DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

Estevez, Francisco J.; Glösekötter, Peter; González, Jesús

doi:10.3390/s16070960

Cited by 10 publications

(16 citation statements)

References 24 publications

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“…In these scenarios, several parameters, as in previous works [5], were taken into account. Thus, convergence time, number of messages and consumed battery during set-up time are the parameters used for comparison purposes between the mentioned protocols.…”

Section: Resultsmentioning

confidence: 99%

mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

Estevez

Castillo-Secilla

González

et al. 2017

Sensors

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Smart Cities are called to change the daily life of human beings. This concept permits improving the efficiency of our cities in several areas such as the use of water, energy consumption, waste treatment, and mobility both for people as well as vehicles throughout the city. This represents an interconnected scenario in which thousands of embedded devices need to work in a collaborative way both for sensing and modifying the environment properly. Under this scenario, the majority of devices will use wireless protocols for communicating among them, representing a challenge for optimizing the use of the electromagnetic spectrum. When the density of deployed nodes increases, the competition for using the physical medium becomes harder and, in consequence, traffic collisions will be higher, affecting data-rates in the communication process. This work presents mDARAL, a multi-radio routing algorithm based on the Dynamic and Adaptive Radio Algorithm (DARAL), which has the capability of isolating groups of nodes into sub-networks. The nodes of each sub-network will communicate among them using a dedicated radio frequency, thus isolating the use of the radio channel to a reduced number of nodes. Each sub-network will have a master node with two physical radios, one for communicating with its neighbours and the other for being the contact point among its group and other sub-networks. The communication among sub-networks is done through master nodes in a dedicated radio frequency. The algorithm works to maximize the overall performance of the network through the distribution of the traffic messages into unoccupied frequencies. The obtained results show that mDARAL achieves great improvement in terms of the number of control messages necessary to connect a node to the network, convergence time and energy consumption during the connection phase compared to DARAL.

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Section: Resultsmentioning

confidence: 99%

mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

Estevez

Castillo-Secilla

González

et al. 2017

Sensors

Self Cite

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“…The new information is significantly obtainable to maintain the consistency of the routing table with dynamically changing topology of the wireless sensor network [13] [14]. To extend the lifetime of the nodes close to the sink, the mobile sink is utilized.…”

Section: Joint Routing With Mobile Sinkmentioning

confidence: 99%

Enhancing the Performance of Wireless Sensor Networks thorugh Clustering and Joint Routing with Mobile Sink

2019

ijeat

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In wireless sensor network, randomly deployed nodes are formed as a clusters of varying size for each area depending upon the numbers of users. This paper deals with the cluster based joint routing with mobile sink and with static sink in cognitive based wireless sensor network. The Joint Routing (JR) is designed to overcome the problems, due to data gatherings of the sensor nodes for any application. Channel resources usually may vary among the different routing methods based on the traffic characteristics and application they require, which poses a great challenge to guarantee time delivery services. These problems poses a great challenge for cognitive radio based WSN. The resource allocation technique overcomes the problems like spatial priority, time delay, transmission delay and energy loss and here the channel resources are allocated with the help of TDMA technique. The static sink in networks consumes more energy which results the early die out of the nodes. Hence throughput of the networks declines which badly affect the network life time. To overcome these issues, static sink is replaced by mobile sink, which consumes less energy, before each transmission in a sensor networks. The networks with mobile sink provide us optimal solution and performance as well, while comparing with network with static sink. It is shown that the proposed system achieves 15% of improved throughput, 20% of less packet loss and 35% of less delay when compare with the system having centralized sink.

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“…For small area communications it is possible to use sensor modules with WiFi or Ethernet adopters but, these communication methods are not proper for long distance communications according to their high power consumption. There are some methods to reduce the energy consumed by sensor nodes such as using DARAL [13] -charging sensor batteries over WiFi network -but DARAL restricts the communication range of sensor nodes [8]. Another solution is the Low Power Wide Area Network (LPWAN) protocol which, helps modules to communicate over a long distance using a comparatively small amount of energy.…”

Section: Related Workmentioning

confidence: 99%

Design and implementation of an automated and decentralized pollution monitoring system with blockchains, smart contracts, and LoRaWAN

Niya

Jha

Bocek

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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This work proposes an IoT-and Blockchain-based, distributed system, for automated measuring, storing, and monitoring of water and air quality in environments such as lakes, mountains, urban areas, or factories. Comparable state-of-the-art solutions, require human interaction to access the data or require high power consumption or space requirements, or they are based on centralized architectures. The proposed pollution monitoring system here, on one hand, employs LoRa to address the high power consumption and long-range transmission challenges of IoT protocols. On the other hand, it is designed to be fully decentralized by using the Ethereum Blockchain to store and retrieve the data recorded by IoT sensors. Thus, data integrity is provided without the need for a Trusted Third Party (TTP) and data is collected and captured automatically without any manual operations needed. Observations on the four different types of sensors for measuring Potential Hydrogen (PH), Turbidity, Carbon monoxide (CO), and Carbon dioxide (CO2), revealed a high accuracy with the expected time-lines of measurements, non-falsified experimental values collected and can be used as reliable evidence of presence of pollution.

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DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

Cited by 10 publications

References 24 publications

mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

Enhancing the Performance of Wireless Sensor Networks thorugh Clustering and Joint Routing with Mobile Sink

Design and implementation of an automated and decentralized pollution monitoring system with blockchains, smart contracts, and LoRaWAN

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