Abstract: Cloud-integrated Internet of Things (IOT) is emerging as the next-generation service platform that enables smart functionality worldwide. IOT applications such as smart grid and power systems, e-health, and body monitoring applications along with large-scale environmental and industrial monitoring are increasingly generating large amounts of data that can conveniently be analyzed through cloud service provisioning. However, the nature of these applications mandates the use of secure and privacy-preserving implementation of services that ensures the integrity of data without any unwarranted exposure. This article explores the unique challenges and issues within this context of enabling secure cloud-based data analytics for the IOT. Three main applications are discussed in detail, with solutions outlined based on the use of fully homomorphism encryption systems to achieve data security and privacy over cloud-based analytical phases. The limitations of existing technologies are discussed and models proposed with regard to achieving high efficiency and accuracy in the provisioning of analytic services for encrypted data over a cloud platform.
Keywords: IOT; e-Health; m-Health; Healthcare; Body sensor; Cloud storage; Remote monitoring;
1. INTRODUCTION
In n telecommunication fields there is a new technology called Internet of Things (IOT). The Internet of Things (IOT) is ‗‗the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, permitting these objects to gather and interchange data‘‘. IOT technology is the next major step in the new technology sector, but with the great difference that it carries massive changes in business functionality. Over the next years, a flare in the number of connected devices as well as located sites, and the functions they will perform, is expected.
In addition, the main strength of the IOT idea is the high impact that it will have on several aspects of the everyday-life and behavior of potential users. The most obvious effects of the Internet of Things, as a private user could observe, would be visible in both domestic and working fields. In the first case, some examples of the possible application scenarios in which the new paradigm, that is the Internet of Things, will play a leading role in the near future are domestics, e-health, assisted living, and enhanced learning. In the second case, business users could observe the similar consequences which are traceable in some fields such as logistics, intelligent transportation of
people and goods, automation and industrial manufacturing, and business/process management. The Internet of Things is composed of three main parts:
1. The ‗‗things‘‘ (objects).
2. The communication networks that connect them.
3. The computer systems using data streaming from and to objects.
For example, home security systems already allow you to check remotely the locks on your doors, and thermostats in the house. But what if it was possible to act proactively on your behalf? Imagine you opened the windows to ventilate your house before arriving, based on your personal preferences, weather conditions, and the distance from your house.
To summarize, the Internet of Things is a type of network of some physical objects or things which, embedded with software, electronics, sensors and connectivity that enables them, achieves greater value and service by exchanging data with manufacturers, operators and some other connected devices. Thus, the intensive computations and the mass storage, which are supported by clouds, are often inefficient. Some examples include the limitations of storage, communication capabilities, energy and processing. Such inefficiencies motivate us to combine the technology of Mobile Cloud Computing (MCC) and the Internet of Things. As an emerging technology, Mobile Cloud Computing integrates multiple technologies for maximizing capacity and performance of the existing infrastructure.
Moreover, there is another technology, called Mobile Cloud Computing (MCC), which improved through the recent years by a new generation of services which is made its appearance, based on the concept of the ‗‗cloud computing‘‘ which aims to provide access to the information and the data from anywhere at any time by restricting or eliminating the need for hardware equipment. More specifically, Mobile cloud computing is defined as an integration of cloud computing technology and mobile devices in order to make mobile devices resourceful in terms of computational power, memory, storage, energy, and context awareness. Also, Mobile cloud can be defined as a contemporary approach to innovative services for firms and institutions. Mobile cloud computing is the outcome of interdisciplinary approaches, which consists of mobile computing and cloud computing. The term mobile cloud is generally referred to in two perspectives: (a) infrastructure based, and (b) ad-hoc mobile cloud. In infrastructure based mobile cloud, the hardware infrastructure remains static, and provides services to the mobile users. As a result of the operations of Cloud Computing, it could be used as useful bases for both Internet of Things and Video Surveillance SECURE DATA ANALYTICS FOR CLOUD-INTEGRATED INTERNET OF THINGS APPLICATIONS
1Mrs. TAHMEENA FATIMA, 2Mrs. SYEDA NAUSHEEN 1
Department of Computer Science, Prince Sattam Bin Abdul Aziz University, Saudi Arabia 2
technologies and could provide improvements on their functions.
2. LITERATURE REVIEW
For the purpose of this paper we study and analyze previous literature which has been published in the field of cloud computing and Internet of Things, and their integration. The following paragraphs present the papers which contributed significantly in our study.
