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 13 publications

(3 citation statements)

References 139 publications

(166 reference statements)

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“…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

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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).

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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

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“…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

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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).

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“…With the integration of computational capabilities, it has become capable of achieving a huge leap in a variety of applications including home, military, healthcare, entertainment, and many other disciplines [9][10][11]. The Wireless Sensor Network (WSN) is the base technology underpinning the IoT, which has recently sped up advancements in several applications [12][13][14][15][16][17][18]. WSN can be comprehended as a contemporary information technology that integrates a variety of components, such as sensors, wireless communication, distributed data processing, and low-power embedded components, into a single system.…”

Section: Introductionmentioning

confidence: 99%

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Osamy

Khedr

Alwasel

et al. 2023

Sensors

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Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

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

Cited by 13 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

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

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