Energy efficient data compression and aggregation technique forwireless sensor networks
01 Mar 2017-pp 209-212
TL;DR: An energy efficient data compression and data aggregation algorithm is presented which results in the whole network lifetime prolonged by about 24%.
Abstract: This paper present and analyze an energy efficient data compression and data aggregation algorithm which results in the whole network lifetime prolonged by about 24%. In this paper, a new idea is proposed for sensor values compression based on a technique that involves feedback mechanism. In this technique, the base node in the sensor network generates Huffman code for the sensor data that needs to be compressed and broadcast the Huffman code in to the sensor network. All nodes in the sensor network receives Huffman code, compress the sensor data and transmit to base node. For data aggregation, secure data aggregation algorithm is used which does not necessitate additional phase for data integrity verification and also it eludes extra transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. The whole idea was tested on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki OS-simulator Cooja. A comparison is also done with existing compression algorithms in terms of lifetime of the sensor network.
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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
14 Nov 2006
TL;DR: This work proposes cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels, and presents an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system.
Abstract: Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach.
1,042 citations