Cross-layer multi channel MAC protocol for wireless sensor networks in 2.4-GHz ISM band

Karwal, Vikram; Narayana, Komanapalli Venkata Lakshmi

doi:10.1109/cast.2016.7914986

Cited by 5 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the numbers of particles are in coexistence to each other the kind cooperation among them obtains a best possible solution. The particles in the group self-updates by tracking the following two "extreme values": P best(i) value is learnt from the individual particle (7) The right channel assignment to every node in the network is the main criteria of the proposed algorithm, the another aim is to reduce the maximum load on any channels within two-hop neighborhood of the network so PSO technique was used for load aware channel estimation and assignment.…”

Section: III Particle Swarm Optimization (Pso) Based Load Aware Channel Estimationmentioning

confidence: 99%

“…IoT [3], health care monitoring [4], home automation [5], Smart Grid [6] [7], industrial automation [8], structural monitoring [9], environmental monitoring [10], and surveillance [11].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Assures secured and reliable data transmission [6] (2) Provides the flexible network configuration [7] (3) Assures high network life time and low equipment costs [8] (4) Increases the reliability of the network [3]. The IEEE 802.15.4 elucidates the first two layers PHY and MAC for data communication.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Load Aware Channel Estimation and Channel Scheduling for 2.4GHZ Frequency Band Based Wireless Networks for Smart Grid Applications

Karwal

Sahoo

2017

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

The advanced monitoring and control applications consider Wireless Sensor Networks (WSN) as a promising technology for modern applications like the Internet of Things (IoT), Smart Grid and Wireless Body Area Networks (WBAN). The WSN has important features like less cost, low power usage, supportable data rates and complexity. There is a need for continuous research on improving characteristics and abilities of WSN. The reliable performance of WSN depends on the latency necessities depending on the type of application and Quality of Service (QoS) parameters. The technologies like Zigbee, WiFi, and Bluetooth operating in 2.4GHz are mostly considered for deploying the WSN. Because of coexistence environment, the performance of Zigbee gets affected in terms of channel switching and causes the significant amount of delay. Also, the data transmission should be performed without any collision. In this paper, initially, the pseudorandombased interference evading scheme is introduced for efficient data communication. During this scheme, if node attains a channel it must wait for a network reconfiguration time for moving to next channel. Hence, during this time other nodes are allowed for moving to the new channel. Secondly, for moving to the new channel load aware channel estimation is proposed to assess the possibility of traffic weight assignment at each channel. Finally, the Particle swarm optimization (PSO) based collision avoiding multiple-channel based superframe scheduling is proposed for IEEE 802.15.4 based wireless networks working under the influence of IEEE 802.11b network. The channel with best energy function is selected for data transmission. The work proposed in this paper is evaluated based on the comparison to the existing works. From the results obtained it is inferred as proposed work shows better performance in terms of packet error rate, packet delivery ratio, and energy consumed when compared to the existing algorithms.

show abstract

Section: III Particle Swarm Optimization (Pso) Based Load Aware Channel Estimationmentioning

confidence: 99%

“…IoT [3], health care monitoring [4], home automation [5], Smart Grid [6] [7], industrial automation [8], structural monitoring [9], environmental monitoring [10], and surveillance [11].…”

Section: Introductionmentioning

confidence: 99%