CHAPTER 1 Introduction
1.4 Organization
intermittently in the environments of the delay tolerant networks, the efficiency of forwarding messages reduces greatly. In spite of many researches use the social network analysis methods to solve the forwarding messages problem, most of them focus on forwarding messages to single destination or relay nodes. Further, most of those studies are confined to forward message to the destinations which are knowable.
The main object of this thesis is that user can forward message to multiple uncertain destinations by multicasting. Besides, the information of the community relations between users and each nodes or destination is investigated and applied to
The rest of thesis is organized as follows. Chapter 2 introduces the overviews of the related works, including the Common DTN Routing Protocol, Social-based Multicast Scheme and the Social-based DTN Routing Protocol. Chapter 3 shows the details of different components and main features of the methodology. Chapter 4 presents, analyzes, and discusses the simulation results. Finally, the conclusions and future work are presented in Chapter 5.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
5
CHAPTER 2
Related Work
In this chapter, we will introduce the research of routing protocols. Here, we were divided into three parts, including the Common DTN Routing Protocol, Social-based Multicast Scheme and the Social-based DTN Routing Protocol.
2.1 Common DTN Routing Protocol
In this section, we will introduce common DTN routing protocol including Epidemic Routing, Spray-and-Wait Routing and PRoPHET Routing.
Epidemic Routing [11], provide a message of delivery in disconnected delay tolerant networks, in which the basic idea deliver the message to other nodes, if the node encounter with other nodes. In other words, the Epidemic routing select all nodes as relay nodes in the network. Upon encounter with two nodes exchange their summary vector to decide which nodes will not be seen. In this way, the message should be delivered to everywhere all in the network. Theoretically, the Epidemic routing would the biggest delivery ratio and lowest delivery delay, but the delivery overhead is highest when the buffer is unlimited.
Spray-and-Wait Routing, flooding based, presented by Spyropoulos et al. [13]
the copies of message should be restricted to a fixed number, called L copies. Then,
‧
A number of the solutions is to reduce the delivery overhead that employed some form of probability and these metrics are based on contact history between nodes
In this section, we will introduce social-based Multicasting scheme including The Social-Aware Multicast in Disruption-Tolerant Networks and Social Network Aided Multicast Delivery Scheme For Contact-Based Networks.
The Social-Aware Multicast in Disruption-Tolerant Networks [9]. This paper studies to the social network perspective based on delay tolerant network architecture.
For use multicasting way to select a relay node and analytical models, and further explore the unicast and multicast difference. This research proposed a new method to select appropriate relay nodes to multiple destinations based on quantitative data social networks. The design of focuses on the concept of node centrality and social community both quantitative data and to ensure that be able to deliver the message to the destination within the time constraint. The simulation results present that the
‧
delivery overhead by decrease the relay nodes used.Social Network Aided Multicast Delivery Scheme For Contact-Based Networks [23]. This paper should design more efficient dimension strategy. In real world, people who are in multiple grouping are good message sender. Thus, the ability to determine the different groupings from the different communities traces. In this paper, the author use human mobility trace data from the real world to simulate to evaluate the performance of multicast delivery scheme in human-contact based DTNs.
The Social Network Aided Multicast Delivery Scheme For Contact-Based Networks algorithm is, first determine the communities which node belong to this scheme use K-clique algorithm to detect the community. Then, we identify the connectors help us to forward message. The simulation result which delivery performance is similar to multi-copy epidemic scheme, but the overhead is smaller.
2.3 Social-based DTN Routing Protocol
In this section, we will introduce social-based DTN routing protocol including Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs and BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks.
Social Network Analysis for Routing in Disconnected Delay-Tolerant MANETs [6]. Message delivery in sparse hoc networks (MANETs) is difficult thing. Node can move freely so the key problem is to find a route through to provide the better performance. This paper expressed in the network, there are various ways of learning to do to solve the information transmitted. To achieve this purpose, a number of relay nodes can use centrality to connect the adjacent node and exchange
‧
indirectly relevant to neighbors, it can use relay nodes to help us to forward message.Because the complexity of centrality metrics based on the concept of the ego networks, the nodes can not to exchange their information about the entire network.
