Latency and Energy Optimization Using MAC-Aware Routing for WSNs

Hebal, Sara; Louail, Lemia; Harous, Saad

doi:10.4018/ijbdcn.2020010102

Cited by 4 publications

(10 citation statements)

References 8 publications

(2 reference statements)

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“…Aoun et al [92] addressed the problem of clustering in WSNs, subject to upper bounds on the maximum latency, the energy consumed by intermediate nodes, and clusters size. Hebal et al [93] proposed latency and energy medium access control aware routing for WSNs using information of the time-division multiple-access and exploiting the information of the energy consumed by each node in order to optimize the latency of communications and the energy consumption when relaying information to the sink in a WSN. Essalhi et al [94] presented an approach based on a Fog-IoT architecture with the objective of ensuring smart energy management during communication and processing of offloaded tasks in IoT applications while respecting the latency constraint.…”

Section: H Latency Constraint Algorithmsmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multisink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multisink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

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Section: H Latency Constraint Algorithmsmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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“…There are very few protocols that use the TDMA schedule and the routing tree to reduce the energy consumption consumed and latency. To the best of our knowledge, there are only three classic approaches MAC-aware [44][45][46] (for more details, see Section 3) in the literature that use the random TDMA schedule information to optimize only energy consumption and latency.…”

Section: Motivationmentioning

confidence: 99%

“…ORW 44 protocol uses duty cycle principle. Protocols MAR 45 and LEMAR 46 use, respectively, a two-hop (requires several exchanged messages) and one-hop neighborhood knowledge to determine the sender node next hop. Moreover, MAR exploits the number of slots and geographic distance.…”

Section: Motivationmentioning

confidence: 99%

“…The related contributions [44][45][46] have limitations in the determination of best paths, which node should be the first sender (random character in MAC layer does not allow to select carefully the node that starts first the transmission) and its best neighbor chosen by a classic approach that uses in best cases one-hop neighborhood knowledge and neglects other necessary metrics. Moreover, for experimental results, they only consider latency and energy consumption to measure their performance.…”

Section: Motivationmentioning

confidence: 99%

“…In this work, we are interested in the monitoring-oriented approach and the multi-hop communication. The main purpose is to propose a new cross-layer routing protocol ACOMAR taking advantages of previous related works [44][45][46] while overcoming their disadvantages. This protocol contains two phases: During the first phase, it builds a distancebased TDMA instead of the random one used in the previous protocols.…”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

ACOMAR: ACO‐based MAC‐aware routing protocol

Mechta

Mahgoun

Harous

2021

Int J Communication

Self Cite

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In this work, we present a new nature-inspired cross-layer routing protocol named ACO-based MAC-aware routing protocol (ACOMAR). An enhancement of time division multiple access (TDMA) algorithm is proposed where slots allocation algorithm considers the geographical distance between nodes and the sink. The nodes that are far away from the sink have better chance to be scheduled first. This strategy will result in more efficiency than a usual random TDMA schedule. This algorithm uses a metric based on the pheromone's value of Ant Colony Optimization (ACO) to choose the best next hop with the aim of reducing latency and energy and extending the network lifetime. Comparisons are made between ACOMAR, MAC-aware routing (MAR) and the latest cross-layer protocol latency and energy MAC-aware routing (LEMAR). The experimental results show that ACOMAR outperforms considerably LEMAR by 80%, 20%, 81%, and 54% in latency, energy, hop count, and throughput, respectively. Consequently, ACOMAR is a suitable solution for healthcare applications where WSN-based critical IoT devices interact quickly in emergence cases (high rate in latency optimization 80%).

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Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

Lv,

Wan

2024

JCSANDM

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To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

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Latency and Energy Optimization Using MAC-Aware Routing for WSNs

Cited by 4 publications

References 8 publications

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

ACOMAR: ACO‐based MAC‐aware routing protocol

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

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