The design and analysis of routing algorithm is an important issue in wireless sensor networks (WSNs). Most traditional geographical routing algorithms cannot achieve good
performance in duty-cycled networks. In this paper, we propose a
Studying the behavior of dynamic sensor networks becomes a hot topic. Movements of nodes make the wireless sensor networks (WSNs) [
It will be much more uncertain when we consider the routing issue in a duty-cycled network, since the duty-cycle scheduling aims to prolong the network lifetime [
Conventional routing algorithms concentrate on finding the shortest path, without much concern about critical issues such as energy efficiency and network lifetime. The problem we discuss here is how to route efficiently in a duty-cycled sensor network. The basic idea behind the algorithms is to divide the network into a number of overlapping clusters. A node's sleep scheduling leads to a change in the network topology, then the membership of cluster changes as well. We propose a cluster formation scheme called
The rest of this paper is organized as follows. Section
Similar to other networks, overload balance, prolonging lifetime, and scalability are the major design concerns of wireless sensor networks. In conventional multihop communications in WSNs, sensors close to the sink are often overloaded, resulting in increased latency and reduced network life span. Such overload might cause latency in communication and reduce life span of network. In addition, the original architecture is not scalable for larger set of sensors covering a wider area of interest. To allow the network to cope with additional load and to be able to afford a large area of interest, clustering routing has been pursued. The main aim of clustering routing is to efficiently maintain the energy consumption of sensor nodes by involving them in multihop communication with a particular cluster and by performing data aggregation and fusion to reduce the number of transmitted messages to sink.
Many routing protocols [
Shu et al. propose an efficient two-phase geographic greedy forwarding (TPGF) [
However, these traditional routing algorithm will overload relay nodes with the increase in sensor density. Besides, convergence characteristics of these algorithm are not good enough to meet the need of dynamic networks, such as duty-cycled networks.
In the last few years, many algorithms have been proposed for clustering routing in wireless sensor networks [
In [
In addition, the TEEN [
To the best of our knowledge, there is only one clustering algorithm that specifically controls overlapping in the formation of clusters, that is, KOCA [
We consider a multihop wireless sensor network where all nodes are alike. We assume that each node has a unique id. The locations of sensor nodes can be obtained by GPS. All sensors transmit at the same power level and hence have the same transmission range
All communications are over a single shared wireless channel. A wireless link can be established between a pair of nodes only if they are within wireless range of each other. The
To ensure the network connectivity and prolong its lifetime, we assume that all nodes operate under CKN-based [
We use the same radio model defined in [
The energy consumed by receiving this packet is
In
(*Run the following at each node
(22) (23)
(*For each node
Return.
(i) Any two nodes in 2-hop neighbors that have (ii) Any node in
In this section, we present the description of the cluster head's selection process as well as the clusters' generation; we then give an example to illustrate a cluster generated by
In
However, if a CH
Each node maintains a table,
The
Note that
We demonstrate our algorithm with Figure
An example of overlapping clusters in a network with 38 nodes. After executing
Single hop clustering. Each node in the network is not more than 1-hop away from a cluster head.
In Figure
We first discuss the necessary data structures to be maintained at each node for the routing algorithm, as shown in Table
During the interior and exterior routing phase, routes are constructed between all pairs of nodes. The routing recovery phase takes care of maintaining routing table considering sleep schedule and recovering from an individual node failure.