To begin with, a survey of the different security risks that pose a threat to the cloud is presented. Also, it was given a survey more specific to the different security issues that has emanated due to the nature of the service delivery models of a cloud computing system. Moreover, an exploration of the roadblocks and solutions to provide a trustworthy cloud computing environment presented. Cloud computing is an evolving paradigm with tremendous momentum, but its unique aspects exacerbating security and privacy challenges.
Concerning the integration of Internet of Things and Cloud Computing, there have been made some previous studies. A propose of a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities‘ needs is given. Additionally, a presentation of a framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed analyzed and controlled by distributed cloud-based services shown. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IOT and CC in manufacturing are investigated. Furthermore, a CC- and IOT-based cloud manufacturing (CMfg) service system (i.e., CC IOT-CMfg) and its architecture are proposed, and the relationship among CMfg, IOT, and CC is analyzed. And finally, the advantages, challenges, and future works for the application and implementation of CC IOT-CMfg are discussed. The mainly focuses on a common approach to integrate the Internet of Things (IOT) and Cloud Computing under the name of Cloud Things architecture the state of the art for integrating Cloud Computing and the Internet of Things, and examine an IOT-enabled smart home scenario to analyze the IOT application requirements. At the end, the Cloud Things architecture, a Cloud-based Internet of Things platform which accommodates Cloud Things IaaS, PaaS, and SaaS for accelerating IOT application, development, and management proposed. Furthermore, a presentation and discussion about some of the integration challenges of Iot and Cloud Computing that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.
A presentation of an approach to the development of Smart Home applications by integrating Internet of Things (IOT) with Web services and Cloud computing are shown. The approach focuses on: (1) embedding intelligence into sensors and actuators using Arduino platform; (2) networking smart things using Zigbee technology; (3) facilitating interactions with smart things using Cloud services; (4) improving data exchange efficiency using JSON data format. Also, it is shown an implementation of three use cases to demonstrate the approach‘s feasibility and efficiency, i.e., measuring home conditions, monitoring home appliances, and controlling home access. The presents a Cloud centric vision for worldwide implementation of Internet of Things the key enabling technologies and application domains that are likely to drive IOT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public clouds, is also presented. Finally, it concludes the IOT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community. Internet of Things (IOT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IOT‘s could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IOT‘s and develop smart applications for the users. This type of integration is referred to as Cloud of Things. With IOT‘s, anything can become part of the Internet and generate data.
Moreover, data generated needs to be managed according to its requirements, in order to create more valuable services. For the previous purpose, integration of IOT‘s with cloud computing is becoming very important. This new paradigm is termed as Cloud of Things (CoTs) and it is presented. The focuses in the attention of the authors on the integration of Cloud and IOT, which is what we call the Cloud IOT paradigm also, many works in literature have surveyed Cloud and IOT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, these works lack a detailed analysis of the new Cloud IOT paradigm, which involves completely new applications, challenges, and research issues. The focuses on some of the key challenges involved in CoT and the proposal of smart gateway based communication. Cloud of Things, requires smart gateway to perform the rich tasks and preprocessing, which sensors and light IOT‘s are not capable of doing. Finally, the presents a survey of integration components: Cloud platforms, Cloud infrastructures and IOT Middleware. In addition, some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.
Finally, we study integration algorithms and methods about the aforementioned technologies. In the authors focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. Furthermore, the algorithms which have been studied need long execution
times. Also, the authors of accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. At the end, the authors reach the achievement performance enhancement by up to 8.9×without compromising the segmentation accuracy. The main aim of this is to perform a review of the basic methods used for-such techniques and finding the emerging trends of the research in this area. The authors of primary focus on summarize some well-known methods of face recognition in video sequences for application in biometric security and enumerate the emerging trends. The in order to address the challenge of the lack of investigating on effective and efficient evaluations and measurements for security and trustworthiness of various social media tools, platforms and applications, surveys the state-of-the-art of social media networks security and trustworthiness particularly for the increasingly growing sophistication and variety of attacks as well as related intelligence applications. Also, the authors of highlighted a new direction on evaluating and measuring the fundamental and underlying platforms. Furthermore, the authors propose a hierarchical architecture for crowd evaluations based on signaling theory and crowd computing, which is essential for social media ecosystem.
3. INTERNET OF THINGS
The Internet of Things is a network of devices that transmit, share, and use data from the physical environment to provide services to individuals, corporations, and society. The objects-things function either individually or in connection with other objects or individuals, and have unique IDs (identifiers). Also, the Internet of Things has different applications in health, transport, environment, energy or types of devices: sensors, devices worn/carried (wearable), e.g. watch, glasses, home automation (domestics) (see Fig. 1).
