Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism,
Wireless sensor networks (WSNs) are a class of wireless ad hoc networks which consist of a set of sensor nodes and aim at several applications, such as industrial sensing and control and environment monitoring [
Location-aware routing protocols seem to possess high routing efficiency. However, there are two extremes in location-aware routing, the greedy strategy and the robust strategy. Greedy strategies may suffer failures to route packets to destination, while robust strategies need very high flooding rates to ensure reliability and rapid delivery of data. Thus, many location-aware routing protocols are mostly to propose methods to overcome the mentioned drawbacks. GPSR [
The energy-aware routing attracts more attention of researcher than location-aware routing for the significance of energy. For maximizing the network lifetime, Rao and Fapojuwo [
In recent years, bio-inspired technology has been concerned by researchers [
Based on our prior works [
This paper considers large multihop WSNs which consist of
In WSNs, each node
An example of WSNs’ topology.
The transmission range of
If the node
In addition, acquiring energy residues of neighbors is important for choosing next hop to balance the energy of sensor's nodes. We think of the basic theory of wireless transmission combing with Figure
In order to acquire the energy residue of neighbors, we add a new field
When the
The orbit of the
In this section, we draw a selecting next hop model (SNH) based on physarum foraging mechanism. From paper [
Physarum forages for distributed food sources through adapting the adaptive behavior of the plasmodium. Consider
Firstly, we discuss the replacement of physical quantities in (
Because of the same characteristic of each node, we suppose that the
Secondly, we analyze the adaptive behavior of plasmodium referring to Figure
(a) If there are two candidates for next hop, the node who has the greater
In this section, we introduce the data which should be conserved in each node. Each node
If node
Receive the positioning information of the
Divide each node
Calculate each
Each node
The first node in
If the next hop
If
Otherwise, each
The process is repeated, like a rolling wheel, until the
In this section, we analyze the feasibility of SNH by mathematical theoretical analysis. We study cases in which two nodes connected to the same node compete to be the next hop, as shown in Figure
There are four nodes
Since
Setting
From (
From (
Similarly,
Namely, equilibrium point is given by
Asymptotic behavior of the solution in
We design a simulation platform using C++ to validate P-bRS. In the simulation, 400 sensors are relatively regularly deployed in the field of 200 m × 200 m, and the
Sensors distribution.
In order to validate the energy equilibrium, we only choose the nodes in the center or peripheral simulation field (enclosed by red dashed circle or two red dashed rectangles in Figure
Figure
Lifetime of network.
Figure
Distribution of dead sensor nodes.
P-bRS,
GEAR,
Figure
Needing hops of transmission of each round.
In the case of
In the case of
The physarum forages for patchily distributed food sources through accommodating its body to form networks with comparable efficiency, fault tolerance, and cost. We draw inspiration from the physarum model and improve it to suit the routing choice for WSNs. The P-bRS algorithm can deal with the trade-off between routing efficiency and energy equilibrium in WSNs, which greatly reduces the processing delay and saves the energy of sensors. Based on the simulation results, we discuss the P-bRS's performance. In future work, we consider introducing actual mobility model of nodes into P-bRS to make it fit in with mobile WSNs. Moreover, we consider the model may also provide a useful help to develop the routing protocols in other networks, which will be our future focus.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is partially supported by the National Natural Science Foundation of China (NSFC), under Grant nos. U1204614, 61142002, 61003035, and 61370221, and by the key project of the Education Department Henan Province under Grant no. 14B520031, and by Key Project of Science and Technology Department of Henan Province under Grant no. 112102210187, and by the Plan for Scientific Innovation Talent of Henan Province under Grant no. 124100510006.