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Sensor nodes compute their inactivity periods taking into account the inactivity periods of their neighboring nodes. The signals are reconstructed at the sink node, from the sampled values received from sensor nodes. DECA is designed to keep the reconstruction error of the monitored process below a given fraction of its actual value.
Since DECA works on the application layer, it can be combined with medium access control and routing protocols to further improve energy management. The proposed algorithm is evaluated with simulations using real data. Since a strategy for managing the sleeping period in routing nodes is proposed, a metric for evaluating the effects of sleeping periods on network connectivity is also proposed.
Recent advances in micro-electronics and wireless communications made it possible to develop and deploy low cost, low energy consumption, and tiny sensors.
These have been used to build wireless sensor networks WSNs [ 1 ]. A WSN can be applied in several domains [ 2 , 3 , 4 ], such as i in medical applications, to remotely monitor patients and their biometric data; ii for military purposes, to monitor forces; iii in industrial automation; and iv to sense variables in a region of interest.
In this work, we consider that a WSN is employed to sense a physical variable, a field—a process whose value depends on space coordinates x,y , z and on time t. In a WSN, the sink is usually more robust than the sensor nodes, and it can be used as a gateway [ 1 ]. The problem that we consider is: How can we make an energy-efficient usage of the WSN while providing an acceptable reconstruction of the sensed field for the monitoring application?
As acceptable reconstruction, we assume that the reconstruction error is kept below a predefined value, while energy efficiency involves improving the network autonomy, by increasing its lifetime. More specifically, we assume that the network lifetime is the time until the first node dies, i. In this work, we propose a distributed energy conservation algorithm DECA that runs in the application layer for wireless sensor networks in monitoring applications.
Roughly, each sensor independently considers information which is expected to be available at the sink node to predict a future measured value and this in turn is used to compute the inactivity period the interval while the node stays in sleep mode of a given node S i.
Each sensor node sleeps during its inactivity period, and then the node wakes up, measures the desired quantity, and transmits it if the error between the current measurement and the predicted behavior of the monitored field is larger than a given threshold, reiterating the process.
Energy saving and the resulting increase in network lifetime is achieved by both reducing the amount of transmissions and also by putting nodes to sleep during their inactivity periods. The decision whether or not to sleep is performed by each node individually, that is locally.
Nodes do not depend on the reception of sleeping commands or data from the sink. Therefore, when making this decision, two aspects need to be considered. The first is that for computing its inactivity period IP i , each node S i must consider how the sink reconstructs the process from the measurements the sink receives.
As a consequence, in order to guarantee a trusty reconstruction of the process, we impose the constraint of keeping the reconstruction error within an acceptable distortion criterion at the sink and not solely at the sensor. Therefore, since each node takes its own decision, the proposed strategy is decentralized and distributed.
How does putting a given node to sleep impact the network connectivity? Inversely, when deciding its inactivity period, each node must account for the impacts of this decision on network connectivity, that is, on other nodes.
This is considered in DECA; nodes forward their own inactivity periods together with the measurements and an inactivity period is used by the first-hop router when it computes its inactivity period. Therefore, although running in the application layer and the decision of how long to sleep being taken autonomously by each sensor, network topology and routes awareness are inherent to DECA since the inactivity periods of sensors being routed by a node are considered by the node when deciding for how long to sleep.
In addition, a fringe benefit of forwarding inactivity periods is that sensor nodes are not required to be synchronized, since the algorithm does not demand an absolute time base, employing just relative times time intervals.
Since nodes are put to sleep during their estimated inactivity periods, in order to evaluate the impact of this in the network connectivity, we propose a metric that we call success ratio. The proposed algorithm is assessed by simulations. In addition real environmental data are employed. We have also verified that network connectivity is not impaired by the algorithm, what, indeed, makes it worth to use DECA on any monitoring application alike the one considered by DECA.
This work is structured as follows. Section 2 presents related works and proposals dealing with energy conservation in WSNs. In Section 3 , we present a model for WSN based monitoring applications.
In Section 5 , we present the sensor node energy model used in this work and the simulation aspects. In Section 6 , we present results obtained with simulations considering real environmental data.
At last, conclusions are discussed in Section 7. In [ 6 ], a survey of energy saving methods for WSNs is presented, including a taxonomy for energy saving schemes. Some energy saving methods are briefly mentioned hereafter.
According to [ 1 ], communication i. This means that it may be advantageous for a node to process data, in order to compress it or to decide whether to transmit it or not and thus save energy while sleeping [ 7 ]. In [ 8 ], spatial and temporal correlations between measured samples are used to decrease the amount of transmissions, saving sensor node energy. The sink predicts the field being sensed by sensor nodes using information that it receives from the nodes.
