Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

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.

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

“…Research on data compression for wireless sensor networks has been done extensively and there are a lot of surveys and reviews on the data compression techniques and their applicability [27]. According to the authors in [27], data compression techniques can be summarized into three main categories; data aggregation compression techniques, local data compression techniques, and distributed data compression techniques.…”

“…• Data aggregation compression techniques: These techniques have been heavily investigated according to literature [28]. However, they are known to extract summaries of statistical data from sensory data such as minimums, maximums, and averages [27]. The techniques are more useful to certain applications, which require information that is limited.…”

“…However, there is also a group of distributed source coding techniques , which are more practical such as the Slepian-Wolf coding [29] that perform data compression from the sources [28]. Data aggregation techniques can be classified into Tree Structured, Chain-Based, Cluster-Based, Sector-Based, and QoS-Based data aggregation compressions [27]. Moreover, Tree structured types include Energy-Aware Distributed Heuristic approach (EADAT) [30], Tree based Tiny Aggregation (TTA) [27], and Power Efficient Data gathering and Aggregation Protocol (PEDAP) [27].…”

“…Data aggregation techniques can be classified into Tree Structured, Chain-Based, Cluster-Based, Sector-Based, and QoS-Based data aggregation compressions [27]. Moreover, Tree structured types include Energy-Aware Distributed Heuristic approach (EADAT) [30], Tree based Tiny Aggregation (TTA) [27], and Power Efficient Data gathering and Aggregation Protocol (PEDAP) [27]. Power Efficient Data Gathering Protocol for Sensor Information Systems (PEGASIS) [31] is a type of chain-based data aggregation techniques.…”

Design an efficient internet of things data compression for healthcare applications

“…IoT environment produce huge amounts of information and IoT networks do not have sufficient bandwidth due to constrained resources [15]. To save IoT network bandwidth and prevent network congestion issues, and network overhead, so the IoT data must be compressed prior to transmission to decrease size of transmitted data [16], [17].…”