Wireless Communications and Mobile Computing

Volume 2018, Article ID 1571974, 9 pages

https://doi.org/10.1155/2018/1571974

## Information Transmission Probability and Cache Management Method in Opportunistic Networks

Correspondence should be addressed to Jia Wu; moc.361@0115uwaij

Received 19 July 2017; Revised 11 January 2018; Accepted 31 January 2018; Published 5 March 2018

Academic Editor: Sabrina Gaito

Copyright © 2018 Jia Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In real network environment, nodes may acquire the communication destination during data transmission and find a suitable neighbor node to perform effective data classification transmission. This is similar to finding certain transmission targets during data transmission with mobile devices. However, the node cache space in networks is limited, and waiting for the destination node can also cause end-to-end delay. To improve the transmission environment, this study established Data Transmission Probability and Cache Management method. According to selection of high meeting probability node, cache space is reconstructed by node. It is good for nodes to improve delivery ratio and reduce delay. Through experiments and the comparison of opportunistic network algorithms, this method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.

#### 1. Introduction

Opportunistic network is a type of multihop wireless network. It has emerged in recent years [1]. The key for distinguishing the features of opportunistic networks from ad hoc [2–4] is that an end-to-end path will never occur. However, the union of networks may present an end-to-end path at snapshots over time. The application areas of opportunistic networks include military communications [5], interplanetary networks [6], networks in underdeveloped areas [7], field tracking [8], and disaster rescue [9].

In opportunistic networks, the traditional algorithm paradigm for the Internet and ad hoc networks, where routing algorithms are computed based on topological information, becomes inadequate. The first approach to routing in opportunistic networks is a variation of controlled flooding. All messages are flooded and are limited by time to live, and then messages are delivered to their destination. This approach contacts the node that is receiving the message during flooding. Several advanced proposals have replaced topological information with higher-level information while attempting to limit flooding cost.

For social network application scenarios, data take up significant cache space device because people use portable mobile devices during data transmission and no suitable transmission range target is responding, which eventually causes transmission delay. On the one hand, a number of pieces of awaiting information are stored in the device. Some information may be stored for a long time without user acceptance and response status. On the other hand, new data is received and emergency information is released in real time, because the cache space is big, resulting in the storage of new data [10, 11].

To solve these problems, this work presents an Information Transmission Probability and Cache Management (ITPCM) method based on node data information cache. The algorithm is based on the node that can identify surrounding neighbors to evaluate nodes between the meeting probabilities, which will cache data distribution adjustment, ensure the high probability of node preferential access to data, and achieve the objectives of cache adjustment. Meanwhile, to avoid deleting cached data, the cache task of the node is shared through the neighbor node writing method, and the effective data shunt is performed.

The contributions of this study are as follows:

By analyzing the relationship between nodes, the probability of meeting the neighbor is evaluated.

The list of nodes is sorted after evaluation and node cache reconstruction.

Through the effective caching adjustment method, this algorithm can improve delivery ratio, and then the delay of end-to-end data transmission is reduced.

#### 2. Related Works

Research on opportunistic networks currently focuses on routing algorithms. Existing routing algorithms can be used in different areas through improvement. Some methods adopted in opportunistic networks are as follows.

Grossglauser and Tse [12] suggested a store-and-forward mechanism Epidemic algorithm that simulated the transmission mechanism of infectious diseases. In this algorithm, two nodes exchange a message that is not stored by the other when they meet. This method is similar to exclusive or transmission (EX-OR) and allows the nodes to obtain additional information. The route where a node reaches the destination node and transmits the message can be guaranteed to be the shortest by increasing network bandwidth and buffering memory space. In real applications, however, congestion can occur in the message transmission network as the number of nodes included in the transmission increases, given that related resources in real networks are limited. In actual applications, this method cannot obtain good result due to the limitation of resources.

Wang et al. [13] proposed the spray and wait algorithm based on the Epidemic algorithm. This algorithm consists of two phases, namely, spraying and waiting. The source node initially counts the available nodes around it for message transmission and then transmits its message to the nodes through spraying. In the waiting phase, the message is transmitted to the destination node through direct delivery to fulfill the transmission process if no available node can be found during the spraying phase. This method is a modified algorithm that improves the flood transmission feature of the Epidemic algorithm. Furthermore, the spraying phase may waste source nodes if a huge number of neighbor nodes that consume considerable space exist in the source nodes. Hence, this algorithm can cause the death of nodes by randomly overspraying source nodes in several networks.

Spyropoulos et al. [14] recommended the PRoPHET Algorithm. This algorithm improves the utilization of a network by first counting the available message transmission nodes and then calculating the appropriate transmission nodes to form message groups. Leguay et al. [15] established the MV algorithm based on the probability algorithm. This algorithm calculates transmission probability based on records and statistics in the meeting and area visiting processes of the nodes.

Burgess et al. [16] presented the MaxProp algorithm based on array setting priority. This algorithm features determining the transmission sequence according to a settled array priority when two nodes meet. This method decreases the consumption of resources and the efficiency of the algorithm is improved by arranging a reasonable sequence for message transmission. Leguay et al. [17] suggested the MobySpace algorithm. In this algorithm, node groups or pairs with high relevance form into a self-organizing transmission area to realize optimal communication among nodes.

Burns et al. [18] recommended the context-aware routing algorithm based on calculating the transmission probability of the source nodes reaching the target nodes. This algorithm obtains the middle node by calculating the cyclic exchange transmission probability and then collects and groups messages to guide the middle node in transmitting messages directly to the node with higher transmission probability.

