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The interest in cloud computing by organizations has driven a core desire to become more effective and efficient with information technology (IT). Cloud computing enables organizations to utilize instantly provisioned scalable IT resources on a pay-per-use basis. The wireless grid provides a new model for heterogeneous devices to share physical and...
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... and reduce the cost of the service [60]. Multi-tenancy enables sharing of resources (and costs) among a large pool of users, provides reliability by way of multiple redundant sites, which makes it suitable for business continuity and disaster recovery. Clouds are advertised as a cheap alternative to supercomputers and specialized clusters. The cloud platform is much more reliable than grid platforms and it is more scalable [52]. The IAAS in cloud computing relies on large collections of commoditized computing resources provisioned via hypervisors. Although cloud computing is in its early stages and its definitions vary greatly, the technologies used for the cloud include grid computing, utility computing, and virtualization technologies. Grid computing is a form of distributed, parallel computing whereby processes are split up to leverage the available computing power of multiple central processing units (CPU) acting in concert [23]. Utility computing allows users to purchase computing capacity, such as CPU, storage, and bandwidth, from an IT service provider and to be billed based on actual consumption [3]. Virtualization technologies are virtual servers and virtual private networks and they provide the ability to quickly reconfigure available resources on demand and provide the necessary security assurance, such as increased security protocols to protect against threats from the cloud environment and the data transported within the cloud [7]. Among the substantial challenges that hinder the widespread adoption of cloud computing are the security and privacy concerns of data in the cloud. Security has emerged as arguably the most significant barrier to faster and more widespread adoption of cloud computing [11]. Although cloud computing offers the benefits of moving computing resources to a cloud computing environment to take advantage of flexibility and cost savings, data confidentiality and integrity controls for the cloud need to be applied to limit exposure to unauthorized users [28]. Moving information assets to a cloud computing environment can reduce the costs of information management and storage but cloud-based systems are open to security threats and loss of control of data [64]. The cloud computing service deployment and architecture (e.g. private, community, public or hybrid) chosen must fit the security and privacy needs of the organization adopting this type of environment [15, 39, 46]. Treglia et al. [68] define wireless grids as ad-hoc dynamic sharing of physical and virtual resources among heterogeneous devices, content and users. As the future of distributed computing, wireless grids (see Figure 2) will enable resource sharing among dynamic groups or social networks with individual profiles that are assigned a specific status relative to similar objects and resources with no dedicated server needed to manage the network [45]. Grid computing involves the aggregation of network connected computers to form a distributed system for coordinated problem solving and resource sharing [16, 17, 57]. McKnight et al. [44] can be credited with describing wireless grid infrastructures along three dimensions: the physical layer, the networking infrastructure, and the middleware. Recent literature from Li et al. [42] and Ahuja and Myers [2] identify wireless grids as three distinct architectures: (1) wireless senor grid, (2) mobile wireless and (3) fixed wireless. McKnight et al. [44] provide a classification of the wireless grid which differs from a typical wired network such as the: (1) applications aggregating information from the range of input/output interfaces found in nomadic devices, (2) applications leveraging the locations and contexts in which the devices exist and, (3) applications leveraging the mesh network capabilities of groups of nomadic devices. The wireless grid extends grid resources to wireless devices of varying sizes and capabilities such as sensors, mobile phones, laptops, special instruments, and edge devices [1]. These devices might be statically located, mobile, or nomadic, shifting across institutional boundaries and connected to the grid via nearby devices such as desktops [45]. Resources can be searched, found, viewed, and manipulated remotely from any other grid-enabled device or service and devices on the wireless grid network can serve as both servers and clients. Agarwal [1] identifies these grids as an augmentation of a wired grid that facilitates the exchange of information and the interaction between heterogeneous wireless devices. These grids will enable shared resources among dynamic groups or social networks of computing and communication devices and are composed of objects and resources with individual profiles that are assigned a specific status relative to similar objects and resources [45]. Wireless grids can take ubiquitous computing to the next level by providing seamless wireless extensions to the wired grid. The architecture for these types of grids are of importance because they can be deployed to provide autonomous nodes that communicate with each other in a decentralized manner and how each node of the network connects others with wireless links, and acts as both a host and a router in sending and receiving ...
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... This is called "wireless cloud" or "mobile cloud" that is depicted in Figure 3 below. Numerous wireless cloud-based applications have been developed such as education (distance learning) ( Wireless cloud extends resources to varying static, mobile, or nomadic wireless devices with different capabilities; including cars, trains, airplanes, sensors, mobile phones, and laptops (Brooks et al. 2012). The independence a wireless device allows accessing the cloud resources regardless of the location or the type of the cloud's wireless device. ...
As the demand on the cloud services increases, networks in the wireless cloud environment would not be able to handle the big amount of data traffic without some procedures that control the arriving traffic from outside the cloud and manage the connections inside the cloud. Thus, for efficient traffic monitoring and control, it is essential to have some measures that would improve the cloud performance. Since congestion phenomenon can negatively affect the utilization of the cloud resources and reduce the performance experienced by the end-uses, it is highly necessary to diminish congestion phenomena in order to optimize the cloud service utilization and provide end-users with appropriate level of performance that meets their requirements. The paper aiming at improving the performance in wireless cloud computing environment in frame of delay, packet loss, throughput, and utilization.