Using the received data, the sink estimates for how long each node can sleep and then sends different messages for the distinct nodes conveying their sleeping periods. This strategy is centralized as the sink decides for how long the nodes can sleep, and a given node may not be put to sleep at all, in the case that packets are lost, thus severely impacting energy saving.
Differently, our strategy is completely decentralized, since each sensor node computes its own sleeping period. Similarly, in [ 9 ], the temporal pattern of samples measured by sensor nodes is used to reduce the amount of transmissions to the sink node.
The monitored process is compared against its expected behavior, if they match then the node does not transmit its value in order to increase network lifetime. If the measurements do not match the expected behavior, then the nodes transmit the values. Since this scheme does not involve node sleeping, a confinement of its gain in network lifetime is obtained.
DECA tries to track the monitored process and the sensor nodes do not need to know a priori the behavior of the process that is being sensed. Moreover, DECA makes node to switch into a sleeping mode, increasing the autonomy of sensor nodes. In [ 10 ], a sleep-awake scheme is proposed, in which a network coordinator periodically transmits a beacon frame with a sleeping command. Therefore, this scheme is centralized and nodes enter the sleeping state synchronously upon the reception of this command.
To improve energy saving, a power control mechanism in the MAC sublayer is also proposed, based on the distance between neighboring nodes. In our proposal, sensor nodes do not receive any sleeping command which could eventually be lost because of the wireless channel. Furthermore, DECA runs at application layer of sensor nodes, independently of lower-layer protocols. It simply allows each node to verify if the measured quantity did not change enough, in which case it is not transmitted.
This approach resembles the one presented in [ 11 ]. The energy conservation algorithm in [ 11 ] aims at reducing the amount of transmissions by managing the need for them. A sample is transmitted only if the percentage variation between it and the last transmitted sample is greater than a given threshold and nodes sleep between transmissions.
The current proposal outperforms the previous one [ 11 ] because DECA obtains a constrained reconstruction error. The proposals in [ 3 , 8 , 9 ] consider a uniform sampling interval for the sampled measurements. However, once a node is put into sleep mode, the sampling is not uniform anymore; this aspect emerged in [ 11 ] and is fully considered by the algorithm presented in this work.
Finally, DECA can be used together with other energy saving protocols, as for example the ones proposed to save energy in the network layer and in the MAC sublayer [ 13 , 14 , 15 , 16 , 17 , 18 ] in order to further augment energy saving. The monitored process is considered to be sufficiently smooth to be tracked with a simple predictor.
The received samples are equal to the transmitted samples, except by eventually lost data. We want to reconstruct the process s x,y , t within an acceptable error criterion. In order to save energy nodes do not transmit all measured samples, i.
Replacement of the superscript M by R provides a predictor that can be applied at the sink node. Assuming that all transmissions are successful, i. A future sample is guessed using linear interpolation which requires very low computational complexity and memory requirements.
As depicted in Fig. Nevertheless, one wants to keep the reconstruction error at the sink within the bounds in Eq. Therefore, the influence of the quantities known at the sink on the sensor decision to sleep or not needs to be addressed. All information that is available at the sink about the measurements of a sensor is known by the sensor itself.
On the other hand, the contrary is hardly true. Let us resort the indices of the last two transmitted received samples and their instants of transmissions with respect to the sample indices at the sensor. The sensor is aware of its transmissions to the sink. Therefore, one applies the sink reconstruction model at the sensor for obtaining an acceptable range for its inactivity period, IP i [ n ].
This range is guaranteed by choosing the smallest case between Eqs. The acceptable range of IP i [ n ] for a given sensor node i guarantees a reconstruction error within a prescribed error at the sink.
Considering the use of a simple linear predictor, we derived for how long a node can sleep while the reconstruction is within an allowed distortion range. For that each sensor compares the predictor model available at the sink for its measurements against a predictor model for the sensor, which is more frequently updated, i.
It is worth noticing that other more sophisticated interpolators as spline, cubic, or polynomial could also be evaluated. Next, we use this linear predictor to propose DECA. DECA works directly in the application layer of each sensor node, regardless, for example, of the routing protocol used. The steps of the algorithm are showed in Algorithm 1. The inactivity period is initially set to T granularity seconds and it is updated by DECA at each sensor.
After that, the sensing—processing— eventual transmission—sleeping procedure occurs. For ease of understanding, Algorithm 1 is presented in two parts: