This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic clusterbased sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.
Recent advances in microelectromechanical systems are driving the developments of lowcost and and lowpower wireless sensors, with diverse applications in the physical world in areas such as environmental monitoring, disaster recovery, industrial process control, and smart environments. With sensors placed close to an event, wireless sensor networks can observe the phenomenon and receive data. However, having too few active sensors or excessive ones may result in reduced sensing coverage or severe interference, which will have a great influence on network performance features such as energy and bandwidth efficiency, and sensing quality. Therefore, sensing scheduling schemes may be implemented to tackle basic problems of sensor networks (e.g. energy constraints and communication interference) in order to reduce energy consumption and prolong network lifetime.
Sensor scheduling aims to maintain a balance of network resources. Recent research has found that significant energy savings can be achieved by dynamic power management in sensor networks [
For the CASA scheme, given the local information such as neighboring connectivity, the round determination problem may be solved centrally by the clusterheads. For the DASA scheme, as the clusterhead broadcasts a message to start the scheduling assignment, each sensor initializes a random waiting timer with a value which is related to the cluster topology and the neighbor information. If the random waiting timer expires, then the sensor broadcasts a message proclaiming that it is a good candidate to be a group member, which also serves to notify its neighbors that it has a higher priority for the sensing task. Based on the received messages from its neighboring cluster members, each cluster member may use the data gathering strategy (detailed in Section 3.3) to schedule itself to a specific round.
In order to facilitate performance evaluation of a sensor scheduling protocol design, two analytical models, a neural network model and a probabilistic model, are proposed. For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry is applied to abstract the properties of the algorithm. Moreover, based on the analysis, the sensor lifetime and cluster lifetime is further explored to show how the operations of the proposed schemes may prolong the network lifetime.
The organization of this paper is as follows: Section 2 reviews the current literature on the sensor scheduling management. Section 3 describes the system model and algorithm for sensor scheduling in a clusterbased network topology. In Section 4, a neural network model and a probabilistic model are built up to approximate the desire performance and estimate the sensing rounds of the proposed schemes. Section 5 summarizes the performance of the proposed scheduling methodology. Finally, Section 6 draws conclusions and shows future research directions.
A large number of sensor scheduling and coverage maintenance protocols have been proposed [
Hohlt
Cheng
Since energy efficiency and reasonable sensing coverage can be achieved by exploiting the sensing spatial redundancy, redundant sensors may be turned off to save energy [
In contrast, the approaches of this paper consider coverage, connectivity, and sensing spatial redundancy simultaneously in order to improve energy efficiency in a hierarchical network structure. For the CASA approach, the clusterhead collects local topology information to manage the sensing schedule centrally. By approximating the network behavior throughout the neural network learning process, the clusterhead may be able to roughly predict the performance of the scheduling management. For the DASA approach, the setting of the random waiting timer allows each sensor to exploit the information about coverage, connectivity, and sensing spatial redundancy such that a balance of network resources can be maintained. Due to the randomized property of the waiting timer, the probabilistic model is proposed to abstract global network behavior. The comparison of the proposed approaches and the other clusterbased schemes [
This section describes two scheduling management schemes for organizing the sensing tasks, the Centralized Adaptive Scheduling Algorithm (CASA) and the Distributed Adaptive Scheduling Algorithm (DASA). The main assumptions of the network are: (1) All sensors are homogeneous with the same transmission range; (2) The sensors are fixed without location information; (3) Symmetric communication channel: all links between sensors are bidirectional; (4) All sensors perform the sensing task periodically. Note that there are no base stations to coordinate or supervise activities among sensors.
When sensors of a network are first deployed, they may apply the Clustering Algorithm via Waiting Timer (CAWT) from [
Sensors update their neighbor information (i.e. a counter specifying how many neighbors it has detected) and decrease the random waiting time based on each ‘new’
After applying the CAWT, there are three different kinds of sensors: (1) the clusterheads (2) sensors with an assigned cluster ID (3) sensors without an assigned cluster ID, which will join any nearby cluster and become 2hop sensors. In this phase, each sensor initiates two rounds of local flooding to its 1hop neighboring sensors, one for broadcasting sensor ID and the other for broadcasting cluster ID, to select clusterheads and form 2hop clusters.
