A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy, Walid; Khedr, Ahmed M.; Salim, Ahmed; Ali, Amal Ibrahim Al; El‐Sawy, A. A.

doi:10.7717/peerj-cs.1089

Cited by 17 publications

(4 citation statements)

References 139 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig.9Scientific fields that are related or close to Artificial Intelligence. This figure is the fusion of those which are proposed by Osamy et al[123], Cao et al[124], Kaveh and Mesgari[125] …”

supporting

confidence: 55%

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Luzolo

Galland

Elrawashdeh

et al. 2023

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologiesthat have long been applied to the development of smart systems. These systemscover various areas, such as smart cities, energy management, autonomous cars,etc. Intelligence, autonomy, and real-time monitoring are the fundamental ele-ments that characterize these application areas. The convergence of multi-agentsystems (MAS), IoT, and AI has opened up new possibilities for creating smarterbuildings and cities. This integration leverages the power of MAS to enable intelli-gent communication and collaboration among various entities, while IoT providesa vast network of interconnected sensors and devices that collect and transmitreal-time data. On the other hand, AI algorithms process and analyze this data toderive valuable insights and make informed decisions. Multi-agent systems tech-nology is one of the most used for the implementation of such intelligent systems.This article aims to focus on multi-agent systems, although it also covers otherrelated topics by carrying out a Systematic Literature Review (SLR) on relevantresearch works in the previous ten years. Eleven research questions are consideredat the beginning of the review, including typical research topics and applicationdomains. From the SLR results the research directions are: (i) Development ofa methodology that shows how to integrate the different applications indepen-dent of the scenarios that are deployed in. Additionally, elaboration of the toolsused in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology andInternet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

supporting

confidence: 55%

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Luzolo

Galland

Elrawashdeh

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…9 Scientific fields that are related or close to Artificial Intelligence. This figure is the fusion of those which are proposed by Osamy et al [115], Cao et al [116], Kaveh and Mesgari [117] have fewer articles, but they are more impactful and influential in the field. Furthermore, the research topics of the articles and the quality of the studies should also be considered when evaluating the contributions of each family of models to the field.…”

Section: Application Domainsmentioning

confidence: 72%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

View full text Add to dashboard Cite

A Multiagent System (MAS) refers to a network of agents collaborating to achieve same objective. This system comprises numerous individual programs or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems. The Multiagent System (MAS) is not a substitute or rival for Artificial Intelligence (AI) methods. Instead, AI techniques can be integrated within the agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous. The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologies that have long been applied to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Intelligence, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of artificial intelligence (AI) and IoT, known as AIOT, Allows those electronic devices to make intelligent, autonomous and more automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze this data to derive valuable insights and make informed decisions. The authors devoted efforts on the critical analysis of AIOT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, the authors contribution is in the formulation of innovative research directions, outlining a clear guide for researchers and professionals in the expansion of knowledge in AIOT integration. Research results in a significant contribution to the continuous advance of the area, enriching the understanding of challenges and boosting the development of solutions and strategies in this technological convergence. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results the research directions are: (i) Development of a methodology that shows how to integrate the different applications independent of the scenarios that are deployed in. Additionally, elaboration of the tools used in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology and Internet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

“…In the network architecture of the Internet of Things (IoT), some new distributed models ( Bouarourou, Boulaalam & Nfaoui, 2021 ; Labib et al, 2021 ) and multi-hop clustering scheme ( Muthukkumar et al, 2022 ) for heterogeneous wireless sensor networks (WSNs) to efficiently deal with heterogeneous sensors in a dynamic IoT environment, it greatly increases the service life and communication efficiency of node networkst. In the security architecture of the IoT, ( Alansari et al, 2023 ) presents a novel lightweight system for anomaly detection of grayhole, blackhole, and selective forwarding attacks, and a survey ( Osamy et al, 2022 ) reviews and analyzes the research trends related to the utilized artificial intelligence (AI) methods for IoT and the potential enhancement of IoT. In terms of key management, due to the complexity of the structure and high mobility of the IoT structure, the key management strategy based on static implementation cannot be directly applied to the IoT environment.…”

Section: Introductionmentioning

confidence: 99%

Dynamic coefficient symmetric polynomial-based secure key management scheme for Internet of Things (IoT) networks

Liu,

Luo

2023

PeerJ Computer Science

View full text Add to dashboard Cite

Background With the extensive application and continuous expansion of the Internet of Things (IoT), the access of a large number of resource-limited nodes makes the IoT application face a variety of security vulnerabilities and efficiency limitations, and the operating efficiency and security of IoT are greatly challenged. Key management is the core element of network security and one of the most challenging security problems faced by wireless sensor networks. A suitable key management scheme can effectively defend against network security threats. However, among the key management schemes that have been proposed so far, most of them do not take into account the efficiency in terms of connectivity rate and resource overhead, and some of them even have security risks. Methods In this article, based on the symmetric polynomial algorithm, a dynamic coefficient symmetric polynomial key management scheme is proposed to better solve the IoT security problem. In this scheme, the nodes’ IDs are mapped into the elements of the shared matrix M by the identity mapping algorithm, and these elements are used to construct polynomials P(x,y) to generate pairwise keys. The communicating nodes have their own coefficients of P(x,y) and thus have higher connectivity. Results The overall performance evaluation shows that the scheme significantly improves the resilience against node capture and effectively reduces the communication and storage overheads compared to the previous schemes. Moreover, the scheme overcomes the λ-security of symmetric polynomial key management scheme, and is able to provide a large pool of polynomials for wireless sensor networks, facilitating large-scale application of nodes.

show abstract

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Cited by 17 publications

References 139 publications

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Dynamic coefficient symmetric polynomial-based secure key management scheme for Internet of Things (IoT) networks

Contact Info

Product

Resources

About