Modelling and Simulation Challenges in Internet of Things

Kecskeméti, Gábor; Casale, Giuliano; Jha, Devki Nandan; Lyon, Justin; Ranjan, Rajiv

doi:10.1109/mcc.2017.18

Cited by 88 publications

(45 citation statements)

References 16 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the heart of a simulator is a complex mathematical model that captures the environment. Although simulators are advantageous, numerous modeling challenges will need to be addressed in designing an ideal (or even a reasonable) simulator [51]. An ideal simulation environment should incorporate programming APIs, management of configuration files and UI dashboards for easy modeling with minimum manual effort.…”

Section: The Modeling Challengementioning

confidence: 99%

Addressing the Challenges in Federating Edge Resources

Baktır

Sonmez

Ersoy

et al. 2019

Fog and Edge Computing

View full text Add to dashboard Cite

Throughout this chapter, we use the general term "Edge" to refer to a collection of technologies that aim towards decentralizing data center resources for bringing computational resources closer to the end user. Mobile Cloud Computing (MCC), Cloudlets, Fog computing, Multi-access Edge Computing (MEC) can all be considered as instances of Edge computing [9]. Therefore, generally speaking, the principles discussed in this chapter can be broadly applied to the aforementioned technologies.The remainder of this chapter is organized in line with the above discussion and is as follows. Section 2.2 considers the networking challenge. Section 2.3 considers the management challenge. Section 2.4 presents the resource and modeling challenges. Section 2.5 concludes this chapter.

show abstract

Section: The Modeling Challengementioning

confidence: 99%

Addressing the Challenges in Federating Edge Resources

Baktır

Sonmez

Ersoy

et al. 2019

Fog and Edge Computing

View full text Add to dashboard Cite

show abstract

“…That is the reason they are inclined to attacks [3]. The scientific classification of assaults in IoT are satirizing, modifying, replaying directing data, Sybil attack [8], Denial of Service (DoS) assaults [9], assaults dependent on node property, assaults dependent on combatant position, assaults dependent on combatant area and assaults dependent on data damage level [1] and so forth. So as to handle these assaults, a required arrangement should be light-weighted and protected down to get the trust of IoT clients [10].…”

Section: Related Workmentioning

confidence: 99%

Implementation of Link Fingerprint Verification and Data Provenance using RSSI

Venkateswarlu*,

Maheswari

2019

IJITEE

View full text Add to dashboard Cite

Due to constrained assets and adaptability, security protocol for Internet of Things (IoT) should be light-weighted. The cryptographic solutions are not possible to apply on little and low-power devices of IoT in view of their power and space impediments. In this paper, a light-weight protocol to verify the information and accomplishing information provenance is introduced for multi-hop IoT arrange. The Received Signal Strength Indicator (RSSI) of conveying IoT nodes are utilized to produce the connection fingerprints. The connection fingerprints are coordinated at the server to process the relationship coefficient. Higher the estimation of connection coefficient, higher the rates of verified information move. Lower worth gives the recognition of ill-disposed node in the middle of a particular connection. Information provenance has additionally been accomplished by examination of pocket header with all the accessible connection fingerprints at the server. The time unpredictability is processed at the node and server level, which is O(1). The power scattering is determined for IoT nodes and overall network. The outcomes demonstrate that the power utilization of the framework and time complexity. Exploratory outcomes show that up to 97% connection is accomplished when no attacker node is available in the IoT network.

show abstract

“…The simulators provide many advantages to the researchers. However, from the simulator developers' perspective, there are some modeling challenges . When compared to the traditional cloud computing simulators, we envision that simulating edge computing environment will bring more complex questions and challenges.…”

Section: Introductionmentioning

confidence: 99%

EdgeCloudSim: An environment for performance evaluation of edge computing systems

Sonmez

Özgövde

Ersoy

2018

Trans Emerging Tel Tech

289

171

View full text Add to dashboard Cite

Edge computing is a fast growing field of research that covers a spectrum of technologies bringing the cloud computing services closer to the end user. Growing interest in this area yields many edge computing approaches that need to be evaluated and optimized. Experimenting on the real cloud environments is not always feasible due to the operational cost and the scalability. Despite increasing research activity, this field lacks a simulation tool that supports the modeling of both computational and networking resources to handle the edge computing scenarios. Existing network simulators can model the network behavior at different levels of granularity. The cloud computing simulators support the modeling and simulation of the computational infrastructures and services efficiently. Starting from the available simulators, a significant programming effort is required to obtain a simulation tool meeting the actual needs. On the other hand, designing a new edge computing tool has many challenges such as the scalability, extensibility, and modeling the mobility, network, and virtualized resources. To decrease the barriers, a new simulator tool called EdgeCloudSim streamlined for the edge computing scenarios is proposed in this work. EdgeCloudSim builds upon CloudSim to address the specific demands of edge computing research and support the necessary functionalities. To demonstrate the capabilities of EdgeCloudSim, an experiment setup based on different edge architectures is simulated. In addition, the effect of the edge server capacity and the mobility on the overall system performance are investigated.

show abstract

Modelling and Simulation Challenges in Internet of Things

Cited by 88 publications

References 16 publications

Addressing the Challenges in Federating Edge Resources

Addressing the Challenges in Federating Edge Resources

Implementation of Link Fingerprint Verification and Data Provenance using RSSI

EdgeCloudSim: An environment for performance evaluation of edge computing systems

Contact Info

Product

Resources

About