Optimized routing protocol for broadband hybrid satellite constellation communication IP network system

Hybrid networks have both wireless and wired components. A wide variety of networks can be treated according to this framework, including multisatellite constellation network with optical fiber crosslinks [4]. The interconnection to the global communication grid is needed to install a backhaul cable, fiber optics. Most infrastructure-based hybrid networks require significant effort, time, and capital to build and deploy. However, the tremendous adoption of some of these networks is a great testament to their usefulness and commercial viability. There are numerous other communication capabilities to serve such as temporary events, battlefield communications, remote sensing, and robotic networks [5].

A LEO satellite constellation consists of a set of satellites orbiting the Earth with a high constant speed at a relatively low altitude, using orbits much lower than the geostationary orbit, in order to give global coverage, more frequency reuse, and higher system capacity as a result of this frequency reuse. With the LEO satellite system, each satellite is equipped with a fixed number of antennas that allow it to communicate with terrestrial networks and with other satellites. Routing algorithms are needed to determine the best way to traverse the mesh; a flexible packet-based, rather than static circuit-based, routing approach can take advantage of the redundancy inherent in this mesh [6, 7].

The most straightforward hybrid architecture envisaged is based upon a high-speed forward link using a broadcast lower satellite, whereas the return link takes benefit of the already existing, high bandwidth network architectures such as the synchronous transport module connections. This also enables the seamless migration of broadband satellite constellation systems providing triple-play services into hybrid systems. Such hybrid systems already exist. One can quote, for instance, that the AstraNet system providing IP telecommunication services uses an ASTRA satellite in the forward link and a terrestrial telephone line in the return link. Satellite/terrestrial LTE networks are integrated for IP services delivery, for instance, in the SkyTerra/LightSquared with LTE/satellite network system [8].

The COMMStellation™ satellite network [9, 10] is an orbit with an altitude range around 1000 km. It consists of six orbital planes with 12 satellites in each plane. Those planes have an additional two redundant satellites in every orbit. The spacing between the orbital planes is 30° apart. The Earth station is 10° in the minimum elevation angle to maximize the coverage area of satellites. This will improve the link quality by decreasing multipath fading and having positive impact on link quality when compared with lower elevation of the Earth station. Both the uplink and downlink of the satellite link bandwidth is 1.1 Gbps, and the capacity of the satellite node is 8.8 Gbps. There are two types of stations; they are (1) trunk station that always connects to the Internet backbone and (2) user station for individual client to connect to the satellite link. The available bandwidth of the Internet backbone of the trunk station has a setup link which is the STM-4 transmission and cross link communication which is the STM-1 transmission.

In this research, the network topology consists of four user stations with triple-play service applications as illustrated in Fig. 1. The source nodes are assumed to be located in Bangkok, Thailand; Yangon, Myanmar; Vientiane, Laos; and Oil platform, Indian Ocean, while the destination nodes are located in North Boston, MA; Tallahassee, FL; Saint Paul, MN; and Lincoln, NE [2]. The reason for support to choose the located in Asia and North America is almost content in the world has location over there, and we selected Asia because this continent is growing internet usage statistics more than other continents from Internet users in the world by geographic regions. The error model produced two loss links on both wired and wireless network links [3].

The plot script [11] for this research shown in Fig. 2 illustrates the location of the satellite, trunk station, and user station of the COMMStellation™ satellite system [2, 12] which has NS-2 satellite plot scripts. In addition, our previous [2] research shows a constellation satellite layout, trunk station layout, and Internet backbone layout. Our Internet backbone traffic density model is similar to the model described in [13]. Table 1 summarizes the key properties of the presented COMMStellation™ satellite system which is used throughout the simulations.