Fig.3.1. Internet of Things technology
Recently, IOT (Internet of Things) has studied in the various application fields of human living space. IOT is based on the smart sensors and the middleware for connecting between clients and terminal devices. It can provide the
public with the interesting information about various things deployed in our surrounding environment.
INTERNET of Things (IOT) [1] is a technology that connects all things and the Internet in smart spaces. By implementations of intelligence with sensing devices, IOT has been widely applied to different fields, such as smart homes.
The application fields in smart homes [4] incorporate smartness into home areas for comfort, safety, security, healthcare, and energy conservation [5], [6]. The need for comfort and a convenient life are especially important in smart homes. Thus, home automation is one of the most essential and critical components for the IOT-based smart home technology. Home automation systems are used to control home devices or appliances in smart homes and provide automatic remote control inside or outside homes. Nevertheless, although remote control provides convenience and ease of use, some major problems require consideration and improvement, such as how to provide an intuitive and user-friendly remote control scheme in IOT-based smart homes.
Internet of Things (IOT) is becoming more and more popular around the world. IOT is the network of physical objects or "things" embedded with electronics, software, sensors and connectivity to enable it to achieve greater value and service by exchanging data with the manufacturer, operator and/or other connected devices. Typically, IOT is expected to offer advanced connectivity of devices, systems, and services that goes beyond IOT environment and covers a variety of protocols, domains, and applications.
In order to provide the IOT service, IIC (Industrial Internet Consortium), All Seen Alliance, OIC (Open Interconnect Consortium), etc. have been organized. These consultative groups have published open source projects for developing the IOT service.
3.1. Growth and Prevalence of the Internet of Things Internet-connected devices enable seamless connections among people, networks, and physical services. These connections afford efficiencies, novel uses, and customized experiences that are attractive to both manufacturers and consumers. Network-connected devices are already becoming ubiquitous in, and even essential to, many aspects of day-to-day life, from fitness trackers, pacemakers, and cars, to the control systems that deliver water and power to our homes. The promise offered by IOT is almost without limit.
3.2. Internet of Things: advantages of the data
What does it mean when the devices and sensors are networked together and communicate with each other? How can the Internet of Things affect our daily life? GPS systems, alarm systems, and thermostats, all send and receive constant feeds to monitor and automate activities in our daily lives. And the not so obvious: Mosaic, cups, clothes and other everyday objects can also join network to
send and receive data over the Internet. Opportunities where the streaming data will create new markets in order to inspire positive change or to enhance existing services are examined by businesses. Some examples of sectors that are at the heart of these developments are listed below:
(a) Smart solution in the bucket of transport: Smart solutions in the bucket of transport, achieve a reduction of traffic on the roads, reduce fuel consumption, set priorities in vehicle repair programs, and save lives.
(b) Smart power grids incorporating more renewable: Smart power grids incorporating more renewable improve system reliability, and reduce the charges consumers, thus providing cheaper electricity.
(c) Remote monitoring of patients: Remote monitoring of patients provides easy access to health care, improves the quality of services, increases the number of people served, and saves money.
(d) Sensors in homes and airports: Sensors in homes and airports, or even in your shoes or doors, improve safety by sending signals when left unused for a certain period of time or when used in the wrong time.
(e) Engine monitoring sensors that detect & predict maintenance issues: Engine monitoring sensors that detect and predict maintenance issues, improve inventory replenishment, and even define priorities in scheduling maintenance work, repairs, and regional operations.
3.2. Internet of Things Security
IOT security is the area of endeavor concerned with safeguarding connected devices and networks in the Internet of things. The Internet of Things involves the increasing prevalence of objects and entities – known, in this context as things – provided with unique identifiers and the ability to automatically transfer data over a network. Much of the
increase in IOT communication comes from computing devices and embedded sensor systems used in industrial machine-to-machine (M2M) communication, smart energy grids, home and building automation, vehicle to vehicle communication and wearable computing devices.
