Computer communications

Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000–2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

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A Survey of Smart Parking Solutions

This work proposes an innovative infrastructure of secure scenario which operates in a wireless-mobile 6G network for managing big data (BD) on smart buildings (SBs). Count on the rapid growth of telecommunication field new challenges arise. Furthermore, a new type of wireless network infrastructure, the sixth generation (6G), provides all the benefits of its past versions and also improves some issues which its predecessors had. In addition, relative technologies to the telecommunications filed, such as Internet of Things, cloud computing (CC) and edge computing (EC), can operate through a 6G wireless network. Take into account all these, we propose a scenario that try to combine the functions of the Internet of Things with CC, EC and BD in order to achieve a Smart and Secure environment. The major purpose of this work is to create a novel and secure cache decision system (CDS) in a wireless network that operates over an SB, which will offer the users safer and efficient environment for browsing the Internet, sharing and managing large-scale data in the fog. This CDS consisted of two types of servers, one cloud server and one edge server. In order to come up with our proposal, we study related cache scenarios systems which are listed, presented, and compared in this work.

IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network

Smart parking in IoT-enabled cities: A survey

With more and more data generated, it has become a big challenge for traditional architectures and infrastructures to process large amounts of data within an acceptable time and resources. In order...

Big data analytics: a literature review

This work addresses the processing of top-k queries in mobile ad hoc peer to peer (M-P2P) networks using economic schemes. Our proposed economic model, designated as EcoTop, issues economic rewards to the mobile peers, which send relevant data items (i.e., those that contribute to the top-k query result), and penalizes peers for sending irrelevant items, thereby incentivizing the optimization of communication traffic. The main contributions of our work are three-fold. First, we propose the EcoTop economic model for efficient top-k query processing in M-P2P networks. Second, we propose two schemes, namely ETK and ETK+, for assigning rewards/penalties to peers and for enabling peers to re-evaluate the scores of their data items for item re-ranking purposes. Third, we conduct a performance study, which demonstrates that EcoTop is indeed effective in improving the performance of top-k queries, while minimizing the communication traffic. Notably, our novel economic incentive model also discourages free-riding in M-P2P networks

EcoTop: an economic model for dynamic processing of top-k queries in mobile-P2P networks

Economic incentive-based brokerage schemes for improving data availability in mobile-P2P networks

This work proposes the edPAS system for the efficient processing and handling of dynamic parking allocation requests in vehicular networks. In edPAS, base-station and communicators collaboratively provide the effective event-based parking allocation process for vehicles. Edpas offers the best available parking-lot to vehicle, which is relevant to its event-place. Furthermore, edPAS tracks the vehicles through communicators for providing runtime changes in allotted parking-lot, if any, to the vehicles, thereby optimizing the parking-lot utilization. The main contributions of edPAS are three-fold. First, it proposes the architecture of edPAS with static and dynamic-allocation processes. Second, it proposes two parking-lot allocation schemes, FCFS and PR, using first-come-first-serve and priority algorithms respectively, for parking-queue mechanism. Third, our performance evaluation shows that our schemes are indeed effective for improving the edPAS functionality in terms of maximizing parking-lot utilization and reducing overall time at low communication cost.

edPAS: Event-Based Dynamic Parking Allocation System in Vehicular Networks

Crowdlearning is an incentive-based learning platform for crowd, denoted as iCL, to inspire experts for knowledge sharing and to incentivize them for their contributions in crowd. In iCL, collaborators provide learning platform to the crowd of interested learners. The relativeness of interest of contributors and learners is compared by match-making process. The core objective of iCL is to motivate learners to join common-interest crowd and to collaboratively perform Crowdlearning activities. Moreover, incentivization motivates learners to grow crowd. The main contributions of iCL are three-fold. First, it presents the architecture of iCL with the crowd formation process that includes the processes of initialization and collaboration. Second, it proposes three incentive-based fees distribution schemes defined as Equally Distributed Incentive (EDI), Level-based EDI (LEDI) and Commission-oriented LEDI (CLEDI). Third, our performance evaluation shows that our schemes are indeed effective to motivate learners for participation and to inspire experts for their contribution through incentivization in crowdlearning.

Crowdlearning: An incentive-based learning platform for crowd

We propose the E-VeT system for efficient vehicular traffic management in road networks using economy-based reward/penalty schemes. In E-VeT, base stations collaboratively facilitate dynamic vehicular route assignments for reducing the traffic congestion, average time of arrival and fuel consumption. The main contributions of this work are two-fold. First, it proposes an R2A (Revenue-based Route Allocation) scheme, which rewards vehicles for following system-assigned longer-time paths, and charges a fee for following system-assigned shorter-time paths. Furthermore, it penalizes vehicles for any deviations from the system-assigned paths. Second, it discusses a route allocation algorithm, which gives lesser-time paths as a preference to vehicles that have earned higher revenue based on the R2A scheme. Preliminary performance study shows that E-VeT is indeed effective in managing vehicular traffic in road networks by reducing the average time of arrival and fuel consumption.

Nilesh Padhariya

Papers

EcoTop: an economic model for dynamic processing of top-k queries in mobile-P2P networks

Economic incentive-based brokerage schemes for improving data availability in mobile-P2P networks

edPAS: Event-Based Dynamic Parking Allocation System in Vehicular Networks

Crowdlearning: An incentive-based learning platform for crowd

E-VeT: Economic Reward/Penalty-Based System for Vehicular Traffic Management