- Open Access
A comprehensive-integrated buffer management strategy for opportunistic networks
© Pan et al.; licensee Springer. 2013
- Received: 12 December 2012
- Accepted: 25 February 2013
- Published: 17 April 2013
Opportunistic networks aim to provide reliable communications in an intermittently connected environment. The research in cache management of opportunistic networks has been done a lot in those aspects, such as queue strategy, cache replace, redundancy delete, etc. But most of the existing studies only focus on a subdivision of the buffer management. To deal with such case, this article proposes a comprehensive integration buffer management strategy, called comprehensive-integrated buffer management (CIM), which takes all information relevant to message delivery and network resources into consideration. The simulation experiments show that the CIM strategy improved the performance in terms of delivery ratio, overhead ratio, and average delivery delay.
- Opportunistic networks
- Buffer management
- Message delivery
Opportunistic network has captured much attention from researchers in recent years as a natural evolution from mobile ad-hoc network . It utilizes the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implements communications between nodes based on the manner of storing–carrying–forwarding transmission. Opportunistic networks are characterized by sparse connectivity, forwarding through mobility and fault tolerance. To deal with the unpredictability in connections and network partitions, many routing protocols [2–10] adopt flooding-based schemes to improve the message delivery, where a node receives packets, stores them in their buffers, carries them while moving, and forwards them to other nodes when they encounter each other. The excessive multi-copies spraying in the network causes serious congestion and exhaust nodes’ buffer space, thus influences the performance of transmission dramatically. Therefore, the buffer management plays a very important role in the transmission, and the limited buffer in each hop should be used reasonably. How to design an efficient and effective buffer management strategy in opportunistic networks becomes a crucial issue.
The main objectives of buffer management are (a) to delete the redundant information in the system, (b) to formulate reasonable queue strategy, (c) to control congestion, and (d) to build up the cache replacement policy. There has been prior work done in designing buffer management strategies.
Most of the existing studies of buffer management for opportunistic networks have focused on a subdivision of the field, such as queuing strategy, cache replacement, or redundancy removal. But few works take all of these factors into consideration at the same time. The conventional buffer management strategies including drop-random, drop-front, drop-tail, drop oldest, drop–least-recently-received, evict most forwarded first, and history-based drop [7, 8] have shown some improvement for dissemination of the message. The research in cache management of opportunistic networks has been done a lot in those aspects, such as queue strategy, cache replace, redundancy delete, etc. But the researches did not propose an integrated cache management strategy. How to fully utilize the characteristics of opportunistic networks to design the buffer management strategy is still an open issue.
In this article, we integrate the different parts of buffer management, and take all information relevant to message delivery and the network resources into account. Based on statistics and analysis of the state of the messages, and considering the delivery history of the node and location information, combined with the relevant information from mutual learning between nodes, this article proposes a comprehensive integration buffer management strategy. We have implemented the proposed strategies including all aspects in the opportunistic network simulation ONE [11, 12]. Simulation experiments show that the CIM strategy improved the performance in terms of the delivery ratio, the overhead ratio, and the average latency.
The remainder of the article is organized as follows. Section 2 gives the problem statement, In Section 3, we give an overview and detailed information of our algorithm. We evaluate our scheme through simulation in Section 4. Finally, we summarize our conclusions and discuss future work in Section 5.
In opportunistic networks, nodes not only forward data, but also store data in the cache and keep the data for a long time (store–carry–forward). Several factors, such as the mobility of nodes, the number of copies of the messages and buffer space of each node, etc., should be considered [13–19].
First, we observe that the mobility of nodes greatly affects the delivery of messages. Mobility of nodes result in a limited encountering time of the nodes, thus the number of successful delivery of messages is also limited. Based on this observation, we give high priority to those messages which can choose the encountering nodes as their relay and have higher delivery probability. This helps to suppress spreading of the messages with low delivery probability, which may result in more messages staying in the buffer and the congestion. So, without the help of effective buffer management, node may relay more messages with low delivery probability in this limited encountering time, and wastes the buffer resource, and degrades the performance of the system.