The main problem is that because the idea of networking appliances and other objects is relatively new, security has not always been considered in product design. IOT products are often sold with old and un-patched embedded operating systems and software. Furthermore, purchasers often fail to change the default passwords on smart devices—or if they do change them, fail to select sufficiently strong passwords. To improve security, an IOT device that needs to be directly accessible over the Internet, should be segmented into its own network and have network access restricted. The network segment should then be monitored to identify potential anomalous traffic, and action should be taken if there is a problem. Security experts have warned of the potential risk of large numbers of unsecured devices connecting to the Internet since the IOT concept was first proposed in the late 1990s. In December of 2013, a researcher at Proof point, an enterprise security firm, discovered the first IOT botnets. According to Proof point, more than 25% of the botnets was made up of devices other than computers, including smart TVs, baby monitors and other household appliances.
3.2.1 Prioritizing IOT Security
While the benefits of IOT are undeniable, the reality is that security is not keeping up with the pace of innovation. As we increasingly integrate network connections into our nation‘s critical infrastructure, important processes that once were performed manually (and thus enjoyed a measure of immunity against malicious cyber activity) are now vulnerable to cyber threats. Our increasing national dependence on network-connected technologies has grown faster than the means to secure it.
The IOT ecosystem introduces risks that include malicious actors manipulating the flow of information to and from network-connected devices or tampering with devices themselves, which can lead to the theft of sensitive data and loss of consumer privacy, interruption of business operations, slowdown of internet functionality through large-scale distributed denial-of-service attacks, and potential disruptions to critical infrastructure.
Last year, in a cyber-attack that temporarily disabled the power grid in parts of Ukraine, the world saw the critical consequences that can result from failures in connected systems. Because our nation is now dependent on properly functioning networks to drive so many life-sustaining activities, IOT security is now a matter of homeland security.
It is imperative that government and industry work together, quickly, to ensure the IOT ecosystem is built on a foundation that is trustworthy and secure. In 2014, the President‘s National Security Telecommunications
Advisory Committee (NSTAC) highlighted the need for urgent action.
IOT adoption will increase in both speed and scope, and [will] impact virtually all sectors of our society. The Nation‘s challenge is ensuring that the IOT‘s adoption does not create undue risk. Additionally…. there is a small—and rapidly closing—window to ensure that IOT is adopted in a way that maximizes security and minimizes risk. If the country fails to do so, it will be coping with the consequences for generations.2
The time to address IOT security is right now. This document sets the stage for engagement with the public and private sectors on these key issues. It is a first step to motivate and frame conversations about positive measures for IOT security among IOT developers, manufacturers, service providers, and the users who purchase and deploy the devices, services, and systems. The following principles and suggested practices provide a strategic focus on security and enhance the trust framework that underpins the IOT ecosystem.
4. SECURE CLOUD-BASED SENSOR DATA ANOMALY DETECTION
Sensor data anomaly detection is another application that‘s compatible with secure cloud integration using FHE models. Although many solutions to secure WSNs exist, we focus exclusively on securing sensed data through FHE with regard to facilitating service provisioning over the cloud. IOT applications in a variety of domains gather large volumes of industrial- or environmental/meteorological-monitoring data through ubiquitous sensing. Most of these applications use large-scale WSNs consisting of small nodes collaborating to sense and communicate data.
A key analytics application common to these implementations is effective anomaly detection. Data anomalies should be determined dynamically through an efficient and accurate process to identify events of interest, detect attacks, and ensure the integrity of the sensed data for decision support. This mandates adapting different unsupervised learning models, which are typically of high computational complexity. However, given the nature of a WSN deployment, the data is generated in a distributed manner, supporting the viability of having a distributed anomaly detection process over a distributed data-processing framework. Such a distributed anomaly-detection model will lessen the amount of data processed at a given time, reducing computational complexity and thereby enabling the use of an FHE scheme. Therefore, fully homomorphic encryption schemes with higher capabilities and support for most mathematical operations can be adapted over a distributed set of cloud resources that cater to the provisioning of the required anomaly-detection service.
We propose a distributed anomaly-detection model based on fuzzy data modeling as uniquely suitable for implementing secure cloud-based analytics as a service complemented with FHE. Elsewhere, fuzzy c-means
clustering is adapted for iterative anomaly detection over a hierarchical data-processing framework that distributes the main data analytic tasks to composition nodes reflecting the WSN environment. The key factors contributing to the viability of an approach based on this earlier work for FHE-based cloud adaptability are:
• It consists of a hierarchical topology of distributed data-processing nodes that can be replicated in virtual machines on a cloud datacenter for efficient analytics for encrypted data.
Fig.4.1. Anomaly-detection services for wireless sensor network-based IOT monitoring. Large-scale WSNs can be arranged on a hierarchical topology and detection of anomalies performed over different network tiers. Unsupervised anomaly-detection methods can then be employed in a distributed manner over subsets of data to reduce complexity of FHE-based approaches by replicating the hierarchical topology on cloud virtual machines.
