A Multi-Agent-Based Data Collection and Aggregation Model for Fog-Enabled Cloud Monitoring

Bulla, Chetan; Birje, Mahantesh N.

doi:10.4018/ijcac.2021010104

Cited by 18 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 60 ] data mining classification technique has been used in order to group the connected devices based on the collected data and then detect the nodes which generate erroneous data. A multi-agent-based data collection and aggregation model is proposed for monitoring fog infrastructure in [ 61 ]. Secure decentralized spatial crowdsourcing scheme for 6G-Enabled Network is proposed in [ 62 ] in which nodes can gather and transmit information on the blockchain without depending on third party.…”

Section: Related Workmentioning

confidence: 99%

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

et al. 2021

View full text Add to dashboard Cite

Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.

show abstract

Section: Related Workmentioning

confidence: 99%

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The IoT devices have limited processing power in computing and storage resources to perform advanced analytical tasks. Cloud computing provides a very powerful solution for IoT application, service, and resource management [22][23][24]. The integration of cloud infrastructure with IoT architecture brings significant advantages to IoT including data analytical tasks [22].…”

Section: Background and Related Workmentioning

confidence: 99%

Live Data Analytics with IoT Intelligence-Sensing System in Public Transportation for COVID-19 Pandemic

Alamri¹,

Alamri²

2021

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The COVID-19 pandemic has presented an unprecedented challenge to the entire world. It is a humanitarian crisis on a global scale. The virus continues to spread throughout nations, putting health systems under enormous pressure in the battle to save lives. With this growing crisis, companies and researchers worldwide are searching for ways to overcome the challenges associated with this virus. Also, the transport sector will play a critical role in revitalizing economies while simultaneously containing the spread of COVID-19. As the virus is still circulating, the only solution is to redesign public transportation to make people feel safe. In this paper, we have proposed a system based on computer vision and IoT sensor infrared technology with live data analytics. The proposed system is capable of gathering, storing, analyzing, processing, and handling the vast amount of data produced by the IoT sensors, and provides the users with real-time information on potential events affecting public transport, thereby enabling users to make well-informed and timely decisions. The evaluation showed that, despite the complexity of the system, it performs well.

show abstract

“…Based on our knowledge, the selection of multiple coordinators from a set of candidate nodes in IOTA network has not been studied so far, and this paper is the first attempt in this regard. We propose a two‐layered architecture for IOTA network including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). Employing a set of fog nodes in our proposed architecture improves the capacity of storage and computing resources in IOTA network during the process of transaction validation in the consensus phase 29–33 We simulate the proposed architecture using iFogSim simulator and investigate the performance of the MCS algorithm in terms of average throughput, average response time, system utilization, number of data packets sent across the network, and total cost. …”

Section: Introductionmentioning

confidence: 99%

“…Employing a set of fog nodes in our proposed architecture improves the capacity of storage and computing resources in IOTA network during the process of transaction validation in the consensus phase. [29][30][31][32][33] 4. We simulate the proposed architecture using iFogSim simulator and investigate the performance of the MCS algorithm in terms of average throughput, average response time, system utilization, number of data packets sent across the network, and total cost.…”

mentioning

confidence: 99%

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Alavizadeh,

Erfani,

Mirabi

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryIOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

show abstract

A Multi-Agent-Based Data Collection and Aggregation Model for Fog-Enabled Cloud Monitoring

Cited by 18 publications

References 26 publications

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

Compressive sensing based secure data aggregation scheme for IoT based WSN applications

Live Data Analytics with IoT Intelligence-Sensing System in Public Transportation for COVID-19 Pandemic

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

Contact Info

Product

Resources

About