ELECTION: Energy-efficient and Low-latEncy sCheduling Technique for wIreless sensOr Networks

1. 1 ELECTION: Energy-efficient and Low-latEncy sCheduling Technique for wIreless sensOr Networks Hi, I am Shamim Begum from University of southern california. Today I will present election: an energy efficient and low latency scheduling technique for wireless sensor networks. I collaborated with shaocheng wang, professor bhaskar and professor helmy in electrical engineering department of university of southern california Hi, I am Shamim Begum from University of southern california. Today I will present election: an energy efficient and low latency scheduling technique for wireless sensor networks. I collaborated with shaocheng wang, professor bhaskar and professor helmy in electrical engineering department of university of southern california

2. 2 Motivation Sensor network of homogenous active sensors Monitor some phenomenon to detect abnormalities Application: chemical monitoring, machine fault detection Exhibits spatio-temporal correlation Phases of operation: Phase1 (normal operation): Energy efficiency Phase2 (event detection+): Latency and responsiveness Here is a wireless sensor network of homogenous active sensors with a far away base station. The network is deployed to monitor some phenomenon to detect any abnormalities in the underlying phenomenon. Examples are chemical plant monitoring, machine fault detection etc in which the underlying phenomena exhibits spatio-temporal correlation. For example, all sensors in the small area r measure the same phenomenon in a spatially correlated environment. Now, requirements of such applications depend on the phases of the application. Typically, during normal phase of operation, we expect the network to be energy efficient, however, when some abnormality occur in the phenomenon it is more important to ensure low latency and high responsiveness than energy efficiency.. Here is a wireless sensor network of homogenous active sensors with a far away base station. The network is deployed to monitor some phenomenon to detect any abnormalities in the underlying phenomenon. Examples are chemical plant monitoring, machine fault detection etc in which the underlying phenomena exhibits spatio-temporal correlation. For example, all sensors in the small area r measure the same phenomenon in a spatially correlated environment. Now, requirements of such applications depend on the phases of the application. Typically, during normal phase of operation, we expect the network to be energy efficient, however, when some abnormality occur in the phenomenon it is more important to ensure low latency and high responsiveness than energy efficiency..

3. 3 LEACH: Heinzleman et. al., HICSS 2000 Data driven, passive sensor Achieves energy efficiency Periodic clustering Rotation of cluster head High latency TEEN: Manjeshwar et. al., IPDPS 2001 Event driven, passive sensor Periodic cluster and rotation of cluster head Sleeps with fixed sleep cycle Achieves low latency Sense continuously Stay awake when the event is detected (threshold reached) ELECTION: Event driven, active sensor Takes advantage of the spatio-temporal correlation to adaptively adjust sleep cycle Achieve energy efficiency in phase 1: turn radios off Ensures low latency and high responsiveness in phase2 Motivation Given this network model let us see how existing scheduling schemes meet the dynamic objectives of the monitoring applications. LEACH by Weindy Heinzleman is a hierarchical clustering scheme that forms periodic clusters. It achieves energy efficiency by rotating cluster heads among different nodes. But leach does not ensure low latency which is a key requirement and the reason is that it is based on data driven philosophy. So TEEN by Arati Manajeshwar was designed based on event driven philosophy by incorporating the data threshold at which the event or abnormality occur in the environment. Nodes in TEEN sleep with a fixed sleep cycle, sense continuously and stay awake when the threshold is reached. TEEN ensures low latency, however, the dynamic objective of energy efficiency still is not met. So we propose ELECTION based on the event driven philosophy, but we assume active sensors instead of passive. We achieve energy efficiency in the first phase by turning the radios off in this phase. We exploit that spatio-temporal correlation of underlying physical phenomenon to adjust the sleep cycle and ensure low latency and high responsiveness when threshold is reached.Given this network model let us see how existing scheduling schemes meet the dynamic objectives of the monitoring applications. LEACH by Weindy Heinzleman is a hierarchical clustering scheme that forms periodic clusters. It achieves energy efficiency by rotating cluster heads among different nodes. But leach does not ensure low latency which is a key requirement and the reason is that it is based on data driven philosophy. So TEEN by Arati Manajeshwar was designed based on event driven philosophy by incorporating the data threshold at which the event or abnormality occur in the environment. Nodes in TEEN sleep with a fixed sleep cycle, sense continuously and stay awake when the threshold is reached. TEEN ensures low latency, however, the dynamic objective of energy efficiency still is not met. So we propose ELECTION based on the event driven philosophy, but we assume active sensors instead of passive. We achieve energy efficiency in the first phase by turning the radios off in this phase. We exploit that spatio-temporal correlation of underlying physical phenomenon to adjust the sleep cycle and ensure low latency and high responsiveness when threshold is reached.

