A Pareto optimization-based approach to clustering and routing in Wireless Sensor Networks

Clustering and routing in WSNs are two well-known optimization problems that are classified as Non-deterministic Polynomial (NP)-hard. In this paper, we propose a single multi-objective problem formulation tackling these two problems simultaneously with the aim of finding the optimal network configuration. The proposed formulation takes into consideration the number of Cluster Heads (CHs), the number of clustered nodes, the link quality between the Cluster Members (CMs) and CHs and the link quality of the constructed routing tree. To select the best multi-objective optimization method, the formulated problem is solved by two state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs), and their performance is compared using two well-known quality indicators: the hypervolume indicator and the Epsilon indicator. Based on the proposed problem formulation and the best multi-objective optimization method, we also propose an energy efficient, reliable and scalable routing protocol. The proposed protocol is developed and tested under a realistic communication model and a realistic energy consumption model that is based on the characteristics of the Chipcon CC2420 radio transceiver data sheet. Simulation results show that the proposed protocol outperforms the other competent protocols in terms of the average consumed energy per node, number of clustered nodes, the throughput at the BS and execution time

A Full Area Coverage Guaranteed, Energy Efficient Network Configuration Strategy for 3D Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), providing full area coverage while maintaining connectivity between the sensors is considered an important issue. Coverage-aware sleep scheduling is an efficient way to optimize the coverage of WSNs while maximizing the energy consumption. On the other hand, clustering can provide an efficient way to achieve high connectivity in WSNs. Despite the close relationship between the coverage problem and the clustering problem, they have been formulated, discussed and evaluated separately. Furthermore, most existing WSN strategies are designed to be applied on Two-Dimensional (2D) fields under an ideal energy consumption model that relies on calculating the Euclidean distance between any pair of sensors. In reality, sensors are mostly deployed in a Three-Dimensional (3D) field in many applications and they do exhibit a discrete energy consumption model that depends on the sensors' status rather than the distance between them. In this paper, we propose a Pareto-based network configuration strategy for 3D WSN s. In the proposed protocol, deciding the status of each sensor in a 3D WSN s is formulated as a single multi-objective minimization problem. The proposed formulation considers the following combined properties: energy efficiency, data delivery reliability, scalability, and full area coverage. The performance of the proposed protocol is tested in 3D WSNs and under a realistic energy consumption model which is based on the characteristics of the Chip con CC2420 radio transceiver data sheet

Application of Machine-Learning Algorithms to the Stratigraphic Correlation of Archean Shale Units Based on Lithogeochemistry

Data-driven methods have increasingly been applied to solve geoscientific problems. Incorporation of data-driven methods with hypothesis testing can be effective to address some long-standing debates and reduce interpretation uncertainty by leveraging larger volumes of data and more objective data analytics, which leads to increased repro-ducibility. In this study, lithogeochemical data from regionally persistent Archean shale units were aggregated from literature, with special reference to the Kaapvaal Craton of South Africa�namely, shales from the Barberton, Witwatersrand, Pongola, and Transvaal Supergroups�and the Belingwe and Buhwa Greenstone Belts of the Zimbabwe Craton. We examine the feasibility of using machine-learning algorithms to produce a geochemical classification and demonstrate that machine learning is capable of accurately correlating stratigraphy at the formation, group, and supergroup levels. We demonstrate the ability to extract highly useful scientific findings through a data-driven approach, such as geological implications for the uniqueness of the sediment compositions of the Central Rand and West Rand Groups. We further demonstrate that when lithogeochemistry and machine-learning algorithms are used, only about 50 samples per geological unit are necessary to reach accuracy levels of around 80�90 for our shale samples. Consequently, for many traditional tasks, such as rock identification and mapping, some expensive analyses and manual labor can be replaced by an abundance of cheaper data and machine learning. This approach could transform large-scale geological surveys by enabling more detailed mapping than currently possible, by vastly increasing the coverage rate and total coverage. In addition, the aggregation of historical data facilitates data reuse and open science. These results justify the need to bridge data-and hypothesis-driven techniques for the stratigraphic correlation and prediction of rock units, which can improve the accuracy of the inferred stratigraphic correlation and basin setting. © 2022 The University of Chicago. All rights reserved

A coverage and obstacle-aware clustering protocol for wireless sensor networks in 3D terrain

