A Context-Aware Routing Protocol on Internet of Things Based on Sea Computing Model

Chen, Zhikui; Wang, Haozhe; Liu, Yang; Bu, Fanyu; Wei, Zhe

doi:10.4304/jcp.7.1.96-105

Cited by 23 publications

(16 citation statements)

References 3 publications

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“…These researches are Context-Awareness in Sea Computing Routing (CASCR) Protocol that was introduced by Chen et al in [30], Routing Protocol Based on Energy and Link Quality (REL) that was introduced by Machado et al in [31], and Markov Decision Ant colony routing selection (Markov-A) that was introduced by Cheng et al in [32]. These researches are Context-Awareness in Sea Computing Routing (CASCR) Protocol that was introduced by Chen et al in [30], Routing Protocol Based on Energy and Link Quality (REL) that was introduced by Machado et al in [31], and Markov Decision Ant colony routing selection (Markov-A) that was introduced by Cheng et al in [32].…”

Section: Related Workmentioning

confidence: 99%

“…There are three researches that can be considered as the most closed related works to the IoT routing problem. These researches are Context-Awareness in Sea Computing Routing (CASCR) Protocol that was introduced by Chen et al in [30], Routing Protocol Based on Energy and Link Quality (REL) that was introduced by Machado et al in [31], and Markov Decision Ant colony routing selection (Markov-A) that was introduced by Cheng et al in [32]. CASCR is considered as a kind of analysis of gathered information and controlling technique for the IoT systems.…”

Section: Related Workmentioning

confidence: 99%

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Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization

Said

2016

Int J Communication

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In this paper, a routing algorithm to optimize the selection of the best path for the transmitted data within the Internet of Things (IoT) system is proposed. The algorithm controls the use of ant colony ideas in the IoT system to obtain the best routing benefit. It divides the IoT environment into categorized areas depending on network types. Then, it applies the most suitable ant colony algorithm to the concerned network within each area. Furthermore, the algorithm considers routing problem in intersected areas that may arise in case of IoT system. Finally, Network Simulator 2 is used to evaluate the proposed algorithm performance. Simulation results demonstrate the effectiveness of the proposed routing algorithm in terms of end-to-end delay, packet loss ratio, bandwidth consumption, throughput, overhead of control bits, and energy consumption ratio. that the real IoT system consists of four different networks: WSN, HAP, Mobile Ad Hoc, and RFID. These networks are communicated with each other using the Internet. Each network has its own ACO algorithm. The equations describing different ACO algorithms, which are used in the simulation process, are stated as follows. Equations (1)-(4) represent the general form of the ACO algorithm, while the ACO algorithm for WSN is expressed by Equations (5)-(9). Equations (10)-(17) represent the ACO algorithm for the HAP network. The ACO algorithm equations for mobile ad hoc network are described in Equations (18)-(22). The ACO algorithm equations for RFID network are stated in [37][38][39], and the ACO algorithm equations for the Figure 2. The proposed IoT routing algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization

Said

2016

Int J Communication

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“…The simulation results show that the variance of the residual energy of EEPR is smaller than the variance of the residual energy of AODV. The work in [31] proposed the Context Awareness in Sea Computing Routing Protocol (CASCR). It exploits the idea of sea computing that was first announced at the Chinese Academy of Science High Technology Planning Seminar in 2010.…”

Section: Iot Routing Protocolsmentioning

confidence: 99%

Energy-Efficient Content-Based Routing in Internet of Things

Chelloug¹

2015

JCC

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The convergence of the Internet, sensor networks, and Radio Frequency Identification (RFID) systems has ushered to the concept of Internet of Things (IoT) which is capable of connecting daily things, making them smart through sensing, reasoning, and cooperating with other things. Further, RFID technology enables tracking of an object and assigning it a unique ID. IoT has the potential for a wide range of applications relating to healthcare, environment, transportation, cities… Moreover, the middleware is a basic component in the IoT architecture. It handles heterogeneity issues among IoT devices and provides a common framework for communication. More recently, the interest has focusing on developing publish/subscribe middleware systems for the IoT to allow asynchronous communication between the IoT devices. The scope of our paper is to study routing protocols for publish/subscribe schemes that include content and context-based routing. We propose an Energy-Efficient Content-Based Routing (EECBR) protocol for the IoT that minimizes the energy consumption. The proposed algorithm makes use of a virtual topology that is constructed in a centralized manner and then routes the events from the publishers to the intended interested subscribers in a distributed manner. EECBR has been simulated using Omnet++. The simulation results show that EECBR has a significant performance in term of the energy variance compared to the other schemes.

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“…However, without comprehensive cognitive capability, IoT is just like an awkward stegosaurus, all brawn and no brains. To fulfill its potential and deal with growing Li et al [4] Low -Good ---High Hong et al [5] Low ---Low --Baloch et al [6] Low -Poor -High Better -Zhu et al [7] Low --High -Good -Yang et al [8] Low -Good ----Bhardwaj et al [9] Low -Good High -Good Low Chen et al [10] Low Better -High --High Naser et al [11] Low ---Low --Feng et al [12] ---Low -Better - • used in developing resource constrained devices • useful for embedded system developers challenges, we must take the cognitive capability into consideration and empower IoT with high-level intelligence, i.e., brain-empowered IoT or CIoT. In this paper, we have analyzed some measures like energy consumption, network life time, resource allocation, throughput, time duration, quality of service and delay researched by various authors (Table 1).…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data Perception in Cognitive Internet of Things

Bhajantri¹,

Balugari

2019

JTIT

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A Cognitive Internet of Things (CIoT) is a brand of Internet of Things (IoT) with cognitive and agreeable mechanisms, which are incorporated to advance performance and accomplish insights into real world environments. CIoT can perceive present system’s conditions, analyze the apparent information, make smart choices, and increase the network performance. In this survey paper, we present classiﬁcations of data perception techniques used in CIoT. This paper also compares the data perception works against energy consumption, network life-time, resource allocation, and throughput, as well as quality of data and delay. In addition, simulation tools for IoT and their performance are discussed. Finally, we provide the model of cognitive agent-based data perception in CIoT for future research and development, which ensures the network performance in terms of reliability, energy eﬃcient, accuracy, scalable, fault tolerant, and quality of data.

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A Context-Aware Routing Protocol on Internet of Things Based on Sea Computing Model

Cited by 23 publications

References 3 publications

Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization

Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization

Energy-Efficient Content-Based Routing in Internet of Things

A Survey on Data Perception in Cognitive Internet of Things

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