... (Hoang & Chen 2010), and multimedia (seminars and conferences)( Dinh et al. 2013). Wireless cloud extends resources to varying static, mobile, or nomadic wireless devices with different capabilities; including cars, trains, airplanes, sensors, mobile phones, and laptops ( Brooks et al. 2012). The independence a wireless device allows accessing the cloud resources regardless of the location or the type of the cloud's wireless device. ...
As the demand on the cloud services increases, networks in the wireless cloud environment would not be able to handle the big amount of data traffic without some procedures that control the arriving traffic from outside the cloud and manage the connections inside the cloud. Thus, for efficient traffic monitoring and control, it is essential to have some measures that would improve the cloud performance. Since congestion phenomenon can negatively affect the utilization of the cloud resources and reduce the performance experienced by the end-uses, it is highly necessary to diminish congestion phenomena in order to optimize the cloud service utilization and provide end-users with appropriate level of performance that meets their requirements. The paper aiming at improving the performance in wireless cloud computing environment in frame of delay, packet loss, throughput, and utilization.
... The assumption is that the most highly skilled, motivated hackers would attack organizations that implement a wireless cloud within its environment. [5] Therefore, in a secure wireless cloud environment, organizations will need to shift their focus from perimeter-based security models to a service-level view of security, with emphasis not so much on ownership and control, but on network identities, trust, and authorization of both users and applications. The architectural construct of a secure wireless cloud computing environment is identified in Figure 11 [9] identifies the wireless cloud as a natural extension of the wireless grid, which provides seamless access to the internet, networked devices and computing capabilities. ...
The Open Specifications Model and the Wireless Grid Internet of Things (WiGIT) virtual organization and its wireless grid architecture for edgeware have been developed under the auspices of the U.S. National Science Foundation Partnerships for Innovation (PFI) grant #0227879 for WiGiT (Wireless Grid Innovation Testbed). Syracuse University (SU) and Virginia Tech (VT) created the first national WiGiT distributed experimental testbed in 2009. Working software prototypes were first demonstrated at Syracuse University in 2003, and have been field tested and evaluated since 2005. More than a dozen doctoral theses have been authored at MIT, Tufts, Syracuse University, and Virginia tech since 2004 that hhave contributed to understanding. Hardware, software, and cloud to edge service implementations have been lab tested iteratively over the years, with initial field testing in 2008/9 and a new series of field trials begun once again in 2011/2012. In August 2012 an enhanced Advanced Situational Awareness System was demonstrated, with additional evaluations in 2013 prior to initial efforts to commecialize somewhat related implementations beginning in 2014 with the conclusion of the NSF PFI WiGIT project. Field tests of WiGiT specifications, applications, services, and devices are ongoing beyond the scope of the project with suport of WiTec, which is presently a Syracuse University student club anticipating expansion. In cooperation with WiGiT partner firms, schools, public agencies, healthcare institutions, universities, and emergency managers. Students and alums will be welcomed to WiTec, while all are welcome to join the WiGIT virtual organization and related efforts, at no cost, and no obligation, as the Open Specifications Model gains users, adapters, and adopters.
... Wireless grids have been theorized for use with emergency response [12], learning in collaborative educational environments [11], health [15], energy [16], and security [17] in cloud computing spaces [18]. The importance of social interactions using wireless grids [11] has been developed in relation to learning, gaming [11] and the value of game theoretics [2]. ...
... This in turn may enable the security development teams to recognise and develop countermeasures to deal with classes of vulnerabilities by dealing with the vulnerabilities at a higher level of abstraction. For example, the Brooks et al. (2012b) article detailed four cyber-attack classes for a wireless cloud (i.e., wireless grid, cloud computing, data-in-transit, and insider) contributing to the identification of 37 specific vulnerabilities, such as inconsistent parameter validations, implicit sharing of privileged/confidential data, inadequate identification/authentication/authorisation and exploitable programme logic errors enabling circumvention of access control. Since the wireless cloud applications are device and location independent and compatible with many device types (including mobile and nomadic devices, phones, tablets, laptops, and network computers), it is important to understand the vulnerabilities to this infrastructure. ...
... The combination of threats and vulnerabilities illustrates the risks that this wireless grid Edgeware technology is exposed to. Since this technology plans to provide a more interoperable, scalable and flexible approach to provide ways for intelligent devices to provide communication, the research performed by Brooks (2014) Brooks et al. (2012aBrooks et al. ( , 2012b) uncovered a significant high risk due to the number of vulnerabilities and its increase dependence on cloud and virtualisation technology making it an increasingly attractive target for hackers. ...
... This in turn may enable the security development teams to recognise and develop countermeasures to deal with classes of vulnerabilities by dealing with the vulnerabilities at a higher level of abstraction. For example, the Brooks et al. (2012b) article detailed four cyber-attack classes for a wireless cloud (i.e., wireless grid, cloud computing, data-in-transit, and insider) contributing to the identification of 37 specific vulnerabilities, such as inconsistent parameter validations, implicit sharing of privileged/confidential data, inadequate identification/authentication/authorisation and exploitable programme logic errors enabling circumvention of access control. Since the wireless cloud applications are device and location independent and compatible with many device types (including mobile and nomadic devices, phones, tablets, laptops, and network computers), it is important to understand the vulnerabilities to this infrastructure. ...