Parameters	Value
Satellite altitude	1000 km
Inclination	90°
Number of orbital plane	6
Number of satellite per orbital plane	12
The cutoff elevation angle	10°
Spacing between orbital plane	30°
Satellite bandwidth	8.8 Gbps
Link bandwidth	1.1 Gbps
Trunk stations	35
User stations	4

There has been an exponential growth in multimedia applications over Internet due to the increasing demand of combining voice, video, and data services known as ‘triple-play bundling’ [14]. In order to support ‘triple-play’, a satellite system needs to be integrated with the Internet to provide appropriate performance across a range of applications that required capacity, minimum jitter, minimum delay, etc. It is a challenge to maintain the QoS for an IP-based satellite network that share communication channel’s capacity among transmitting media. The voice is VoIP [12, 15] with codec G.711 over IPv6, the video is Internet Protocol television (IPTV) [12, 16–18] with codec H.264 part 10 (1920×1080 @24 fps) over Ipv6, and file transfer using the File Transfer Protocol (FTP) that uses TCP Westwood [19] over Ipv6 (512 B payload). These are chosen as the traffic loads in the simulations.

The routing algorithm for hybrid satellite networks is proposed based on a weighted graph model to solve the QoS routing problem in BHSCCS. The metric selection of the proposed technique based on optimization with the relationship ratio of QoS metric for triple-play services which have strict requirements on bandwidth, delay variations, and availability. Hence, five significant dynamic parameter functions are considered. The first metric is the propagation link delay, which is determined by an advanced propagation delay model from the source to the destination. The second metric is the queuing delay. It is affected by the traffic load on a particular satellite and its outgoing links, as the packet traverses varying traffic gateways. The third metric is link hop count. The fourth metric is link utilization in which the throughput part of all the next neighborhood nodes confirms the best path. The fifth metric is link length that chooses the shortest link between user/trunk stations to both satellites. The five matrices are used to reduce the end-to-end link delays, deteriorate the rerouting frequency, and chose the best route path in the routing table.

5.1 End-to-end delay metric

The metric that considers the end-to-end delay [20–22] measures the time taken for a message to journey from the source to the destination. In a network, variations of the end-to-end delay are bounded by the variations of the propagation, processing, and transmission delays. It also impacts the experience for triple-play services. Therefore, we will calculate all of the wired and wireless networks. Figure 3 illustrate the end-to-end delay metric of the simulation model. E ₁ is the sum of a propagation delay for a source uplink into a satellite node. E ₂ is the sum of a propagation delay for a source downlink into a trunk station. E ₃ is the sum of a propagation delay for a high-speed Internet backbone. E ₄ is the sum of a trunk station propagation delay for a destination uplink into a satellite node. E ₅ is the sum of a propagation delay for a destination downlink into a destination node. Then, the advanced E2E propagation delay (P _d) can be calculated using the following Eq. (8). The goal of this metric is finding the lowest end-to-end delay in the network route.

The end-to-end delay (P _d) can be calculated as

$$\begin{array}{@{}rcl@{}} E_{1} &=& \frac{d_{\text{ts\_src}}}{c}+\frac{L}{B_{1}}+P \end{array} $$

(1)

$$ \begin{aligned} {d_{\text{ts\_src}}} = \sqrt{(x_{\text{sat}}-{x_{\text{term}}})^{2} + (y_{\text{sat}}-{y_{\text{term}}})^{2} + (z_{\text{sat}}-{z_{\text{term}}})^{2}} \end{aligned} $$

(2)

$$\begin{array}{@{}rcl@{}} E_{2} &=& \frac{d_{\text{st\_src}}}{c}+D+\frac{L}{B_{1}}+P \end{array} $$

(3)

$$ \begin{aligned} {d_{\text{st\_src}}} = \sqrt{(x_{\text{term}}-{x_{\text{sat}}})^{2} + (y_{\text{term}}-{y_{\text{sat}}})^{2} + (z_{\text{term}}-{z_{\text{sat}}})^{2}} \end{aligned} $$

(4)

$$\begin{array}{@{}rcl@{}} E_{3} &=& ((N-1).T_{\text{cross}})+\left(N.D+\frac{L}{B_{2}}+P\right) \end{array} $$

(5)

$$\begin{array}{@{}rcl@{}} E_{4} &=& \frac{d_{\text{ts\_dst}}}{c}+\frac{L}{B_{1}}+P \end{array} $$

(6)

$$\begin{array}{@{}rcl@{}} E_{5} &=& \frac{d_{\text{st\_dst}}}{c}+D+\frac{L}{B_{1}}+P \end{array} $$