• It provides an unsupervised means of anomaly detection without requiring prior knowledge while being adaptable and scalable in a dynamic manner.
• The granular analysis of a distributed process is naturally suitable for the application domain of discrete sensor nodes that collect data in WSN based IOT applications.
• The operations in the clustering process can be broken down into simple mathematical operations that are straightforward to implement on encrypted data.
• It supports efficient application of FHE operations owing to the distributed processing of subsets of data over a hierarchy.
• Each processing node in the model deals with a small subset of data, reducing computational costs.
• The iterative anomaly-detection process further reduces communication and computational overhead.
Fig.2 gives a graphical overview of the proposed model for sensor data anomaly detection in the cloud using the distributed data clustering approach. Fig.3 gives a more detailed view, including the different tasks performed on
different granular levels in a hierarchical topology. Anomalies are evaluated at different levels in providing more fine-grained detection capability to the application concerned. Therefore, we can adapt the earlier work to accommodate homomorphic encryption over a distributed data-processing model in the cloud without changing the underlying data analytic model.
Having a distributed data clustering approach based on fuzzy c-means for anomaly detection requires a more powerful FHE scheme than the scheme proposed for smart grid applications. A scheme such as Domingo-Ferrer‘s, though lightweight, isn‘t applicable because it has limited capabilities and offers minor support for different mathematical operations. Therefore, we need a more complex but capable scheme that leverages a distributed data-processing approach and the significant computational power available in cloud facilities. We propose using the functions available in the HElib library (https://github.com/shaih/HElib) in the implementation of our distributed anomaly detection model for analyzing encrypted data as a service in cloud.
HElib is a functional software implementation of homomorphic encryption; however, it only supports operations on encrypted integers. To overcome this limitation, we use the IEEE Standard for Floating-Point Arithmetic (IEEE 754) to perform floating point computation domain operations in an integer computation domain, making the required data analysis possible with HElib. Therefore, floating-point arithmetic can be performed in a homomorphic manner by converting the representation of float point numbers to its integer representation based on the IEEE 754 standard. We performed experiments to validate this approach; Table 1 gives some preliminary results. The table shows the accuracy over different float-point levels with the associated execution time in seconds for a multiplication operation using HElib functions based on the described approach. We used a multiplication approach as most data analytic operations can be reconstituted as a combination of addition and multiplication operations, with multiplication being the more complex to implement and execute in the current context.
Fig.4.2. Hierarchical framework for distributed anomaly detection different analytic tasks such as clustering and
detection with discrete operations are performed in a distributed manner at different levels over subsets of data.
Thus, the HElib library is a feasible measure to encrypt the data and perform cloud-based anomaly detection for IOT sensor network applications. The high overheads can be managed by employing cloud resources in a distributed manner over different subsets of the data.
5. CONCLUSION
The Cloud Computing technology offers many possibilities, but also places several limitations as well. Cloud Computing refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. In this paper, we present a survey of Internet of Things Technology, with an explanation of its operation and use. Moreover, we present the main features of the Cloud Computing and its tradeoffs. Cloud Computing refers to an infrastructure where both data storage and data processing happen outside of the mobile device. Also, the Internet of Things is a new technology which is growing rapidly in the field of telecommunications, and especially in the modern field of wireless telecommunications. The main goal of the interaction and cooperation between things and objects sent through the wireless networks is to fulfill the objective set to them as a combined entity. In addition, based on the technology of wireless networks, both the technologies of Cloud Computing and Internet of Things develop rapidly. In this paper, we present a survey of IOT and Cloud Computing with a focus on the security issues of both technologies. Specifically, we combine the two aforementioned technologies (i.e. Cloud Computing and IOT) in order to examine the common features, and in order to discover the benefits of their integration. Concluding, the contribution of Cloud Computing to the technology IOT, and it shows how the Cloud Computing technology improves the function of the IOT was presented. At the end, the security challenges of the integration of IOT and Cloud Computing were surveyed through the proposed algorithm model, and also there is a presentation of how the two encryption algorithms which were used contributes in the integration of IOT and Cloud Computing. This can be the field of future research on the integration of those two technologies. Regarding the rapid development of both technologies the security issue must be solved or reduced to a minimum in order to have a better integration model. These security challenges that surveyed in this paper could be the sector for further research as a case study, with the goal of minimizing them.
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