Utilizing Mobile Nodes for Congestion Control in Wireless Sensor Networks

Nicolaou, Antonia; Temene, Natalie; Sergiou, Charalampos; Georgiou, Chryssis; Vassiliou, Vasos

doi:10.1109/pimrc.2019.8904192

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…The MSSM refers to the treatment method in [30] and adopts a dynamic and greedy idea. Its purpose is to keep each MN as busy as possible at the same time and to reduce the waiting time for emergency events in sensitive areas.…”

Section: Multimobile Service Node Scheduling Model (Mssm)mentioning

confidence: 99%

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

Han

Shang²,

Pan

2022

Electronics

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The localization of continuous objects and the scheduling of resources are challenging issues in wireless sensor networks (WSNs). Due to the irregular shape of the continuous target area and the sensor deployment in WSNs, the sensor data are always discrete and sparse, and most network resources are also limited by the node energy. To achieve faster detection and tracking of continuous objects, we propose a convolution-based continuous object localization algorithm (named CCOL). Moreover, we implement the idea of greedy and dynamic programming to design an energy-saving and efficient strategy model (named MSSM) to respond to emergencies caused by multiple continuous targets in most specific WSNs. The simulation experiments demonstrate that CCOL is superior to other localization algorithms in terms of time complexity and execution performance. Furthermore, the feasibility of the multinode scheduling strategy is verified by setting different mobile nodes to respond to the target area in certain green WSNs.

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Section: Multimobile Service Node Scheduling Model (Mssm)mentioning

confidence: 99%

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

Han

Shang²,

Pan

2022

Electronics

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“…The congestion is also one of the fields that deal with MWSNs. Mobile Congestion Control Algorithm (MobileCC) was proposed to utilize the mobile nodes for controlling the congestion of the network 43 . In case of congestion, the sink forwards to the mobile sensors to move and create alternative routes.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive survey of routing protocols for Mobile Wireless Sensor Networks

Rady

El-Rabaie

Shokair

et al. 2021

Int J Communication

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Summary Mobile Wireless Sensor Networks (MWSNs) have emerged as a new evolution trend in Wireless Sensor Networks (WSNs) technology as a result of the growth and popularization of mobile terminal technology. WSNs' feature spectrum is greatly expanded by the mobility of nodes introduced by MWSNs. MWSN is a network with lots of sensors that are initially distributed randomly and collaborate to gather, process, and transmit information around targets to the sink node. MWSN is an overgrowth and emerging technology with significant applications that provide free moving for sensors and adaptable communicating with each other without fixed infrastructure. Because of their small size, they have limited radio capability, storage, and power. Therefore, MWSNs are utilized to improve network lifetime, energy expenditure, and channel capacity than fixed WSNs. Many routing protocols have recently been introduced to perform advances in the field of energy expenditure for MWSN data collection. This paper introduces a survey on hierarchical‐based routing protocols for MWSNs. The survey outlines the classification of the routing techniques in the aspect of mobility. The comparisons between Mobile Sensor Node (MSN) protocols, Mobile Sink (MS) protocols, and protocols for mobile of both sink and sensor node together are introduced individually. Moreover, the commercial software programs for MWSNs are discussed. Finally, the challenges and future trends in MWSNs are presented.

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“…An energy-harvesting WSN constructed by applying the LB scheme has been described in De Cuypere et al [10]. Congestion control in WSNs has attracted interest from many researchers, and various methods have been proposed [11]. This is because a high level of congestion causes loss or delay (long waiting time) of the data to be transmitted, thereby deteriorating the performance of WSNs severely.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Choi

Lim

2020

Sustainability

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This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous.

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Utilizing Mobile Nodes for Congestion Control in Wireless Sensor Networks

Cited by 10 publications

References 10 publications

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

Comprehensive survey of routing protocols for Mobile Wireless Sensor Networks

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

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