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Control aspects in Wireless sensor networks
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Control aspects in Wireless sensor networks



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  • Ambiguity (ઍમ્બિગ્યુઇટિ) : દ્વિઅર્થી, સંદિગ્ધ, અનિશ્ચિત
  • predict : પ્રિડિક્ટિવ: ભવિષ્ય ભાખવું, આગાહી કરવી
  • Subsequent : Following in time or order
  • Scavenge (સ્કૅવિન્જ) : ભંગી હોવું –નું કામ કરવું, કચરો, મેલ ઇ. સાફ કરવુંHarvest(હાર્વિસ્ટ) : કાપણી લણણી
  • ambient : ઍમ્બિઅન્ટ : પરિસરનું
  • Endure (ઇન્ડ્યુઅર) : સહન કરવું, વેઠવું, ટકવુંExposure (એક્સપૉઝર) : ખુલ્લું મૂકવું, ખુલ્લું મુકાવું તે,


  • 1. Control Aspects in WSN 1 ]Rushin $hah UNIT : 5 27 February 2014
  • 2. Control aspects in WSN 1. Admission Control 2. Connection control 3. Power Control 4. Baud rate Control 5. Congestion Control 6. QoS Control 2 ]Rushin $hah 27 February 2014
  • 3. Congestion Control in ATM Networks ATM is a key Technology for integrating broad-band multimedia  services in heterogeneous networks. ATM Service Category Guaranteed Service Unspecified Bit Rate (UBR) Best effort Services Absolute Bit Rate (ABR)  ABR guarantees zero Loss.  UBR source neither specifies nor receives a Bandwidth, Delay or Loss guarantees. 3 ]Rushin $hah 27 February 2014
  • 4. Ambiguity of Traffic Control & Congestion Congestion: It is define as condition of an ATM network where the  network does not meet a stated performance objective. Traffic control: It contain combination of Connection Admission Control  (CAC) to avoid congestion. The most simplest form of congestion control scheme is the binary feed  back with FIFO queuing. - Here when the buffer occupancy exceeds a predefined threshold value, the switch begins to issue a binary notifications to the sources and it continues to do so until the buffer occupancy falls below the threshold. Second scheme is Rate Feedback Control  4 ]Rushin $hah 27 February 2014
  • 5. Congestion Control in Neural Network Number of  methods are available to reduce congestion in communication network.  One of them is NN( Neural Network) Based approach.  NN based scheme possess function approximation and learning capabilities which can be used directly in many applications. The NN based methods can be broadly categorized based on the  learning scheme they employ both offline and online. The offline learning schemes are used to train the NN, once trained  NN, weights are not updated during run time. 5 ]Rushin $hah 27 February 2014
  • 6. Congestion Control in Neural Network The online learning NN Scheme:    Relaxes the offline training phase,  Avoids the weight initialization problems and  6 Requires More real computation, Performs learning and adaptation simultaneously. ]Rushin $hah 27 February 2014
  • 7. When Congestion Occurs ?  Σ Input rate > available link capacity  Congestion occurs when the input rate is more than available link Capacity 7 ]Rushin $hah 27 February 2014
  • 8. When Congestion Occurs ? Most congestion control scheme consist of adjusting the input rates to  match the available link capacity. Depending upon the duration of congestion, different scheme can be  applied. If congestion time is less then the connection time then end to end  feedback control scheme can be applicable. 8 ]Rushin $hah 27 February 2014
  • 9. Network Model 9 ]Rushin $hah 27 February 2014
  • 10. Network Model  x(k +1) = f (x(k)) +T*u(k) + d(k)  x(k) – buffer length  T – Sampling time  f(.) – non linear traffic accumulation  d(k) – disturbance at time instant k  Ini – traffic arrival rate at destination buffer  Hd – buffer size  Q – bottle neck queue level  Sr – Service Capacity 10 ]Rushin $hah 27 February 2014
  • 11. Predictive Congestion Control for WSN  Predictive congestion control scheme for wireless sensor network takes into account energy efficiency and fairness.  This scheme will be implemented through feed back obtained from one hop.  Existing congestion control scheme, for example Transport Control Protocol when applied to wireless network, result in:  Large number of Packet drops,  Unfair scenario and  Low throughput with significant amount of wasted Energy due to Retransmission. 11 ]Rushin $hah 27 February 2014
  • 12. Predictive Congestion Control for WSN  The network congestion occurs when either: - The incoming traffic exceeds the capacity of the outgoing link or - Link bandwidth drops because of channel fading caused by path loss. 12 ]Rushin $hah 27 February 2014
  • 13. Predictive Congestion Control for WSN  To predict onset of congestion, the proposed scheme uses both queue utilization and transmission power under the current channel state at each node.  When node is become congested, the traffic will accumulate the nodes because there will be an excess amount of incoming traffic over the outgoing one. Hence queue utilization has been selected as an indicator of the onset of congestion.  On the other hand in wireless networks during fading, the available bandwidth is reduced and ongoing rate will be lowered.  The channel fading is estimated by the feedback information provided by DPC protocol for the next packet transmission. 13 ]Rushin $hah 27 February 2014
