FARMS: Fusionable ambient renewable MACS

Iyer, Vasanth; Iyengar, S. S.; Phoha, Vir V.; Srinivas, M.B.

doi:10.1109/sas.2009.4801800

Cited by 8 publications

(6 citation statements)

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“…The fusion of energy harvesting applications with power-aware MAC was studied in terms of deployment of low-level protocols for 802.11, CSMA, and B-MAC. Furthermore, if the data is routed using multi-hop algorithms and is MAC-centric, then the distributed sleep scheduling [2] is observed to reduce the percentage of energy lost during overhearing and collision. The performance of routing algorithms with MAC losses has a long tail which is similarly observed in Power Law.…”

Section: Discussionmentioning

confidence: 99%

“…The simulated results calculated are prioritized for parameters that are not dependent on large constant energy resources but are based on Qos, which include data reliability, MAC latency, and energy-savings owing to sleep scheduling. These MAC-dependent parameters are adapted for ultra-low-duty cycles for renewable lifetime model of energy-harvesting application that rely on ambient energy (FARMS) [2]. This simulation approach allows the integration of specific needs for emulating a real mote, as well as the study of the adaptation of MAC to Qos, which is independent of the lifetime model.…”

Section: Motivationmentioning

confidence: 99%

“…For a multi-hop sensor network using a lifetime renewable energy model [2], an active node that is ready to transmit at a given instance will take a time period t to reach its neighbors, and the response time for receiving the message back will be 2 × t or a preset TTL value. To efficiently multi-hop, we need to at least have one neighbor that can respond to the active node, but in a large WSN network, the traffic is directly proportional to the number of neighbors.…”

Section: Renewable Energy Model and Energy Harvestingmentioning

confidence: 99%

“…In this study, we assumed a population of M sensor nodes, sharing the same noiseless radio channel, without any hidden or exposed terminals. In addition, we also considered a single-hop fully connected cluster of nodes, i.e., all the sensors can hear transmission from any sensor in the cluster [2]. Furthermore, no central control existed and each sensor node had an equal probability of generating a packet for transmission to any of the rest of the M − 1 nodes in the cluster.…”

Section: Performance Analysis Of Wsn Data Aggregation Algorithmsmentioning

confidence: 99%

“…Sensor network harvesting provides mechanisms for network protocols to operate efficiently, and the three key elements of the renewable energy protocol design are, data reception, data transmission and neighbor Management. This phase of the enhancement of the simulator allows the improvement on the scheduling of the nodes that participate during routing using application control, as described in Theorem 1, more detailed of the protocol implementations are provided in the FARMS paper [2].…”

Section: Mac Performance Using Ultra-low Duty Cyclementioning

confidence: 99%

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Multi-hop scheduling and local data link aggregation dependant Qos in modeling and simulation of power-aware wireless sensor networks

Iyer

Iyengar²,

Murthy

et al. 2009

Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wireless

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In this study of wireless sensor networks (WSN) protocols, the application Qos, system, and protocol performance metrics are measured for a large scalable wireless deployment using a typical wireless radio and an energy model. As there are many different types of WSN algorithms, we have categorized it into pro-active, re-active, and query driven information processing. A typical Qos is based on the useful lifetime of sensor nodes, after which reliability of the sensor data cannot be guaranteed and typically, a threshold such as a percentage of the sensor drains out of energy or a minimum through-put of real-time data from the sensor network is expected, which is used to compare the Qos of the routing algorithm. The results from lifetime based Qos, measured in simulation seconds, for the implemented protocols show that with varying sampled data sources for a BE Qos multi-hop deployment and varying percentage of cluster heads in a time-synchronized deployment, the lifetime is based on network size and protocol invariant. However, low sensing ranges result in dense networks, and therefore, it becomes necessary to achieve an efficient medium-access protocol subjected to power constraints. Scalability of sensor network applications are based on energy energy-harvesting techniques in which the various layers of the network interoperate and extend the system network lifetime, the battery residual power per node, and the application reliability in terms of cross-layer energy savings. In this study, we have extended the lifetime metrics from a constant metrics into a break down of how much percentage of time is spent for Tx, Rx, and Idle tasks, respectively. This helps one to highlight the cross-layer energy dissipation per node and how the performance of an algorithm differs in terms of duty-cycling.Furthermore, we have shown that the energy savings due owing to the distributed algorithms in a large sensor network will not be practical without a complimentary lowerlayer MAC. We show have demonstrated that the Qos is very much related to the ambient conditions, namely, are the Rx and Idle modes. From these preliminary results, we have added a new category of WSN protocols which are based ion the renewable energy resources, namely, the Fusion Ambient Renewable Measuring Sensors (FARMS). The study of sensor FARMS -harvesting applications allows one to measure the impact on Idle, Sleep, and renewable energy cycles as well as their unique deployment (density) needs, as all the sensor are not active(Rx) at all times. We have also shown that the efficiency of cross-layer Qos performance of routing algorithms with MAC losses has a long tail which is similarly observed in Power Law. In this sensor network model we like to show the complexity of clustering, messaging and data rate in terms of ( (N ) log N ), (N ) and (log 2 N ) where N is the number of nodes.

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Renewable Energy Model and Energy Harvestingmentioning

confidence: 99%

Section: Performance Analysis Of Wsn Data Aggregation Algorithmsmentioning

confidence: 99%

Section: Mac Performance Using Ultra-low Duty Cyclementioning

confidence: 99%

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