Sensor-cloud Infrastructure Research Articles

APPLICATION OF ALGORITHMS FOR ANOMALY DETECTION IN HEALTH-ENABLED SENSOR-CLOUD INFRASTRUCTURE

Real-time patient monitoring and early disease diagnosis are two ways that the healthcare industry is benefiting from the integration of sensors and cloud technology. In order to detect changes in patient's health, a variety of non-invasive sensors are applied to the skin to monitor various physiological parameters. The collected data are then wirelessly communicated to the cloud data center. However, the transmitted data are susceptible to several sources of interference called anomalies. Anomalies is when a sudden change occurs from the expected sensor data generated. This may be as a result of sensor faults, measurement faults, injection and alteration by malicious attackers. Therefore, this research tends to conduct a survey on existing algorithms or techniques used for the detection of anomalies in health-enabled sensor-cloud infrastructure. The processes adopted by the algorithms were identified and discussed exhaustively. In addition, the simulation setup and programming languages adopted to implement and evaluate the existing algorithms, followed by the limitations of the algorithms, which may lead to future research directions are captured in this paper. The outcome of the research shows that machine learning algorithms were predominantly adopted for detecting anomalies with the support of clustering and classification processes. Furthermore, Visual Basic.Net simulation tool and Python programming language was mostly adopted for experimentation and evaluation of the existing techniques. Limitations such as overfitting, under-fitting, computation complexity (time and memory space), and missing data are hindering the optimal performance of existing algorithm, which needs to be addressed in future researches.

Extending CloudSim to simulate sensor networks

With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.

QoS-Aware Dynamic Cost Management Scheme for Sensors-as-a-Service

In this article, we study the problem of quality of service (QoS)-aware cost management of sensor-cloud comprising multiple sensor-cloud service providers (SCSPs) and sensor-owners. The rapid adaptation of the wireless sensor network (WSN) and Internet-of-Things (IoT) technology led to the conceptualization of the sensor-cloud infrastructure which primarily aims to reduce the complexities associated with operating WSN-based applications by rendering Sensors-as-a-Service (Se-aaS). However, the oligopolistic market scenario of sensor-cloud involving multiple SCSPs and sensor-owners significantly impacts its profitability and QoS. Thus, there is a need to explore the dynamics of this market competition elaborately in order to maintain the usability of sensor-cloud. The existing works fail to address the aforementioned issues in sensor-cloud. Hence, in this work, we analyze the interactions among the sensor-owners and the SCSPs using a game-theoretic approach. We propose a QoS-aware dynamic cost management scheme, named QUEST, to determine the optimal strategies of the various actors in sensor-cloud market. Through simulations, we observe that, using QUEST, the price paid by the end-users decreases by 10.31-20.43 percent and the revenue of sensor-owners improves by 66.83-89.94 percent. Moreover, QUEST ensures the service satisfaction of the end-users while optimally distributing the services among the SCSPs and the sensor-owners.

Enabling Internet of Things through Sensor Cloud: A Review

With the inception of the Internet of Things (IoT), wireless technology found a new outlook where the physical objects can interact with each other and can sense the environment. The IoT has found its way in the real world and has connected billions of devices throughout the world. However, its limitations, such as limited processing capability, storage capability, security and privacy issues, and energy constraints prevent the IoT system to be properly utilized by the real-world applications. Hence, the integration of IoT with various emerging technologies like big data, software defined networks, machine learning, fog computing, sensor cloud, etc., will make the IoT system a more powerful technology. The sensor cloud provides an open, secure, flexible, large storage and a computational capable infrastructure which makes the ensemble architecture of IoT and sensor cloud more efficient. An extensive review of the IoT system enabled sensor cloud is presented in the paper, and with this context, the paper attempts to summarize the sensor cloud infrastructure along with its challenges. In addition, the paper presents the possible integrated architecture of the IoT and the sensor cloud which enables the network to be properly utilized. Further, the importance of integrating these two promising technologies and research challenges associated with it is also identified. Finally, the paper analyses and discusses the motivation behind the ensemble system along with future research direction.

