Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations; HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep Compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper contributes by introducing a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression, and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia’s air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value<0.0005), achieving a higher compression ratio (CR) by 1.4x while reducing data size by up to 59%. Furthermore, this achievement is attained while utilizing less than 80 KB of IoT memory and consuming at most 44 mAmps per slide compression. Furthermore, the compression performance correlated linearly with the number of patterns in each sliding window. With such excellent performance, using HDC at IoT edge is a considerable solution to reduce the smart-cities network traffic.