QoS guaranteeing robust scheduling in attack resilient cloud integrated cyber physical system

Chejerla, Brijesh Kashyap; Madria, Sanjay

doi:10.1016/j.future.2017.02.034

Cited by 20 publications

(5 citation statements)

References 23 publications

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“…The key to widespread adoption of computing remotely is security that needs to be guaranteed by a provider [96,97,98]. In the traditional cloud, there are significant security risks related to data storage and hosting multiple users, which are mitigated by robust mechanisms to guarantee user and user data isolation.…”

Section: Guaranteeing Enhanced Securitymentioning

confidence: 99%

Next generation cloud computing: New trends and research directions

Varghese

Buyya

2018

Future Generation Computer Systems

642

270

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The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

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Section: Guaranteeing Enhanced Securitymentioning

confidence: 99%

Next generation cloud computing: New trends and research directions

Varghese

Buyya

2018

Future Generation Computer Systems

642

270

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“…▪ QoS [257] ▪ Communication Control [258] ▪ PiccSIM simulations may have a challenging learning curve, particularly for users unfamiliar with NS-2 and MATLAB. ▪ It does not support the simulation of large-scale WSNs.…”

Section: Piccsimmentioning

confidence: 99%

Investigating and Analyzing Simulation Tools of Wireless Sensor Networks: A Comprehensive Survey

Adday,

Subramaniam,

Zukarnain

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) serve as the backbone for the Internet of Things (IoT) and smart cities, enabling the gathering of essential data and vital information. The widespread deployment of sensors created new scenarios that presented new domains for various applications. Due to their numerous advantages, WSN's simulators are still gaining considerable attention as the primary method for testing and evaluating new protocols and approaches for WSN. The scientific community has developed various simulators, some tailored explicitly for WSNs and others for general use. In addition, an old but renovated direction has been enormously grown recently, representing the researcher's attention to building its own simulator. However, many researchers struggle to select the most appropriate tool for performance analysis, which requires extensive research into available options. Choosing a suitable simulator to meet the necessary simulation criteria necessitates exhaustive research into the available options. The published studies on WSN's simulators have limitations, such as the limited number of simulators under examination, ignoring the essential functions of the simulators, and inadequate performance criteria for precise comparison. To get beyond these restrictions in previous studies and offer a fresh study that compares the simulation tools comprehensively, an analysis of thirty-three different simulators was conducted based on a new taxonomy. Furthermore, this study examines the advantages and constraints of each simulator regarding many specific academic areas in WSNs. Moreover, the study presented a detailed comparison among the performance analysis tools according to many famous performance metrics.

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“…Likelihood Est. Univ Georgia,US [45] Bayesian Networks Univ Limerick,IE [123]; George Mason Univ,US [76]; Benedict Coll,US & Univ Illinois,US [69]; Missouri Univ Sci Tech,US [81]; World Islamic Sci Edic Univ,JO & Royal Jordanian Air Forces,JO [ [118] Bounded sensor reading Nanyang Tech Univ,SG [47] Convex Optimization South China Univ Tech,CN [127] Distributed Attack Iran Sci Univ Tech,IR [54] Hierarchical Modeling MIT,US [77] LiSM: Land in Sand Miner NEC Labs Amer,US & Univ Illinois,US & BBN Tech,US [86] Montecarlo Politech Milan,IT & Univ Paris,FR [48] Penetration Testing Malek Ashtar Univ Tech,IR & NIOPDC,IR [26]; US Air Force,US [29] Process Northeastern Univ,CN [128]; Singapore Univ Tech Design,SG & Optiwater,IL & Technion Israel Inst Tech,IL [49]; Shenandoah Res Tech,US [90] Reliability Univ Idaho,US & Texas A&M Univ,US [57] Robust predictive control Northeastern Univ,CN [119] Stochastic Hypothesis Testing Univ Florida,US & Univ Sao Paulo,BR [70] Continued on next page Survey General Shanghai Univ,CN [67]; Univ Hull,UK [92]; MIT,US [82] 6 Analysis and future work…”

Section: Generalmentioning

confidence: 99%