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Available online at www.ijarmate.com International Journal of Advanced Research in Management, Architecture, Technology and Engineering (IJARMATE) Vol. 1, Issue 3, October 2015

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Intelligent Routing in Multilayered Satellite Network S.Sivasundari1, N.Sandhiya Rajeshwari2, E.Sahana3, S.Sakthi4, G.Rufia5, S.Narendran6 U.G. Scholars, Department of ECE, Francis Xavier Engineering College, Tirunelveli 1,2,3,4,5 Assistant Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli6

Abstract— Multilayered satellite network, taken as reference constellation in this method, consists of two satellite layers, LEO layer in the lower part and MEO layer at the top. LEO layer has the same constellation properties defined in the first scenario.MEO layer constellation is slightly different than only MEO satellite constellation case defined in the previous section. There are 6 satellites in MEO layer divided into two MEO planes, achieving global coverage. There are 3 MEOs in each MEO plane. LEO satellites in the footprint of MEO form a group and this LEO group is shown by LGi. MEO is named as “group manager” and shown

by GMi. Each group has only one GM and all group members are aware of their GM. Actually a LEO might be covered by more than one satellite, but it assume that the MEO with the longest service time (depending on the satellite calendar) is designated as GM. There are ISLs among each MEO pairs. Previously mentioned LEO ISLs are still valid. Moreover, LEOs are also linked to their MEO group managers by IOLs. There are no direct links between GS and MEO, and GSs can communicate only with LEOs. Routing strategy is now different than the previous cases.Voice packets are classified by the source LEO satellites according to the distance between the source satellite and the destination satellite.

Index Terms— LEO layer, MEO Layer, ISL, LDV I. INTRODUCTION A path is assigned for each packet by thesource LEO using the routing table. Shortest propagation delaypath is calculated using Dijkstra’s Shortest Path Algorithm. Minimum delay paths are accepted as optimal paths.If the calculated path’s delay is greater than threshold delay Dthrsh, then this voice packet is marked as Long DistanceVoice (LDV). Otherwise, it is Short Distance Voice (SDV) packet. In the packets are classified according to the calculated path’s hop count. Since ISL lengths are noticeably different from each other at different parts of the Earth i.e. at Polar Regions and Equator, hop count does not reflect the realdelay values.

Hence, the base these marking schemes on ISL delays rather than hop count of the path. Initially, SDV andbackground (non-real time traffic) packets are forwarded tothe next hop of the calculated path on the LEO layer. LDV traffic is forwarded by the source LEO to its GM. After

getting the packet, MEO assigns a new path and forwards the packet to the next hop either in MEO layer or LEOlayer. If the destination LEO is in its managed LEO satellitegroup, it directly sends packet to the destination LEO. If itis in one of other MEOs’ coverage, it forwards the packet to the corresponding MEO. Using the MEO layer especially for time-sensitive traffic allows the system to meet two goal ssimultaneously: balance the link utilization rates and prevent excessive jitter and delay values. Depending on the values of Dthrsh, SDV and LDV traffic percentage will change and therefore load on LEO layer will change in return.This interaction makes determining Dthrsh value a design issue.

Delay and jitter sensitive voice traffic must be differentiated from delay tolerant background traffic. Due to satellites ‘processing limitations, queuing policy must be both simple and fast. Weighted Round Robin Queuing (WRRQ) is quite efficient for on-board processing in that sense. Strict priority may be an alternative policy. However, this policy leads to suffering of data packets of high delay values and may cause“starvation” anomaly. In this scenario, LEO satellites apply round robin queuing.

Satellite channels are known to suffer relatively large Bit Error Rates (BER) caused by losses or errors due to fading, propagation anomalies, intentional jamming, or other user interference. Packet loss due to noticeable packet errors may cause degradation in the transmission quality, if no correction mechanism is applied. However, ECC brings extra processing delay. The delay depends on the correction strength of the algorithm. To have modeled ECC by using a variable threshold for the receiver side for each link determining whether a packet is lost.

