Special issue on service delivery management in broadband networks

Bellavista, Paolo; Chen, Chi-Ming; Hassanein, Hossam S.

doi:10.1016/j.jnca.2012.05.004

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…A nomadic community, according to this model, is a collection of mobile devices that travel together. The community's movements are controlled by a set of guidelines that imitate the behavior of actual nomadic communities [43]. According to the NCMM, nomadic communities follow a cyclical pattern of movement, with each cycle consisting of a specific sequence of waypoint visits by the community [44].…”

Section: Correlated Mobility Modelsmentioning

confidence: 99%

“…The NCMM is a helpful tool for modeling situations in which a collection of mobile devices moves in unison in a cyclical manner, as in military, disaster relief, and wildlife tracking applications [43]. Researchers can examine how mobile devices behave and how mobility affects network performance in a controlled setting by utilizing the NCMM.…”

Section: Correlated Mobility Modelsmentioning

confidence: 99%

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Zigbee Based Mobile Sensing for Wireless Sensor Networks

Coboi,

Nguyen,

Pham

et al. 2023

Comput. Networks Commun.

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Wireless sensor networks, have drawn a lot of interest because of their adaptability and range of uses in different industries. WSNs face significant challenges when it comes to energy efficiency because sensor nodes are usually battery-powered and have limited resources. Several energy-efficient methods and protocols, such as duty cycling, data aggregation, and topology management, have been put forth to address this problem. Moreover, new potential for mobile wireless sensor networks are presented by the integration of WSNs with mobile and static devices, such as drones, tablets, and smartphones. In this study, we suggest utilizing Zigbee technology to establish a robust and flexible monitoring system for both stationary and mobile sensors. Numerous industries, including healthcare, smart agriculture, asset tracking, energy management, smart home automation, and industrial monitoring and control, have made extensive use of Zigbee. By leveraging Zigbee's capabilities, we hope to improve the protocol's performance while establishing dependable communication links between nodes, analyzing the communication range, and assessing the influence of environmental conditions. In this study, a system model for Zigbee deployment in mobile robots will be presented. It will address the basics of Zigbee, communication difficulties, networking with Zigbee, and simulations or real-world outcomes. We will learn about the strengths and weaknesses of Zigbee-based systems in terms of creating reliable communication links in mobile wireless sensor networks by looking at their architecture and functionality. The results of this study will help us comprehend Zigbee's potential to improve monitoring systems and make better decisions across a range of industries. The study's emphasis on mobile monitoring systems signifies a step forward in addressing the evolving needs of wireless sensor networks in dynamic environments.

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Section: Correlated Mobility Modelsmentioning

confidence: 99%

Section: Correlated Mobility Modelsmentioning

confidence: 99%

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Coboi,

Nguyen,

Pham

et al. 2023

Comput. Networks Commun.

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“…The HO management process includes itself several components, namely HO prediction [5], HO triggering [6], HO decision [7], HO optimization [8] and HO evaluation [9]. Our work is focused on HO forecasting, a key aspect in the HO management process, allowing for more efficient resource (e.g.…”

Section: Introductionmentioning

confidence: 99%

Handover Forecasting in Cellular Networks: A Spatio-temporal Graph Neural Network Approach

Djuikom Foka,

Stanica,

Naboulsi

2023

2023 14th International Conference on Network of the Future (NoF)

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The proliferation of connected devices, as well as the continuous integration of data demanding applications, have brought significant challenges to mobile network operators. Indeed, mobile networks need to accommodate the resulting exponential growth of traffic, while still ensuring a decent Quality of Service (QoS) and Quality of Experience (QoE) to their users. To do so, adequate management solutions are needed. So far, a variety of problems have been tackled, including resource allocation and network planning. Yet, handovers (HO) management has not received much attention, despite its importance for users QoS and QoE. In this work, we study the problem of mobile devices HO forecasting, over time, among base stations. To solve the problem, we introduce a Handover Graph Convolutional Long Short-Term Memory (HGC-LSTM) neural network forecasting approach. Our approach allows capturing spatio-temporal correlations among neighboring pairs of base stations in the forecasting process. The evaluation on a real-world dataset shows that the proposed HGC-LSTM approach outperforms other methods in the state-of-art in terms of accuracy and execution time.

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Special issue on service delivery management in broadband networks

Cited by 2 publications

References 7 publications

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Zigbee Based Mobile Sensing for Wireless Sensor Networks

Handover Forecasting in Cellular Networks: A Spatio-temporal Graph Neural Network Approach

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