CHBR: Contact History Based Routing in Time Varying Approach

In Delay tolerant network having intermittent connectivity so there is no guarantee of finding a complete communication path that connecting the source and destination. There no any end to end connectivity for delay-tolerant network selection of routing protocol is important to deliver the message in an efficient way and increases chance to deliver a message to the destination. Some existing routing protocols improve the delivery ratio but it also increases the overhead. Our paper proposed Contact History Based Routing (CHBR) that use Neighborhood Index and Time varying properties such as temporal distance, Temporal Diameter and centrality for benchmarking the existing routing protocol. First, temporal metrics are evaluated for synthetic and real trace data. Then CHBR protocol is compared with the Epidemic and PROPHET for delivery ratio, overhead and the number of messages dropped. This has been carried using Opportunistic Network Environment simulator under real and synthetic datasets.


Introduction
Provide examples of relevant academic disciplines for this journal: E.g., History; Education; Sociology; Psychology; Cultural Studies; Delay tolerant networks [1] or networks with intermittent connectivity networks are wireless networks often where, a communication path between a source node and destination node does not exist, either directly or through established routes by intermediate nodes. This situation occurs if the network is sparse and partitioned due to high mobility, or when the network extends over long distances; in these cases, the traditional routing protocols have been developed for ad hoc networks proved to be insufficient because they require the existence of end to end path in order to route the packets. Hence, researchers have proposed different techniques for exchanging the message in such environment based on store carry and forward mechanism.
Broad routing techniques in DTN environment can be classified as forwarding and replication. Forwarding technique only keeps the single copy of message in network. e.g. direct transmission, first contact [2]. This protocol utilizes the available resources optimally but suffers from lower delivery ratio. In replication, copies of each message spread across the network. E.g. epidemic routing [3]. This protocol give better delivery ratio [3] but, wastes resources such as bandwidth, buffer of network. Since, contacts are almost opportunistic, past history stored at each node may be use for future contact prediction. PROPHET [4] protocol uses the predictability values. These values are calculated using history of encounters and based on that forwarding decision is taken. With Prophet we get better delivery ratio compare to other routing but it increases overhead ratio. The work presented in this paper describes author's contribution as: 1. Temporal and Static representation of network 2. Proposed protocol based on contact history in time varying Approach

Performance analysis and comparison using ONE simulator
The rest of the paper is organized as section II introduces about routing protocols and mobility models, temporal graph simulator used respectively. Section III gives the information about the CHBR routing protocol and characteristics of it. Section IV discusses about the performance analysis our propose CHBR routing protocol with existing multi-copy routing scheme.

Related Work
Routing in delay-tolerant networking concerns itself with the ability to transport, or route, data from a source to a destination, which is a fundamental ability all communication networks must have. Delay-and disruption-tolerant networks (DTNs) are characterized by their lack of connectivity, resulting in a lack of instantaneous end-to-end paths. In these challenging environments, popular ad hoc routing protocols such as AODV [5] and DSR [6] fail to establish routes. This is due to these protocols trying to first establish a complete route and then, after the route has been established, forward the actual data. However, when instantaneous end-to-end paths are difficult or impossible to establish, routing protocols must take to a "store and forward" approach, where data is incrementally moved and stored throughout the network in hopes that it will eventually reach its destination [7] [2]. A common technique used to maximize the probability of a message is successfully transferred is to replicate many copies of the message in hopes that one will succeed in reaching its destination [8].
DTN focus is mainly on being as efficient as possible with few available paths [4]. Hence routing protocols have been classified according to practical scenarios. These protocols can be classified according to methodology used to find destinations and whether replicas of messages are transmitted or not. Consequently routing protocols can be broadly characterized by 1. Knowledge based (Forwarding) protocols 2. Flooding based (Replica) protocols

Coding based protocols
The Direct Delivery Routing: is protocol that delivery message direct to destination, so until the destination is not in range of source, the source will carry the message. According to behavior of direct delivery routing the delivery ratio is to small compare to above discussed epidemic and prophet protocols The epidemic routing protocol [9]: Messages propagate through the network like an outbreak of disease. This approach ensures that the message reaches its destination as much as possible, but it also wastes a lot of resources by unnecessary transfers of messages. A p r i l 1 0 , 2 0 1 4

The PROPHET [Probabilistic ROuting Protocol using History of Encounters and Transitivity] Routing[4]
: is protocol is one of the routing algorithm that have been proposed to use their resource properly. Prophet introduced a metric called Delivery Predictability, P(a,b) in [0, 1]. This metric is calculated by each node a of the DTN network and for each known destination b and will be used to decide which messages to be exchanged whenever two nodes meet.
The calculations of the delivery predictabilities have three parts. The first thing to do is to update the metric whenever a node is encountered, so that nodes that are often encounteredhave high delivery predictability. This calculation is shown in Eq. (i), where Pint in [0, 1] is an initialization constant.
If a pair of nodes does not encounter each other in a while, they are less likely to be good forwarders of messages to each other, thus the delivery predictability values must age, being reduced in the process. The aging equation is shown in Eq.
(ii), where α is the aging constant, and k is the number of time units that have elapsed since the last time the metric was aged. The time unit used can differ, and should be defined based on the application and the expected delays in the targeted network.
The delivery predictability also has a transitive property, that is based on the observation that if node A frequently encounters node B, and node B frequently encounters node C, then node C probably is a good node to forward messages destined for node A to. Eq. (iii) shows how this transitivity affects the delivery predictability, where β Є [0,1] is a scaling constant that decides how large impact the transitivity should have on the delivery predictability.
NECTAR protocol: NECTAR [10] protocol is based on the contacts history in order to create a Neighborhood Index and then determine the most appropriated route for DTNsIt uses the occurrence of an opportunistic contact to calculate a Neighborhood Index and spread messages in a controlled manner. During the contact period, nodes fist start the transmission of messages whose destination is the node that established the contact, then exchange information about the neighborhood (Neighborhood Index), and eventually forward other messagesThe NECTAR protocol can operate as the Epidemic Protocol. MinEpidemicLevel and MaxEpidemicLevel parameters were defined to allow this mode of operation and determine how many times a message can flood the network.
If the expression ( TTL) < MinEpidemicLevel is true, then the message will be transmitted to all neighbors. If MinEpidemicLevel parameter is set to one, only the source node will flood the network. If MinEpidemicLevel parameter is set to two, the source node will flood the network, as well as the source's direct neighbors.To avoid buffer overflow problem and network congestion, when the expression MinEpidemicLevel < (TTL) < MaxEpidemicLevel is true, neighbors only accept this kind of message if the buffer occupation is below a certain threshold.

