MacroServ: A Route Recommendation Service for Large-Scale Evacuations

Khan, Muhammad Usman Shahid; Khalid, Osman; Huang, Ying; Ranjan, Rajiv; Zhang, Fan; Cao, Junwei; Veeravalli, Bharadwaj; Khan, Samee U.; Li, Keqin; Zomaya, Albert Y.

doi:10.1109/tsc.2015.2497241

Cited by 32 publications

(37 citation statements)

References 15 publications

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“…With the evolution of intelligent transportation system (ITS), a large number of sensors are deployed in city premises that collect traffic data on daily basis [107] [108]. The live streams captured through video cameras require real-time processing and minimal latency, and therefore, the information cannot be sent to the traditional cloud as the response Fig.…”

Section: Smart Livingmentioning

confidence: 99%

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

et al. 2018

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Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name "edge computing" for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of "being near" to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users' request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service provider's data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.

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Section: Smart Livingmentioning

confidence: 99%

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

et al. 2018

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“…Shahabi and Wilson proposed a Capacity-Aware Shortest Path Evacuation Routing (CASPER) framework for emergency traffic evacuation, which utilizes an advanced traffic model and an intelligent routing algorithm to calculate the optimal evacuation plan for evacuees [5]. Khan et al used the Intelligent Transportation System (ITS) to compute the maximum traffic flow and the routes with least traffic congestion towards safe places for evacuees based on realtime traffic conditions [19]. Pourrahmani et al considered the uncertain evacuee demand at the pick-up source points by utilizing fuzzy credibility theory to present a genetic algorithm (GA) for handling the stated evacuation problem [20].…”

Section: Prescriptive Methodsmentioning

confidence: 99%

“…end if (18) end if (19) end if (20) when evacuees leave source vertices, be the to-be-solved evacuation planning route set, and be the historical evacuation route set.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Capacity-Constrained Contraflow Adaption for Lane Reconfiguration in Evacuation Planning

Wang

et al. 2018

Journal of Advanced Transportation

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This paper presents a heuristic contraflow-based reconfiguration evacuation algorithm, which is named Capacity-Constrained Contraflow Adaption (CC-Adap). First, it effectively calculates optimal candidate routes for evacuation. Second, an evaluation method is proposed for estimating these candidate routes. Third, CC-Adap utilizes a contraflow-based method to reconfigure the evacuation routes to improve capacity constraints. Fourth, traffic conditions are updated in real time. Fifth, CC-Adap reuses historical evacuation routes to reduce the computational cost and accelerate the evacuation process. Experimental results show that CC-Adap generates high-performing evacuation strategies and can be used to tackle large-scale evacuation planning.

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“…After effective storage and representation of big data, another major task is to transmit the reduced data over the underlying SG network efficiently [13]. In this context, authors in [14], [15] reviewed the key aspects of smart metering process with a focus on the type of data generated and techniques required to process it.…”

Section: Related Workmentioning

confidence: 99%

“…(T uni × n U ) e1e2....e k−1 e k e k+1 ....en (13) where, e 1 e 2 ....e k−1 e k e k+1 ....e n are the dimensional attributes of n th order tensor.…”

Section: Problem Formulationmentioning

confidence: 99%

Tensor-Based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective

Kaur

Aujla

Kumar

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, firstly the Frobenius norm is applied on high-order tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks (SDN) for minimization of the load and effective bandwidth utilization on the network infrastructure. The proposed scheme minimizes the transmission delay occurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using 'R' programming and Matlab. The big data traces considered for evaluation consist of more than 2 million entries (2075259) colecetd at 1 minute sampling rate having hetrogenous features such as-voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such as-network delay, accuracy, and throughput.

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MacroServ: A Route Recommendation Service for Large-Scale Evacuations

Cited by 32 publications

References 15 publications

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

Capacity-Constrained Contraflow Adaption for Lane Reconfiguration in Evacuation Planning

Tensor-Based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective

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