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Wireless integrated network sensors
Open accessAbstract
For pervasive computing performance, exploit the physical limits of these densely distributed networks of embedded sensors, controls, and processors.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 G.
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Cited by
Directed diffusion for wireless sensor networking
Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2002.808417 · Cited by 2,359
Gossip-based computation of aggregate information
Kempe, D., Dobra, A., & Gehrke, J. (2004) · 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings. · doi:10.1109/sfcs.2003.1238221 · Cited by 1,480
Cybersecurity of industrial cyber-physical systems: A review
Kayan, H., Nunes, M., Rana, O., Burnap, P., & Perera, C. (2022) · ACM Computing Surveys · doi:10.1145/3510410 · Cited by 261
(2012) · doi:10.1109/jsen.2012.2198054 · Cited by 37
Application-specific compression for time delay estimation in sensor networks
Vasudevan, L., Ortega, A., & Mitra, U. (2003) · Proceedings of the 1st international conference on Embedded networked sensor systems · doi:10.1145/958491.958520 · Cited by 33
Metadata
- Authors
- Pottie, G. J., & Kaiser, W. J.
- Venue
- Communications of the ACM
- Published
- 2000-05
- Volume
- 43
- Issue
- 5
- Pages
- 51-58
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2121255383
- Cited by
- 3,219
- References
- 0
- FWCI
- 121.86
- Type
- journal article
- Language
- en
- Open access
- bronze
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