4. 4 Assumptions Active/smart sensors Able to sense the environment in a responsive and timely manner Schedules sensors and communication radios independently The underlying phenomenon exhibits spatio-temporal correlation Here is the assumptions we make: Our protocols is designed for active sensor in which sensor act as a smart agent and they can sense the environment in a responsive and timely manner. Each sensor can schedule its sensor and communication radio independently. We also assume that the underlying phenomenon exhibits spatio temporal correlaitonHere is the assumptions we make: Our protocols is designed for active sensor in which sensor act as a smart agent and they can sense the environment in a responsive and timely manner. Each sensor can schedule its sensor and communication radio independently. We also assume that the underlying phenomenon exhibits spatio temporal correlaiton

5. 5 Outline Motivation Description of Algorithms Performance Analysis Conclusion Here is the outline of the rest of my talk: First I will present the basic algorithm. Then I will present the performance analysis of the protocol using analytical and simulation results. Finally the conclusion with a brief outline of future workHere is the outline of the rest of my talk: First I will present the basic algorithm. Then I will present the performance analysis of the protocol using analytical and simulation results. Finally the conclusion with a brief outline of future work

6. 6 System Parameters Initial sleep cycles: Sin Data threshold: Dth Gradient threshold: Gth Gradient: rate of change of the phenomenon Sleep reduction function: Fsr Here is a list of system parameters: sin is the initial sleep duration a sensor node in election start with. Dth and gth are the preset data threshold and gradient threshold at which the abnormality in detected. A sensor node in election use these thresholds and instantaneously sampled data and its gradient to adaptively control its sleep cycle using a function called Sleep reduction function fsrHere is a list of system parameters: sin is the initial sleep duration a sensor node in election start with. Dth and gth are the preset data threshold and gradient threshold at which the abnormality in detected. A sensor node in election use these thresholds and instantaneously sampled data and its gradient to adaptively control its sleep cycle using a function called Sleep reduction function fsr

7. 7 Basic Algorithms Here is the timing diagram and state diagram of a sensor node. A node operates in three phases: synchronization, monitor and report. In phase 1 a node turns its radio completely off and senses using a phenomenon dependent scheduling. In phase 2, it senses and communicates to report. Now, initially it spends some time for synchronization in phase 0. It then enters into phase 1 with initial sleep cycle sin. At sleep it turns its sensor and radios off. When it wakes up, it turns only the sensor on, samples the environment once and based on the sampled data and its gradient it adjusts its sleep cycle. If gradient is less than gradient threshold, it does not change its sleep duration. If data is less than data threshold or gradient is greater than gradient threshold it perceives an increase in the underlying phenomenon, so it reduces its sleep cycle using the function fsr which I will describe in next slide. If the data is greater than data threshold it goes to phase 2 by turning both its sensors and radios on. At this time, sensor nodes select a cluster head which adapts a TDMA schedules to gather data from cluster members, aggregates the data and send it to base station.Here is the timing diagram and state diagram of a sensor node. A node operates in three phases: synchronization, monitor and report. In phase 1 a node turns its radio completely off and senses using a phenomenon dependent scheduling. In phase 2, it senses and communicates to report. Now, initially it spends some time for synchronization in phase 0. It then enters into phase 1 with initial sleep cycle sin. At sleep it turns its sensor and radios off. When it wakes up, it turns only the sensor on, samples the environment once and based on the sampled data and its gradient it adjusts its sleep cycle. If gradient is less than gradient threshold, it does not change its sleep duration. If data is less than data threshold or gradient is greater than gradient threshold it perceives an increase in the underlying phenomenon, so it reduces its sleep cycle using the function fsr which I will describe in next slide. If the data is greater than data threshold it goes to phase 2 by turning both its sensors and radios on. At this time, sensor nodes select a cluster head which adapts a TDMA schedules to gather data from cluster members, aggregates the data and send it to base station.

