Cyber-physical system (CPS) cooperates with physical processes, computing, communication, and control (3C) into multiple levels of information processing and operation management to streamline and fortify the operation of physical systems. Due to the unique characteristics, such as unpredictable node mobility, low node density, lack of global information and network intermittent connectivity, an algorithm for data forwarding in CPS is a considerably difficult and challenging problem, and there is no good solution to it in existing works. In this paper, we propose a fully-fledged data forwarding algorithm tailored to the CPS environment. The proposed protocol, called data forwarding based on Delaunay triangulation (DFDT), takes into account the computational geometry based on Delaunay triangulation to form a few triangle communities according to nodes’ connectivity. Data in a community are forwarded to other nodes once a node comes into this community to increase the data delivery ratio. DFDT achieves a good performance by data gathering and sending data to other nodes with higher probability of meeting the link. An extensive simulation has been performed to validate the analytical results and to show the effectiveness of our approach compared with the three existing popular data forwarding algorithms.
As an emerging field, cyber-physical system (CPS) [
CPS is composed of and interconnected by various components (e.g., sensors, monitors, controllers, actuators, and embedded computers) through communication networks. Information collected by sensors from physical infrastructure in CPS is adapted to cyber components with using communication links to intelligently feedback control the physical components, which is computed by information processing center, such as cloud computing. There is typically no one-to-one correspondence between the elements of the two networks, which complicates the understanding of their interaction. Critical infrastructure systems reliant on intelligent monitoring and control are among the most important CPS and also prime examples of pervasive computing systems, as they exploit computing to provide “anytime, anywhere” transparent services. While the added intelligence offers the promise of increased utilization, its impact must be assessed, as unrestricted information control can actually lower the reliability of existing infrastructure systems. Compared with the traditional networks, CPS owns characteristics, such as self-judgment, self-control and self-regulation, node mobility, and network intermittent connectivity.
CPS has gained increasing attention in a wide range of application domains, such as transportation system, modern airplane, nuclear power plant, highway traffic, embedded medical devices, oil refiner, power grid, and health care. Such system must be able to operate safely, dependably, securely, efficiently, and in real-time, potentially highly uncertain or unstructured environments. CPS is expected to have great technical, economic, and social impacts in the near future.
The convergence of sensing, communication, processing, control, and coordination in CPS poses enormous challenges. The aspects seem particularly challenging [
The data gathering and forwarding schemes in the traditional networks depend on a large number of nodes deployed densely with short communication range to form a connected end-to-end networks. It is unreasonable to assume end-to-end connectivity in CPS, such as mobile ad-hoc networks (MANTs) [
Thus, how to maintain not only a relatively longer system lifetime but also a higher information delivery ratio with the lower transmission overhead and data delivery delay has become the most important problem to solve. Different techniques have been proposed for data gathering and forwarding. Researchers in those papers [
In this paper, we propose a fully fledged data forwarding algorithm tailored to CPS environment. The proposed protocol, called data forwarding based on Delaunay triangulation (DFDT), takes into account the computational geometry for nodes deployment and wireless signal coverage to increase the data delivery ratio. DFDT algorithm is discussed and thoroughly analyzed. DFDT achieves a good performance by gathering and sending data to other nodes with higher probability of meeting the link. An extensive simulation campaign has been performed to validate the analytical results and to show the effectiveness of our approach compared with the three existing popular data forwarding algorithms, namely DT, epidemic, and SpWT. Simulation results show that DFDT does not only achieve a relatively low data forwarding overhead but also gets the higher message forwarding ratio with lower data forwarding delay.
The remainder of the paper is organized as follows. We summarize the recent related work on CPS in addition to existing node deployment for networks in Section
Our work is inspired and motivated by some research efforts on delay tolerant mobile sensor networks (DTMSNs). Due to the wide range, the novel field and the limited space of related work in CPS, we only briefly introduce the work closely related to our proposal.
