A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks

Khedim, Farah; Labraoui, Nabila; Ari, Ado Adamou Abba

doi:10.1016/j.jnca.2018.09.001

Cited by 22 publications

(10 citation statements)

References 33 publications

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“…Therefore, the mine risk assessment problem shows randomness; moreover, the basic concept of ambiguity can be characterized as the uncertainty contained in the event that has occurred but is difficult to accurately define. The method has been applied in many fields [39][40][41][42][43][44][45][46][47][48] and achieved good evaluation results.…”

Section: Definition Of the Cloud Modelmentioning

confidence: 99%

Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

2021

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Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σc (I1), ratio of the uniaxial compressive strength to the tensile strength σc/σt (brittleness coefficient, I2), elastic deformation energy index Wet (I3), ratio of the maximum tangential stress to the uniaxial compressive strength σθ /σc (stress coefficient, I4) of the rock, depth of the roadway H (I5), and integrity coefficient of the rock mass Kv (I6) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.

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Section: Definition Of the Cloud Modelmentioning

confidence: 99%

Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

2021

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“…The user can address requests to other nodes in the network, specifying the type of required data and harvesting them through the sink node. Routing in LS-WSNs remain a very active area of research, and several routing protocols have been proposed [12,36,100,103,105,106,107,108]. Recently, routing protocols for WSNs have been the subject of several studies, and various studies have been published.…”

Section: Data Transferring In Ls-wsnsmentioning

confidence: 99%

Big Data Collection in Large-Scale Wireless Sensor Networks

Djedouboum

Ari

Guéroui

et al. 2018

Sensors

102

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Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

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“…IoT finds applications in many fields such as smart Buildings, smart cities, smart agriculture etc [2]. Typically IoT is described by tiny devices in the modern world, widely distributed with finite storage and processing capabilities focusing on issues such as efficiency, output, and privacy protection [3,4]. And from the other hand, cloud computing, having huge potential in terms of storing and processing power, is a highly developed technology which helps the IoT to partially solve its problems.…”

Section: Introductionmentioning

confidence: 99%

Resource Efficient Security Mechanism for Cloud of Things

Bashir¹,

Sholla²

2021

IJWMT

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Cloud of Things (CoT) relates to the convergence between Cloud Computing (CC) and the Internet of Things (IoT) and has significantly transformed the way services are delivered in the ubiquitous realm of devices. This integration has become essential because of the huge data being generated by IoT devices, requiring an infrastructure for storage and processing. Such infrastructure is provided by Cloud Computing services with massive space for data storage and exceptional platform to process complex data. IoT networks are vulnerable to multiple security breaches because of the growing usage of IoT devices in user personal systems. This leads to security and privacy threats that need to be addressed. IoT consists of resource limited devices which have feeble computing power, battery source and storage capacity. This paper addresses security issue by proposing usage of obfuscation and encryption techniques to scramble the data at IoT devices which is later on stored in encrypted form at the cloud server. The data at IoT devices is classified into highly critical or less critical and accordingly the appropriate technique between encryption and obfuscation is applied. The proposed mechanism is evaluated in terms of processing time for cryptographic operations at IoT devices. Evaluation results depict that the proposed mechanism is 1.17 times faster than [22] in terms of encryption and decryption times.

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A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks

Cited by 22 publications

References 33 publications

Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

Big Data Collection in Large-Scale Wireless Sensor Networks

Resource Efficient Security Mechanism for Cloud of Things

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