Second, flooding-based storing–carrying–forwarding transmission does not control the number of copies of the messages. The unrestricted flooding will inevitably lead to network congestion and frequent loss of messages, so the integrated buffer management strategy should involve this factor.
Third, there are still a large number of redundant copies of a message stay in the buffer when the message is successfully delivered to the destination. Normally, these copies either stay in the buffer until the time-to-live (TTL) of the copy expires , or continue spreading the copies of the message in the networks, this will increase the overhead and waste the network resources. So, we consider the redundancy deletion in the buffer management.
Finally, buffer constraints can severely affect the performance of transmission in opportunistic networks, studies show that flooding-based routing, e.g., epidemic routing (ER), has minimum delivery delay under no buffer constraints, but performs poorly when buffer sizes are limited. If the buffer size is full, the buffer management defines which message to drop when a new message is to be accommodated. Therefore, our buffer management takes the buffer replacement into consideration.
In order to improve network performance of transmission in opportunistic networks, we design a comprehensive integration cache management to tackle the problem discussed above.
The design of buffer management strategy should get along with the mobility model of the nodes and the routing algorithm. Most of the studies use the random-based mobility model to simulate the movement of the nodes. While the study in  shows that the typical random-based mobility models are significantly different from the real-move pattern of the daily lives of human being. Most people usually travel in some locations (such as home, dormitory, working place, athletic field, etc.), and only 3% of people leave home out of 100 km frequently. The opportunistic network mainly relies on the encountering opportunities of nodes, these nodes refer to mobile devices, and normally they are held by individuals. So, the mobility of nodes is characterized with sociality. Some of the nodes are more active than the others, and they have more chances to forward messages for others. Taking these factors into consideration, we design the queuing policy with the sociality of nodes. In addition, studies in [4–6, 22, 23] also show that the message delivery ratio increases as the TTL of the messages, the number of copies of the messages, and the number of forwarding increase, so if a message has a larger number of these factors, then it is more likely that the message has successfully been delivered, so we preferentially remove the messages with larger number of these parameters. Based on this idea, we propose our buffer replacement policy. Although the flooding-based routing has a worse performance when the buffer is constrain, but constrains of buffer and the CPU resources will not be a problem with the fast development of mobile terminators. On the other hand, flooding-based routings have the merits with large throughput, short delay. The future mobile devices are equipped with powerful hardware and large buffer size, which will help to increase the delivery of the messages of the flooding-based routing in the opportunistic networks. In our design, we combine the design of buffer management with the flooding-based routing. By collecting and analyzing the status of message, and considering the information of historical deliver and location, we propose a comprehensive integration cache management strategy. We give the detail of our design in the following sections.
3.1. Queuing policy
The queuing policy of our buffer management strategy uses two levels of priority queuing scheme. In the first level queuing, each node maintains a state information packet (SIP).The nodes update the state information every once in a while. Before the node sends messages to its relay nodes, it exchanges its information packet with the encountering nodes.
The structure of the SIP includes the node’s ID, message abstract, time stamp, position coordinate information of nodes, and most frequently contacting node list. The node’s ID refers to the node holding the packet. The message abstract of the SIP includes the list of message ID which the node is holding. From the message abstract, a node can learn which message it does not have. The most frequently contacting nodes refer to the nodes which get the number of messages ranked the top five. To maintain the frequently contacting nodes list, we first define the contact range as a circle which takes the current coordinator of the node as its center, LocationRang as its radius, where we sets up LocationRang 200 m. We also define a regular contact nodes list, nearHostList.
After the node gets the SIP of its encountering node, it can learn if the destination of the new coming message is in the recently contacting nodes list. If so, it then preferentially inserts the message into the queue.
The second level of queuing deals with the remaining messages after the first level of queuing.
3.2. Buffer replacement policy
where α, β, and γ are the weighted factor, which represent the impact of TTL, message size and average transmission times of the message, respectively. TTLmin is the remaining time of TTL of the message. TTLo is the initialization TTL for the message. BS j is the buffer size of node j. Smi is the size of the message, countav refers to the average forwarding time of all messages in node j, and count is the forwarding time of message i.
where M j is total number of messages in the buffer.