II. EXISTING SYSTEM In the light of this, it is just one consequent step to make the whole space segment itself a real network by designing low earth orbit (LEO) satellite constellations with inter-satellite links (ISLs) [1] to form a dynamic mesh trunk network. Providing truly broadband capacity on the air interface bottleneck by means of frequency reuse, such constellation networks should, however, not only serve as “last mile” broadband access solution. Rather than should also provide a substantial benefit to both operators and end users by offering truly global, low-latency, secure and flexible

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end-to-end communication with controllable quality-of-service (QoS) in one autonomous network.

Additional impetuses for the midterm implementation of really broadband LEO constellation networks are :1) mature laser technology for optical ISLs: and 2) the appealing prospect that lies in transferring WDM concepts from terrestrial fiber to space, being potentially combined with on-board optical switching and add/drop-multiplexing and last, but not least, the appeal of multi protocol label switching (MPLS) as a promising basis for the co-existence of IP- and non-IP traffic routing on an ATM-based infrastructure in space.

Regular mesh topologies, as conveniently designed for inclined Walker “delta” constellations, are considered as strong candidates for such an ISL backbone. However, such regular topologies still exhibit dynamic features in terms of considerable inter orbit link distance variations and the traffic patterns on both space-ground and inter-satellite links show extreme variations due to the rapid relative movement between serving satellites and served (global and inhomogeneous) user distributions.

Adaptive routing is, therefore, an essential requirement in the ISL backbone, from the user QoS perspective and in the operator’s interest to optimize the utilization of installed payload capacities. With the growing dominance of Internet and any kind of packet-oriented services, it is,thus, attractive to study packet-oriented routing approaches, which are supposed to be well suited for mesh topologies due to their inherent flexibility.

A rather “generic” analysis of routing performance under various reference traffic scenarios,as provided in this paper, should be of basic importance to gain insight and expertise for the proper design of concrete routing implementations for any such system, given the constellation parameters, service, and traffic demand profiles from the business case and the QoS specifications.

The distributed algorithms proposed [2] so far have inherent disadvantages due to the lack of the global traffic information. Moreover, most of the proposed algorithms neglect the geographical characteristic of traffic distribution.

To deal with the limitation of distributed routing scheme, it has proposed ALBR (agent-based load balancing routing) for Courier-like constellation. ALBR employs ISL (inter-satellite link) cost modification factor to increase the cost of paths through hot spot zones.

With the help of mobile agent, excellent load balancing over entire network is achieved. Unfortunately, there exist three deficiencies in ALBR. Firstly, how to reduce packet loss caused by the periodical handover of inter-plane ISL still remains open. Secondly,ALBR has higher end-to-end delay, even at the low traffic area. Thus, it is very necessary to optimize ISL Cost Modification Factor (ICMF) to further reduce end-to-end delay. Last but not least, there is a lack in analyzing the impact of ICMF on the observed performance.

Following ALBR, this paper focuses on presenting an Optimized Load Balancing Routing (OLBR) scheme based on agent for Polar-orbit satellite constellation by redesigning

ICMF, and thoroughly reveals the impact of ICMF on the observed performance. The main objective of OLBR will be to achieve better throughput and end-to-end delay with good load balancing over the entire satellite system. For this purpose, to propose new ISL cost modification factor considering both periodically topological change and geographic characteristic of traffic distributionon Earth.

Similar to ALBR, OLBR employs stationary and mobile agents simultaneously. Stationary agents perform ISL cost estimation and routing items updating on satellites. By migrating autonomously, mobile agents gather local information of visited satellites, such as ISL cost, latitude.

A system similar to Motorola’s Celestri [3], is examined in terms of traffic analysis and delay performance.

The orbit altitude of the proposed system is taken equalto 1381 Km instead of 1400 Km. This modification is necessary for the reduction ofthe system period (the time needed for twosatellites to be placed exactly over thereference earth point) from approximately 3days to 205.15 minutes.

This simulation periodis sufficient for the consideration of any possible combination of traffic distribution and constellation pattern. Although Motorola has announced the merging of Celestri and Teledesic, nothing is known yet about thedesign of the new constellation. However the developed code is adaptable to many other systems after very slight modifications.

