Abstract
Energy consumption has risen to be a bottleneck in wireless sensor networks. This is caused by the challenges faced by these networks due to their tiny sensor nodes that have limited memory storage, small battery capacity, limited processing capability, and bandwidth. Data compression has been used to reduce energy consumption and improve network lifetime, as it reduces data size before it can be forwarded from the sensing node to the sink node in the network. In this paper, a survey and comparison of currently available data compression techniques in wireless sensor networks are conducted. Suitable sets of criteria are defined to classify existing data compression algorithms. An adaptive lossless data compression algorithm (ALDC) is analyzed through MATLAB coding and simulation from the reviewed data compression techniques. The analysis aims to discover strategies that can be used to reduce the amount of data further before it is transmitted. From this analysis, it was discovered that encoding residue samples, rather than raw data samples, reduced the bitstream from 112 bits to a range of 30 to 36 bits depending on the sample block sizes. The average length of data samples to be passed to the encoder was minimized from the original 14 bits per symbol to 1.125 bits per symbol. This demonstrated a 0.875 code efficiency or redundancy. It resulted in an energy saving of 67.8% to 73.2%. This work further proposes a data compression algorithm that encodes the residue samples with fewer bits than the ALDC algorithm. The algorithm reduced the bitstream to 26 bits. The average length of the code is equal to the entropy of the data samples, demonstrating zero redundancy and an improved energy saving of 76.8% compared to ALDC . The proposed algorithm, therefore, shows improved energy efficiency through data compression.
Highlights
Large-scale data collection for many applications can be made possible by deploying wireless sensor networks (WSNs) through continuous monitoring
Security: Some wireless sensor networks applications demand a certain level of security, creating a conflict with data compression
Suitable conditions for CS are that the signal should be compressible, as in WSN data, where source nodes are constrained and allow simple coding, but complex decoding is realized at the sink, which is not limited in energy supply and processing capability [46]
Summary
Large-scale data collection for many applications can be made possible by deploying wireless sensor networks (WSNs) through continuous monitoring. The approach taken by this work to address this energy consumption challenge is to reduce the size of data before it is transmitted and to employ efficient means of transmitting this data across the network and eventually to its destination or sink This can be achieved through data compression, the application of efficient data aggregation techniques, and efficient routing techniques. Complexity in processing and memory capacity Typical processing speeds for wireless sensor nodes range around 4 – 8MHz with instruction memories of 128kB and data memories of 4kB This calls for the design of less complex compression algorithms with limited code size. It is desirable to design algorithms that perform most of the data processing at the sink instead of performing compression at individual nodes In this case, sensors with lower processing performance are considered more efficient in data compression [6], [8], [9]. This increases data processing at both the transmitter and receiver [8]
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