This paper proposed the SimBet routing scheme which is pre-estimated betweenness centrality metrics and social similarity. This paper simulations use reality trace data to present the delivery ratio is similar to Epidemic routing and decrease delivery overhead. Besides, studies show that where the nodes have low connectivity, the SimBet is outperforms PRoPHET routing.
BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks [8]. It is use for unicast message delivery. The BUBBLE Rap considered the node centrality and hierarchical community based on social community knowledge. The BUBBLE Rap proposed a forwarding message scheme based on the social network. The BUBBLE Rap algorithm, first of forwarding scheme is deliver message to other node that is more popular than itself. Then, this scheme is to determine the nodes of communities, and use them as relay node. This scheme is implemented in a randomly distributed and compare to the PRoPHET routing. However, the BUBBLE Rap could the limit the performance metrics, because the node centrality cannot represent the contact probability which does not represent the capability of nodes to contact others. The simulation uses the trace data from the real world.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
9
CHAPTER 3
Methodology
In this chapter, we will show the details of different components and main features of the methodology. We develop a new forwarding message approach in DTNs to solve the delivery message problem based on the social networks and multicasting concept of the data source reach to multiple and uncertain recipients.
Here, our mechanism is divided into three parts, we are the first to introduce the system environment and scenario, the second explains how to group nodes in the distributed community, and the last investigate to select the appropriate nodes as relays and formulate our own strategy. In the following, we will explain each of the three parts in detail.
3.1 System Environment
This research that we are taking into consideration the situation in real world, we often do not know the destination of the road pedestrian encounter, or the direction where they want to go, causing the people mobility is unpredictable. That is, we assumed that the unknown circumstances, each node could not know each other the destination and direction of movement, and the node that is randomly distributed can
‧
transmission capacity, for example Bluetooth and Wi-Fi Direct. In a limited range of communication, node can transmit message to each other.3.1.1 Scenario
We use the NCCU reality traces data, so our scenario assumed in the NCCU campus, but our mechanism can be used in any environment. The Securities Research Society wants to hold a financial management seminar. Student S, is public relations officer, should need to deliver this message to students, teachers and the community, but not the school’s wireless work smooth. As many people could connect to the Internet, wireless network congestion often occur, resulting in cannot surf the Internet or network connection have intermittent condition, so S can use multicasting scheme and social relations to deliver quickly this information to everyone who may be interested in this message. This scenario similar to distribute flyers that hope more people know this message.
3.1.2 Technical Challenges
In the current delay tolerant networks (DTNs) organization and the research of social networks, most message forwarding schemes deliver message to single and certain destination, but in reality we are almost impossible to know directly the nodes’
destination and direction of movement, it must be formed according to social relations among people. The key challenge is how to establish the social relationship strategy to select appropriate nodes as relays, and furthermore to use multicasting to disseminate effectively as many destinations as possible to improve performance of
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
11
delivery success ratio and delay latency and delivery cost and delivery accuracy.
3.1.3 Operating Environment
Consider distributed communities in DTNs of real world, nodes all had its own living sphere, for example, interest as the classification, formed a grouping. Some networks can consist of grouping where metrics based on direct or indirect encounters, whether direct or indirect encounters can become a target for receiving message.
The grouping method detailed as shown in 3.2.
Consider three disconnected groups as shown in Figure1 [6]. Source node S wants to deliver message to destination node D. A mobile node may encounter multiple nodes. If node D is interested in node S’s message, however, node S cannot directly deliver this message to node D. This is difficult to make the decision of selecting a relay node to forward this message. If node S can be found the appropriate relay node 𝑅1 in its group to forward message, in the same way, the three groups are connected by the relay node 𝑅1, 𝑅2, 𝑅3 and 𝑅4. The relay nodes 𝑅1 to 𝑅2 and 𝑅3 to 𝑅4 illustrated by dashed lines.
Figure 1 Disconnected Grouping
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
12
3.2 Grouping
In real world, the people there are mobile nodes are randomly distributed in the network. Each node more has its own local community, so we use similarity detection scheme which allows each mobile node will be effective to determine whether nodes are in the same grouping.
In the following, one special component focuses on grouping discussed in section 3.2.1, and the other proposed social-aware local utility calculation (SALUC) routing protocol scheme to enhance performance will be shown in chapter 3.3. In Figure 2 presents the flow of forwarding message.