3.3. Congestion control
Congestion control is generally divided into two parts. The first part is how to choose the discarding message when butter space is filled up. This part is now separated, whereby the buffer replacement policy. The second part is how to control the copies of messages spread in the network. We mainly focus on the second part in this section.
where RelayCount i is the relay time of message i.
To control the number of copies spread in the network, the node only forward the message whose number of forwarding hops is less than HopsCountTh, and the relay time less than RelayCountTh.
3.4. Redundant deletion
Flooding-based routings spread multi-copies of the messages in the network to increase the chance of delivery. In the transmission process, when one of the copies reaches the destination, the transmission process of other copies of the same message should be terminated. While other copies of the message still accommodate the buffer of the relay nodes and spread in the network until the TTL elapsed. These useless copies of the messages compete for the network resources with other useful copies, which results in unnecessary network resources consumption. Therefore, the redundant deletion should remove these copies of the message. We introduce a learning mechanism, each node maintains a list of messages, which have successfully been delivered. When two nodes encounter each other, they exchange the lists, and spread to other encountering nodes. By this way, the redundant message will be cleaned up and the buffer space will be released soon.
4.1. Network model and simulation environment
Simulation environment parameters
10000 × 8000 m2
Number of nodes
Electric motor car
Electric motor car
2 Mbps/10 m
20 Mbps/100 m
100 Mbps/1000 m
500 kB–1 MB
Message generation interval
25 s ,35 s
The following metrics are used in our simulations.
Delivery ratio, which is defined as the ratio of the number of delivered messages to the total number of sent messages.
Overhead ratio, which is defined as the average number of relays used for one delivered message.
Average delay, which is defined as the average delay of all messages received by destination nodes.
In the performance evaluation, we implement the proposed CIM strategy in the representative flooding-based routing, ER, and we also apply Random (a node will forward a message in its buffer randomly) and FIFO (when two nodes encountered, the most recently received message in the node buffer will be forwarded last) buffer management strategies to the representative flooding-based routings, including ER, ProPHET routing (PRO), and Spray and Wait(SNW), respectively.
We run all these routing in the same scenario with the above parameters, and compare their performance with regard to the success delivery rate and delivery delay under different buffer size, TTL, and total number of messages, respectively.
4.2. Overall performance
4.3. Impact of buffer size
Opportunistic networks aim to provide reliable communications in an intermittently connected environment. The performance of the flooding-based storing–carrying–forwarding transmission may get worse when the buffer size is limited. The existing studies of buffer management for opportunistic networks have focused on a subdivision of the field, such as queuing strategy, cache replacement, or redundancy removal. To deal with such case, we integrate the different parts of buffer management, and take all information relevant to message delivery and the network resources into account. We propose a comprehensive integration buffer management strategy. Extensive results are provided to evaluate the proposed routing protocol with ONE simulator. Simulation experiments indicate that the proposed buffer management strategy is effective and outperforms the existing solutions.
This study wassupported by the National Natural Science Foundation of China under Grant no.61172087 and by the Natural Science Foundation of Guangdong Province under Grant no. 06300923.
