Huffman coding algorithm for compression of sensor data in wireless sensor networks
27 Aug 2009-pp 296-301
TL;DR: An orthogonal approach for compressing sensor readings based on a novel feedback technique that generates Huffman code for the compression of sensor data and broadcasts it into sensor networks as feedback information and this modified Huffman coding is called sHuffman coding.
Abstract: Sensor data exhibit strong correlation in both space and time. Many algorithms have been proposed to utilize these characteristics. However, each sensor just utilizes neighboring information, because its communication range is restrained. Information that includes the distribution and characteristics of whole sensor data provides other opportunities to enhance the compression technique. In this paper, we propose an orthogonal approach for compressing sensor readings based on a novel feedback technique. That is, the base station or a super node generates Huffman code for the compression of sensor data and broadcasts it into sensor networks as feedback information. All sensor nodes that have received the information compress their sensor data and transmit them to the base station. We call this approach as feedback-diffusion and this modified Huffman coding as sHuffman coding. In order to show the superiority of our approach, we compare it with the existing data compression algorithms in terms of the lifetime of the sensor network. As a result, our experimental results show that the whole network lifetime was prolonged by about 30%.
Citations
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TL;DR: The novel concept of Kernel Dataset, which can represent the vast information carried by big sensory data with the information loss rate being less than $\epsilon$, and two distributed algorithms of maintaining the correlation coefficients among sensor nodes are developed are developed.
Abstract: The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has already exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we introduce the novel concept of $\epsilon$ -Kernel Dataset , which is only a small data subset and can represent the vast information carried by big sensory data with the information loss rate being less than $\epsilon$ , where $\epsilon$ can be arbitrarily small. We prove that drawing the minimum $\epsilon$ -Kernel Dataset is polynomial time solvable and provide a centralized algorithm with $O(n^3)$ time complexity. Furthermore, a distributed algorithm with constant complexity $O(1)$ is designed. It is shown that the result returned by the distributed algorithm can satisfy the $\epsilon$ requirement with a near optimal size. Furthermore, two distributed algorithms of maintaining the correlation coefficients among sensor nodes are developed. Finally, the extensive real experiment results and simulation results are presented. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.
155 citations
24 Aug 2015
TL;DR: The concept of e-dominant dataset is defined, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small.
Abstract: The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we define the concept of e-dominant dataset, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small. We prove that drawing the minimum e-dominant dataset is polynomial time solvable and provide a centralized algorithm with 0(n3) time complexity. Furthermore, a distributed algorithm with constant complexity (O(l)) is also designed. It is shown that the result returned by the distributed algorithm can satisfy the e requirement with a near optimal size. Finally, the extensive real experiment results and simulation results are carried out. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.
125 citations
Patent•
06 Apr 2011
TL;DR: In this paper, a compression method for sensor network data based on Huffman encoding and a random optimization policy is proposed, which aims at the fact that an encoding threshold is a dynamically changing value in the practical application, and by executing the random optimisation policy, a base station can judge the values received by nodes every time in real time to determine the optimum transmission threshold, and the energy consumption of the nodes for receiving the base station information and energy consumption for transmitting raw data or codes can be effectively balanced.
Abstract: The invention discloses a compression method for sensor network data based on Huffman encoding and a random optimization policy The current data compression methods have inefficiency The invention aims at the fact that an encoding threshold is a dynamically changing value in the practical application, and by executing the random optimization policy, a base station can judge the values received by nodes every time in real time to determine the optimum transmission threshold, and the energy consumption of the nodes for receiving the base station information and the energy consumption for transmitting raw data or codes can be effectively balanced, thereby adapting to different network sizes and the fluctuation range of different monitoring values By using the method of the invention, the average energy consumption of the nodes can be effectively reduced, the accuracy of the base station restoring the perception data of the nodes is improved, and an overall indicator formed by data accuracy, the average energy consumption of transmitting unit data nodes and the number of network failure nodes is optimized, thereby prolonging the service life of the network and expanding the overall performance of the network
4 citations
29 May 2012
TL;DR: A calculable and convenient encoding method named ‘concatenate encoding’ has been designed to improve the communication of the data longer than 8bits, which is needed by the high-accuracy data acquisition (with the resolution over 8bits).
