Performance Evaluation of Scheduling Approaches for Wireless Sensor Networks

Al-Ghamdi, Bandar; Ayaida, Marwane; Fouchal, Hacène

doi:10.1007/s11277-019-06775-3

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The realization of the positioning system adopts the sensor node of the IRIS model, which adopts the AtmelRF230 wireless transceiver and the At-mega128l microprocessor conforming to IEEE802.15.4. The internal program of the node is written in the nesC language on the basis of the Tiny OS operating system [20,21].…”

Section: Resultsmentioning

confidence: 99%

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Li¹,

Sharma

2022

Journal of Sensors

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In order to solve the abnormal behaviors in wireless sensor networks, such as attacks, intrusions, node failures, and data anomalies, a data anomaly detection method for sensor networks based on DNN algorithm and cluster analysis is proposed. The deep neural network is introduced into the wireless sensor network, and each wireless sensor data is described by the neuron to construct the neural network element model. The traditional neural network model is improved, and the neural network model of the wireless sensor is used to realize the fusion and extraction of the data collected by the wireless sensor network. The clustering technology is used to complete the abnormal data judgment of the nodes, and at the same time, the spatial correlation of the sensing data between neighboring nodes is used to filter the noise data, extract the abnormal event information, and assist the system decision-making. The results show that when the number of neighbor nodes increases, the number of nodes with similar physical locations in the optimal neighbor node set gradually increases, and the accuracy rate is also continuously improved. However, if too many nodes are selected, the algorithm will use the data of nodes with a longer physical distance to vote, which will lead to an increase in the error rate. Therefore, when determining the number of neighbor nodes, 25%-30% of the total number of nodes in the positioning scenario can be selected. Therefore, the algorithm can effectively detect and distinguish environmental noise and abnormal events in the network.

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Section: Resultsmentioning

confidence: 99%

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Li¹,

Sharma

2022

Journal of Sensors

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“…Contiki OS provides an efficient simulation environment for WSN with detailed statistical parameters. The Unit Disk Graph Medium (UDGM), with distance loss propagation model, was adopted with Constant Bit Rate (CBR) traffic model which is most suitable for resource-constrained devices generating data at a constant rate [20,29] with maximum frame size set to 127 bytes, which include 25 bytes of MAC Header and 102 bytes of payload [9,17] operating within the standard operational carrier frequency of 2.4 GHz and channel data rate of 250 kbps [4,9] (see Table 5).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Wireless Sensor Networks (WSN) [ 2 , 3 ] forms the building block of IoT. WSN is made up of tiny resource-constrained sensor devices called nodes or motes [ 4 , 5 ]. Functioning cooperatively, these nodes are deployed in target locations to sense changes in the physical parameters of the target environment.…”

Section: Introductionmentioning

confidence: 99%

“…With the Industrial, Scientific, and Medical (ISM) bands, IEEE 802.15.4 operates at the 2.4GHz frequency with a data rate of 250kbps [ 4 ]. Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism is implemented in two ways; Slotted and Non-slotted CSMA/CA.…”

Section: Introductionmentioning

confidence: 99%

“…The active period is further divided into 16 time-slots which are shared by Contention Access Period (CAP) and Contention Free Period (CFP) (the CFP may sometimes be optional), while the inactive period is used by nodes to conserve energy when nodes are not undergoing any active transactions [ 15 , 16 ]. The 16 equally spaced time-slots run from zero to fifteen (0–15 time-slots), where zero (0) indicates the first time-slot [ 4 ]. The duration of a unit time-slot is equivalent to 0.96ms [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimized backoff scheme for prioritized data in wireless sensor networks: A class of service approach

et al. 2020

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Data prioritization of heterogeneous data in wireless sensor networks gives meaning to mission-critical data that are time-sensitive as this may be a matter of life and death. However, the standard IEEE 802.15.4 does not consider the prioritization of data. Prioritization schemes proffered in the literature have not adequately addressed this issue as proposed schemes either uses a single or complex backoff algorithm to estimate backoff time-slots for prioritized data. Subsequently, the carrier sense multiple access with collision avoidance scheme exhibits an exponentially increasing range of backoff times. These approaches are not only inefficient but result in high latency and increased power consumption. In this article, the concept of class of service (CS) was adopted to prioritize heterogeneous data (realtime and non-real-time), resulting in an optimized prioritized backoff MAC scheme called Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). This scheme classifies data into high priority data (HPD) and low priority data (LPD) by computing backoff times with expressions peculiar to the data priority class. The improved scheme grants nodes the opportunity to access the shared medium in a timely and power-efficient manner. Benchmarked against contemporary schemes, CSTP-MAC attained a 99% packet delivery ratio with improved power saving capability, which translates to a longer operational lifetime.

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Weighted high priority packet transmission using ozmos bio-inspired mechanism for traffic load distribution (WH-DTO) in WSNs

Ganesan

Vijayalakshmi²

2022

Wireless Netw

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Performance Evaluation of Scheduling Approaches for Wireless Sensor Networks

Cited by 5 publications

References 9 publications

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

Optimized backoff scheme for prioritized data in wireless sensor networks: A class of service approach

Weighted high priority packet transmission using ozmos bio-inspired mechanism for traffic load distribution (WH-DTO) in WSNs

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