Kavitha and Altman [19] presented the message ferry algorithm, which refers to the grouping and transmission of messages. This algorithm classifies and groups the messages collected by the source nodes that are going to be transmitted and then counts the existing transmission traces for each ferry node in the network. The movement rule of ferry nodes can be achieved. The source node will move to the ferry node automatically during message transmission. Transmission effects can be improved by predicting the node moving trace in the algorithm.

This paper discusses and demonstrates the application of opportunistic networks to social networks based on the analysis and summary of related works.

#### 3. System Model Design

##### 3.1. Analysis Model of Node Connection Status

The capability to forward and cache the messages conveyed by the encounter node becomes robust when the node establishes a strong connection. According to the connections, the running time of the nodes in the network can be divided into connection interval time and duration. The following analyses can be obtained by examining the state of the node.

It is assumed that the nodes in the network exhibit independent and distributive properties, and the motion state of the nodes is unaffected by the motion state of other nodes. Therefore, the connection events in the nonoverlapping time domain are independent of each other; the constraint conditions are expressed in

Among the constraints, and represent the connection probability between nodes and at times and , respectively.

Given that the nodes that meet other nodes can be described as the average for Poisson distribution, the node of the connection state is related to its connection strength and time interval monitoring. In the time interval [], the connection probability of node can be expressed in

Among the constraints, indicates that node has established a connection in . is the connection strength, and is the high-order infinitesimal of .

The nodes continuously establish two or more connections with a minimal probability event at a brief time interval, as shown in

In (1), (2), and (3), the connection is established among the nodes for a random event at a given period, which is equivalent to the number of Poisson processes; then, unknown nodes establish two connection intervals by exponential distribution [5]. In addition, relevant studies show that the duration of node connection is subdivided [8]. Node connection is established by the state analysis model. The node can be analyzed by the specified message node to transfer service capability to estimate the message transmission probability and to provide the decision basis for forwarding and deleting a message.

##### 3.2. Calculation Method for Data Transmission Probability

The arrival strength of the node connection reflects the strength of the node service capability. The duration of node connection demonstrates a robust randomness and is limited by media-sharing characteristics. In addition, a link conflict is observed among the nodes. Therefore, the service capability of the node should fully consider the probability of connection arrival strength and fluid connection availability.

The nodes in the network can be analyzed by the established analysis model of distributed connection status. The average connection of node is determined at interval time and can be established by the local record of connections between and the current system running time , as shown in

Furthermore, the connection of node can be obtained from strength , as shown in

For node , connection time is determined by three parameters, namely, the connection setup time , connection broken time , and connection time , as expressed in

According to the historical information of local recording, the average connection duration of node can be obtained, as presented in

Apparently, the service nodes within a given time interval rate are directly related to service node number. The fast rate of service nodes with the same connection strength can service a considerable number of nodes, can cache a considerable number of messages, and can show the robust capability of service nodes. The service rate of node can be connected to the average duration of obtained , as provided in

The node connection at time in the established state probabilities and in the off-state probability through nature and differential equation of in the node queue model satisfies the following:

The connection state of the node at any time must have one established connection and disconnection, namely, and , respectively, to satisfy the regularity, as expressed in

The density of the network node distribution is relatively sparse, and the initial time node in the system changes from static to a critical state. Hence, any node and other nodes are connected to a minimal probability event, and the availability is shown in

According to the nodes that meet other nodes which can be described as the average for Poisson distribution, the node of the connection state is related to its connection strength and time interval monitoring [5]. Equations (9), (10), and (11) display the connection probability of node connected at time , as presented in

The operating state of the node transition set integrates the elements with the interoperability; thus, the stationary distribution of exists. So,

According to (3) is high-order infinitesimal [8], from , . So, the network at a steady state time point connection may be revealed in the off-state probability, including for the load intensity of node , as shown in

A new node connection at any time should satisfy the basic condition that the node connection is in a disconnected state. Therefore, the connection probability of node at any time in a disconnected state is equivalent to the fluid availability of connection , as presented in

The service force of node can be obtained, as expressed in

The message delivery status is related to the relay node that transmits the message. The service capability of the relay node that has been stored in the message should be considered when estimating the delivery probability of the message. In this study, the relative service capability of each node in the network can be obtained according to the proposed method of node service capability, as provided in

denotes the maximum node service capability, which can be obtained through information interaction among the nodes. Considering the relative service capability of relay nodes in the message, the delivery probability can be obtained, where , and is the number of paths stored in the message transmission. The large equates to a high probability of message delivery and to continuously reduced forwarding and storing of the message.

In this study, the basic principle of network news transmission is used by using node communication opportunities, interaction in the news propagation path among the nodes, and node information service capability. After a brief convergence time, the node can approximately obtain the transmission path of local news and the service capability of each node when the network is stable.

##### 3.3. Information Delivery and Cache Management in Algorithm

The node in opportunistic network has a strong social attribute, which not only needs to cache the information it generates but also provides cache and forwarding services [20]. However, to ensure that the messages they generate are delivered successfully, nodes usually show a certain degree of selfishness; that is, the messages generated for themselves are given higher caching priority. When many news networks exist, the node easily obtains news of storage, carrying, and forwarding, and other nodes produce a fewer cache spaces for news distribution, which causes serious cache competition problem. Therefore, when designing the cache management strategy, you should consider the source of the message and cache the resource allocation for the messages it and its nodes generate. Generally, the news source node cache is not lost. The node cache is divided into local and cooperative caching areas, and the news of their local community produces storage node and other nodes generated message. Its structure is shown in Figure 1.