Assume that a cluster of sensor nodes share a common view of a local clock time [
There are many possible data gathering strategies to accomplish the sensing tasks. In many applications, the monitored area may be used to determine the group members in a specific round. For instance, since the core sensing field is covered by the sensing area of 1hop cluster members in the clusterbased topology, the round determination problem can be expressed by the coverage subject to the number of 1hop cluster members covered in the round sensing area,
When developing the sensing schedule, two rounds of local flooding are initiated in order to gather topology information for the clusterhead in the 2hop cluster structure. Hence, given the local information such as neighboring connectivity, a clusterhead may choose a 2hop cluster member and a 1hop relay node to initialize the proposed scheduling algorithm. After that, the clusterhead may randomly pick 1hop cluster members, which have no communication links with the chosen group members, as the new round group members. The purpose for this selection policy is to reduce the overlap between group members in the same round. Note that the relay node can be selected as the group member in the following round since it is not responsible for sensing at this round. If all the 2hop cluster members have been selected for initializing the sensing rounds, the clusterhead will select a 1hop cluster member for starting the new round. This procedure is repeated until all of the cluster members have been assigned. Then, with a common local clock time in the cluster, the clusterhead triggers two rounds of 1hop flooding for broadcasting the sensor scheduling information throughout the 2hop cluster topology.
Observe that the overlap is only approximately minimized; in our experiments we have noticed that the answers tend to be close to the optimal. The pseudocode of the proposed algorithm is presented in
After establishing the sensing schedule in each cluster, network connectivity may be maintained with two phases of operation (observation and confirmation phases). The period of the observation phase may last several sensing cycles (
There are four possible scenarios when determining the gateway nodes: (1) When the sensing node receives only one broadcast message from an active node in the nearby cluster during its sensing period, these two nodes form a pair of distributed gateways. Hence, a sensing node or a relay node in the nearby cluster may be a gateway under this condition; (2) If the sensing node receives multiple broadcast messages from the same nearby cluster, the nearest active node in this specific cluster might be chosen as a gateway node based on distance information, which could be estimated by the received signal strength. Similar to Scenario 1, a sensing node or a relay node may be a gateway in this case; (3) When no broadcast message is received during the sensing period, the sensing node may choose the nearest node in an adjacent cluster as a gateway node; (4) If the clusters are too far apart (outside the range of communication
Built upon the learning process in the observation phase, the sensing node and the candidate of the gateway node acknowledge the role assignment in the confirmation phase. Thus, each pair of distributed gateways send 1hop broadcast messages to confirm the gateway selection with each other. Accordingly, the result of gateway selection is that each round group member assigns a single member of each nearby clusters such that network connectivity during the sensor scheduling operation may be assured. Therefore, the CASA approach provides a virtual backbone for sensing coverage and network connectivity maintenance. The procedures of gateway selection is depicted in
The distributed method operates much like the CAWT [
If the random waiting timer expires (i.e.
The message for communication among the cluster members consists of: (1) the ID of the sending sensor, (2) the round ID of the sending sensor, and (3) the relay round ID of the selected relay sensor. At the beginning, the round ID and the relay round ID of each sensor is one and zero, respectively. Based on
In order to maintain the correct round ID information when receiving multiple messages among neighboring nodes, the ID updating strategy may be described as follows. Given a cluster member with round ID
By following the above procedures, the round IDs and relay IDs can be determined for each cluster member. Based on the received broadcast messages for updating round ID information from 1hop cluster members, the clusterhead can obtain the number of sensing rounds
Under the operation of the DASA scheme, pairs of distributed gateways for intercluster communication can be decided by applying the same approach as described in Section 3.2. The sensing coverage and connectivity performance will be further explored in Section 5.
Two analytical tools are provided to estimate the number of sensing rounds of the proposed schemes. For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry and the Lindeberg Theorem [
This subsection reviews the neural network algorithm [
For the hidden layer:
For the output layer:
Note that
