Integrating pervasive computing, infostations and swarm intelligence to design intelligent context-aware parking-space location mechanism

Gupta, Abhishek; Sharma, Venimadhav; Ruparam, Naresh Kumar; Jain, Satbir; Alhammad, Abdulmalik; Ripon, Md. Afsar Kamal

doi:10.1109/icacci.2014.6968231

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…Context‐aware intelligent parking‐space location mechanism based on ACO is designed by Gupta et al 121 to find the vacant parking space while driving the vehicle. Wireless sensors and microsensors are mounted on the vehicles to gather context information regarding the environment, drivers, and the vehicle.…”

Section: Applicability Of Nature‐inspired Algorithms In Iovmentioning

confidence: 99%

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

Sharma

Kaushik

2021

Int J Communication

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The concept of the Internet of Vehicles (IoV) was evolved to cater to the increasing demands of emerging and advanced vehicular applications such as intelligent transportation systems (ITS). IoV plays a vital role in the development of smart cities and smart transportation. As IoV involves real-time data, so efficient routing mechanisms, as well as secure protocols, are very much needed to enhance the efficiency and security of the IoV network. In the existing literature, numerous researchers have focused on enhancing the efficiency of the IoV network. However, there still exist some flaws keeping in view the new security vulnerabilities that keep on emerging. So, there is a need to optimize the capabilities of existing frameworks for dealing with such issues.Nature-inspired algorithms are a category of powerful tools to solve problems of optimization. In this first of its kind state-of-the-art study, the potential of applicability of nature-inspired algorithms in IoV networks for improving the overall performance of IoV is explored. First, various categories of natureinspired algorithms are presented to make the readers well acquainted with the concepts of nature-inspired algorithms. Different nature-inspired algorithms having their applicability in different aspects of IoV are studied with a special focus on security, routing, and parking space management for strengthening the existing IoV network. Various open issues and research challenges in the use of different nature-inspired algorithms in IoV are also presented. Based upon the study conducted, it has been analyzed that natureinspired algorithms can optimize the overall performance of IoV networks.

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Section: Applicability Of Nature‐inspired Algorithms In Iovmentioning

confidence: 99%

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

Sharma

Kaushik

2021

Int J Communication

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“…IPCISSI: 14 A smart parking system formed by the Integrating Pervasive Computing, InfoStations, and Swarm Intelligence (IPCISSI) group. The architecture of the system consists of VANET, OBU, info‐station (IS), swarm intelligence algorithm for process optimization, and wireless sensor network (WSN).…”

Section: Prominent Smart Parking Systemsmentioning

confidence: 99%

A survey on smart parking systems in urban cities

Zulfiqar

Haque

Tariq

et al. 2021

Concurrency and Computation

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Summary Parking vehicles in densely populated areas are often challenging, stressful, and sometimes it becomes a monotonous job for the drivers in jam‐packed areas. There are several reasons for the delay in finding parking spaces such as scarcity of parking slots, disordered or unmanaged parking of vehicles, lacking or unaware of parking information at the destination, which further leads to the wastage of time, fuel, energy and increase in environmental pollution. Literature has revealed a significant number of smart parking solutions based on the Internet of Things (IoT) and context‐awareness with the incorporation of routing strategies and vehicle detection techniques in a pervasive computing environment. With the rapid escalation of the smart and intelligent devices along with their applicability in a highly decentralized environment, real‐time traffic monitoring, and finding parking spaces have become quite trivial. Smart parking sensors and technologies assist drivers in finding vacant parking slots while they are on the way to their destination. Considering the needs, wants, and demands of metropolitan cities, in this article, we have reviewed the recently published articles, mostly from the last 5 years, on smart parking systems augmented with sensors, embedded systems, context‐awareness capability, and IoT which yields in saving time, fuel, energy, and reduces the stress of the drivers. To accomplish this, we have reviewed different models on smart parking solutions based on algorithmic formalisms, theoretical frameworks, formal models, smart device‐based prototypes as well as real‐time applications, and verifying the correctness properties of the system. The results shown may provide a base for the state of the art future research directions.

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“…The authors of [22] put forward a swarm intelligence-based vehicle parking system employing context awareness and wireless communications. In this system, parking areas are instrumented with web-based tools and with a wireless sensor infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Data Analytics for Smart Parking Applications

Piovesan

Turi

Toigo

et al. 2016

Sensors

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We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

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Integrating pervasive computing, infostations and swarm intelligence to design intelligent context-aware parking-space location mechanism

Cited by 9 publications

References 27 publications

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

A survey on nature‐inspired algorithms and its applications in the Internet of Vehicles

A survey on smart parking systems in urban cities

Data Analytics for Smart Parking Applications

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