The research on data forwarding originates from
Some other data forwarding strategy approaches concentrate on trading off data complexity versus increasing the likelihood of data delivery. To limit the number of data single copy routing schemes, allow only one copy of the data to be present in the network at a time [
To address overhead problems in flooding, different forms of controlled flooding have been proposed. One of the basic approaches for data forwarding is a present
Later studies develop data forwarding strategies to approach the performance of epidemic routing with lower forwarding cost, which is measured by the number of data copies created in the network. The data forwarding metric, which measures the nodes’ capability of contacting others, is generally independent from the data forwarding strategies mentioned above. Various metrics can be applied to the same forwarding strategy for different performance requirements.
At first, we review the definition of Delaunay triangulation. We assume that all wireless nodes are given as a set
In the CPS network, it has its unique characteristics.
Due to those characteristics of CPS described above, traditional proactive and reactive routing schemes fail to work in CPS scenario. However, this does not mean that the packets cannot be delivered to the destination. Because of node mobility, different links come up and down over time, enabling nodes to achieve eventual delivery through a stored-forward approach, which uses buffers to hold the message until the next link comes up in the end-to-end path due to node mobility. A necessary condition for this approach to work is the existence of an end-to-end path between source and destination in a combined connectivity graph formed by overlapping connectivity graphs over a time interval.
After deploying nodes in a target area, there is no established network among these nodes. The nodes do not have any idea about the location of the other nodes and the architecture of the network. We can take advantage that the nodes in CPS have ability of self-configuring, self-control, and self-organizing, and consider that the intermittent network connectivity and data gathering and forwarding between the communities are connected by the mobile nodes, which have the ability of joining communities quickly while being sure of their own locations in certain communities.
In this paper, we suppose that all wireless nodes given as a set The node mobility model is mainly used to describe the location and velocity of the node and its variation, which is an important tool to describe the encounter characteristics between the nodes. Data forwarding and transfer depend on meeting opportunities during node mobility, and node mobility model determines the probability of encounters between nodes and encounter time distribution, which is the basis of designing data forwarding algorithm. In order to study the data forwarding of CPS nodes, this paper first assumes CPS node connectivity scene. A certain number of nodes make random movement in the area according to random waypoint (RWP) mobility model [ All the nodes have the same maximum transmission range Each node can only communicate directly with other nodes in the same community or the adjacent community area (sharing the same line or angle is called adjacent), and all the nodes are all surely in their own communities. All the nodes have the attached extra location devices, such as a low-power GPS or some other way, to easily attain their locations [
Illustration of network model and communities.
In this section, we propose a novel data forwarding based on DFDT in CPS environment. Considering the unique characteristics of CPS, DFDT aims to attain a high data delivery ratio with the minimum data delivery overhead and delay.
In CPS environment, due to the node random mobility and the limitation communication range, the whole CPS is intermittent connectivity and divided into a certain number of stable nonconnective communities. As shown in Figure
In this paper, the first problem to be solved is how to divide a community. We firstly consider creating a triangle, which is big enough to surround all the nodes to form a community. In a random node set
How should we set another two vertexes of
A node makes a decision on whether a target is detected or not based on its measurement on the intensity of target signal. The noise of a node obeys a Gaussian distribution with zero mean, and the target signal obeys a Gaussian distribution with nonzero mean. According to paper [
Node
In this paper, one of the key design objectives of community and routing maintenance for computational complexity is to minimize the community (re-)configuration and node detection time. The computational cost for determining the node and community in the network is combinatorial due to the need to consider the combination of detection decisions from multiple nodes. It can be seen from (
After the accomplishment of the discovery of the big triangle community
and the node is deployed at the vertex of the community.
Each sensor node in Upon receiving alone with the location information of the current node; Update the neighborhood list according to the acknowledgements of the The directly communicative nodes store the information of other sensor nodes' locations; Sort locations from as
//Update the triangle community information Connect Search the nodes which can communicate with Put the formed triangles
Delete the co-edge of the triangle, and connect the three vertexes of Delete
Different communities can communicate with each other based on the mobility nodes based on RWP model outside communities, which store, carry, and forward data to other communities and realize the whole CPS connectivity. When a node moves nearby a community, a node in the community detects this mobility node and forward data in its wireless range.