Figure 2 The flow of forwarding message
3.2.1 Similarity
The similarity component is the special one of the proposed method. Such as centralized scheme are prior to do mobile nodes classification for offline data analysis.
‧
instant grouping, rather than pre-grouping, because should more close to the reality of the situation.The nodes have the same grouping, when there is a high degree of similarity between two nodes. Therefore, we can determine these nodes in the same grouping.
In our research scenario, mobile nodes all have its interest, so we group according to the interest of each node. If the nodes encountered, we will quantify the interest similarity value. If the higher degree of interest similarity between the nodes, they are more representative of the same grouping.
We defined two nodes that are 𝑛𝑖 and 𝑛𝑗. The node 𝑛𝑖 and 𝑛𝑗 indicates degree of interest similarity between two nodes that may belong to the same grouping.
By this way, we hope that if node 𝑛𝑖 met node 𝑛𝑗 and can use the interest similarity value (𝑆𝑖𝑚𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡(𝑛𝑖, 𝑛𝑗)) to determine whether the node 𝑛𝑖 and node 𝑛𝑗 has a high degree of interest similarity, that is node 𝑛𝑖 and node 𝑛𝑗 are in the same grouping.
‧
The method depends on local information calculations not makes assumption of global knowledge, so it can reduce the cost. For example, we divided five kinds of interests, including art, service, entertainment, sports and academic. We denote two vectors 𝑛⃑⃑⃑ = (1, −1,1,1,0) and 𝑛𝑖 ⃑⃑⃑ = (1, −1,0,1,1). The values of vector denote 𝑗 -1 meaning exactly non-interest, 1 meaning exactly interest, with 0 usually indicating not has any option. According to formula (1), the interest similarity value is 35. However, the common approach is only taking into account single-oriented. We refer to this interest similarity method that not only provides positive perspective but also includes negative perspective. Thus, it can be more realistic and increase delivery success ratio.
3.3Social-Aware Local Utility Calculation (SALUC) Routing Protocol
From the aspect of social networking analysis, the DTNs comprise a set of people forming social grouping. In the previously mentioned to the purpose of community determine is to search these groupings in section 3.2.1. In the following, we focus isn’t interest to the message, but it’s a popular node which has higher weights of local utility with similarity, calculation centrality and probability than others nodes. The
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
15
weights of local utility detailed in section 3.3.3.
Figure 3 A Randomly Distributed in the Networks
In this section, such knowledge is critical to calculate the cumulative contact rate and node centrality to multiple destinations. Then, we develop social network and probability annalistic to select a relay node to forward message. In our problem is as much as possible to forward message quickly to each node that is interest to the messages and to determine the appropriate relay selection scheme within the time constraint. As shown in Figure 4 and 5 describes the message forwarding process.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
16
Figure 4 Message Forwarding Process – Direct
In Figure 4 icon definition same with Figure 3, the node S delivers the message to the interest nodes who are interested in this information, when nodes who can directly encounter are in the same grouping. The result which the node S may broadcast message to destinations 𝐷1, 𝐷2 and 𝐷3 in the same grouping. As shown in Figure 5 icon definition same with Figure 3. On the other hand, if the nodes indirectly encounter the node S that is with a message, it can be helped by interest and popular nodes or non-interest and popular nodes. The node S can be multicast to multiple destinations. For example, if the destination 𝐷5 and 𝐷6 didn’t encounter the node S, message can be transmitted via node 𝐷4. Although the relay node 𝑅1 isn’t interest to the message, but it is a popular node which has higher weights of local utility than others nodes can be an appropriate relay node. If any the destination 𝐷7 and 𝐷8 didn’t encounter the node S, message can be transmitted via relay node 𝑅1. The solid line denotes direct encounter and dashed line denotes indirect encounter which should use social forwarding. The main flow of forwarding message detailed in section 3.3.3.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
17
Figure 5 Message Forwarding Process – Indirect
3.3.1 Centrality Metric
We based on centrality for exchange metadata information between encounter nodes which adopt local information instead of global topology information of entire network to decrease delivery cost. We estimated a node’s centrality in the distributed network in order to link different interest grouping in the social network analysis. For instance, how influential and most important people is in the social network. The centrality of node is measured the node’s capability of forwarding message and centrality metric can be calculated by the message source encounter other nodes based on the local information as its relay node. According to centrality features, we can select the appropriate relay node that has a higher degree of centrality as a bridge to link different groupings. Therefore, we could compare node centrality to each other to choose the least node as relay nodes to forward message, if the nodes didn’t belong to the same grouping. In Figure 6, described social relation of node [9].
