Real-Time Energy-Efficient Reliable Traffic Aware Routing for Industrial Wireless Sensor Networks

Elfouly, Fatma H.; Ramadan, Rabie A.

doi:10.1109/access.2020.2980682

Cited by 23 publications

(25 citation statements)

References 36 publications

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“…As for data communication between two nodes, we have adopted the energy consumption model, and the model of lossy links in WSNs is based on [28]. Besides, each node runs the AODV routing protocol [29] to send and forward data packets.…”

Section: B Methodologymentioning

confidence: 99%

Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

et al. 2021

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Wireless Sensor Networks (WSNs) became essential in developing many applications, including smart cities and Internet of Things (IoT) applications. WSN has been used in many critical applications such as healthcare, military, and transportation. Such applications depend mainly on the performance of the deployed sensor nodes. Therefore, the deployment process has to be perfectly arranged. However, the deployment process for a WSN is challenging due to many of the constraints to be taken into consideration. For instance, mobile nodes are already utilized in many applications, and their localization needs to be considered during the deployment process. Besides, heterogeneous nodes are employed in many recent applications due to their efficiency and cost-effectiveness. Moreover, the development areas might have different properties due to their importance. Those parameters increase the deployment complexity and make it hard to reach the best deployment scheme. This work, therefore, seeks to discover the best deployment plan for a WSN, considering these limitations throughout the deployment process. First, the deployment problem is defined as an optimization problem and mathematically formulated using Integer Linear Programming (ILP) to understand the problem better. The main objective function is to maximize the coverage of a given field with a network lifetime constraint. Nodes’ mobility and heterogeneity are added to the deployment constraints. The importance of the monitored field subareas is also introduced in this paper, where some subareas could have more importance than others. The paper utilizes Swarm Intelligence as a heuristic algorithm for the large-scale deployment problem. Simulation experiments show that the proposed algorithm produces efficient deployment schemes with a high coverage rate and minimum energy consumption compared to some recent algorithms. The proposed algorithm shows more than a 30% improvement in coverage and network lifetime.

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Section: B Methodologymentioning

confidence: 99%

Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

et al. 2021

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“…The paper proposes a digital architecture that meets these standards and demonstrates use cases in machine utilization and health monitoring to achieve this goal. Paper [19] proposes a hierarchical routing graph construction (HRGC) to meet the needs of real-time and reliable communication in IWSN. In IWSN, quality of service (QOS) is expressed as the accuracy with which the sensor covers or monitors the target set within its sensing range.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

et al. 2021

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The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

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“…While taking limited energy as well as a real-time requirement into account, congestion-aware routing needs to cover the mentioned demands. El-Fouly et al [26] presented the real-time energy-efficient trafficaware approach (RTERTA) in industrial wireless sensor networks. In RTERTA, congestion can be avoided by utilizing underloaded nodes with a hop count to the sink node that is measured by the buffer occupancy in a node.…”

Section: Congestion-aware Routingmentioning

confidence: 99%

Q-Learning Based Routing Protocol for Congestion Avoidance

Godfrey¹,

Kim²,

Miao³

et al. 2021

Computers, Materials &Amp; Continua

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The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state, Q-value, and reward function to set the desired path toward the destination. A new reward function that consists of a buffer occupancy, link reliability and hop count is considered. Moreover, look ahead algorithm is employed to update the Q-value with values within two hops simultaneously. This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account, accordingly. Finally, the simulation results presented approximately 20% higher packet delivery ratio and 15% shorter end-to-end delay, compared to those with the existing scheme by avoiding congestion adaptively.

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Real-Time Energy-Efficient Reliable Traffic Aware Routing for Industrial Wireless Sensor Networks

Cited by 23 publications

References 36 publications

Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

Q-Learning Based Routing Protocol for Congestion Avoidance

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