In order to estimate the number of schedule rounds in a given topology when applying the CASA scheme, the threelayer perceptron neural network is presented. For selecting the network parameters (weights and biases) that best approximate a given function, the backpropagation learning algorithm is considered to minimize the mean square error performance as described in (
This subsection introduces a particular problem considering the mean and variance of the overlap of geometrical figures [
Suppose that
Referring to Theorem 1, the probability of a point (
Accordingly, in our case we may interpret
This subsection reviews the probability that is used when analyzing the performance of the model. Readers may refer [
Suppose for each
Denote
For our case, the Lindeberg condition [
Observe that
Assume that each sensor will be grouped with probability
The main objective of the dynamic sleep scheduling approaches is to extend the lifetime of the clusters so that the network may remain functional longer. Say that the cluster lifetime ends when the first sensor in the cluster fails. Therefore, it is worthwhile to understand the lifetime of individual sensors.
Depending on the traffic model of the network, the expected sensor lifetime may be different. Suppose that the sensors measure periodically and transmit the data back to the clusterhead for further processing with a steady traffic. We also assume that the clusterhead collects the information from cluster members and communicates with the base station with a steady traffic flow [
Accordingly, the impact of the sleep scheduling approach on cluster lifetime is further examined. For a cluster without sleep scheduling strategy, the expected lifetime of sensor
For a cluster with sleep scheduling strategy, the expected lifetime of sensor
Based upon the definition of the cluster lifetime, the cluster lifetime is equal to the minimum of the expected lifetime of sensors. That is,
Now we provide an example on how the cluster lifetime can be extended by applying the dynamic scheduling techniques. Assume that sensors of the network have identical initial energy levels and power dissipation. Therefore, the cluster lifetime factor (CLF) yields:
This subsection assesses the performance of the proposed schemes in terms of communication and time complexity for network operations.
When developing the sensing schedule, two rounds of local flooding are initiated in order to gather topology information for the clusterhead in the 2hop cluster structure. Hence, the time complexity is
Consider a sensor, say sensor
Since the algorithm is mainly executed in the clusterhead, the computation cost analysis of a clusterhead is presented. Based on the procedures of the CASA scheduling scheme in
In order to show the frequency of operations on the system resource and explore the impact of gathering all the information by the clusterhead, memory usage analysis is provided in terms of information processing perspective. Assume each node has a
By using the CASA algorithm, the periodic onoff scheduling problem can be solved efficiently due to a sleeping schedule for each sensor node in a cluster. However, the drawback is using a centralized accumulator host to gather topology information of each sensor such that it can execute the scheduling management. The problem arises when some of the sensors can not transmit the required information to the accumulator host or the accumulator host malfunctions.
In the DASA approach, the clusterhead triggers two rounds of 1hop flooding to initialize the sensor scheduling management process in the 2hop network topology. Next, 1 round of local flooding is applied for updating the round ID of each node. Then, the clusterhead generates another two rounds of local flooding for broadcasting the sensor scheduling information. Finally, the gateway nodes are selected using two rounds of 1hop flooding. Therefore, the time complexity is
Accordingly, the total energy consumption is
Due to the operation of the DASA scheme, the computation burden is distributed among the sensors. Thus, the computation cost analysis is considered with respect to the clusterhead and cluster members, respectively. For the clusterhead, it arranges the sensing schedule based on the largest received round ID. Hence, the computation complexity for updating the round ID in a clusterhead is
Suppose that each sensor node has a
Note that although the DASA scheme has a higher time complexity due to the round ID updating process, the DASA allows the cluster members to organize themselves into round groups and complete the scheduling assignment automatically with only local neighboring information.
Assume that
The first set of experiments illustrates two examples of generating a sensing round with the CASA approach and the DASA approach, respectively. According to the procedures of the CASA approach, as shown in
In
Given a cluster topology, the second set of experiment studies the impact of parameter settings on network performance. With varying the number of sensors
Similarly,
Furthermore, in order to describe the interaction between the parameter settings and the network performance,
The third set of experiments explores the performance of the neural network model. The efficiency of neural network training can be improved with certain preprocessing steps performing on the network inputs and targets [