Similar studies are rarely presented in the literature,due to the high complexity and the changing topology of the system, so it tried to obtain results that can be easily usedby other researchers investigating different LEO constellations. As far as to know this packet level simulation tool is one of the first tools available in the scientific community for an in-depth study ofthe integrated (end to end) routing and traffic issue for systems with continuous changes in topology and sensitivity to transmission delays.

Non-geostationary (NGEO) satellite networks, with their inherent multicast capability and global coverage potential in comparison to terrestrial networks, and their advantages of offering services with lower latency and terminal power requirements over geostationary (GEO) satellites, have become an attractive infrastructure to accommodate the burgeoning communication demands in current networks. Major countries in the world are paying much attention to this field, regarding NGEO satellite networks as an indispensable part of the Next Generation Networks (NGN).In order to efficiently transmit service data in NGEO satellite networks, an effective routing strategy is essential.

Previously, most proposed routing strategies focus on finding a transmission route that has the shortest end-to-end propagation delay. These strategies are simple to operate, and can get rather good results if the network traffic is not heavy. However, as the communication traffic increases, it begins to show two defects: the packet drop rate at network layer becomes abnormally high, and the cumulative queuing delay during transmission gets non-ignorabley large. To consider that it is the neglect of congestion that should be blamed.

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As to know, most world population lives around the equator or in middle-latitude regions, so the communication demands there are much larger than those from other areas.

This phenomenon directly leads to an unbalanced traffic distribution over the whole constellation: some satellites are congested while others remain underutilized.

To cope with issues mentioned above, researchers begin to take the expected queuing delay and congestion status into account when computing the optimal route for packet transmission. The multi-path routing mechanism is also introduced to better utilize free Inter Satellite Links (ISLs) and realize load balance of the entire constellation.

Due to the dynamic topology characteristic of NGEO satellite networks, traditional terrestrial Internet routing strategies, such as OSPF or RIP, cannot be directly applied to them. Researchers hence proposed lots of specific routing schemes that could deal with the routing complexity of NGEO satellite networks. Generally, according to whether the routing schemes adopt periodicity-based or on-board computation, it could classify them into two categories.

Schemes of the former category make full use of the periodic and predictable variations of constellation topology and divide the system period into several slots.

Routing table for each slot is calculated on ground and stored onboard in advance. When the topology changes, corresponding routing information will be retrieved to meet the routing demands. The advantage of this kind of schemes is their simplicity and easy operability. And the major drawbacks are their large storage requirements, weak fault tolerance, and poor adaptive capabilities. Among all these schemes, two concepts called Dynamic Virtual Topology Routing (DVTR) and Virtual Node (VN) deserve to be mentioned. In DVTR, the system period is divided into a series of time intervals.

On-off operations of ISLs are supposed to be performed only at the beginning of each interval and the whole topology keeps unchanged during each interval. Under such assumptions, the complex dynamic topology is transformed into a group of simple static topologies and traditional Dijkstra Shortest-Path (DSP) algorithm could be utilized. In VN-based schemes, virtual nodes are supposed to be set above the surface of the Earth to represent certain physical satellites. A virtual node and a physical satellite have a one to one correspondence at any time and such correspondence will not change until physical satellite flies out of and other flies into the coverage of a VN. The VN keeps state information, such as routing table entries or channel allocation situation for the physical satellite.

However, it imposes significant challenges for the space devices as well, especially in terms of the required computational and processing capability. These years, a lot of onboard routing schemes have been proposed in the literature.

For some examples, Jianjun et al: in proposed an onboard routing scheme which is based on a distributed hierarchical link state update mechanism: The defined plane speakers at first collect state information of all the links within the plane, and then exchange obtained information with other plane

speakers to build a routing information base (RIB) for the network.

Finally, the converged RIB can be distributed to all satellites through the intra-plane and inter-plane ISLs. Each satellite then can calculate the routing table based on this RIB for themselves. Another onboard routing scheme which proposed similar state information collecting mechanism is involved, and the only difference is that this scheme adopts multilayer topology architecture instead of defining a plane speaker is another example which proposes an on-demand computing and caching centralized routing strategy.