(7)

$$\begin{array}{@{}rcl@{}} P_{\mathrm{d}} &=& (E_{1}+E_{2}+E_{3}+E_{4}+E_{5}) \end{array} $$

(8)

where:

$$\begin{aligned} &x_{\text{sat}}=(R+h).\cos\theta_{\text{sat}}.\cos\emptyset_{\text{sat}} &&x_{\text{term}}=R.\cos\theta_{\text{term}}.\cos\emptyset_{\text{term}}\\ &y_{\text{sat}}=(R+h).\cos\theta_{\text{sat}}.\sin\emptyset_{\text{sat}} &&y_{\text{term}}=R.\cos\theta_{\text{term}}.\sin\emptyset_{\text{term}}\\ &z_{\text{sat}}=(R+h).\sin\theta_{\text{sat}} &&z_{\text{term}}=R.\cos\theta_{\text{term}}\\ &\theta=\text{latitude} \quad\quad\quad\quad\quad\quad\quad\quad \emptyset=\text{longitude} &&R=6378.137~\text{Km} \ (\text{earth radius})\\ &c=299792~\text{Km/s (light speed)} &&h=1000~\text{Km}\\ &D=2~\text{ms (processing delay of node)} &&L=\text{length of data} \\ &P=1~\text{ms (deviation delay)} &&N=\text{number of nodes in the path}\\ &B_{1}=\text{bit ratio of satellite link} &&B_{2}=\text{bit ratio of Internet backbone}\\ &T_{\text{cross}}= 5~\text{ms (propagation delay of Internet backbone)}\\ &d_{\text{ts\_dst}}=\text{uplink of destination following with equation}\ 2\\ &d_{\text{st\_dst}}= \text{downlink of destination following with equation}\ 4 \end{aligned} $$

5.5 Link length metric

A COMMStellation™ model is used in this research. The orbit parameters of every satellite in the COMMStellation™ system at a time point are obtained in a snapshot of the COMMStellation™ satellite constellation at one time point via NS-2 [2, 28]. The differences of a snapshot of the satellite constellation are chosen at different time points. In the snapshot, we notice the satellite orbital parameters, which will be used to calculate the link parameters [28–30].

From the satellite orbit parameters, the required topology parameter can be calculated from the link length metric to find the best path between trunk/user stations and satellite nodes. For the three dimensional coordinates of satellite orbit, to calculate the link length between the satellite node A and satellite node B, the latitude and longitude of COMMStellation™ satellite orbit parameters are known by a snapshot of the satellite constellation. It is only needed to find the central angle, α. Figure 4 illustrates the three-dimensional coordination of a satellite orbit. With the equation of distance between two nodes in three-dimensional geometry, the following (16) can be derived. Hence, this metric will find the shortest link length between satellite and ground station.

$$ \sin^{2} \frac{\alpha}{2} = \sin^{2} \frac{\varphi_{1}-\varphi_{2}}{2}+\sin^{2}\frac{\delta_{1}-\delta_{2}}{2}\cos\varphi_{1}\cos\varphi_{2} $$

(16)

where

$$\begin{aligned} &\varphi_{1}\ \text{and}\ \varphi_{2}\ \text{are the latitudes of satellite}\ A\ \text{and}\ B,\ \text{respectively}\\ &\delta_{1}\ \text{and}\ \delta_{2}\ \text{are the longitudes of satellite}\ A\ \text{and}\ B,\ \text{respectively} \end{aligned} $$

From Eq. (16), the value of α can be obtained. So the link distance between satellite nodes A and B, P _l, is

$$\begin{array}{@{}rcl@{}} P_{\mathrm{l}}=AB=2R\sin\frac{\alpha}{2} \end{array} $$

(17)

where R is assumed to be the distance between the core of the earth and the satellite.

In this research, in order to investigate the performance of the routing algorithm in a broadband hybrid satellite constellation communication system, the proposed ORPHSN algorithm was built on a NS-2 version 2.34 simulation platform.

