A wireless sensor network (WSN) essentially ad hoc networks consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location
Sensor module collects the observations from surrounding environmental analog information such as light, sound, shocks, etc, converts it to the digital signal via the analog to digital converter (ADC), and then transfers to the processor unit Processor module manages the cooperation between the units in the sensor, the collaborations between other WSN nodes in the network. Wireless communication module communicate between WSN nodes and Base Station
Wireless Sensor Network Technology Operating systems Contiki,ERIKA Enterprise, Nano-RK, TinyOS,LiteOS,OpenTag, NanoQplus Industry standards ANT, 6LoWPAN, DASH7, ONE-NET, ZigBee, Z-Wave, Wibree, WirelessHART, , MiWi Programming languagesC, LabVIEW,nesC HardwareIris Mote, Sun SPOT, Xbee, Arduino Software TinyDB, TOSSIM, NS-2, OPNET, NetSim, LinuxMCE Applications Key distribution, Location estimation, Sensor Web, Telemetry ProtocolsAODV, DSR, TSMP Used in ProjectOS (TinyOS), platform(Micaz), Programming language C++,Protocol(AODV, DSR, TSMP)
Sensor Web/ Sensor Grid, Internet of things,M2M Ubiquitous Computing : Smart home/Cities, smart meter, smart TV/appliances,. Future with Graphene Smart dust is hot. Message Queue Telemetry Transport (MQTT). Used by Facebook,IBM smart planet initiatives Already in use Disaster management, Alternative energy, health monitoring, agriculture, defense.
WSN nodes are very small and design is dominates by size of battery. WSN are using various technology like energy harvesting,piezoelectric material technology to reduce size of battery. Power consumption in sensor networks can be divided into three domains: sensing, communication and data processing. Router node consume more power than leaf nodes. Due to unbalanced energy consumption, WSN nodes on busy routing paths may drain their batteries faster than other nodes, Energy aware routing is important.
data-centric: like directed diffusion, sensor protocols for information via negotiation (SPIN) and power aware many-to-many routing fall into this category cluster-based: Low-energy adaptive clustering hierarchy (LEACH) is an example of a cluster-based sensor network routing algorithm. location-based: minimum energy communication network (MECN) and geographic adaptive fidelity (GAF) are location-based routing algorithms.
Q Overall = Q CPU + Q RadioTrans + Q RadioRcv Q CPU = P CPU * T CPU = P CPU * (B Enc * TB Enc + B Dec * TB Dec +B Mac * TB Mac + T RadioActive ). P X in Eq. 2 represents the power of device X. T X means the computation time of device X TB Y denotes the per-byte time consumed for doing operation Y. B Y indicates the amount of bytes to be computed by operation Y Enc, Dec and Mac denote the encryption, decryption, and MAC digest generation operations respectively T RadioActive represents the radio transceiver’s active time, as the processor module remains active while the radio chip is turned on. Q RadioTrans = P RadioTrans * (K Trans * B Trans + T Startup ) Q RadioRcv = P RadioRcv * (K Rcv * B Rcv + T Idle )
Decide the price of adding security to WSN routing protocols or further estimate the network lifetime of their WSNs Mathematical models used to estimate extra energy consumption of routing protocols due to security features in WSN. Empirical values and physical actual values are compared to validate model for different routing protocols.
S.No.TaskFrom dateTo Date 1Setting up MicaZ platformMarch March Routing protocols setup in on hardware and OS platform. 15 March March Vulnerability and security of protocols 31 March April Writing code on TinyOS on MicaZ 15 April May Testing codes for various routing protocols 15 May May Taking measure for each protocol and applying model 30 May June Comparing empirical values with model values 30 June July Presentation of results15 July July 2013