The strategy is designed for satellite network topology dynamic grouping, its route calculation is divided into three phases: direction estimation, direction enhancement, and congestion avoidance. The strategy provides significant advantages of high efficiency, low complexity, flexible configuration and great potential in scalability.

Previously, in the context of satellite networks, since the traffic load is not so heavy, numerous researchers presume that the propagation delay is the dominating factor in the communication delay. Therefore, it focused on developing routing mechanisms that find minimum propagation delay paths with minimal hop count for communication.

However, in recent years, with the traffic load increasing in the NGEO satellite network, queuing delay has become an important factor that could not be ignored any more. The avoidance from congested node and traffic load balance are also taken into account when designing a routing scheme. The CEMR algorithm periodically collects the expected queuing delay at each hop through an orbit speaker scheme. When it computes the routes, it takes both the expected queuing delay and propagation delay into account.

However, since the expected queuing delays are collected in advance, it may not completely conform to the actual situation.

So, there may be some potential congested nodes in the calculated routes, which will cause serious packet loss if packets are still sent there. Therefore, in order to decrease the packet loss rate and get better load balance, ELB takes the state of next hops into account when it makes a choice of the best next hop. However, it still fails to anatomize some special cases, in which even if the state of the next hop shows “free�, the packet should not be sent there, for some packets may be dropped even before it is sent out if the current hop is overloaded. In a distributed agent-based load balancing routing scheme for low-earth orbit (LEO) satellite networks is presented.

Two kinds of agents are used there. Mobile agents migrate autonomously to explore the path connecting source and destination, to gather ISL cost, identifier and latitude of visited satellites. Meanwhile, stationary agents employ exponential forgetting function to estimate ISL queuing delay, calculate ISL cost using the sum of propagation and queuing delays; evaluate path cost considering satellite geographical position as well as ISL cost, finally update routing items. The scheme is shown to achieve good load balancing, and can especially decrease packet loss ratio efficiently, guarantee 3 All Rights Reserved Š 2015 IJARMATE

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better throughput and end-to-end delay bound in case of high traffic load.

However, it needs the favor of many agents, which means a lot of cost and complexity. In a load balancing mechanism based on a new congestion-prediction method is devised. The author thinks that, since it is possible to know which LEO satellite is going toward the congested area in mesh constellations, the satellite in the congested area could preliminarily informs the neighboring satellite following itself with the coordinates of the congested area. The congested area then can be defined as a circle with its center at the informed coordinate.

The radius of the circular field could be determined according to the configuration of the satellite constellation and the LEO satellites coverage area. By exchanging such information, the satellite approaching the congested area can predict network congestion and immediately begin traffic detouring upon entering the area without awaiting detection of actual network congestion events. In to effectively resolve the problem of traffic congestion, the author proposes new Multi-Layered Satellite Networks (MLSNs) model by envisioning a method to distribute the flow of packets between the two layers of the considered MLSNs for minimizing the packet delivery delay of the network.

Moreover, it analyzes the effect of the method on the packet delivery delay by considering propagation and queuing latencies. In this paper, to aim at developing a routing strategy which considers both the expected and real-time queuing delays, concerns both current and next hop congestion, and adjusts the pre-calculated route according to the real-time situation, aiming at reducing packet drops, lowering queuing delay and distributing traffic burdens fairly.

The envisioned multihop NGEO satellite constellation is based on an Iridium-like backbone which consists of S satellites, where m represents the number of orbits and N represents the number of satellites in each orbit.

In this constellation, a satellite can set up ISLs with four neighbors at most, with two in its own orbit and the other two in the neighboring orbits. It can also establish several ground-satellite links (GSLs) with the terminals in its coverage.

The linking relationships among them, where all terminals are represented by a single ground antenna for simplicity. In the satellite, a buffer queue is allocated for each ISL to temporarily store packets to be sent out through this ISL to the next hop. A traffic light is also maintained for each ISL to indicate the traffic condition at this direction. A description about how-to set the color of traffic lights is given progressively in section. It starts from considering the congestion status of a buffer queue at the current hop. Then, the congestion status of the next hop is taken into account, for it will directly influence the packet drops if bad congestion happens there.

