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26
Dozer: ultra-low power data gathering in sensor networks
- In IPSN ’07
, 2007
"... Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this longevity in unattended operation it is crucial to minimize energy consumption of the battery-powered sensor nodes. This p ..."
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Cited by 118 (5 self)
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Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this longevity in unattended operation it is crucial to minimize energy consumption of the battery-powered sensor nodes. This paper proposes Dozer, a data gathering protocol meeting the requirements of periodic data collection and ultra-low power consumption. The protocol comprises MAC-layer, topology control, and routing all coordinated to reduce energy wastage of the communication subsystem. Using a tree-based network structure, packets are reliably routed towards the data sink. Parents thereby schedule precise rendezvous times for all communication with their children. In a deployed network consisting of 40 TinyOSenabled sensor nodes, Dozer achieves radio duty cycles in the magnitude of 0.2%.
Software-based on-line energy estimation for sensor nodes
- in Fourth Workshop on Embedded Networked Sensors
, 2007
"... Energy is of primary importance in wireless sensor networks. By being able to estimate the energy consumption of the sensor nodes, applications and routing protocols are able to make informed decisions that increase the lifetime of the sensor network. However, it is in general not possible to measur ..."
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Cited by 81 (20 self)
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Energy is of primary importance in wireless sensor networks. By being able to estimate the energy consumption of the sensor nodes, applications and routing protocols are able to make informed decisions that increase the lifetime of the sensor network. However, it is in general not possible to measure the energy consumption on popular sensor node platforms. In this paper, we present and evaluate a softwarebased on-line energy estimation mechanism that estimates the energy consumption of a sensor node. We evaluate the mechanism by comparing the estimated energy consumption with the lifetime of capacitor-powered sensor nodes. By implementing and evaluating the X-MAC protocol, we show how software-based on-line energy estimation can be used to empirically evaluate the energy efficiency of sensor network protocols. 1.
Dynamic point coverage problem in wireless sensor networks: a cellular learning automata approach
- Journal of Ad hoc and Sensors Wireless Networks
, 2010
"... One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynamic point coverage problem, which is to detect some moving target points in the area of the sensor network, a node is need ..."
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Cited by 15 (9 self)
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One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynamic point coverage problem, which is to detect some moving target points in the area of the sensor network, a node is needed to be active only when a target point is in its sensing region. A node can be aware of such times using a predicting mechanism. In this paper, we propose a solution to the problem of dynamic point coverage using irregular cellular learning automata. In this method, learning automaton residing in each cell in cooperation with the learning automata residing in its neighboring cells predicts the existence of any target point in the vicinity of its corresponding node in the network. This prediction is then used to schedule the active times of that node. In order to show the performance of the proposed method, computer experimentations have been conducted. The results show that the proposed method outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
Data Aggregation in Sensor Networks using Learning Automata
"... Abstract: One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many n ..."
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Cited by 14 (7 self)
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Abstract: One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may results from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata based data aggregation method in sensor networks when the environment's changes can not be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.
A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Computer Networks doi:10.1016/j
, 2010
"... The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target p ..."
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Cited by 12 (6 self)
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The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
How much energy saving does topology control offer for wireless sensor networks? – A practical study
- Elsevier/ACM Computer Communications
, 2007
"... Topology control is an important feature for energy saving, and many topology control protocols have been proposed. Yet, little work has been done on quantitatively measuring practical performance gains that topology control achieves in a real sensor network. This is because many existing protocols ..."
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Cited by 11 (0 self)
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Topology control is an important feature for energy saving, and many topology control protocols have been proposed. Yet, little work has been done on quantitatively measuring practical performance gains that topology control achieves in a real sensor network. This is because many existing protocols either are too complex or make too impractical assumptions for a practical implementation and analysis. A rule of thumb or a practical upper bound on the energy saving gains achievable by topology control would assist engineers in estimating the overall energy budget of a real sensor system. This paper proposes a new topology control protocol simple enough to permit a straightforward stochastic analysis and also a real implementation in Mica2. This protocol is currently deployed in our testbed network of 42 Mica2 nodes. Our contribution is not on the novelty of this protocol but on a practical performance bound we can study using this protocol. The stochastic analysis reveals that topology control can achieve a power gain proportional to network density divided by a factor of eight to ten. Our experiment result from the real testbed tests confirms this finding. We also find a tradeoff in terms of throughput loss due to reduced density by topology control which amounts to about 50 % throughput loss. These performance figures represent rough rules of thumb on energy efficiency achievable even by a very simple, unoptimized protocol.
