Load Balancing Optimization for RPL Based Emergency Response Using Q-Learning

Sebastian, A.; Sivagurunathan, S.

doi:10.20319/mijst.2018.42.7492

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…In a complete fuzzy network, the arc is the geodesic among its end nodes. Therefore, a complete fuzzy graph's node set is its only geodesic cover [12]. The fuzzy end nodes of a fuzzy tree form a distinct geodesic basis.…”

Section: Geodetic Fuzzy Subgraphmentioning

confidence: 99%

Novel Geodetic Fuzzy Subgraph-Based Ranking for Congestion Control in RPL-IoT Network

2024

Teh. vjesn.

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Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.

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Section: Geodetic Fuzzy Subgraphmentioning

confidence: 99%

Novel Geodetic Fuzzy Subgraph-Based Ranking for Congestion Control in RPL-IoT Network

2024

Teh. vjesn.

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“…As a result, one of the main goals of RPL improvement is to seek a good balance. In addition, load imbalance may result in network unreliability [28,29].…”

Section: Energy Consumptionmentioning

confidence: 99%

QFS-RPL: RPL Based Energy and Mobility Aware Multi Path Routing Protocol for the Internet of Mobile Things Data Transfer Infrastructures

Alilou

Sangar

Majidzadeh

et al. 2023

Preprint

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The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.

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