After cluster head selection and cluster generation procedure, each node completes the construction of two tables: when when when when
Let us illustrate it with an example. Tables
CH table of
CHID | PID | PEA | CID | CRID | CREA |
---|---|---|---|---|---|
|
|
5 J, 4 J, 4 J | |||
|
Null | Null |
|
|
3 J, 4 J, 5 J |
|
|
5 J, 3 J, 4 J |
CH table of
CHID | PID | PEA | CID | CRID | CREA |
---|---|---|---|---|---|
|
|
5 J |
|
|
4 J, 5 J |
CH table of
CHID | PID | PEA | CID | CRID | CREA |
---|---|---|---|---|---|
|
|
5 J | Null | Null | Null |
|
|
3 J | Null | Null | Null |
NCH table of
NCHID | NCHEA | RID | REA |
---|---|---|---|
|
4 J |
|
4 J |
|
5 J |
|
3 J, 5 J |
NCH table of
NCHID | NCHEA | RID | REA |
---|---|---|---|
|
3 J |
|
4 J, 3 J |
|
4 J |
|
5 J |
NCH table of
NCHID | NCHEA | RID | REA |
---|---|---|---|
|
3 J |
|
5 J |
|
5 J |
|
4 J |
Data structures table.
Data structures | Description |
---|---|
|
A message containing CHID (the ID of cluster head), CHEA (the energy availability of cluster head), HC (hop count), and SEA (the energy availability of message sender) |
|
A message containing ID (the ID of join node), SEA (the energy availability of message sender) |
|
A message containing NCHID (the ID of neighbor cluster head) |
|
A table containing CHID (the ID of cluster head), PID (the ID of parent node), PEA (the energy availability of parent node), CID (the ID of its children node), CRID (the ID of children's relay node), and CREA (the energy availability of children's relay node) |
|
A table containing NCHID (the ID of neighbor cluster head), RID (the ID of neighbor cluster head's relay node), and REA (the energy availability of neighbor cluster head's relay node) |
|
A routing table containing destination and next hop |
In the routing table of
In the routing table of
In the routing table of
Routing table of
Destination | Next hop |
---|---|
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|
|
|
|
|
|
|
|
|
|
|
*.* | Outer Routing |
Routing table of
Destination | Next hop |
---|---|
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|
|
|
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|
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*.* |
|
Routing table of
Destination | Next hop |
---|---|
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|
|
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*.* |
|
We consider each cluster as a node in exterior cluster routing phase. Each CH node takes the responsibility of each cluster. An original routing algorithm (e.g., GPSR and TPGF algorithms) is running exterior clusters. As shown in Figure
Exterior cluster routing. Nodes
This phase begins when nodes' status change due to duty-cycle scheduling. The route maintenance in our approach basically boils down to cluster maintenance. After a change in topology, all the nodes have the complete cluster information in the form of
The new cluster information will be propagated throughout the network. Among exterior neighbor clusters, it should be noted that only the boundary nodes are responsible for broadcasting and rebroadcasting any new information. This helps in quick dissemination of information across the network. Thus, the convergence of the cluster-based protocols is very quick. When a node of a cluster stops working, after a certain time, all its neighbors will detect this event. In interior cluster, only its parent node will update the information of
Let us illustrate it with an example, as shown in Figures
Each cluster can be transformed into a spanning tree.
Routing recovery. When node
To verify the correctness and effectiveness of the proposed
Simulation parameters.
Variables | Values |
---|---|
Communication range | 30 m |
Number of nodes | 400 |
Total energy of each sensor | 5 Joules |
Packet size | 240, 1200 bits |
Energy dissipated for receiving | 50 nJ/bit |
Energy dissipated for transmission | 50 nJ/bit |
Energy dissipated for transmit amplifier | 100 pJ/bit/m2 |
In this section, we evaluate traditional GPSR and GPSR in
First we compare energy consumption of each sensor in traditional GPSR and
Comparison of energy consumption in traditional GPSR and in
Energy consumption of traditional GPSR
Energy consumption of
Finally, we compare network lifetime, which is more attractive to application scientists and system designers. We set the value of
Comparison of network lifetime by using traditional GPSR and
Figure
Comparison of routing recovery time by using traditional GPSR and
Demonstration of
In this paper, we propose a
We present the pseudo code of
This work is supported by the Natural Science Foundation of China under Grants no. 61070181, no. 61272524, and no. 61202442.