An Architecture for Robust Sensor Network Communications
, 2005
"... Node clustering in sensor networks increases scalability, robustness, and energy-efficiency. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. We propose REED (Robust Energy ..."
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Cited by 6 (0 self)
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Node clustering in sensor networks increases scalability, robustness, and energy-efficiency. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. We propose REED (Robust Energy-Efficient Distributed clustering), for clustering sensors deployed in hostile environments in an interleaved manner with low complexity. Our primary objective is to construct a k-faulttolerant (i.e., k-connected) clustered network, where k is a constant determined by the application. Fault tolerance is achieved by selecting k independent sets of cluster heads (i.e., cluster head overlays) on top of the physical network, so that each node can quickly switch to other cluster heads in case of failures. The independent cluster head overlays also give multiple vertex-disjoint routing paths for load balancing and security. Network lifetime is prolonged by selecting cluster heads with high residual energy and low communication cost, and periodically re-clustering the network. We prove that REED asymptotically achieves k-connectivity if certain conditions on node density are met. We also discuss inter-cluster routing and MAC layer considerations, and investigate REED clustering properties via extensive simulations.
Efficient Clustering for Improving Network Performance in Wireless Sensor Networks
"... Abstract. Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the ..."
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Cited by 6 (2 self)
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Abstract. Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time. The main objective of this paper is to provide a useful fully-distributed inference algorithm for clustering, based on belief propagation. The algorithm selects cluster heads, based on a unique set of global and local parameters, which finally achieves, under the energy constraints, improved network performance. Evaluation of the algorithm implementation shows an increase in throughput in more than 40 % compared to HEED scheme. This advantage is expressed in terms of network reliability, data collection quality and transmission cost.
Energy-efficient clustering/routing for cooperative MIMO operation in sensor networks
, 2008
"... Abstract — Employing multi-input multi-output (MIMO) links can improve energy efficiency in wireless sensor networks (WSNs). Although a sensor node is likely to be equipped with only one antenna, it is possible to group several sensors to form a virtual MIMO link. Such grouping can be formed by mean ..."
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Cited by 5 (1 self)
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Abstract — Employing multi-input multi-output (MIMO) links can improve energy efficiency in wireless sensor networks (WSNs). Although a sensor node is likely to be equipped with only one antenna, it is possible to group several sensors to form a virtual MIMO link. Such grouping can be formed by means of clustering. In this paper, we propose a distributed MIMO-adaptive energyefficient clustering/routing scheme, coined cooperative MIMO (CMIMO), which aims at reducing energy consumption in multihop WSNs. In CMIMO, each cluster has two cluster heads (CHs), which are responsible for routing traffic between clusters (i.e., inter-cluster communications). CMIMO has the ability to adapt the transmission mode and transmission power on a per-packet basis. The transmission mode can be one of four transmit/receive configurations: 1 × 1 (SISO), 2 × 1 (MISO), 1 × 2 (SIMO), and 2 × 2 (MIMO). We study the performance of CMIMO via simulations. Results indicate that our proposed scheme achieves a significant reduction in energy consumption, compared to nonadaptive clustered WSNs. I.
Putting the software radio on a low-calorie diet
- In Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks 2010, (Hotnets ’10
"... Modern software-defined radios are large, expensive, and power-hungry devices and this, we argue, hampers their more widespread deployment and use, particularly in low-power, size-constrained application settings like mobile phones and sensor networks. To rectify this problem, we propose to put the ..."
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Cited by 4 (1 self)
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Modern software-defined radios are large, expensive, and power-hungry devices and this, we argue, hampers their more widespread deployment and use, particularly in low-power, size-constrained application settings like mobile phones and sensor networks. To rectify this problem, we propose to put the software-defined radio on a diet by redesigning it around just two core chips – an integrated RF transceiver and a Flash-based, mixed-signal FPGA. Modern transceivers integrate almost all RF front-end functions while emerging FPGAs integrate nearly all of required signal conditioning and processing functions. And, unlike conventional FPGAs, Flash-based FPGAs offer sleep mode power draws measured in the microamps and startup times measured in the microseconds, both of which are critical for low-power operation. If our platform architecture vision is realized, it will be possible to hold a software-defined radio in the palm of one’s hand, build it for $100, and power it for days using the energy in a typical mobile phone battery. This will make software radios deployable in high densities and broadly accessible for research and education.