Issues with NECTAR:
With NECTAR the node having a higher Neighborhood index for destination is select for next rely node but, if there is no contact in future then ?NECTAR protocol is designed based on static graphs for Neighborhood index calculation thus; it reveals inaccurate information about node's neighbor. Hence , there is scope to extend the work of extracting Neighborhood index and propose a new routing protocol using Time Varying graph NECTAR is flooding based routing algorithm so each node require high buffer size, so there is scope for integration of good message drop policy

DTN as Time Varying Graph
Time varying graph (Temporal graph) can be represented in a sequence of time windows. Where for each of window we consider a snapshot of the network state at that particular time interval [11] [12].It behaves like a Tool for understanding the dynamic properties of network over periods of time. Classical studies on social networks have focused on a static Representation of nodes and edges. After all static means, all edges are assumed to occur at the same time in temporal graph. However, in reality, such edges come and go across time. Temporal distance, temporal centrality and temporal diameter is important metrics that used to get dynamic properties of network

CHBR routing
Proposed protocol uses the occurrence of an opportunistic contact to calculate a Neighborhood Index and spread messages in a controlled manner. During the contact period, nodes first start the transmission of messages whose destination is the node that established the contact, then exchange information about the neighborhood (Neighborhood Index), and eventually forward other messages. The proposed routing protocol also uses the temporal distance and temporal diameter for efficient information diffusion.
The spread of the Neighborhood Index [10] allows a more accurate knowledge of network topology, which, inherently, aids the routing task. Although, to avoid unnecessary consumption of network resources, each node maintains a cache of neighbors it has contacted recently. Hence, nodes i and j can establish a new contact only after Tslot time slots (units of time) elapsed from the last contact between them, and if the Neighborhood Index table or message storage area (buffer) has changed

Neighborhood Index calculation
The Neighborhood Index is based on recent contact's history, in such a way that nodes that are frequent neighbors present a high Neighborhood Index. .

Computation of Time Window size
There are six nodes (1,2,3,4,5,6) in network, if we wish to calculate Time window then find the total contact duration of each pair and also count the number of connections in each pair.

Experimental Setup
We used ONE simulator [18] to evaluate our proposed protocol CHBR and generate message state reports of for specific scenario of simulation Performance evolution of CHBR is compared with existing epidemic and Prophet routing.
Objective for simulation: to measure delivery ratio and overhead ratio of our proposed protocols and existing routing protocol. Delivery Ratio is define as ratio of total numbers of created message by the number of successfully delivered messages and Overhead Ratio is define by Equ (i)

Dataset Information
Dataset being downloaded from Community Resource for Archiving Wireless Data at Dartmouth [19] (CRAWDAD) is a wireless network data resource for the research community site. Synthetic data is generated on ONE simulator and make compatible with existing real dataset.

Address IDs 0 -62
First the temporal metrics is evaluated using Networkx tool for rollernet and synthetic (RWP) dataset . The evaluated temporal metrics of dataset is given in table 4  In rollernet traces are for short duration and Neighborhood index is higher compared to RWP. Thus, CHBR drops more messages for rollernet. This leads to higher overhead compared to RWP.

Dropped Messages:
In Synthetic dataset, the number of connection edges is less due to randomize behavior of mobility model (RWP), also the contact period is very short. This leads to a lower node degree and in turn number of messages dropped for RWP compared to rollernet.

No of Messages
Rollernet Synthetic   Epidemic is a pure replication based technique. It floods the network with message copies. Thus, it drops the messages at relay nodes. PROPHET forwards the message copies based on probability and transitivity. Though numbers of copies are flooded by PROPHET is more compared to CHBR, resulting in higher message drops.

Conclusion
CHBR uses a neighborhood index technique for forwarding the message copies. It does base on time intervals. This resulted into accurate information regarding per node neighbor's compared to similar probability based protocols. It has been further extended for suitable integration of drop policy-MOFO. It keeps track of forward count of messages per relay node. MOFO selects candidate for drop messages with highest forward count. Thus, it helps in reducing the overhead and improving the delivery ratio

AKNOWLEGDEMENT
We express our sincere gratitude to the management of Ganpat University -Mehsana for providing us research opportunities and their wholehearted support for such activities. Finally, our acknowledgement cannot end without thanking to the authors whose research papers helped us in making this research.