WSN WIRELESS WSN WSN WSN. Application agriculture SENSOR. Application geophysical. Application agriculture NETWORK: 11/30/2016

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1 WIRELESS SENSOR NETWORK: ENERGY CONSERVATION APPROACHES agriculture Localized environmental conditions varies over short distances. Yitian Shui agriculture Problems of traditional monitoring techniques Scalability Single point of measurement based agriculture [ 1 ] geophysical Monitoring volcanic eruption Solution 1 Build stations on variable sites around volcano; Manually retrieve date. Stations are costly Lack scalability Cost much Solution 2 1

2 Sensor Node Types Sensor Node Limitations Generic node (sensor node) measure physical attributes Gateway node (sink) gather data from generic nodes and relay them to the base station. higher processing capability, battery power, and transmission (radio) range limited processing and computing resources,limited memory limited communication range, low bandwidth limited amount of energy. limitation usage is a very important concern in a. Nodes are battery-powered devices. Reduce the energy consumption so that the network lifetime can be extended. the goal! harvesting or conservation harvesting nodes replenish its energy from an energy source; re-charge Options: Solar cells Vibration Fuel cells Acoustic noise Mobile supplier harvesting limitation: Unstable, environment dependent e.g. Solar cells may not be a option for under water wireless sensors. conservation 1. Duty cycling 2. Data-driven approach 3. Mobility-based approach 2

3 1. Duty cycling a) select only a subset of nodes to remain active. b) Other nodes go to sleep: switch off the radio, put it in the low-power sleep mode when there is no network activity Major issue: finding the optimal subset of nodes that guarantee connectivity. GAF (Geographical Adaptive Fidelity Protocol) Divide sensing area into small virtual grids. All nodes within the same grid are equivalent : one node at a time need to be active. Coordinate with each other to decide which one can sleep and which one goes active (periodical re-election). 1.2 Connective driven protocol P. Walia and A.Mehta. efficient geographic adaptive fidelity in wireless sensor networks Periodic re-election 1.2 Connective-driven protocol ASCENT (Adaptive Self-Configuring sensor Networks Topologies) Activate: broadcast discovery message and enter active state. While active: periodically re-broadcast discovery message Active to sleep: receive some other equivalent node s discovery message and go to sleep state. Wake up: wake up after a sleeping and enter discovery state Initially, only some nodes are active, others are passive (only listening but not transmitting). a. If active node (e.g. sink node) find that local data loss rate is over threshold,it sends help message to passive neighbors (passive nodes). b. Passive neighbors receive help message, decide whether to join the network. c. Passive nodes join the network, loss rate goes down. Repeat until network is stable. (a) Message loss (b) Help message. A. Cerpa and D. Estrin. ASCENT: Adaptive self-configuring sensor networks topologies. (c) Stable network 3

4 2. Data-driven approach Idea: reducing energy used to process data. 2.1 data-reduction schemes: reduce unneeded(redundant) samples while transmitting. 2.2 energy-efficient data acquisition schemes: reducing the energy spent by the sensing subsystem. 2.1 data-reduction Data prediction: Nodes builds model on previous data. When new data sensed: if (new data fit the model ) { do not transmit; gateway node take predicted data (computed by model)as result; } else { transmit new data to gateway node; } Update the model if necessary. 2.2 energy-efficient data acquisition hierarchical sampling simple sensor low power consumption, low resolution advanced sensor power hungry, high resolution example: target tracking.(target detection & target identification) 3. Mobility-based approach Motivation: packets traverse network to sink node following multi-hop path. Additional data processing and transmission on mid-way nodes; Nodes close to sink have to process more data (consume more energy). Why mobility-based approach works? Mostly short-range radio transmission. Lower link error rate, less contention overhead, less forwarding 4

5 3.1 mobile sink sink node is mobile 3.2 mobile relay add mobile device(mobile relay) to the network. Sink node can move to a number of locations in coverage area: single-hop communication not in coverage area: multi-hop communication Greedy Maximum Residual (GMRE): Mobile sink select new location that covers maximum residual energy of sensor nodes. Sensor nodes: Sampling data and store in local buffer temporarily. Data Mule (mobile ubiquitous LAN extension): Collect data from sensor nodes with in coverage area. Access point (sink node): Receive information from Mule when the Mule passes by.. W. Zhao, M. Ammar and E. Zegura. A message ferrying approach for data delivery in sparse mobile ad hoc networks. reference G. Anastasi, M. Conti, M. Di Francesco and A. Passarella. conservation in wireless sensor networks: A survey. Ad Hoc Networks 7(3), pp DOI: /j.adhoc [2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci. Wireless sensor networks: A survey. Computer Networks 38(4), pp DOI: /S (01) [3] J. Yick, B. Mukherjee and D. Ghosal. Wireless sensor network survey. Computer Networks 52(12), pp DOI: /j.comnet [4]J. Chang and L. Tassiulas. Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking (TON) 12(4), pp DOI: /TNET [5] A. Cerpa and D. Estrin. ASCENT: Adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing 3(3), pp DOI: /TMC [6] W. Zhao, M. Ammar and E. Zegura. A message ferrying approach for data delivery in sparse mobile ad hoc networks. 2004,. DOI: / [7] Z. M. Wang, S. Basagni, E. Melachrinoudis and C. Petrioli. Exploiting sink mobility for maximizing sensor networks lifetime. 2005,. DOI: /HICSS [8] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees and M. Welsh. Deploying a wireless sensor network on an active volcano. IEEE Internet Computing 10(2), pp DOI: /MIC [9] P. Walia and A.Mehta. efficient geographic adaptive fidelity in wireless sensor networks, PP e-ISSN p-issn Volume 17, Issue 5, Ver. I [10] Micro Strain s Inc SensorCloud enables condition based agriculture at shelburne vineyard. Q&A 5