Input Pattern Classification Based on the Markov Property of the IMBT with Related Equations and Contingency Tables

In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is provided to decide under which conditions IMBT outperforms other data structures typically used in the field, as a function of the statistical characteristics of the commonly occurring anomalies in the arrival of data packets. We use in the modeling Bernstein theorem, Markov property, Fibonacci sequences, bipartite multi-graphs, and contingency tables.

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Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor

Applied Sciences ◽

10.3390/app112110240 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10240

Author(s):

Qianhao Chen ◽

Wenqi Wu ◽

Wei Luo

Keyword(s):

Conditional Distribution ◽

Lossless Compression ◽

General Purpose ◽

Statistical Characteristics ◽

Sequential Data ◽

Digital Signals ◽

Sensor Applications ◽

Neural Architecture ◽

Efficient Data ◽

Sensor Signals

The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.

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An Energy Efficient Data Transfer Mechanism for Wireless Sensor Network

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v6i3.4459 ◽

2013 ◽

Vol 6 (3) ◽

pp. 359-369

Author(s):

Partha Pratim Bhattacharya ◽

Jyoti Saraswat

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

Data Transfer ◽

Wireless Sensor ◽

Delivery Ratio ◽

Level Energy ◽

Data Packets ◽

Traffic Pattern ◽

Efficient Data ◽

Correlated Information

Wireless Sensor Networks (WSNs) are generally energy and resource constrained. In most WSN applications the traffic pattern is from sensor-to-sink and for effective utilization of available resources in network data aggregation is employed. If a data packet is lost due to node failure or collision the correlated information content by data packets is lost. Existing protocols that provide reliable data transfer for sensor-to-sink traffic are either not energy efficient or they provide reliability at the event level. Energy efficiency can be improved by employing proper duty cycle values. By extending the concept of monitors the proposed protocol provides packet level reliability and improves the energy efficiency by employing duty cycles. To further decrease the energy consumption only a subset of nodes is chosen as active nodes to transfer the data. The performance of the proposed protocol is evaluated using Matlab. Results show that protocol has significant improvement in terms of energy saving, throughput and packet delivery ratio.

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Dynamic Multi Constraint Routing Based Efficient Data Transmission in Manet for Improved Sink Point Streaming

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l1154.10812s19 ◽

2019 ◽

Vol 8 (12S) ◽

pp. 627-632

Keyword(s):

Routing Algorithm ◽

Poor Performance ◽

Mobile Nodes ◽

Hop Count ◽

Data Packets ◽

Mobile Adhoc Network ◽

Adhoc Network ◽

Routing Performance ◽

Efficient Data

The mobile adhoc network (Manet) has been identified as keen networking scenario in modern internet world. The most networking solutions have been enabled to access through mobile devices. The physical characteristics of mobile nodes keep changing the topology of network at each second. However, achieving higher streaming performance is most important in point of quality of service. There exist numerous techniques to route data packets between the source and sink/destination nodes, but suffer with poor performance. To overcome the deficiency, a dynamic multi constraint routing algorithm has been presented in this paper. The method considers different parameters like energy, lifetime, traffic, mobility speed, direction and hop count in route selection. According to the above mentioned parameters, the multi constraint algorithm estimates streaming support score (SSS) for any route to perform routing of packets. The algorithm improved routing performance of routing and increases the streaming rate in Manet.

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A Reliable Energy Efficient Data Aggregation Algorithm for Wireless Sensor Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j7429.079920 ◽

2020 ◽

Vol 9 (9) ◽

pp. 686-691

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Data Aggregation ◽

Energy Efficient ◽

Cluster Head ◽

Life Time ◽

Environmental Data ◽

Wireless Sensor ◽

Data Packets ◽

Efficient Data

One of the popular and emerging networks is wireless sensor networks (WSN), where it comprises of an unlimited number of sensors deployed dynamically and irregularly in a geolocation, for a specific purpose. Each sensor node in the network sense, collect and transmit the environmental data from one location to other location. All the nodes have the capabilities of transmitting and receiving the documents. The major problem in WSN is energy efficiency and network lifetime. By reducing the energy consumption, the network life time can be increased. Clustering, scheduling and other related methods are used to reduce the energy consumption, during the data transmission and receiving. This paper proposed a Reliable Energy Efficient Data Aggregation (REEDA) method for improving the energy efficiency. All the common nodes or the cluster head nodes gather, aggregate, and transmit the data where it reduces the energy consumption. The aggregation method is applied according to correlation of data packets generated by entire node. Simulations results prove that the proposed algorithm provides a good solution for minimizing communication and computation cost.

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