Big-Sensor-Cloud Infrastructure: A Holistic Prototype for Provisioning Sensors-as-a-Service

The proposed work relates to the development of Big-Sensor-Cloud Infrastructure (BSCI) that enhances the usability and management of the physical sensor devices. Traditional sensor networks are manufactured in a proprietary, vendor-specific design, and cannot handle application switching dynamically at runtime due to the presence of monolithic kernel. Thus, the applications are inaccessible to the common people who do not own physical sensor devices. Recently, sensor-cloud infrastructure has been viewed as a substitute for traditional sensor networks. However, with the increasing growth in the velocity, variety, and variability of data, the management becomes a serious concern and difficulty. Thus, existing systems are not able to capture, analyze, and control the present data efficiently, in real-time. BSCI is a distributed framework for Big sensor-data storage, processing, virtualization, leveraging, and efficient remote management. The framework interfaces between the physical and cyber worlds, thereby acquiring real-time data from the physical WSNs into the cloud platform. This data are processed and delivered to the end-users as a simple service - Sensors-as-a-Service (Se-aaS). Multiple organizations with heterogeneous demand can be successfully served with Se-aaS. From a user-perspective, BSCI allows the naive users to envision the typical sensor devices as simple accessible services like electricity, and water.

SenSchedule: Scheduling Heterogeneous Resources in Sensor-Cloud Infrastructure

In a sensor-cloud infrastructure scheduling and allocation of resources are challenging tasks. Unlike computational cloud infrastructure, sensor may have varying capabilities and contexts of services. Moreover, some sensors may be available for a fixed time duration periodically. Depending on some dynamic factors, performance of a sensor may also vary. Such non-uniform performance may be predicted from previous observations. This article proposes novel scheduling algorithms for heterogeneous resources having varying capabilities, contexts of usage and non-uniform performance over time. Simulation results show that the algorithms perform efficiently.

SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things.

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.

A SURVEY ON SECURITY ISSUES IN SENSOR CLOUD ENVIRONMENT FOR AGRICULTURE IRRIGATION MANAGEMENT SYSTEM

Wireless sensor networks are used in various fields, such as military, healthcare, agriculture, environment monitoring, Monitoring and preparation of critical infrastructure. However, due to some of the restrictions of WSNs on storage, power consumption, communication, computation, scalable, security and privacy; people are looking forward cloud environment with large storage framework for the processing and storing of real time data. The cloud environment is providing large storage space and better services to the people in scalable manner. Sensor-cloud infrastructure is popular, providing an open, flexible and reconfigurable platform for many monitoring and control applications. In this work presenting a sensor-cloud infrastructure for storing data related to agriculture, Infrastructure that shows how to transfer data from WSNs to the cloud. In addition we addressed malicious attacks, issues and challenges in sensor cloud infrastructure. The idea of our proposed work is to collect all the samples for the soil. We choose the most favored plants around the India, then, select three plants, namely rice, sugarcane and wheat. After selecting the plants we collect data related to agriculture irrigation from different types of sensors such as soil moisture, temperature, humidity, and soil PH, CO2 and light sensors. Based on the data collected, our proposed system advice to farmers that usage of pesticides, fertilizers which are most suitable to their lands at the same time, we integrate to improve agriculture productivity in India.

A Reliable Communication and Load Balancing Scheme for Resource-Limited Networks

Sensor-cloud infrastructure provides a storage platform for the massive sensed data, that is flexible and re-configurable, for various application areas which are monitored through the resource-limited networks such as wireless sensor networks (WSNs), ad hoc networks, and Internet of things (IoT). Due to their overwhelming characteristics, these networks are used in different application areas to assist human beings in their daily-life activities. However, these networks have different challenging issues such as reliability in communication and processing, storage of the massive data, efficient utilization of on-board battery, maximum lifetime achievement, minimum possible average packet loss ratio, and reliable routing mechanisms. Although various communication and load balancing mechanisms have been proposed in the literature to resolve this issue, however, these schemes are either application specific or overlay complex. In this paper, a reliable communication and load balancing scheme for the resource-limited networks is presented to resolve these issues, particularly with available resources. To achieve these goals, the proposed scheme bounds every sensing device $C_{i}$ to compute the transmission capabilities of its neighboring devices that is residual energy $E_{r}$ , hop count $H_{c}$ , round trip time ( $RTT_{i}$ ), and processing cost. Initially, to guarantee reliable wireless communication, a source device prefers a neighboring device $C_{i}$ with minimum $H_{c}$ value over those having maximum $H_{c}$ values. Moreover, this scheme bounds every device $C_{i}$ to find four shortest & reliable paths and forward maximum packets on two of these paths preferably on the most reliable and optimal route. Therefore, unlike the traditional shortest path scheme, devices $C_{i}$ reside on these paths do not deplete their on-board battery more rapidly than others. To further improve the reliability of the proposed scheme, the assigned weight-age factors are fine-tuned if one or two of the neighboring devices $C_{i}$ consume 80% of their on-board battery, that is now maximum weight-age is assigned to the residual energy $E_{r}$ and minimum to $H_{c}$ value respectively. Simulation results show the exceptional performance of the proposed reliable communication and load balancing scheme against the field-proven schemes in terms of average packet delivery ratio, average throughput, end-to-end delay, and overall network lifetime.