Moreover, regression analysis is employed as posttraining analysis between the network response and the corresponding targets and three parameters are returned to evaluate the performance. The first two parameters, slope and yintercept of the best linear regression relate targets to network outputs. If the outputs exactly equal to targets, the slope and yintercept would be 1 and 0, respectively. For the 1hop case, slope = 0.79 and yintercept = −6.1 · 10^{−3}. For the 2hop case, slope = 1.0 and yintercept = 2.7 · 10^{−4}. The third parameter is correlation coefficient between the outputs and targets. When the correlation coefficient is equal to 1, then there is perfect correlation between targets and outputs. In this study, the correlation coefficients of 1hop regression analysis and 2hop regression analysis are
In order to simultaneously consider energy conservation, network connectivity, and the data gathering strategy, the fourth set of experiments investigates the impact of the transmission range
Observe that, as shown in
Accordingly, an appropriate transmission range
Note that
The sixth set of experiments studies the network connectivity when using the proposed scheduling approaches. Given a random network with
By following the analysis as detailed in Section 4.4, the seventh set of experiments illustrates the mean memory usage in the cluster members and the clusterhead, respectively. Assume each node has a 36byte data packet to transmit.
The last set of experiments depicts the energy consumption of the proposed algorithms and compare the results with those of other clusterbased scheduling protocols. Assume that clusters are formed in a random network of 100 sensors with side length
Assuming that each node has a 36byte data packet to transmit,
This paper presents hierarchical scheduling algorithms, which use a local criteria to simultaneously undertake the sensing coverage and connectivity such that dynamic clusterbased sleep scheduling can be achieved. An analytical network architecture and a probabilistic model are derived to describe the behaviors of the proposed schemes. The clusterheads may apply the established models to estimate the number of sensing rounds given local topology information. The main objective of the proposed dynamic sleep scheduling approaches is to extend the lifetime of the clusters so that the network may remain functional longer. The experimental results show that the proposed algorithms provide efficient network power control and achieve high scalability in wireless sensor networks.
There are several ways this work may be generalized. For instance, the CASA scheme may exploit the relationship between the monitored area in a cluster and the cluster topology to determine a proper number of group members in a round for the sensing task. Also, the DASA scheme can be generalized to a
In the proposed scheduling solutions, tradeoffs are found between model complexity, energy consumption, computational complexity, and sensible model description in real systems. Future plans will involve generalizing the methods to design energyefficient data dissemination protocols, to consider certain failure scenarios, to explore the sensitivity of the proposed schemes to data gathering strategies and network operation, and to perform actual measurements to investigate the impact of parameter settings on network performance.
The connectivity of the network (left); clusters are formed in a random network of 100 sensors with
The procedures of selecting a pair of distributed gateways in Scenario 1 (a) and Scenario 3 (b).
Virtual sensor scheduling flowchart for the DASA algorithm.
The round ID updating process of the DASA algorithm; the (·) represents the round ID.
The threelayer perceptron neural network architecture for analyzing the CASA scheme.
An example of generating a sensing round with the CASA approach.
An example of generating a sensing round with the DASA approach.
The number of sensing rounds
The number of sensing rounds
Average coverage per round versus
Average sensing overlap per round versus
The preprocessing results of the network inputs and targets.
An independent test of network generalization.
The regression analysis between the network response and the corresponding targets: 1hop regression analysis (left) and 2hop regression analysis (right).
The relationship between the number of sensing rounds
The comparison of the average number of round groups
The number of active nodes in a round
The distribution of the number of sensing rounds
The distribution of the number of sensing rounds
The coverage percentage of the whole network and each cluster (the topright quadrant); the accuracy of the neural network architecture (the bottomleft quadrant) and the accuracy of the Probabilistic Model (PM) in a random network (the bottomright quadrant).
The network connectivity using the CASA scheme in round 1 (left) and round 2 (right).
The network connectivity using the DASA scheme in round 1 (left) and round 2 (right).
Memory usage of sensor nodes in a cluster versus number of sensors
First order radio model as described in [
The relationship between energy consumption per round (Joules) and transmission range
The comparison of the number of rounds as the first sensor node dies in the network using the LEACH, MECH (10 members), CASA, and DASA.
The CASA Scheduling Scheme
Assign 
{ 


\* Selecting 2hop round members *\ 

{ 




} 
\* Selecting 1hop round members *\ 

{ 
Pick sensor 



} 



} 
Procedures of the PM model for analyzing the DASA.
Let Assign the probability
Assign
form members of this round, update

* ⌈·⌉ is the ceiling function. 