Sensor-Cloud for IoT

The technological advancement of wireless sensor networks (WSNs) empowers numerous real-life applications such as target tracking, battlefield monitoring, telemonitoring, ubiquitous monitoring, and several other applications. However, these WSNs are single-user centric. On the other hand, for certain applications such as environment monitoring and telemonitoring, the data from the single sensor networks can be shareed among multiple applications. In these scenarios, Sensor-Cloud can play a huge role to provision the Sensors-as-a-Service (Se-aaS) platform, while satisfying the requirements of multiple applications by forming virtual sensors in a cloud platform. The sensor-cloud architecture has been conceived as a potential solution for multiorganization WSN deployment and data access.

We study the various aspects of the sensor-cloud architecture such as Virtualization, Caching, QoS, and Pricing. We also develop and analyze various models to characterize the performance of sensor-cloud.

 

Conceptualization of Sensor-Cloud

Mathematical Modelling

In this work, we present a mathematical formulation of sensor-cloud, which is very important for studying the behavior of WSN-based applications in the sensor-cloud platform. We also suggested a paradigm shift of technology from traditional WSNs to sensor-cloud architecture. A detailed analysis is made based on the performance metrics – energy consumption, fault-tolerance, and lifetime of a sensor node. A thorough evaluation of the cost-effectiveness of sensor-cloud is also done by examining the cash inflow and outflow characteristics from the perspective of every actor of sensor-cloud.

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Big-Sensor-Cloud Infrastructure

This work relates to the development of Big-Sensor-Cloud Infrastructure (BSCI) that immensely enhances the usability and management of the physical sensor devices. BSCI is a distributed framework for “Big” sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The methods of the proposed BSCI are persuasive as they are equipped with the ability to handle “Big” data with enormous heterogeneous data volumes (in zettabyte) generated with tremendous velocity. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform.

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Virtual Sensor Formation in Sensor-Cloud

Dynamic Mapping of Virtual Sensors

In this work, the problem of dynamic mapping of virtual sensors in sensor-cloud is divided into two subproblems — optimal dispersed node selection and optimal data-rate distribution, and analyze that these problems are NP-complete. Hence, we propose a game theory-based online scheme, named QADMAP, to solve these two problems in polynomial time.

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Economic Model for Virtual Sensors

In this work, we propose a scheme for the formation of DynamIc VIrtual Sensor for Overlapping Region (DIVISOR) in a IoT-based sensor-cloud platform. Using DIVISOR, we ensure that each sensor-owner gets an equal opportunity to earn profit from the deployment of his/her sensor nodes.

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Composition of Virtual Sensors

In this work, we propose algorithms for efficient virtualization of the physical sensor nodes and optimal composition of VSs — within the same geographic region (CoV-I) and spanning across multiple regions (CoV-II).

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Energy Efficient Virtual Sensors

In this work, we present a dynamic virtual sensor provisioning scheme (DVSP) for sensor-cloud based IoT applications to maintain the energy-efficiency of the deployed physical sensor nodes while maintaining the QoS of the service requests. We model the interaction between the Cloud Service Provider (CSP) and the Sensor owners (SOs) using the Single-Leader Multi-Follower Stackelberg game.

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Intelligent Dynamic Virtual Sensors

In this work, we argue that the collaboration between the Cloud Service Provider (CSP) and sensor owners (SOs) can improve dynamic virtual sensor provisioning. We propose a scheme named Intelligent Dynamic Virtual Sensor Provisioning (iDVSP) to enable optimal selection of nodes in a multi-hop path with different SOs. We employ multi-unit single-item combinatorial reverse auction to model the interaction between the CSP and SOs.

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Optimal Configuration of Virtual Sensors

In this paper, we propose a scheme, OPTIVE, for obtaining the optimal configuration of a virtual sensor in the mobile sensor-cloud (MSC) architecture. We use Markov Decision Process (MDP) to select the optimal mobile sensor nodes among the available ones, for configuring the VS in the application area.

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Caching in Sensor-Cloud

Heterogeneous Caching Mechanism

In this work, we focus on designing an optimal, and adaptive data caching mechanism to be implemented within sensor-cloud environment. This work models the data caching to be followed with the External Cache (EC) and the Internal Cache (IC).

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Adaptive Caching Mechanism

In this work, we propose a dynamic, and adaptive caching mechanism for efficient virtualization in sensor-cloud. The proposed caching mechanism is flexible with the varied rate of change of the physical environment.

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Caching-Based Pricing

In this work, we propose a dynamic cache-based pricing scheme, named CASH, for service-oriented sensor-cloud. In CASH, we propose a dynamic pricing model for sensor-cloud using dynamic coalition formation game with transferable utility.

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QoS-Aware Dynamic Caching

In this work, we propose a scheme, named Quality-of-Service (QoS) Aware Dynamic Caching for Destroyed Virtual Machines in Sensor-Cloud Architecture, which enables efficient caching in sensor-cloud, in the presence of heterogeneous sensor nodes.

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Resource Management in Sensor-Cloud

Dynamic Duty Scheduling

In this work, we propose a dynamic duty scheduling scheme for minimizing the energy consumption of the on-field sensor networks in a sensor-cloud application framework.

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Bridge Node Selection

In this work, we focus on obtaining an optimal decision rule to select a bridge node on behalf of a VSN. The work achieves the reduce energy consumption of every node which, in turn, improves the energy expenditure of the entire VSN.

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Data Center Scheduling

In this work, we focus on the problem of scheduling a particular DC that congregates data from various VSs, and transmit the same to the end-user application. The work follows the general pairwise choice framework of the Optimal Decision Rule.

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Gateway Selection

In this work, we study the optimal gateway selection problem in sensor-cloud framework for real-time patient monitoring system by using a zero-sum game model.

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QoS-Based Automated Selection

In this work, we focus focus on an automated selection of Cloud Service Provider (CSP) for a naive end-user in an IoT scenario. In this work, we propose an algorithm QoS based Automated Selection of CSP (QASeC) for automated selection of a CSP from a set of nominated CSPs based on the maximum achievable QoS.

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Optimal Resource Orchestration

In this work, we address the problem of efficient utilization of resource-constrained wireless sensor nodes (WSNs) in sensor-cloud for provisioning high quality of Sensors-as-a-Service (Se-aaS). We propose an optimal resource orchestration scheme for sensor-cloud, named SensOrch, which is based on coalition formation game with transferable utility.

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QoI-Aware Resource Orchestration

In this work, the problem of ensuring profitability for multiple sensor-owners in sensor-cloud, while satisfying the the Quality-of-Information (QoI) requirements of end-users, is studied. A strategic resource allocation scheme, named RACE, is proposed, which introduces the participation of sensor-owners in the node allocation process. Firstly, utility theory is used to calculate the optimum number of nodes to be allocated for a service. Thereafter, single leader multiple followers Stackelberg game is formulated to decide the number of nodes to be contributed by each sensor-owner and the price to be charged.

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Pricing in Sensor-Cloud

Competitive Sensor-Owners

In this work, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit.

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Heterogeneous Pricing

In this work, we propose a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) within the sensor-cloud infrastructure. The proposed pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware (pH) and pricing attributed to Infrastructure (pI). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. On the other hand, pI mainly focuses on the pricing incurred due to the virtualization of resources.

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Pricing with Dumb Nodes

In this paper, we formulate a scheme for dynamic pricing in sensor-cloud environment in the presence of dumb nodes (DISCLOUD). As the presence of dumb nodes in sensorcloud affects the Quality of Service (QoS), we propose a scheme considering QoS of the sensor-cloud.

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Applications of Sensor-Cloud

Agriculture

In this work, we highlight the benefits of using sensor-cloud framework for the efficient addressing of various agricultural problems. We address the specific challenges associated with designing a sensor-cloud system for agricultural applications. We also mathematically characterize the virtualization technique underlying the proposed sensor-cloud framework by considering the specific challenges. Furthermore, the energy optimization framework and duty scheduling to conserve energy in the sensor-cloud framework is presented.

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Target Tracking

This work considers tracking of multiple targets using the sensor-cloud infrastructure.We propose the Dynamic Mapping Algorithm (S-DMA) based on the Theory of Social Choice for ensuring a 'fair' and unbiased mapping of sensors to targets.

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Target Tracking

This work addresses the problem of Quality of Service (QoS) aware sensor allocation for target tracking in a sensor-cloud environment. In this work, using an auction-based mechanism, we find the optimal solution for allocation of a subset of available sensors to achieve efficient target tracking.

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Military Battlefield

In this work, we propose "Mils-Cloud" –a sensor-cloud architecture utilising this infrastructure for developing architecture for the integration of military tri-services in a battlefield scenario. We propose a hierarchical architecture of sensor-cloud with users having different level of priorities.

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