A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Primeau, Nicolas; Falcón, Rafael; Abielmona, Rami; Petriu, Emil M.

doi:10.1109/comst.2018.2850220

Cited by 63 publications

(40 citation statements)

References 173 publications

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“…CI includes evolutionary algorithms (EAs), swarm intelligence algorithms (SIAs), fuzzy system (FS), learning based system (LBS), and hybrid system (HS). These CI techniques have the capability to process imprecise information and search for approximate yet good enough solutions while ensuring robustness and computational tractability [12].…”

Section: Dementioning

confidence: 99%

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: Dementioning

confidence: 99%

“…Also, EAs and SIAs are often combined as both have similar behaviors. Similarly, fuzzy adaptive theory [147] is sometimes hybridized with FL and neural networks (NN) [12]. Recently, some hybrid algorithms have been applied to address critical issues in CC [148]- [151].…”

Section: E Hsmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…Zhong et al [19] and Xie et al [33] adopted the ant colony optimization (ACO) to solve the lifetime maximization and data aggregation in wireless sensor network, respectively. Recently, based on their ideas, Kumar et al [34] also proposed an ACO based mobile sink framework to solve the sink deployment and path determination with a non-uniform data sensing rate. Since the evolutionary algorithm shows a great potential in prolonging the network lifetime, its corresponding researches on wireless sensor networks are concluded in some surveys [35], [36].…”

Section: Related Workmentioning

confidence: 99%

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Huang¹

2020

Preprint

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Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

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“…The OBC method cannot assure the rate at which the correct messages are ordered up to the maximum. To address that issue, an Ordering By Double Confirmation (OBDC) technique was developed to assure the rate of ordering of the right messages up to the maximum [23]. Conversely, the OBDC needs more time than the OBC method.…”

Section: Preserving Temporal Relationships Of the Eventsmentioning

confidence: 99%

Wireless Sensor and Actuator Networks (WSANs): Insights and Scope of Research

Janardhan*¹,

Nandhini²

2019

IJITEE

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Recent years are witnessing the growth of different kinds of networks including Wireless Sensor and Actuator Networks (WSANs). WSANs are self-configured and ad-hoc natured networks without any permanent infrastructure that consists of numerous sensor nodes and few actuator nodes that can collaboratively monitor the characteristics of physical and environmental conditions like vibration, sound, temperature, pressure, motion or pollutants, and determine an appropriate action to take depending upon the sensed data, thereby changing the state of the field of interest by performing the suitable action in it. We can say it as an advancement of Wireless Sensor Networks (WSNs) with the inclusion of actuating component. Along with the existing research challenges of WSNs, the WSANs have many additional research challenges to be addressed. This paper gives an insight into the scope of ongoing and future research in the aspect of preserving temporal relationship among the events, restoring the connectivity in case of node failures, need for QoS parameters, along with several operational details of WSANs.

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A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Cited by 63 publications

References 173 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

Wireless Sensor and Actuator Networks (WSANs): Insights and Scope of Research

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