8. 8 We achieve energy efficiency in first phase by turning the radios off. If this is the case, how do we ensure 1)a node wakesup at the desired event of threashold, and 2) all nodes wakeup at the same time. The sleep reduction function is the key in our design. This slide shows in detail how it works. Sleep duration at time t+1 is a function of sleep duration of time t and gradient at time t. the geared sleep reduction function reduces the sleep cycle just like a gear of a car. For example, sleep cycle at time t+1 is equal to previous sleep cycle if gradient is less than 0. When gradient is between 0 and .005, the cycle is halved. The graph in the right side shows the adjustment of sleep Cycle with the change in phenomenon as indicated by red and green line respectively. Here initial sleep cycle Sin is set to 256 seconds, Data threshold is 95 degrees, simulation duration of 20K seconds. At the beginning the temperature gradient decreases, so the sleep cycle does not change. At around this time temperature gradient increases resulting in shrinking of sleep cycle to about 128 seconds. At around this time, the temperature gradient increasing more sharply reducing the sleep cycle to about 64 seconds and finally here the sleep cycle reduces to 32 seconds. That means, at this point of blue cross when the event occurs the node wakesup would wakeup 32 seconds, and turns both radios and sensor on while going to phase 2. Now, using such sleep reduction function, a sensor node will wakeup at the event of threshold crossing in a temporally correlated environment, and all sensors will wakeup at the same time in a spatially correlated environment We achieve energy efficiency in first phase by turning the radios off. If this is the case, how do we ensure 1)a node wakesup at the desired event of threashold, and 2) all nodes wakeup at the same time. The sleep reduction function is the key in our design. This slide shows in detail how it works. Sleep duration at time t+1 is a function of sleep duration of time t and gradient at time t. the geared sleep reduction function reduces the sleep cycle just like a gear of a car. For example, sleep cycle at time t+1 is equal to previous sleep cycle if gradient is less than 0. When gradient is between 0 and .005, the cycle is halved. The graph in the right side shows the adjustment of sleep Cycle with the change in phenomenon as indicated by red and green line respectively. Here initial sleep cycle Sin is set to 256 seconds, Data threshold is 95 degrees, simulation duration of 20K seconds. At the beginning the temperature gradient decreases, so the sleep cycle does not change. At around this time temperature gradient increases resulting in shrinking of sleep cycle to about 128 seconds. At around this time, the temperature gradient increasing more sharply reducing the sleep cycle to about 64 seconds and finally here the sleep cycle reduces to 32 seconds. That means, at this point of blue cross when the event occurs the node wakesup would wakeup 32 seconds, and turns both radios and sensor on while going to phase 2. Now, using such sleep reduction function, a sensor node will wakeup at the event of threshold crossing in a temporally correlated environment, and all sensors will wakeup at the same time in a spatially correlated environment

9. 9 Performance Metrices Energy Total energy dissipation Sensing energy Communication: Cluster formation + Reporting Latency Delay between report generation and actual time of threshold being reached Responsiveness Difference between reported data value and threshold (e.g. degree of temperature) Now I will present our results of performance analysis of the scheme. We use three performance metrices: energy, latency and responsiveness. Total energy dissipation of a node is divided into sensing and communication energy which is energy of cluster formation and data reporting. Latency is defined as the delay between the report generation and actual time of threshold being reached. Responsiveness is defined as the difference between reported data value and the actual data threshold. Now I will present our results of performance analysis of the scheme. We use three performance metrices: energy, latency and responsiveness. Total energy dissipation of a node is divided into sensing and communication energy which is energy of cluster formation and data reporting. Latency is defined as the delay between the report generation and actual time of threshold being reached. Responsiveness is defined as the difference between reported data value and the actual data threshold.