  • 14. Predictive Congestion Control for WSN  Working:  The DPC algorithm predicts the channel state for the subsequent time interval and calculates the required power, if this power exceeds the maximum threshold then the channel is considered to be unsuitable for transmission and proposed congestion control scheme can initiate back-off process by reducing incoming traffic.  There are many algorithms which can be applied for congestion control like predictive congestion control and Rate adaption.  Predictive congestion control minimized queue overflow by regulating the incoming flow. 14 ]Rushin $hah 27 February 2014
  • 15. Predictive Congestion Control for WSN  The incoming traffic can be calculated by three factors:  Predicted Outgoing flow  Wireless Link State  Queue utilization 15 ]Rushin $hah 27 February 2014
  • 16. Predictive Congestion Control for WSN  Predicted Outgoing flow: The outgoing flow is periodically measured and an adaptive scheme is used to accurately predict the outgoing flow in the next period, moreover the next hop node can reduce the outgoing flow assessment by applying a control over its incoming flow.  Wireless Link State: The predicted outgoing flow rate is further reduced when the DPC protocol predicts a severe channel fading which will disrupt communication on the link.  Queue utilization: The algorithm restricts the incoming flow based on the current queue utilization and predicted outgoing flow, thus reducing buffer overflows. 16 ]Rushin $hah 27 February 2014
  • 17. Energy Harvesting in WSN  Energy harvesting also known as: Power Harvesting or Energy Scavenging.  It is the process by which energy is derived from external sources (e.g., solar power, thermal energy, wind energy and kinetic energy), captured and stored for small wireless autonomous devices, like those used in wearable electronics and wireless sensor networks.  Energy harvesters provide a very small amount of power for low-energy electronics. 17 ]Rushin $hah 27 February 2014
  • 18. Energy Harvesting in WSN  The energy source for energy harvesters is present as ambient background and is free.  For example, temperature gradients exist from the operation of a combustion engine and in urban areas, there is a large amount of electromagnetic energy in the environment because of radio and television broadcasting.  Energy harvesting devices converting ambient energy into electrical energy have attracted much interest in both the military and commercial sectors.  Some systems convert motion, such as that of ocean waves, into electricity to be used by oceanographic monitoring sensors for autonomous operation. 18 ]Rushin $hah 27 February 2014
  • 19. Energy Harvesting in WSN  Future applications may include high power output devices (or arrays of such devices) deployed at remote locations to serve as reliable power stations for large systems.  Another application is in wearable electronics, where energy harvesting devices can power or recharge cell phones, mobile computers, radio communication Equipment, etc.  All of these devices:  must be sufficiently robust to endure long-term exposure to hostile environments and  have a broad range of dynamic sensitivity to exploit the entire spectrum of wave motions. 19 ]Rushin $hah 27 February 2014
  • 20. Accumulation of Energy  Energy can also be harvested to power small autonomous sensors such as those developed using MEMS technology .  These systems are often very small and require little power, but their applications are limited by the reliance on battery power  Scavenging energy from ambient vibrations, wind, heat or light could enable smart sensors to be functional indefinitely. 20 ]Rushin $hah 27 February 2014
  • 21. Storage of Power  In general, energy can be stored in a capacitor, super capacitor, or battery.  Capacitors are used when the application needs to provide huge energy spikes.  Batteries leak less energy and are therefore used when the device needs to provide a steady flow of energy. 21 ]Rushin $hah 27 February 2014
  • 22. Use of the Power  Current interest in low power energy harvesting is for independent sensor networks.  In these applications an energy harvesting scheme puts power stored into a capacitor then boosted/regulated to a second storage capacitor or battery for the use in the microprocessor.  The power harvesting is usually used in a sensor application as the data stored or is transmitted possibly through a wireless method. 22 ]Rushin $hah 27 February 2014
  • 23. Devices  There are many small-scale energy sources that generally cannot be scaled up to industrial size:  Piezoelectric crystals or fibers generate a small voltage whenever they are mechanically deformed. Vibration from engines can stimulate piezoelectric materials, as can the heel of a shoe  Some wristwatches are already powered by kinetic energy (called automatic watches), in this case movement of the arm. The arm movement causes the magnet in the electromagnetic generator to move. The motion provides a rate of change of flux, which results in some induced emf on the coils. The concept is simply related to Faraday's Law. 23 ]Rushin $hah 27 February 2014