Heuristic approach to minimise the energy consumption of sensors in cloud environment for wireless body area network applications

The wireless sensor networks are single user centric and end users who do not own sensors are unable to have access to any wireless network specific application. This sensor cloud is a new paradigm to manage the physical sensors which are deployed in any WSN application. Existing research work on sensor cloud is limited to guaranteeing minimal energy consumption. In this paper, a novel mathematical model based on virtual sensor grouping is proposed to minimise the cumulative energy consumption of sensors in cloud environment. Energy consumption of non-virtual sensor with virtual sensor group for two different applications is compared and the results are shown. Further the proposed sensor cloud infrastructure with power model is compared with traditional WSN with respect to energy efficiency, throughput and performance with quick synchronisation time for random run trials and results were found to be better than the conventional WSN.

Corrigendum to “Towards Smart Healthcare: Patient Data Privacy and Security in Sensor-Cloud Infrastructure”

Corrigendum to “Towards Smart Healthcare: Patient Data Privacy and Security in Sensor-Cloud Infrastructure”

Improved elliptical curve cryptography and Abelian group theory to resolve linear system problem in sensor-cloud cluster computing

At present, the sensor-cloud infrastructure is gaining popularity, since it offers a flexible, open and reconfigurable configuration over monitoring and controlling application. It handles mainly the user data, which is quite sensitive and hence the data protection in terms of integrity and authenticity is of greater concern. Thus, security is a major concern in such system, inclusive of intruders, who tries to access the infrastructure. In this paper, an improved encryption protocol for secured session keying between the users using a trusted services proposed over sensor-cloud architecture. This technique uses modified Elliptical Curve Cryptography (ECC) algorithm to improve the authentication of sensor nodes in the network. Further, Abelian group theory is designed to convert intruder deduction problem to linear deduction problem to resolve the complexity associated with manipulation of finding the intruders in the network.This helps to reduce the computational complexity of generating a secured message transmission and increased possibility to find the intruders in the network. The experimental validation with the proposed ECC in terms of computational cost proves that the proposed method attains lower computational cost and improved detection of intruders in the network. Also, the technique seems efficient and can be applied on practical cases, where other ECC algorithms fails while implementing it on a real time basis.

Towards Smart Healthcare: Patient Data Privacy and Security in Sensor‐Cloud Infrastructure

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor‐cloud infrastructure (S‐CI), is aiding the healthcare domain through real‐time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S‐CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S‐CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute‐based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri‐Mode Algorithm, Dynamic Probability Packet Marking, and Priority‐Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six‐step generic framework for patient physiological parameters (PPPs) privacy and security in S‐CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.

Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new 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. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users’ utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the base station (BS), received signal strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.

Survey for Sensor-Cloud System from Business Process Outsourcing Perspective

Cloud computing is a new IT trend to meet the new business requirements such as business agility and operational efficiency with business process outsourcing (BPO). Sensor-Cloud infrastructure is the extended form of cloud computing to manage the sensors which are scattered throughout the network. Several benefits of adopting cloud computing including cost saving, high scalability, and business risk reductions also can be applied to sensor data collection. As a first investment for new technology, we analyze IT managers' feedbacks to identify the most important factors of decision making for cloud platform adoption and IT outsourcing. Even though the technological motivation is persuasive in Korean IT market, survey results show that the cost efficiency is the most important for cloud platform adoption. Also surveys reveal that IT service consumers have difficulties understanding complicated service contracts and data sharing issues with infrastructure providers, which serve as main blockers of adopting cloud computing.