10. 10 Energy Analysis This slide shows analytical comparison of energy analysis of these three protocols. I just present the results, for details you can see the paper. Nodes in election sense in phase 1 and phase 2, form cluster only once in phase 2, and report at every Tr interval in phase 2. In leach, nodes form cluster at every Tc interval, and sense and report at every Tr interval. In Teen, nodes sense continuosly, form cluster at every Tc and report at every Tr. The net result is: usually cluster formations energy is much greater than sensing energy. So comparing these red circled terms which is the dominant energy cost, election results in most energy efficiency and savings in cluster formation results in major energy savings. When Es greater, savings in sensing predominates In election compared to TEENThis slide shows analytical comparison of energy analysis of these three protocols. I just present the results, for details you can see the paper. Nodes in election sense in phase 1 and phase 2, form cluster only once in phase 2, and report at every Tr interval in phase 2. In leach, nodes form cluster at every Tc interval, and sense and report at every Tr interval. In Teen, nodes sense continuosly, form cluster at every Tc and report at every Tr. The net result is: usually cluster formations energy is much greater than sensing energy. So comparing these red circled terms which is the dominant energy cost, election results in most energy efficiency and savings in cluster formation results in major energy savings. When Es greater, savings in sensing predominates In election compared to TEEN

11. 11 Latency and Responsiveness This slide shows the latency and responsiveness comparison of these protocols. Average latency of election is half of the last sleep cycle which we expect to be very small compared to initial sleep cycle sin because of temporal correlation and sleep reduction function. In leach it is half of the reporting interval, and in teen it is half of the fixed sleep cycle. Now Gmax is the maximum gradient threshold that election responds to. So the worst case responsiveness of election is Gmax times sin, in leach it is gmax times reporting interval and in teen it is gmax time fixed sleep duration S. This slide shows the latency and responsiveness comparison of these protocols. Average latency of election is half of the last sleep cycle which we expect to be very small compared to initial sleep cycle sin because of temporal correlation and sleep reduction function. In leach it is half of the reporting interval, and in teen it is half of the fixed sleep cycle. Now Gmax is the maximum gradient threshold that election responds to. So the worst case responsiveness of election is Gmax times sin, in leach it is gmax times reporting interval and in teen it is gmax time fixed sleep duration S.

12. 12 Simulation Setup High level simulation ELECTION TEEN Hybrid Fixed sleep cycle (like TEEN) On demand cluster formation (like ELECTION) Network simulated 36 uniformly distributed sensors Network divided into 4 quadrant Each quadrant is assigned a sensing pattern Phenomenon simulated Phenomenon 1: changes 100 times during entire simulation Phenomenon 2: changes 20 times We also perform a high level simulation of election, teen and a hybrid of election and teen. The hybrid has fixed sleep cycle like teen, but instead of periodic cluster formation it forms cluster on demand like election. We take a network of 36 nodes and divide them into 4 quadrant and assign each a sensing pattern. We simulated 2 phenomenon: the first changes 100 times and the second changes 20 times during entire simulation We also perform a high level simulation of election, teen and a hybrid of election and teen. The hybrid has fixed sleep cycle like teen, but instead of periodic cluster formation it forms cluster on demand like election. We take a network of 36 nodes and divide them into 4 quadrant and assign each a sensing pattern. We simulated 2 phenomenon: the first changes 100 times and the second changes 20 times during entire simulation

13. 13 Simulation Parameters Simulation time: 600K seconds ELECTION Geared sleep reduction function Initial sleep cycle (Sin): 256 secs TEEN Cluster formation interval (Tc): 6K secs Fixed sleep cycle: 50 secs Hybrid Cluster formation: on demand Fixed sleep cycle: 50 secs Here is a list of simulation parameters. Here is a list of simulation parameters.

