A dynamic multi-sink routing protocol for static and mobile self-organizing wireless networks: A routing protocol for Internet of Things

Daas, Mohamed Skander; Chikhi, Salim; Bourennane, El-Bay

doi:10.1016/j.adhoc.2021.102495

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…Regrettably, packet transmission is implicitly scheduled via a queuing process. Daas et al [32] designed a distributed multi-hop cluster-based routing protocol where paths are selected using both hop count and link state via Signal-to-Noise-Ratio. This protocol also leverages to help balance the load of both sensor nodes and sinks.…”

Section: Rajput and Kumaravelumentioning

confidence: 99%

“…Tables 2 and 4 are inspired by the specifications of the IEEE 802.15.4 standard [38]. We evaluated the ability of three protocols to efficiently transfer data to the sinks through three metrics, namely, the average end-to-end delay, the packet delivery ratio, and the network lifetime [32]. To do this, we randomly and uniformly deployed sensors and sinks varying their population as described in Table 1, respectively using, a 100 and a 2 steps scale so that the sink-to-sensor ratio is 0.02.…”

Section: Experimental Set-upmentioning

confidence: 99%

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Untitled

2023

IJWMN

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Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data collection process must be conducted while copying with packet collisions, nodes' congestion or data redundancy. These issues always result in energy waste which is detrimental to network efficiency and lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks' duty cycling, data aggregation and transmission scheduling in order to minimise message overhead and packet losses. Simulation results show that this solution can mitigate delay while significantly increasing packet delivery and network lifetime.

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Section: Rajput and Kumaravelumentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

Untitled

2023

IJWMN

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“…Romanov et al [22] proposes the use of a self-routing algorithm, originally designed for use in WSNs (wireless sensor networks), in NoCs (networks-on-chip). Daas et al [23] discusses a multi-sink routing protocol for self-organizing wireless networks. Hamrioui [24] proposes a cross-layer approach for IoT devices to self-organize and self-configure their communications.…”

Section: Resilient Iot Networkmentioning

confidence: 99%

Detecting Application-Level Associations Between IoT Devices Using a Modified Apriori Algorithm

Aceron,

Teves,

Tan

2023

Comput. Networks Commun.

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Internet of Things (IoT) for home systems enables new functionalities and results in significant conveniences. However, their reliance on a stable, continuous Internet connectivity reduces their overall reliability. Losing connectivity to the Internet, for many of these devices, translates to the cessation of even the most basic of functionalities (e.g., being able to turn on the light, even from within the house). A possible solution to this problem is to shift some functionalities done by cloud-based servers to the edge (e.g. the home router), but doing so conveniently would necessitate the ability to dynamically identify pairs of IoT devices (usually sensor-actuator pairs) that communicate (their associations), and the rewriting/rerouting of packets or messages between those devices. The first problem is also known as the association detection problem, and is where this paper makes a contribution. We describe a solution to the association detection problem using a modified Apriori algorithm and a method to create its input from network traffic, and then revise the solution to respond to fluctuating network conditions. The final design accurately discovers sensor-actuator pairs using a simple approach with low computational complexity, and with only the hardware addresses of monitored IoT devices as its starting knowledge.

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“…The distributed structure is simple, the error of a single node has little impact on the entire network, all nodes have equal status, it is not easy to cause bottleneck effects and it has high robustness [10][11][12]. The security data of IoT mainly include static data [13,14] and dynamic data [15,16]. Static data (devicerelated data, vulnerability, security measure data) does not change with time, while dynamic data evolves with time, including traffic data, log data and information data collected by the perception layer.…”

Section: Introductionmentioning

confidence: 99%

In-depth basic data detection device based on Internet of Things technology

Xie,

Zhang,

Cheng

et al. 2023

Applied Mathematics and Nonlinear Sciences

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Due to the limited computing power of the perception layer of the Internet of Things (IoT), the ability to analyse and process the collected complex object information data is insufficient, and it is also difficult to complete the storage of a large amount of collected data. Through convolutional neural network-simple recurrent unit (CNN-SRU) deep learning, we preprocess a large amount of complex data in the perception layer. The data collected by the perception layer are first transmitted to the CNN for simple category screening and analysis, and then they reach the SRU link, which is updated and optimised again, to improve the integrity and accuracy of IoT information collection. The results show that the accuracy of gated recurrent unit (GRU), long–short-term memory (LSTM) and SRU algorithms shows a downward trend under the three error evaluation standards of root mean squared error (RMSE), mean absolute error (MAE) and relative error (RE), from 0.034 to 0.015, 0.028 to 0.012 and 0.024 to 0.013, respectively; in terms of training time. The SRU algorithm is increased by 54.52%; the maximum SRU in terms of data storage is increased to 33.22%; and the maximum SRU reduction in data mining energy consumption is 11.45%. This meets the requirements of IoT applications in big data mining.

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A dynamic multi-sink routing protocol for static and mobile self-organizing wireless networks: A routing protocol for Internet of Things

Cited by 13 publications

References 7 publications

Untitled

Untitled

Detecting Application-Level Associations Between IoT Devices Using a Modified Apriori Algorithm

In-depth basic data detection device based on Internet of Things technology

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