After that, the congestion status of the current and next hops will be combined together to set the color of traffic lights, so as to provide more accurate information about surrounding traffic situations. Upon that, a periodic checking and updating mechanism will be introduced. In addition, a

public waiting queue will be constructed at each satellite, aiming at alleviating packet drops when traffic lights at all candidate directions show “RED�. It constructs the public waiting queue by borrowing free space from queue sat each direction. Since the total memory space at a satellite is constant, constructing the public waiting queue in such a way actually means: When traffic at one direction is too heavy, it will borrow some space from other buffer queues to store packets.

This strategy is quite applicable to the NGEO satellite networks, where congestion normally happens due to heavy traffic at one or two directions.

III. PROPOSED SYSTEM This scheme considers a two-dimensional torus network n square region. with n mobile nodes moving on a n Similar to the correlated mobility model introduced in literature, the nodes in the network are partitioned into m groups, m n, and each group consists of an integer number q = n/m of nodes. The mobility of nodes belonging to the same group is confined within a square area, which is referred to as cluster region or simply cluster. This scheme also assumes that the cluster region covers a l l area, with l [1, n] that satisfies the cluster sparse regime (i.e., ml2 =o(n)). In other words, at any time clusters cover only a negligible fraction of the entire network area. Time is divided into slots of equal duration. The movement of groups follows the i.i.d. mobility model, i.e., assuming there is a logical group center for each group, and the position of the group center is uniformly and independently updated in the network area at the beginning of each time slot. Following their respective groups, nodes belonging to the same group will move to a region of area l2 around the group center (i.e., the cluster region). Inside the cluster region, the positions of nodes also change according to the i.i.d. mobility model. Notice that the individual nodes cannot independently roam all over the network area since they are constrained to follow the movement of their groups.

Communication Model schemes adopt the widely used Protocol Model to account for interference among simultaneous transmissions. All nodes adopt the same transmission range r for all their transmissions. Suppose that at time slot t a node i is trying to forward a packet to another node j, and their Euclidean distance is dij(t). According to the protocol model, this transmission can be successful if and only if the following two conditions hold: 1. The distance between i and j is no more than r, dij(t) r: 2. For every other node k

i,j simultaneously transmitting over the same

r, subchannel with i, it is always true that dkj(t) where D is a specified guard-factor for interference prevention.

The main idea of the 2HR-f routing algorithm under the i.i.d. mobility model is that, the source node delivers at most f copies of a packet to distinct relay nodes, while the destination node may finally receive the packet from one relay 4

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node. To support the correlated mobility, we present a three hop relay routing algorithm with f-cast (3HR-f), 1 ≤ f ≤ n 2q,1 which generalizes both the multi-hop routing introduced in literature and the f-cast routing introduced. The main procedure of this scheme is that the source node first transmits at most f copies of a packet P to the encountered relay groups, and then the relay groups deliver the packet to the destination group. Within the destination group, the message is delivered directly to the destination node by some node holding a copy of it. The details of the 3HR-f algorithm are depicted as follows. Without loss of generality, this focus on a tagged flow s → d. Let S denote the group containing source node s, and D the group containing destination node d. The groups other than S and D are potential relay groups of the flow s →d.

In general, the transmission process from s to d consists of three hops: 1st hop. The source node s sends at most f copies of the packet P to different nodes belonging to any relay groups.

Note that more than one nodes in the same relay group are allowed to receive the copies of P. 2nd hop. The relay nodes holding a copy of P deliver the packet to group D. Notice that there are no more than f copies of P will be delivered to D. 3rd hop. Within the destination group D, the node with a copy of P that first encounters the destination node d forwards the packet to the final destination node d.