We implemented five metrics which are (1) end-to-end delay, (2) queuing delay, (3) hop count, (4) link utilization, and (5) link length. These metrics consist routing techniques based on the described network topology and the traffic model given above. The network performance of study routing algorithms is evaluated based on the end-to-end delay between the source and the destination. The source and destination nodes in this experiment are respectively assumed to be located at Bangkok, Thailand, and Boston, MA, with an independent user type.

The simulation results are compared with the conventional algorithms. We managed to simulate the proposed ORPHSN, fixed adaptive routing (FAR) [13, 35], and random adaptive routing (RAR) [13, 35] algorithms with triple-play services load in BHSCCS model network topology.

The instantaneous end-to-end delay associated with a route length regarding hops count of the proposed OPRHSN is illustrated in Fig. 5. The algorithm optimizes paths in a routing table. Thus, all neighboring nodes know each other. When a packet is received by one of these neighboring nodes and is destined to fail, it is deflected to another direction.

The measurement of end-to-end delay of triple-play services traffic between two user terminals in Bangkok and Boston is also calculated. The most important metric of the satellite network is propagation delay. Figure 5 illustrates the simulation results of the proposed ORPHSN, FAR, and RAR algorithms. It plots end-to-end delay versus the route length from the source to the destination. The end-to-end propagation delay for the proposed ORPHSN is 81.66 ms for 10 hops, whereas it is 86.66 and 91.66 for FAR and RAR, respectively. This is because the proposed ORPHSN in BHSCCS is compared during route intervals in a routing table with a hybrid network system. The results show that the proposed technique chose the optimized route by having the least number of hops when compared with the conventional techniques.

In order to see fair results in terms of the end-to-end delay ratio of the proposed ORPHSN algorithm, we have performed simulations for three different traffic loads which are triple-play services, bulk transfer data 2048 kbps, and reliable messaging data 512 kbps. The results of these simulations are presented in Fig. 6. The highest number of the end-to-end propagation delay is triple-play services in the proposed ORPHSN, 81.66 ms in the triple-play services, whereas it is 72.02 and 64.46 for bulk transfer and reliable messaging, respectively. It is also observed that the proposed ORPHSN has the optimized performance for triple-play services, in terms of the propagation delay in the hybrid network system.

As shown in Fig. 7, the proposed ORPHSN has lower end-to-end delay when compared with the FAR and the RAR algorithms. Thus, the probability of packet traversing the whole BHSCCS network is lower. Figure 7 illustrates the comparison of user station bitrate versus end-to-end delay. This figure shows the user station capability of bandwidth started from 100 Mbps until a maximum bitrate at 1.1 Gbps. It is reflex that the user station has more uplink and downlink bandwidth, so the end-to-end delay in networks is lower. Although this is achieved with an occasional terminal bitrate increasing, with a rate of 10 % during intervals, these algorithms reduce end-to-end propagation delay by handling the statistical fluctuations more effectively. When we compared these algorithms for triple-play services presented in Fig. 7, we observe that they are close to each other and have relations in the same direction. However, the proposed ORPHSN outperforms the end-to-end propagation delay in the BHSCCS environment, while FAR and RAR suffer in more propagation delay.

The proposed ORPHSN is implemented on the BHSCCS model, each node connection in network topology is assumed traffic loads constrained with an increasing traffic load until 20 %. The simulation assumes that the connections are of identical traffic characteristic. Figure 8 shows the comparison of the end-to-end delay from the source to the destination when different percentages of traffic loads are applied. Moreover, the average traffic loads in hybrid network system values for this simulation are around 1 % during intervals, which are due to the initially assigned traffic loads. The impact of traffic load rates on the hybrid network system for the proposed ORPHSN, FAR, and RAR algorithms are increasing the end-to-end propagation delay. The figure shows that ORPHSN outperforms with the lowest delay. This is because the proposed algorithm used multimetrics to optimize in that period of lifetime satellite connection for more information in the hybrid network system optimization.

The satellite routing algorithm based on the optimized path always considers many factors, such as the minimum propagation end-to-end delay, the minimum queuing delay, and the optimal link utilization. This article puts forward an ORPHSN routing technique which optimizes metric cost.