In Wireless Sensor Networks (WSNs), clustering techniques are often used to optimize energy consumption and increase Packet Delivery Rate (PDR). To date, most of the proposed clustering protocols assume that there is a Line of Sight (LOS) between all the sensors. In fact, most of the available WSN simulators assume the use of optimistic path loss models that neglect the effect of obstacles on the PDR. However, in real situations such as in 3D terrains, obstacles can interfere this LOS. Moreover, while clustering, it is also important to maintain the coverage of a given Region of Interest (ROI). Therefore, finding an integrated solution for both clustering and coverage problems in an irregular 3D field becomes a pressing concern. In this paper, we first adopt an obstacle-aware path loss model to reflect the effect of obstacles on the communication between any pair of sensors. To that end, the Castalia simulator is adapted to use this proposed path loss model. Then, we introduce a Coverage and Obstacle-Aware Cluster Head Selection (COACHS) protocol to solve the cluster heads selection problem while maintaining a good coverage of a WSN deployed in an irregular 3D field. Simulation results demonstrate that the effect of obstacles on the PDR cannot be neglected. Moreover, comparative evaluation results show that COACHS outperforms other competent protocols in terms of PDR while simultaneously maintaining an acceptable energy consumption and a good coverage of the ROI

A coverage and obstacle-aware clustering protocol for wireless sensor networks in 3D terrain

In Wireless Sensor Networks (WSNs), clustering techniques are often used to optimize energy consumption and increase Packet Delivery Rate (PDR). To date, most of the proposed clustering protocols assume that there is a Line of Sight (LOS) between all the sensors. In fact, most of the available WSN simulators assume the use of optimistic path loss models that neglect the effect of obstacles on the PDR. However, in real situations such as in 3D terrains, obstacles can interfere this LOS. Moreover, while clustering, it is also important to maintain the coverage of a given Region of Interest (ROI). Therefore, finding an integrated solution for both clustering and coverage problems in an irregular 3D field becomes a pressing concern. In this paper, we first adopt an obstacle-aware path loss model to reflect the effect of obstacles on the communication between any pair of sensors. To that end, the Castalia simulator is adapted to use this proposed path loss model. Then, we introduce a Coverage and Obstacle-Aware Cluster Head Selection (COACHS) protocol to solve the cluster heads selection problem while maintaining a good coverage of a WSN deployed in an irregular 3D field. Simulation results demonstrate that the effect of obstacles on the PDR cannot be neglected. Moreover, comparative evaluation results show that COACHS outperforms other competent protocols in terms of PDR while simultaneously maintaining an acceptable energy consumption and a good coverage of the ROI

MCSA: A multi-criteria shuffling algorithm for the MapReduce framework

During the shuffle stage of the MapReduce framework, a large volume of data may be relocated to the same destination at the same time. This, in turn, may lead to the network hotspot problem. On the other hand, it is always more effective to achieve better data locality by moving the computation closer to the data than the other way around. However, doing this may result in the partitioning skew problem, which is characterized by the unbalanced computational loads between the destinations. Consequently, shuffling algorithms should consider all the following criteria: data locality, partitioning skew, and network hotspot. In order to do so, we introduce MCSA, a Multi-Criteria shuffling algorithm for the MapReduce scheduling stage that rests on three cost functions to accurately reflect the trade-offs between these different criteria. Extensive simulations were conducted and their results show that the MCSA-based scheduler consistently outperforms other schedulers based on these criteria. Furthermore, the MCSA-based scheduler can be easily adjusted to the meet the distinct needs of different customers

IoT networks 3D deployment using hybrid many-objective optimization algorithms

International audienceWhen resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or nodes) in the Internet of Things collection networks (formerly known as wireless sensor nodes) is investigated. The aim is to determine the ideal locations of the objects to be added to enhance an initial deployment while satisfying antagonist objectives and constraints. In this regard, a first proposed contribution aim to introduce an hybrid model that includes many-objective optimization algorithms relying on decomposition (MOEA/D, MOEA/DD) and reference points (Two_Arch2, NSGA-III) while using two strategies for introducing the preferences (PI-EMO-PC) and the dimensionality reduction (MVU-PCA). This hybridization aims to combine the algorithms advantages for resolving the many-objective issues. The second contribution concerns prototyping and deploying real connected objects which allows assessing the performance of the proposed hybrid scheme on a real world environment. The obtained experimental and numerical results show the efficiency of the suggested hybridization scheme against the original algorithms