Target Tracking Using Sensor-Cloud: Sensor-Target Mapping in Presence of Overlapping Coverage

This work considers tracking of multiple targets using the sensor-cloud infrastructure. As targets enter the coverage zone of multiple sensors, it becomes crucial to schedule sensors and generate distinct clusters of sensors for each target. It becomes challenging to correctly map sensors to targets, in presence of overlapping coverage, to maintain their privacy and correctness of sensed information about the targets. 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. The distribution of summation of the preference values of sensors allocated to targets exhibit a standard deviation of 0.71 within 99% confidence interval. This implies that S-DMA maintains uniformity while scheduling sensors for every target.

Advanced Applications of Wireless Sensor Network Using Sensor Cloud Infrastructure

It is our pleasure to publish a special issue on advanced applications of wireless sensor network using sensor cloud infrastructure. This special issue contains seven high quality research papers related to the growing technologies of recent wireless sensor networks and sensor cloud infrastructure. The paper “Sensor resource sharing approaches in sensorcloud infrastructure” by Y. Lim and J. Park presents a survey on sensor-cloud infrastructure (SCI) and discusses technical issues of SCI such as architecture, sensor description methods, middleware based on publish/subscribe data exchange model, and case studies. The paper “Multiple wide tables with vertical scalability in multitenant sensor cloud systems” by K. Ma and B. Yang proposes the multitenant multiple wide tables with vertical scalability by analyzing the features of multitenant data and shows the effectiveness of the proposed method by the experiments. The paper “Semantic reasoning with contextual ontologies on sensor cloud environment” by K. Park et al. presents a massive contextual events handling scheme on a big data framework based on a context-aware inference model with the contextual ontologies and shows that the proposed collaboration framework allows many cooperating parties to collaborate with each other by sharing constituent entities. The paper “Modeling and analysis on congestion control for data transmission in sensor clouds” by J. Huang et al. proposes an Improved Random Early Detection (IRED) algorithm that meets sensor cloud networking requirements better and develops a queueing model coupled with an analysis technique to evaluate the performance of the proposed congestion control scheme. The paper “Agriculture sensor-cloud infrastructure and routing protocol in the physical sensor network layer” by K. Kimet al. proposes an agriculture sensor-cloud infrastructure to provide various services efficiently and to process largescale sensor data effectively and evaluates the proposed techniques by simulation experiments. The paper “Competition-based device-to-device transmission scheduling to support wireless cloud multimedia communications” byR. Yao et al. presents a new algorithm to improve the data transmission quality for a mobile multimedia cloud network (MMCN) in a cloud device-to-device (D2D) communication and demonstrates the simulation results to show the effects of the proposed algorithm. The paper “Towards an environmental measurement cloud: delivering pollution awareness to the public” by F. Dominguez et al. proposes a cloud platform with a federated two-layer architecture which integrates an environmental sensor network with a sensor web, in order to offer geographical environmental data to the public and also to serve as a test bed for scientific research on noise and air pollution. We hope that the papers of this issue contribute to the progress of research and the development of Wireless Sensor Networks.

Sensor Resource Sharing Approaches in Sensor-Cloud Infrastructure

Generally, wireless sensor networks have been designed to support predefined application services. The tight coupling between a network and application limits the usability of sensor data. To increase the utilities of wireless sensor networks, sensor-cloud infrastructure has been proposed to provide an open and flexible platform to gather, store, use, and share data from heterogeneous sensors. In this paper, we introduce sensor-cloud infrastructure and present not only comprehensive technical issues like sensor description and publish/subscribe middleware but also applications using sensor-cloud infrastructure. When the issues are tackled in the development of sensor-cloud system, the attractiveness of new applications and services will considerably be increased for end users.

Agriculture Sensor-Cloud Infrastructure and Routing Protocol in the Physical Sensor Network Layer

Nowadays, wireless sensor networks (WSNs) are used in a variety of areas. However, it is difficult to efficiently manage a large number of sensor nodes and their sensing data owing to the limitations of WSNs. Particularly, in agricultural applications, a WSN installed in a specific region is used for multiple services (i.e., greenhouse environment monitoring and control). However, the network resources (i.e., channel, battery, etc.) are currently being utilized for redundant operations requested from multiple service users owing to the lack of an efficient system for managing large WSN data. In this paper, we propose an agriculture sensor-cloud infrastructure (ASCI) to effectively provide various agricultural services using WSNs. In addition, we propose hierarchical source routing (HSR), aggregation gradient routing (AGR), and a priority-based data transmission technique in order to allow packets to be delivered to the destination fast and reliably in large-scale WSNs.

A Survey on Sensor-Cloud: Architecture, Applications, and Approaches

Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.