Reliable spatial and temporal data redundancy reduction approach for WSN

Naz

Computational Intelligence and Neuroscience

et al. 2022

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Section: Related Workmentioning

confidence: 99%

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

Naz

Computational Intelligence and Neuroscience

et al. 2022

“…To avoid this scenario and increase prediction accuracy, the NP aims to check whether one of the targeted sensor neighbouring nodes is transmitting readings. In the case of one or more neighbouring transmit readings, the algorithm calculates the Jaccard similarity between the targeted sensor and its neighbours based on Equation (7). If the similarity is less than the predefined e max then the reproduced non-transmitted reading process (predicted value) will be based on the neighbour's received reading.…”

Section: Data Prediction Phase (Dpp)mentioning

confidence: 99%

“…Due to the limitations mentioned above, the data collected by wireless sensor nodes are enormous, some of which may be unnecessary and faulty. The continuously collected data are highly correlated due to the observed phenomena' physical nature [7,8]. In other words, most of the collected observations are not exclusive, and the deviation between them and the previously collected readings has no significant entropy.…”

Section: Introductionmentioning

confidence: 99%

A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

et al. 2021

Self Cite

Wireless sensor networks (WSNs) are critically resource-constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non-transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…We may extend the network’s lifespan by switching between distinct subsets of sensor nodes that are active at the same time. However, such sleep-active techniques may not be implemented if node redundancy is not available (for example, due to network deployment [ 5 , 6 ] or sensor breakdown [ 7 ]). Sensor nodes utilise a lot of energy when transmitting data, therefore a possible approach is to decrease the amount of data that is delivered.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

Mishra

Gupta

Gui

2022

Sensors

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.