- Fall K: A delay-tolerant network architecture for challenged internet. Proceeding of ACM SIGCOMM 2003, Karlsruhe, Germany, August 25–29 2003, 24-27.Google Scholar
- Vahdat A, Becker D: Epidemic routing for partially-connected ad hoc networks, Technical Report CS-2000-06. USA: Duke University Durham; 2000.Google Scholar
- Spyropoulos T, Psounis K, Raghavendra CS: Spray and wait: an efficient routing in intermittently connected mobile networks. Proceedings of ACM SIGCOMM Workshop on Delay Tolerant Networking (WDTN), Philadelphia, USA 2005, 252-259.Google Scholar
- Nguyen HA, Giordano S, Puiatti A: Probabilistic routing protocol for intermittently connected mobile ad hoc network. Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, Espoo, Finland 2007, 1-6.Google Scholar
- Jouni K, Jörg O: Time scales and delay-tolerant routing protocols. Proceedings of CHANTS’08, San Francisco, California, USA 2008, 13-19.Google Scholar
- Lindgren A, Doria A, Schelen O: Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mob. Comput. Commun. Rev. 2003, 7(3):19-20. 10.1145/961268.961272View ArticleGoogle Scholar
- Santos RM, Orozco J, Ochoa S: A real-time analysis approach in opportunistic networks. ACM SIGBED Rev. 2011, 8(3):2011.View ArticleGoogle Scholar
- Chiara B: Design and analysis of context-aware forwarding protocols for opportunistic networks. Proceedings of MobiOpp ’10, Pisa, Italy 2010, 201-202.Google Scholar
- Boldrini C, Conti M, Passarella A: Exploiting users’ social relations to forward data in opportunistic networks: the HiBOp solution. Elsevier Pervasive Mob. Comput. 2008, 4(5):633-657. 10.1016/j.pmcj.2008.04.003View ArticleGoogle Scholar
- Jindal A, Psounis K: Contention-aware analysis of routing schemes for mobile opportunistic networks. Proceeding of ACM MobiOpp, Puerto Rico, USA 2007, 1-8.Google Scholar
- Keranen A, Ott L: The ONE simulator for DTN protocol evaluation. Proceedings of the 2nd International Conference on Simulation Tools and Techniques (SIMUTools, 2009), Rome, Italy 2009.Google Scholar
- The One http://www.netlab.tkk.fi/tutkimus/dtn/theone/
- Chen Z, Qiu Y, Liu J, Xu L: Incentive mechanism for selfish nodes in wireless sensor networks based on evolutionary game. Comput. Math. Appl. 2011, 62: 3378-3388. 10.1016/j.camwa.2011.08.052MathSciNetView ArticleGoogle Scholar
- Adar E, Huberman BA: Free riding on Gnutella. Technical Report, SSL-00-63. Ecologies Area Xerox Palo Alto Research Center: Palo Alto; 2002.Google Scholar
- Li Q, Zhu S, Cao G: Routing in socially selfish delay tolerant networks. Proceedings of the IEEE INFOCOM 2010, San Diego, CA, USA 2010, 1-9.Google Scholar
- Panagakis A, Vaios A, Stavrakakis I: On the effects of cooperation in DTNs. Proceedings of the Second International Conference on System Software and Middleware (COMSWARE), Bangalore, India 2007, 1-6.Google Scholar
- Resta G, Santi P: The effects of node cooperation level on routing performance in delay tolerant networks. In Proceedings of the 6th Annual Conference on Sensor, Mesh and Ad Hoc Communications and Networks. Washington, DC, USA: IEEE Computer Society; 2009:413-421.Google Scholar
- Altman E, Kherani AA, Pietro P, Molva R: Non-cooperative forwarding in ad hoc networks. Lect. Notes Comput. Sci. 2005, 3462: 100-128.Google Scholar
- Buchegger S, Boudec JL: Performance analysis of the CONFIDANT protocol. Proceedings of the ACM MobiHoc, Lausanne, Switzerland, June9-11 2002, 226-236.Google Scholar
- Prodhan AT, Das R, Kabir H, Shoja GC: TTL based routing in opportunistic networks. J. Netw. Comput. Appl. 2011, 34(5):1660-1670. 10.1016/j.jnca.2011.05.005View ArticleGoogle Scholar
- Gonza’lez MC, Hidalgo CA, Baraba’si L: Understanding individual human mobility patterns. Nature 2008, 453: 779-782. 10.1038/nature06958View ArticleGoogle Scholar
- Nguyen HA, Giordano S: Context information prediction for social-based routing in opportunistic networks. Ad Hoc Networks 2012, 10(8):1557-1569. 10.1016/j.adhoc.2011.05.007View ArticleGoogle Scholar
- Ram R, Richard H, Prithwish B, Regina RH, Rajesh K: Prioritized epidemic routing for opportunistic networks. In Proceedings of MobiOpp, NY, USA. : ; 2007:62-66.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.