Abstract: A calculable and convenient encoding method named ‘concatenate encoding’ has been designed to improve the communication of the data longer than 8bits, which is needed by the high-accuracy data acquisition (with the resolution over 8bits). Different from the others n to 8 encoding method, the concatenate encoding method could achieve a high reliability of communication with concise operation of encoding and decoding. As the result of a GPRS communication test, the failure data and the information misreading caused by the losing and the misplacing of individual data will be effectively avoided with the concatenate encoding method. It will considerably improve the communication quality and ensure the efficiency and reliability of data acquisition.
1 citations
18 Aug 2012
TL;DR: A novel Topology-based Data Compression (TDC) algorithm for wireless sensor networks is proposed, utilizing the topological structure of routing tree to reduce the transmission of message packets.
Abstract: In this paper, we address the problem of Data Compression which is critical in wireless sensor networks. We proposed a novel Topology-based Data Compression (TDC) algorithm for wireless sensor networks. We utilize the topological structure of routing tree to reduce the transmission of message packets. We analyzed the differences and relations between our algorithm and other compression algorithms. Extensive experiments are conducted to evaluate the performance of the proposed TDC approach by using two kinds of data sets: real data set and synthetic data set. The results show that the TDC algorithm substantially outperforms Non-compression algorithm in terms of packets transmitted.
References
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TL;DR: The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections.
Abstract: The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.
14,048 citations
TL;DR: The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security, and opportunities depend on development of a scalable, low-cost, sensor-network architecture.
Abstract: W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system
3,415 citations
07 May 2001
TL;DR: This work identifies opportunities and challenges for distributed signal processing in networks of these sensing elements and investigates some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.
Abstract: Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited.
1,258 citations
Dissertation•
01 Jan 2000
TL;DR: This dissertation supports the claim that application-specific protocol architectures achieve the energy and latency efficiency and error robustness needed for wireless networks by developing two systems.
Abstract: In recent years, advances in energy-efficient design and wireless technologies have enabled exciting new applications for wireless devices. These applications span a wide range, including real-time and streaming video and audio delivery, remote monitoring using networked microsensors, personal medical monitoring, and home networking of everyday appliances. While these applications require high performance from the network, they suffer from resource constraints that do not appear in more traditional wired computing environments. In particular, wireless spectrum is scarce, often limiting the bandwidth available to applications and making the channel error-prone, and the nodes are battery-operated, often limiting available energy.
My thesis is that this harsh environment with severe resource constraints requires an application-specific protocol architecture, rather than the traditional layered approach, to obtain the best possible performance. This dissertation supports this claim using detailed case studies on microsensor networks and wireless video delivery. The first study develops LEACH (Low-Energy Adaptive Clustering Hierarchy), an architecture for remote microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. This approach improves system lifetime by an order of magnitude compared to general-purpose approaches when the node energy is limited. The second study develops an unequal error protection scheme for MPEG-4 compressed video delivery that adapts the level of protection applied to portions of a packet to the degree of importance of the corresponding bits. This approach obtains better application-perceived performance than current approaches for the same amount of transmission bandwidth. These two systems show that application-specific protocol architectures achieve the energy and latency efficiency and error robustness needed for wireless networks. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
1,253 citations
26 Apr 2004
TL;DR: Analytical modeling and simulations reveal that while the nature of optimal routing with compression does depend on the correlation level, surprisingly, there exists a practical static clustering scheme which can provide near-optimal performance for a wide range of spatial correlations.
Abstract: The efficacy of data aggregation in sensor networks is a function of the degree of spatial correlation in the sensed phenomenon. While several data aggregation (i.e., routing with data compression) techniques have been proposed in the literature, an understanding of the performance of various data aggregation schemes across the range of spatial correlations is lacking. We analyze the performance of routing with compression in wireless sensor networks using an application-independent measure of data compression (an empirically obtained approximation for the joint entropy of sources as a function of the distance between them) to quantify the size of compressed information, and a bit-hop metric to quantify the total cost of joint routing with compression. Analytical modelling and simulations reveal that while the nature of optimal routing with compression does depend on the correlation level, surprisingly, there exists a practical static clustering scheme which can provide near-optimal performance for a wide range of spatial correlations. This result is of great practical significance as it shows that a simple cluster-based system design can perform as well as sophisticated adaptive schemes for joint routing and compression.
326 citations