at a specific location on the road network and assisting an open-source routing machine server to generate the shortest and congestion-aware route [28]. Constantinescu and Patrascu find a route for EVs based on a genetic algorithm that minimizes road occupancy and dynamically adjusts the optimal route during the journey [29].…”

Section: B Intelligent Optimization Algorithmsmentioning

confidence: 99%

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Bai

Luan

et al. 2022

IEEE Access

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Emergency vehicles (EVs) play an essential role in emergency services. One of the most intuitive indicators of the emergency service process is the response time of EVs. This survey focuses on reviewing the latest traffic control strategies to reduce response time during EV traveling. Firstly, it classifies traffic control strategies into route optimization, signal preemption, lane reservation, and mixed traffic control strategies. Then, a systematic literature review of traffic control strategies in terms of different algorithms is presented. In addition, this survey classifies the articles by objective metrics. In addition to response time, several other objective metrics are summarized. Finally, this survey reviews the limitations of existing emergency traffic control strategies and critically analyzes them. Based on this, it indicates the core problems and proposes potential research areas to be explored.

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Section: B Intelligent Optimization Algorithmsmentioning

confidence: 99%

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Bai

Luan

et al. 2022

IEEE Access

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“…Therefore, for efficient task offloading management, the FTOM scheme is proposed. The main advantages of using fuzzy logic are that its complexity is low, compared with other decision-making algorithms [ 29 , 30 , 31 ], and it is significantly applied to workload management, vehicle routing, task scheduling, and network congestion-mitigation problems [ 32 , 33 , 34 ]. To satisfy the various security requirements in real time for mobile users, Li et al [ 35 ] introduced a security service-chaining approach based on fuzzy logic for mobile edge computing.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Hossain

Sultana

Hossain

et al. 2021

Sensors

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Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

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“…Various approaches are in practice by researchers and practitioners for smart city. Rout et al [8] presented a model of IoT network based on technique of fuzzy logic data fusion for IoT. e process of data fusion estimates the congestion of location specific through human input and sensory data from the crowd.…”

Section: Related Workmentioning

confidence: 99%

Analyzing and Evaluating Smart Cities for IoT Based on Use Cases Using the Analytic Network Process

Huang

Nazir

2021

Mobile Information Systems

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With the passage of time, the world population is growing. Proper utilization of resources and other devices is tremendously playing an important role to easily examine, manage, and control the resources of the Internet of Things (IoT) in the smart city. Research in the field of IoT has revolutionized the services mostly in smart cities. In the smart city, the applications of IoT are utilized without human involvement. Diverse IoT devices are connected with each other and communicate for different tasks. With the existence of a huge number of IoT devices in the forthcoming years, the chances of privacy breach and information leakage are increasing. Billions of devices connected on IoT producing huge volume of data bound to cloud for processing, management, and storage. Sending of whole data to the cloud might create risk of security and privacy. Various needs of the smart city should be considered for both urgent and effective solutions to support requirements of the growing population. On the other side of rising technology, the IoT evolution has massively produced diverse research directions for the smart city. Keeping in view the use cases of the smart city, the proposed study presents the analytic network process (ANP) for evaluating smart cities. The approach of ANP works well in the situation of complexity, and vagueness exists among the available alternatives. The experimental results of the planned approach show that the approach is effective for evaluating the smart cities for IoT based on the use cases.

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Fuzzy logic-based emergency vehicle routing: An IoT system development for smart city applications

Cited by 44 publications

References 15 publications

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Analyzing and Evaluating Smart Cities for IoT Based on Use Cases Using the Analytic Network Process

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