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
18
Figure 6 social relation of node
As above, relay node 𝑅𝑖𝑗 that i denotes number of relay node and j denotes after number of relay node. If relay node 𝑅𝑖𝑗+1 with centrality greater than centrality of node 𝑅𝑖𝑗, then 𝑅𝑖𝑗 forward the message to 𝑅𝑖𝑗+1. This scheme similar used in Spray-and-Wait [13], but in difference Spray-and-Wait assumes each relay node has equal forward capability to destination. Hence, we use centrality to improve delivery success ratio.
There are common centrality degree, closeness and betweenness measures [24, 25]. Degree centrality is measured as the number of node can be directly connected to the node [6, 25]. Degree centrality, 𝐶𝑑𝑒𝑔𝑟𝑒𝑒(𝑛𝑖), was calculated for node 𝑛𝑖 as follows:
𝐶𝑑𝑒𝑔𝑟𝑒𝑒(𝑛𝑖) =deg (𝑛𝑖)
(𝑁 − 1) (2)
where deg (𝑛𝑖) is a degree number of 𝑛𝑖; 𝐶𝑑𝑒𝑔𝑟𝑒𝑒(𝑛𝑖) is a degree weights of 𝑛𝑖 with large number is regarded as a popular node that is link to many nodes. On the contrary, it’s located in the margin.
‧
Closeness centrality quantifies the distance of the shortest path which a node is reached to all other nodes [6, 25]. A highly closeness centrality may be an important point, because it is close from node to other nodes, and furthermore it can quickly
Betweenness centrality quantifies the number of the shortest path which a node is directly connected to all other nodes [6, 24]. However, betweenness centrality is considered an important node to be a bridge to encounter other nodes. Betweenness centrality, 𝐶𝑏𝑒𝑡𝑤𝑒𝑒𝑛𝑛𝑒𝑠𝑠(𝑛𝑖), is calculated for node 𝑛𝑖 as follows: node in the distributed network. Betweenness centrality measures the importance of the communication capability of node among other nodes. From our social relation calculation define as higher degree of betweenness centrality as possible easy to connect different groupings. In our forwarding message scheme, we should use
‧
betweenness centrality features which can higher chance to encounter the other nodes in the shortest path due to regard as an important node, and furthermore quickly find the better nodes as the appropriate relay nodes to effectively forward message to interest nodes. Betweenness centrality based on the local information measures does not correspond to all nodes in the network. Consequently, nodes can compare their local information betweenness centrality value between each other which with the high betweenness centrality value. In the most important of all that we hope using these features to improve the performance in our scheme.
3.3.2 Probability
In this section, we describe that compute the contact probability between two nodes.
Although the between centrality measures which a node with shortest path to encounter other nodes, and a node with higher betweenness centrality value has better capability to facilitate delivery message to other nodes in the different grouping. In the fact, the betweenness centrality node cannot represent the probability of the node whether the node contact to other nodes. Therefore, we propose a formula of to a random node that is in the randomly network within the time constraint t, and N is
‧
between two nodes. In our system which is in the randomly distributed network for DTNs and unknown destinations which are interest to the message, we should combine contact probability and betweenness centrality to determine whether the node can contact directly and be an important node called popular node as better appropriate relay node to forward message to other nodes who is interest to the message maximum possibly within the time constraint t. For example, assume node 𝑛𝑗 is an important node, if node 𝑛𝑖 has high probability encounter node 𝑛𝑗 rather than no chance encounter to waste the time. Thus, our scheme can ensure that as far as possible disseminate message within the time constraint.3.3.3 Formulation
This section describes utility-based forwarding scheme. We propose a social-aware local utility calculation (SALUC) routing protocol which develop forwarding message strategy selecting the minimum number of relay nodes for multicast is expected to satisfy forwarding message as many destinations as possible are interest to message.
This section describes utility-based forwarding scheme. We propose a social-aware local utility calculation (SALUC) routing protocol which develop forwarding message strategy selecting the minimum number of relay nodes for multicast is expected to satisfy forwarding message as many destinations as possible are interest to message.