Implementation and performance evaluation of two fuzzy-based systems for selection of IoT devices in opportunistic networks

Cuka, Miralda; Elmazi, Donald; Bylykbashi, Kevin; Spaho, Evjola; Ikeda, Makoto; Barolli, Leonard

doi:10.1007/s12652-017-0676-0

Cited by 26 publications

(11 citation statements)

References 20 publications

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“…As shown in Figure , we use triangular and trapezoidal membership functions for FLC because they are suitable for real‐time operation . The x 0 in f ( x ) is the center of triangular function, x 0 ( x 1 ) in g ( x ) is the left (right) edge of trapezoidal function, and a 0 ( a 1 ) is the left (right) width of the triangular or trapezoidal function …”

Section: Proposed and Implemented Systemsmentioning

confidence: 99%

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Effect of node centrality for IoT device selection in opportunistic networks: A comparison study

Cuka

Elmazi

Bylykbashi

et al. 2018

Concurrency and Computation

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Summary Opportunistic networks (Oppnets) are a subclass of delay‐tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end‐to‐end link between communication nodes. The Internet of Things (IoT) presents the notion of large networks of connected devices, sharing data about their environments and creating a diverse ecosystem of sensors, actuators, and computing nodes. IoT networks consist of heterogeneous collections of resource constrained nodes that closely interact with their environment. There are different issues for these networks. One of them is the selection of IoT devices to carry out a task in Oppnets. In this work, we implement and compare two fuzzy‐based systems (FBSs) for selecting the IoT device that completes the task in Oppnets. We call these systems FBSIDS1 and FBSIDS2. For FBSIDS1, we use three input parameters: IoT device storage, IoT device waiting time (IDWT), and IoT device remaining energy. The output parameter is IoT device selection decision. For FBSIDS2, we consider four input parameters by adding IoT device node centrality (IDNC) as a new parameter. Comparing the complexity of FBSIDS1 and FBSIDS2, FBSIDS2 is more complex than FBSIDS1. However, FBSIDS2 is more flexible and makes a better selection of IoT devices than FBSIDS1. The simulation results show that IoT device selection is increased up to 33% and 71% by increasing IDNC and IDWT, respectively, for FBSIDS2, and up to 46% and 61% by increasing IDWT and IDNC, respectively, for FBSIDS1.

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Section: Proposed and Implemented Systemsmentioning

confidence: 99%

“…28 The x 0 in f(x) is the center of triangular function, x 0 (x 1 ) in g(x) is the left (right) edge of trapezoidal function, and a 0 (a 1 ) is the left (right) width of the triangular or trapezoidal function. 29…”

Section: Implemented Systemsmentioning

confidence: 99%

Effect of node centrality for IoT device selection in opportunistic networks: A comparison study

Cuka

Elmazi

Bylykbashi

et al. 2018

Concurrency and Computation

Self Cite

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“…In fact, the emergence of this trend is mainly due to the evolution of wireless technology combined with Internet enhancement and the large demand for high-quality sensing devices enabling smart living applications. From a technological point of view, IoT can be seen as a consequence of machine-to-machine (M2M) architecture and its connectivity [1]. This key concept refers to the interconnection of machines via a network without any human intervention.…”

Section: Introductionmentioning

confidence: 99%

Smart offloading technique for CP-ABE encryption schemes in constrained devices

2020

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Nowadays, we are attending to the wide proliferation of IoT devices in various computing environments. Actually, this trend is the natural way to extend the M2M communication. However, these devices are facing serious resource constraints. To mitigate this problem, the huge amount of the exchanged data is collected from these devices for being stored and processed by the Cloud. Furthermore, since the Cloud is honest but curious, it may reveal personal information about users' habits owning these devices and can even lead to user profiling. Consequently, security mechanisms should be deployed at different levels to preserve data privacy so that only authorized users can gain access to the smallest piece of data according to the collection purpose. By assuming that the Cloud-is-honest-but-curious, we can not provide full or similar access to different untrusted Cloud services. Therefore, we should define the relevant privacy level implementing the data access control for each Cloud service according to some criteria. In this context, CP-ABE is a promising solution addressing this problem. This novel attribute-based public key encryption system provides a flexible fine-grained access control to data for any data requestor. However, performing all the related cryptographic operations on such devices is practically infeasible because of the resource constraints. For alleviating all the computation burden on these resource-limited devices, several schemes have been proposed. In this work, we propose a smart offloading technique that switches dynamically from full encryption to partial encryption according to a wise decision strategy considering the available resources and some crucial parameters like the number of attributes and the size of the data being encrypted. The relevant decision is based on a machine learning algorithm. To the best of our knowledge, this is the first paper proposing an adaptive CP-ABE scheme for constrained device optimizing the overall available resources.

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“…This network enables connected objects to access, interpret, exchange and monitor the information of each other [1]. In IoT, the information are shared to make real time decisions, coordinate intelligence and deliver the ubiquitous services to the users by enabling the collaboration of physical objects without human interference [2]. However, major development in the field is facing potential security threats associated with each layer of its framework.…”

Section: Introductionmentioning

confidence: 99%

Authentication and Access control Mechanism using Elliptical Curve Cryptography with Message Authentication Code

Fathima

2019

IJEAT

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Nowadays, Internet of Things (IoT) provided smart and inter-connected devices, which are ranging from tiny sensors to complex nodes of Fog and Cloud, communication protocols and several networking technologies. IoT plays a significant role in various application areas such as e-commerce, healthcare, research, governance and so on, and these application easily accessible for end users. Although the IoT has many benefits, there are some security challenges associated with the vulnerabilities presents in this area. This research work introduced a new authentication and access control model using Message Authentication code (MAC) with Elliptical Curve Cryptography (ECC) to address the security issues in Message Queuing Telemetry Transport (MQTT). In cryptography, a message can be authenticated by MAC (i.e. to verify that whether the message came from authenticate sender and the content of the message is changed or not). If any changes occur to the message content, the verifier can able to detect those changes using MAC values, which preserves the authenticity as well as data integrity. The validated results stated that the proposed ECC-MAC-MQTT shows better performance when compared with existing techniques in terms of accuracy, precision, recall and f-measure.

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Implementation and performance evaluation of two fuzzy-based systems for selection of IoT devices in opportunistic networks

Cited by 26 publications

References 20 publications

Effect of node centrality for IoT device selection in opportunistic networks: A comparison study

Effect of node centrality for IoT device selection in opportunistic networks: A comparison study

Smart offloading technique for CP-ABE encryption schemes in constrained devices

Authentication and Access control Mechanism using Elliptical Curve Cryptography with Message Authentication Code

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