  • 24. Devices  Photovoltaic is a method of generating electrical power by converting solar radiation (both indoors and outdoors) into direct current electricity using semiconductors that exhibit the photovoltaic effect. Photovoltaic power generation employs solar panels composed of a number of cells containing a photovoltaic material.  Thermoelectric generators (TEGs) consist of the junction of two dissimilar materials and the presence of a thermal gradient. Large voltage outputs are possible by connecting many junctions electrically in series and thermally in parallel. Typical performance is 100-200 μV/K per junction. These can be utilized to capture mW of energy from industrial equipment, structures, and even the human body. They are typically coupled with heat sinks to improve temperature gradient 24 ]Rushin $hah 27 February 2014
  • 25. Devices  Micro wind turbine are used to harvest wind energy readily available in the environment in the form of kinetic energy to power the low power electronic devices such as wireless sensor nodes. When air flows across the blades of the turbine, a net pressure difference is developed between the wind speeds above and below the blades. This will result in a lift force generated which in turn rotate the blades. This is known as the aerodynamic effect.  Special antennae can collect energy from stray radio waves or theoretically even light (EM radiation). 25 ]Rushin $hah 27 February 2014
  • 26. Ambient-Radiation Sources  A possible source of energy comes from radio transmitters.  Historically, either a large collection area or close proximity to the radiating wireless energy source is needed to get useful power levels from this source.  The nantenna is one proposed development which would overcome this limitation by making use of the abundant natural radiation  One idea is to deliberately broadcast RF energy to power remote devices: 26 ]Rushin $hah 27 February 2014
  • 27. Ambient-Radiation Sources  One idea is to deliberately broadcast RF energy to power remote devices:  Biomechanical harvesting  Photovoltaic harvesting  Piezoelectric energy harvesting  Tree metabolic energy harvesting  Blood sugar energy harvesting 27 ]Rushin $hah 27 February 2014
  • 28. Ambient-Radiation Sources  Biomechanical harvesting  Biomechanical energy harvesters are also being created.  One current model is the biomechanical energy harvester of Max Donelan which straps around the knee.  Devices as this allow the generation of 2.5 watts of power per knee.  This is enough to power some 5 cell phones 28 ]Rushin $hah 27 February 2014
  • 29. Ambient-Radiation Sources   Photovoltaic harvesting Photovoltaic (PV) energy harvesting wireless technology offers significant advantages over wired or solely battery-powered sensor solutions: virtually inexhaustible sources of power with little or no adverse environmental effects. Indoor PV harvesting solutions have to date been powered by specially tuned amorphous silicon (aSi)a technology most used in Solar Calculators. In recent years new PV technologies have come to the forefront in Energy Harvesting such as Dye Sensitized Solar Cells (DSSC). The dyes absorbs light much like chlorophyll does in plants. Electrons released on impact escape to the layer of TiO2 and from there diffuse, through the electrolyte, as the dye can be tuned to the visible spectrum much higher power can be produced. At 200 lux a DSSC can provide over 15 µW per cm². 29 ]Rushin $hah 27 February 2014
  • 30. Ambient-Radiation Sources   Piezoelectric energy harvesting he piezoelectric effect converts mechanical strain into electric current or voltage. This strain can come from many different sources. Human motion, low-frequency seismic vibrations, and acoustic noise are everyday examples. Except in rare instances the piezoelectric effect operates in AC requiring time-varying inputs at mechanical resonance to be efficient. 30 ]Rushin $hah 27 February 2014
  • 31. Ambient-Radiation Sources   Blood sugar energy harvesting [edit] Another way of energy harvesting is through the oxidation of blood sugars. These energy harvesters are called Biofuel cells. They could be used to power implanted electronic devices (e.g., pacemakers, implanted biosensors for diabetics, implanted active RFID devices, etc.). At present, the Minteer Group of Saint Louis University has created enzymes that could be used to generate power from blood sugars. However, the enzymes would still need to be replaced after a few years.[47] In 2012 a pacemaker was powered by implantable biofuel cells at Clarkson University under the leadership of Dr. Evgeny Katz.[48] 31 ]Rushin $hah 27 February 2014
  • 32. Ambient-Radiation Sources   Tree-based energy harvesting [edit] Tree metabolic energy harvesting is a type of bio-energy harvesting. Voltree has developed a method for harvesting energy from trees. These energy harvesters are being used to power remote sensors and mesh networks as the basis for a long term deployment system to monitor forest fires and weather in the forest. Their website says that the useful life of such a device should be limited only by the lifetime of the tree to which it is attached. They recently deployed a small test network in a US National Park forest.[49] 32 ]Rushin $hah 27 February 2014
  • 33. 33 ]Rushin $hah 27 February 2014