In our proposed scheme, we create a directed acyclic graph in each community, denoted as
Then, let us make a explanation by the example of the node which contains three leaves as shown in Figure
Changes Process of Node Joining.
When mobile node
Transmitting Process from
In the previous section, we analyze data forwarding algorithms based on Delaunay triangulation in CPS environment and show that it can dramatically reduce costs of data delivery. In this section, we select forwarding algorithms so as to include both well-known existing algorithms as well as algorithms that span a wide range of design choices for CPS.
We work in the following setting: nodes generate messages over time; each message has a particular source and destination. At random times, nodes come into contact, meaning that they are capable of exchanging messages. Messages are transmitted in whole from node to node at time instants during node contact intervals of
In our analysis, the metrics we are concerned with are
Simulation Parameters.
Parameter | Default value |
---|---|
Network size ( |
1000 * 1000 |
Number of sensor node | 50 |
Max node moving speed (m/s) | 15 |
Min node moving speed (m/s) | 10 |
Initial energy of each sensor node (J) | 100 |
Threshold vale of |
8 |
Energy for GPS communication per time (J) | 0.000025 |
MAC layer type | IEEE 802.11 |
Start time of sending data packets (s) | 500 |
End time of sending data packets (s) | 525 |
Interval of sending data packets (s) | 0.25 |
Number of sending data packets | 100 |
Packet buffer size (packet) | 800 |
Data packet size (byte) | 512 |
In this section, we compare the performance of the data forwarding approach with existing data forwarding schemes based on cumulative node data forwarding characteristics. We evaluate the performance of our approach in data delivery ratio and forwarding overhead measured by the number of data copies created in CPS, and each experiment is repeated 500 times for statistical convergence. The data delivery delay is not considered, as long as the data can be delivered on time. We compare our data forwarding metric with the following existing metrics based on cumulative contact characteristics.
We compare the performance of the four data forwarding algorithms, namely, DT, epidemic, and SpWt, with the default parameters, as shown in Table
Simulation Results with Default Parameters.
DT | Epidemic | SpWt | DFDT | |
---|---|---|---|---|
Success Delivery Ratio ( |
67.2 | 69.5 | 75.3 | 91.0 |
Average Delivery Delay (s) | 1201.5 | 890.3 | 520.0 | 268.8 |
Average Delivery Copies | 1 | 12.9 | 11.3 | 10.2 |
As shown in Table
At the same time, we are also interested in the data delivery delay and average overhead. As shown in Table
Figure
Maximum Errors of Data Forwarding Probabilities.
In this scenario, this experiment simulation depicts the impact of sensor node density by varying the total number of sensor nodes, because the connectivity of CPS is closely related to the density of sensor nodes. As shown in Figure
Average Delivery Ratio.
With higher sensor node density, Figure
Average Delivery Copies.
Average Delivery Delay.
This group of experiments illustrates the impact of node transmission range in CPS. As illustrated in Figure
Average Delivery Ratio.
Figure
Average Delivery Copies.
Average Delivery Delay.
In this paper, we propose a fully fledged data forwarding algorithm tailored to CPS environment. The proposed protocol, called data forwarding based on Delaunay triangulation (DFDT), takes into account the computational geometry for nodes deployment and wireless signal coverage to increase the data delivery ratio. The DFDT algorithm is discussed and thoroughly analyzed. DFDT achieves a good performance by data gathering and sending data to other nodes with higher probability of meeting the link with lower energy consumption. An extensive simulation campaign has been performed to validate the analytical results and to show the effectiveness of our approach compared with the three existing popular data forwarding algorithms, namely, direct transmission (DT), flooding, and epidemic. Simulation results show that DFDT does not only achieve a relatively low data forwarding energy consumption but also gets the higher message forwarding ratio with lower transmission overhead and data forwarding delay.
This research is supported by the National Natural Science Foundation of China under Grant no. 61001086 and the Fundamental Research Funds for the Central Universities Grant no. ZYGX2011X004.