... The combination of threats and vulnerabilities illustrates the risks that this wireless grid Edgeware technology is exposed to. Since this technology plans to provide a more interoperable, scalable and flexible approach to provide ways for intelligent devices to provide communication, the research performed by Brooks (2014) Brooks et al. (2012aBrooks et al. ( , 2012b) uncovered a significant high risk due to the number of vulnerabilities and its increase dependence on cloud and virtualisation technology making it an increasingly attractive target for hackers. ...
This article contributes to the foundational understanding of the security vulnerabilities and risk towards wireless grid Edgeware technology. Since communication networks and devices are subject to becoming the target of exploitation by hackers (e.g., individuals who attempt to gain unauthorised access to computer systems), these individuals are gaining ever-increasing knowledge of the often widely-reported exploitable vulnerabilities in these types of innovative technologies; and thus are able to craft increasingly effective computer network attacks (CNA) against such technologies. This research responds to the overall proposition: what security vulnerability enumerations would contribute to the degradation and risk in using a wireless grid Edgeware application in a virtualised cloud environment? Using supporting research pertaining to cyber-attacks and vulnerabilities towards a wireless cloud (e.g., the integration of a cloud computing and a wireless grid architecture), security vulnerabilities in virtualisation environments and specific vulnerabilities exploited against a wireless grid Edgeware application, this research provides a greater understanding of the practical ways wireless grid Edgeware technology can be attacked and the risk in utilising this technology.-Director for the Wireless Grids Testbed (WiGiT) at Syracuse University and is an IEEE senior member. He has more than 20 years of professional experience in the design, development and production of complex information systems/architectures, as well as leading the effort to develop secure information systems architectures for the US DoD. His research interests are in the fields of cyber-security, information assurance, information security architecture and internet of things architectures. He received his Doctorate in Information Management from Syracuse University.
... to consideration that threats should consider the skill of the attacker, their propensity to actually launch an attack, their concern for risk of detection or attribution and the likelihood of success. The assumption is that the most highly skilled, motivated hackers would attack organizations that implement a wireless cloud within its environment. [5] Therefore, in a secure wireless cloud environment, organizations will need to shift their focus from perimeter-based security models to a service-level view of security, with emphasis not so much on ownership and control, but on network identities, trust, and authorization of both users and applications. The architectural construct of a ...
The purpose of this Version 0.2 initial release of open specification technical requirements is to further define and describe the core components of the Wireless Grid Innovation Testbed (WiGiT) first described in Version 0.1, released March 27, 2012. WiGiT is a wireless grid network platform and virtual organization, and several edgeware applications associated with certain use cases for WiGiT have been released (see WiGiT Group Use Cases). New use cases and implementable software are in development and being prepared for testing. Edgeware is a new class of software specifically designed for software applications deployed on wireless grids. Edgeware refers to the software capability to deploy network ‘edge’ devices (aka nodes) as acting servers, hence the ‘serverless’ logic of the architecture. The WiGiT wireless grid architecture and platform enables heterogeneous resource discovery and sharing through the formation of wireless grid virtual networks. Wireless grids are dynamic virtual cognitive networks that exist only while they are in use. Users are able to share and manage available and accessible hardware and software resources through edgeware applications based on the WiGiT product’s core components. WiGiT platform components and edgeware have built in security at multiple levels of the network and are energy and bandwidth efficient by design. These open specifications further define the WiGiT platform and the logical functions of the WiGiT core components in additional detail based upon the 0.1 version.
The ability to process large amounts of data with the integration of the Internet of Things (IoT) and cloud computing environments means organizations can trade sophisticated security estimation techniques for more accurate and simple models. By determining tactical and technical parameters of Cloud-of-Things (CoT) wireless equipment and systems, discovering irregularities of electronic tags and their threat levels and analyzing tags/readers strong and weak APs provide the interoperability for organizing and carrying out CoT defense. The basic task of the steady-state model for the CoT will be to support the defense for the CoT network through detecting technical loopholes and topographic structures from the various CoT networks and the data/information stored inside the system. The CoT system will possess a function of automatic identity recognition and needs a steady-state framework for measuring security components and mechanisms of computing devices, communication networks, and sensing devices.
An over-the-top (OTT) application is an app or service providing a product over the Internet. OTT media and communication services generally are more adaptive to user needs, networks, and devices as well as lower in cost than traditional delivery methods, typically for video content. This paper reviews how Fortune 50 firms and the federal government architecturally organize their information technology and network services using a cloud operating model to create value, over the virtual top, today. Enterprise cloud services offer content, service, and things in an infrastructureless overlay network, Over the Top to create innovative products and services. As business investment in value-creating activities is increasingly focused on cloud services and 'big' data analytics, might regulators inadvertently inhibit innovation through mis-regulation of some of the constituent parts of those services?