To remove the remnant copies and avoid excess congestion in the network, this scheme adopts a mechanism based on the packet sequence number. For the tagged flow, the source node s labels each packet P with a send number SN(P), while the destination node d maintains a request number RN(d) to indicate the send number of the packet it is currently requesting. In fact, every time the source node s generates a new packet (respectively, destination node d receives a new packet), it increases its send number (respectively, request number) by one. Notice that every packet is received in order at the destination node d here. To implement the above routing scheme, this scheme assumes that each node is equipped with: i) one local-queue storing its own generated packets (i.e., packets at hop 1), ii) one already-sent-queue storing packets whose f replicas have been distributed while not received by the destination node yet, iii) n - 2q parallel relay queues for packets at hop 2 (for inter-group transmissions, i.e., the first two hops, each node can be a potential relay for the n - 2q flows excluding the flows originated from or destined for the nodes in its own group), and iv) q-1 relay queues for packets at hop 3 (for intra-group transmissions, i.e., the last hop, one node can serve as a relay for flows destined for the other q - 1 nodes in the same group).

Fig.1. Nodes chooses alternate direction to avoid traffic

Fig.1. shows that nodes choose alternate direction to avoid traffic. The nodes will choose alternate direction and to reach its destination without any traffic.

Fig.2. Node reaches its destination

Fig.2. shows that Node reaches its destination. The nodes will choose alternate direction and to reach its destination without any traffic.

IV. RESULTS AND DISCUSSION 5 All Rights Reserved © 2015 IJARMATE

Available online at www.ijarmate.com International Journal of Advanced Research in Management, Architecture, Technology and Engineering (IJARMATE) Vol. 1, Issue 3, October 2015 [3] X. Yi, Z. Sun, F. Yao, and Y. Miao, “Satellite constellation of MEO and IGSO network routing with dynamic grouping,” International J. Satellite Commun. Netw., 2013.

[4] J. Sun and E. Modiano, “Routing strategies for maximizing throughput in LEO satellite networks,” IEEE J. Sel. Areas Commun., vol. 22, no. 2, pp.

273–286, 2011.

[5] Christo Ananth, S.Esakki Rajavel, I.AnnaDurai, A.Mydeen@Syed Ali, C.Sudalai@Utchi Mahali, M.Ruban Kingston, “FAQ-MAST TCP for Secure Download”, International Journal of Communication and Computer Technologies,Volume 02 – No.13,Issue: 01,March 2014 [6] Y. Kawamoto, H. Nishiyama, N. Kato, and N. Kadowaki, “A traffic distribution technique to minimize packet delivery delay in multi-layered satellite networks,” IEEE Trans. Veh. Technol., 2013

Fig.3. Throughput graph

Fig.3. shows the throughput graph. The graph is plotted between time versus throughput. At the start of simulation the throughput is decreased because of the traffic present in the network. Then after choosing alternate direction to avoid traffic the throughput rate increases.

V. CONCLUSION Multilayered satellite network, taken as reference constellation in this method, consists of two satellite layers, LEO layer in the lower part and MEO layer at the top. LEO layer has the same constellation properties defined in the first scenario.MEO layer constellation is slightly different than only MEO satellite constellation case defined in the previous section. There are 6 satellites in MEO layer divided into two MEO planes, achieving global coverage. There are 3 MEOs in each MEO plane. LEO satellites in the footprint of MEO form a group and this LEO group is shown by LGi. MEO is named as “group manager” and shown by GMi. Each group has only one GM and all group members are aware of their GM. Actually a LEO might be covered by more than one satellite, but it assume that the MEO with the longest service time (depending on the satellite calendar) is designated as GM. There are ISLs among each MEO pairs.

Previously mentioned LEO ISLs are still valid. Moreover, LEOs are also linked to their MEO group managers by IOLs. There are no direct links between GS and MEO, and GSs can communicate only with LEOs. Routing strategy is now different than the previous cases.Voice packets are classified by the source LEO satellites according to the distance between the source satellite and the destination satellite.

REFERENCES [1] J. Bai, X. Lu, Z. Lu, and W. Peng, “A distributed hierarchical routing protocol for non-GEO satellite networks,” in Proc. 2014 International Conf.

Parallel Process., pp. 148–154.

[2] I. Akyildiz, E. Ekici, and M. Bender, “MLSR: a novel routing algorithm for multilayered satellite IP networks,” IEEE/ACM Trans. Netw., vol. 10, no. 3, pp. 411–424, June 2012.

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