Browsing by Author "Thiare, Ousmane"

Now showing 1 - 6 of 6
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    A 2-hop LoRa Approach Based on Smart and Transparent Relay-Device
    (Springer Nature, Switzerland, 2019) Diop, Mamour; Pham, Congduc; Thiare, Ousmane
    LoRa is designed for long-range communication where devices are directly connected to the gateway, which removes typically the need of constructing and maintaining a complex multi-hop network. Nonetheless, even with the advantage of penetration of walls, the range may not sometimes be sufficient. This article describes a 2-hop LoRa approach to reduce both packet losses and transmission cost. To that aim, we introduce a smart, transparent and battery-operated relay-devicethat can be added after a deployment campaign to seamlessly provide an extra hop between the remote devices and the gateway. Field tests were conducted to assess relays’ ability to automatically synchronize to the network without advertising their presence.
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    Enabling privacy and security in Cloud of Things
    (Emerald Publishing Limited, 2019-11-22) Ari, Ado Adamou Abba; Ngangmo, Olga Kengni; Titouna, Chafiq; Thiare, Ousmane; Kolyang; Mohamadou, Alidou; Gueroui, Abdelhak Mourad
    The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.
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    Estimation of Optimal Number of Clusters
    (Blue Eyes Intelligence Engineering & Sciences Publication, 2019-05) Effah, Emmanuel; Thiare, Ousmane
    Clustering of sensor nodes (SNs) is an unsurpassed energy management method in wireless sensor networks (WSNs) that ensures efficient energy balancing and duty-cycling, and improves the lifespan of the network by minimizing intra-cluster communication cost. Thus, since any incidences of misclustering shortens the lifespan of WSN, this paper presents an efficient, unbiased and more stable approach for evaluating the optimality of event-reporting (E-R) clusters in WSNs using the theory symbolic classifiers. Using realistic dataset derived from 1500 randomly deployed SNs, our results showed that the optimal number of clusters that guarantee optimal E-R accuracy and lengthened WSN lifespan by minimizing the intra-cluster communication costs are 240 clusters for classical K-Means method and 390 clusters for Extreme Learning Machine-Auto Encoder (ELM-AE). This method outperformed the classical inertia-based approach by establishing the optimal proxy E-R clusters which ensures higher E-R accuracy and energy efficiency of SNs. The experiment was done using realistic dataset extracted from randomly deployed 1500 SNs, and so our result is credible for the assessment of cluster qualities in other WSNs.
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    HGC: HyperGraph based Clustering scheme for power aware wireless sensor networks
    (Elsevier, 2019-10-25) Gbadouissa, Jocelyn Edinio Zacko; Ari, Ado Adamou Abba; Titouna, Chafiq; Gueroui, Abdelhak Mourad; Thiare, Ousmane
    Due to the energy constraints of sensors owing to the limitation of their built-in batteries, the lifespan of Wireless Sensor Networks (WSNs) are significantly affected. These particular ad-hoc networks have a huge number of applications including surveillance and target tracking. Unfortunately, since sensor nodes are limited in terms of power resources, efficient utilization of these resources is an important goal to design power-aware WSNs. This led researchers to propose numerous methods, such as clustered WSNs, in order to effectively manage the power resources. In this work, we proposed a heuristic clustering based on the hypergraph theory, and called HyperGraph Clustering (HGC) that aims at optimizing the energy of sensor nodes. Theoretical evaluation highlighted that this clustering protocol consumed less energy during the cluster formation phase and the selection of the cluster head. In addition, we evaluated the performance of the proposed HGC and the results showed the effectiveness of our scheme to those we compared in terms of the number of nodes alive, residual energy and the total consumption of the network.
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    Outdoor Localization and Distance Estimation Based on Dynamic RSSI Measurements in LoRa Networks: Application to Cattle Rustling Prevention
    (Research gate, 2019) Dieng, Ousmane; Pham, Congduc; Thiare, Ousmane
    In this paper, we propose a RSSI-based distance estimation scheme for localization of cattle collars communicating with long-range LoRa radios. Cattle localization is designed to prevent theft in livestock. The proposed solution decreases the cost of cattle localization by minimizing the number of collars with GPS and allows accurate localization of collars without GPS. We propose a RSSI-based distance estimation using real time adjustment of RSSI-distance mapping taking advantage of communication between collar nodes and gateway. Log-distance path-loss model is also used as rescue when the map does not provide accurate correspondence. Experimentation results show the validity of the approach with highly accurate localization of non-GPS collars.
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    Resource allocation scheme for 5G C-RAN: a Swarm Intelligence based approach
    (Elsevier, 2019-09-22) Ari, Ado Adamou Abba; Gueroui, Abdelhak; Titouna, Chafiq; Thiare, Ousmane; Aliouat, Zibouda
    The recent fifth generation (5G) system enabled a highly promising evolution of Cloud Radio Access Network (C-RAN). Unlike the conventional Radio Access Network (RAN), the C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH) by allowing BBUs from multiple Base Stations (BSs) to operate into a centralized BBU pool on a remote cloud-based infrastructure and a scalable deployment of light-weight RRHs. In this paper, we propose an efficient resource allocation scheme for 5G C-RAN called Bee-Ant-CRAN. The challenge addressed is to design a logical joint mapping between User Equipment (UE) and RRHs as well as between RRHs and BBUs. This is done adaptively to network load conditions, in a way to reducethe overall network costs while maintaining the user QoS and QoE. The network load has been formulated as a mixed integer nonlinear problem with a number of constraints. Then, the formulated optimization problem is decomposed into two stage resource allocation problem: UE-RRH association and RRH-BBU mapping. Therefore, a modified Artificial Bee Colony is developed as a swarm intelligence based approach to build the UE-RRH mapping (resource allocation). Moreover, an ameliorated Ant Colony Optimization algorithm is proposed to solve the RRH-BBU mapping (clustering) problem. Computational results demonstrate that our proposed Bee-Ant-CRAN scheme reduces the resource wastage and significantly improves the spectral efficiency as well as the throughput.