Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks

Titouna, Chafiq; Gueroui, Mourad; Aliouat, Makhlouf; Ari, Ado Adamou Abba; Adouane, Amine

doi:10.17762/ijcnis.v9i2.1848

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…This gives rise to the IoT Road Network. An IoT road network Ĝ = ( V ; Ê) is a weighted graph that characterizes the road network's topological structure where V represents the number of traffic sensors on a road junction and Ê represents the geographical proximity between the sensors [43][44][45]. Data collection is performed by the sensors positioned at each road junction.…”

Section: System Modelmentioning

confidence: 99%

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Njoya,

Wachira,

Abba Ari

et al. 2023

JERA

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Congestion has become a big issue in major cities throughout the world. Numerous transportation activities have been affected, and travel times have increased as a result of many travelers spending lengthy hours on the road. To overcome these challenges, the Intelligent Transportation System (ITS), which provides efficient traffic service and management, has sparked widespread attention. Collection and analysis of traffic data has been made possible by the algorithms implemented by the ITS. Huge volumes of data are produced by the vast wide range of sensors used in the Internet of Things (IoT), enabling the collection of a variety of traffic information. Development of short-term traffic speed prediction, has been made possible using deep learning models such as Long Short Term Memory (LSTM) and Bidirectional LSTM. Numerous variables, including the weather, the state of the roads, and traffic congestion, can have long-term dependencies and influence traffic speed. The bidirectional architecture of Bi-LSTMs enables them to handle long-term dependencies in sequences and efficiently capture both past and future context in a sequence, which is crucial for producing accurate forecasts of traffic speed. In this paper, the upstream and downstream flow of traffic speed on various pathways has been investigated using a traffic path planning algorithm based on Bi-LSTM models. The algorithm considers the factors affecting the flow of traffic at different seasons and time of the day and tries to predict the average speed associated to that path several timeslots ahead. The experimental results demonstrated that the Bi-LSTM model has the benefit of predicting speed for various timeslots while retaining a high level of accuracy.

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Section: System Modelmentioning

confidence: 99%

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Njoya,

Wachira,

Abba Ari

et al. 2023

JERA

Self Cite

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“…Secondly, to adapt the model in the context of the optimal deployment of relay sensors in the created LS-WSN clusters. Although the physarum polycephalum has been used in the modelling of several works in recent years.For that purpose, let consider an initial connected network, represented by a graph as proposed in [40,42,43]. The nodes of the graph are the sources or crossing points that interconnect the links while the edges are the links through which the nutrients will pass.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Optimizing Relay Sensors in Large-Scale Wireless Sensor Networks: A Biologically Inspired Approach

Ari

Djedouboum

Njoya

et al. 2023

JERA

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In recent years, tremendous advances in communication technologies coupled with the advent of the Internet of Things (IoT) have led to the emergence of the Big Data phenomenon. Big Data is one of the big IT challenges of the current decade. The amount of data produced is constantly increasing and makes it more and more difficult to process. Managing these masses of data requires the use of new data management systems with efficient access methods. Considered as one of the main sources of Big Data, wireless sensors used in networks offer a credible solution to the problem of Big Data management, especially its collection. Several solutions for Big Data collection based on large-scale wireless sensor networks (LS-WSN) are proposed, taking into account the nature of the applications. The hierarchical architecture is the one used for the deployment of these applications. In such an architecture, relay sensors play an important role in finding the balance of the network and maximizing its lifetime. In most LS-WSN applications, once deployed, the LS-WSN does not provide a mechanism to evaluate and improve the positions of the initially deployed relay sensors. This paper proposes, based on the growth model of physarum polycephalum and its ability to prune unnecessary links and retain only those deemed useful for food routing, a mechanism for evaluating and optimizing relay sensors in LS-WSNs. Simulation results indicate that the proposed approach significantly improves the network lifetime compared to the initial deployment and that can be a useful approach for LS-WSNs dedicated to Big Data collection. The effectiveness of the proposed technique is demonstrated by experimental results in terms of connectivity and network lifetime.

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A collaborative WSN‐IoT‐Animal for large‐scale data collection

Abdoul Aziz,

Abba Ari,

Ndam Njoya

et al. 2024

IET Smart Cities

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In recent years, large‐scale data collection systems have developed rapidly in many fields, including agriculture, transport and many others. The internet of things (IoT), whose main platform is wireless sensor networks (WSNs), is behind this development. Comprising thousands of sensors of different kinds, their main purpose is to collect and transmit data. Several data collection techniques have been proposed, including static, mobile and hybrid approaches. The challenges faced by these techniques are considerable, and include energy conservation, planning and trajectory optimisation during data collection, most importantly, the challenges related to the communication between the static sensors generally distributed in a more or less large geographical space and the mobile data collection system (UAV, vehicle, robot etc.). Not to mention the cost, which remains enormous for the agricultural sectors. A hybrid WSN‐IoT‐Animal that is self‐configured to improve data acquisition over large agricultural areas is presented. The main objective and originality of the heterogeneous semi‐modern scheme proposed here oscillating between traditional agriculture and precision agriculture is the use of animals as data collection tools. The main contribution here is the design of a simple and efficient model of data collection that is easily accessible by farmers by adapting the available resources. This model describes and adopts a sensor deployment method based on the notion of the hypergraph, which provides adequate coverage and ensures communication between the mobile sink and a subset of peripheral sensors chosen in alternation. Simulation results verify the effectiveness of the proposed protocol in terms of network lifetime compared to other works. In addition, the amount of data received by the mobile sink demonstrates the importance of this approach in terms of connectivity for large‐scale data collection.

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Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks

Cited by 7 publications

References 22 publications

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Optimizing Relay Sensors in Large-Scale Wireless Sensor Networks: A Biologically Inspired Approach

A collaborative WSN‐IoT‐Animal for large‐scale data collection

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