14. 14 Remaining Energy Analysis This slide shows energy comparison of these protocols considering average remaining energy of the whole network. The x axis represent time and y axis represents average remaining energy of the network. The graphs on left side shows the scenarios when sensors consume 10% of transmission energy and the graph on right side shows the scenarios when sensor consume 1% of transmission energy for example passive sensors. Teen senses continuously, forms cluster at every Tc interval and sleep with a fixed sleep cycle irrespective the environmental condition. Therefore its major cost is in continuous sensing and cluster formation. The hybrid saves cluster formation energy, but spends for continuous sensing. The second phenomena changes slowly which gives election a larger expected sleep duration which in turn reduces sensing energy in the first phase. But it does not buy anything for the other two because they do not adjust sleep cycle based on the environment. The graph in right side shows energy analysis in the case when sensor consumes insignificant amount of energy. Election is still more energy efficient. We see that energy difference between teen and hybrid is larger in this case because cluster formation energy is the dominant energy cost in this scenario which is periodic in teen and on demand in hybrid. This slide shows energy comparison of these protocols considering average remaining energy of the whole network. The x axis represent time and y axis represents average remaining energy of the network. The graphs on left side shows the scenarios when sensors consume 10% of transmission energy and the graph on right side shows the scenarios when sensor consume 1% of transmission energy for example passive sensors. Teen senses continuously, forms cluster at every Tc interval and sleep with a fixed sleep cycle irrespective the environmental condition. Therefore its major cost is in continuous sensing and cluster formation. The hybrid saves cluster formation energy, but spends for continuous sensing. The second phenomena changes slowly which gives election a larger expected sleep duration which in turn reduces sensing energy in the first phase. But it does not buy anything for the other two because they do not adjust sleep cycle based on the environment. The graph in right side shows energy analysis in the case when sensor consumes insignificant amount of energy. Election is still more energy efficient. We see that energy difference between teen and hybrid is larger in this case because cluster formation energy is the dominant energy cost in this scenario which is periodic in teen and on demand in hybrid.

15. 15 Delay and Responsiveness This slide shows the delay and responsiveness of these protocols. We simulated different sensing patterns in different simulation run in x axis. As expected delay in teen and hybrid is about half of the fixed 50 second sleep cycle. Responsiveness in election is clearly higher than teen and hybrid and it guarantees a stable difference of 0.1 degree from the preset threshold. This slide shows the delay and responsiveness of these protocols. We simulated different sensing patterns in different simulation run in x axis. As expected delay in teen and hybrid is about half of the fixed 50 second sleep cycle. Responsiveness in election is clearly higher than teen and hybrid and it guarantees a stable difference of 0.1 degree from the preset threshold.

16. 16 Limitations Dependency on the underlying phenomenon A priori information of the environment may not be available Not suitable for phenomenon that does not exhibit spatio-temporal correlation (e.g. seismic monitoring) Synchronization problem in phase 1 Now lets see the limitation or weaknesses of our scheme. First election is highly dependent on the underlying phenomenon. An a priori information of the environment is available for industrial application which may not available in other contexts. It is not suitable for phenomenon that does not exhibit the spatio-temporal correlation, for example, seismic monitoring. Sensors do not communicate in first phase which may cause synchronization problems in phase 1.Now lets see the limitation or weaknesses of our scheme. First election is highly dependent on the underlying phenomenon. An a priori information of the environment is available for industrial application which may not available in other contexts. It is not suitable for phenomenon that does not exhibit the spatio-temporal correlation, for example, seismic monitoring. Sensors do not communicate in first phase which may cause synchronization problems in phase 1.

17. 17 Conclusion New sleep scheduling scheme for wireless active sensor networks Exploit spatio-temporal correlation of physical phenomenon Adaptively adjust sleep cycle Outperforms LEACH and TEEN with respect to energy, latency and responsiveness To conclude: we designed a new sleep scheduling scheme for wireless active sensor networks. The scheme exploits spatio-temporal correlation to adaptively adjust sleep cycles of nodes. The analytical and simulation results show that our scheme outperforms leach and teen with respect to energy efficiency, latency and responsive.To conclude: we designed a new sleep scheduling scheme for wireless active sensor networks. The scheme exploits spatio-temporal correlation to adaptively adjust sleep cycles of nodes. The analytical and simulation results show that our scheme outperforms leach and teen with respect to energy efficiency, latency and responsive.