A routing algorithm on delay-tolerant of wireless sensor network based on the node selfishness
© Zou et al.; licensee Springer. 2013
Received: 28 June 2013
Accepted: 13 August 2013
Published: 23 August 2013
According to the problem of the intermittent connectivity of the nodes in the delay-tolerant sensor network, considering that the nodes in the network are of social selfishness, we study how to reduce transmission delay and communication overhead on the basis of ensuring the message delivery success rate of the entire network. We present a delay-tolerant routing algorithm based on the node selfishness (DTSNS). Firstly, we divide the activity area into grids. Secondly, we predict the next mobile location of the nodes based on the Markov process and estimate the credibility of the nodes based on the node selfishness in order to reduce the network communication overhead and improve message delivery success rate. Then, we put forward the delay-tolerant algorithm based on delay constraints. Lastly, the simulation results show that the DTSNS has better performance which has higher level of data delivery rate and lower level of message delay and resource, compared with Spray and Focus, Spray and Wait, and Epidemic.
KeywordsDelay-tolerant Node selfishness Wireless sensor network Markov
In recent years, routing has always been a core issue in the studies on wireless sensor network. And the network topology structure is transformed from simple structure (All nodes cannot move.) to not simple, for example, the heterogeneous structure (The monitoring nodes are stationary and the relay nodes are mobile.) and the structure that all nodes in the network move. The problem of the intermittent connectivity due to the random mobility of the nodes in the network brings great challenges to the routing . T Spyropoulos et al. have presented routing for disruption-tolerant networks. It has given routing module that is dependent on the network characteristics exhibited . A Vasilakos et al. have recommended protocols and applications of delay-tolerant networks (DTNs) .
Wireless sensor network is a typical DTN . A Dvir et al. have presented backpressure-based routing protocol for DTNs . To solve the problem of the intermittent connectivity of the nodes in the DTN, we generally adopt the routing strategy based on carrier transfer. The nodes carry the message all the time. After encountering the appropriate nodes which are used to relay messages, the initial node delivers the messages in the form of multiple hops to the destination node. Thus, we know that the performance of the routing algorithm depends much on the intermediate nodes  and the messages arriving to the destination cost much more time (The delay of the network is large). However, in the application in the delay-tolerant wireless sensor network (DTSN), the nodes in the network may show some social selfishness and these nodes will refuse to relay messages for other nodes in order to reduce their own overhead. Therefore we need to take some corresponding measures to reduce the effects of the node selfishness on the network performance [6, 7]. It is of great significance to predict the neighbor nodes within the communication range and deliver data and to ensure the message delivery success rate in the entire network and to reduce the transmission delay and communication overhead under the premise of the relay nodes with selfishness. According to these situations, the paper puts forward a routing algorithm on delay-tolerant of wireless sensor network based on the node selfishness.
The calculation process of the algorithm is divided into three steps. The first step is that we divide the activity area into grids and we predict the next mobile location of the nodes based on the Markov process. We can calculate the probability of reaching at each grid and choose the grid with the largest probability as the next mobile location so that to build the best possible route to reduce the transmission delay. It follows as section 3.2 and section 3.3. The second step is that we estimate the credibility of the mobile relay nodes based on the node selfishness in order to reduce the network communication overhead and improve message-delivery success rate, such as section 3.4. The third step is that we put forward the delay-tolerant algorithm based on delay constraints, showed in section 3.5. We carry out the performance analysis of the algorithm and simulation experiment in the section 4. Section 5 is the summary of the paper.
2. Related research
The copies of message routing and the data forwarding routing are the major in the current researches on sensor network delay-tolerant routing . Routing strategy based on the copies of message is mainly by controlling the number of copies of message to improve the message delivery rate and reduce the message delay. According to the number of the copies of message in the network, it can be divided into simple-copy routing [8, 9] and multi-copy routing [10, 11]. The two strategies have their own advantages and disadvantages. The simple-copy routing has fewer copies compared with multi-copy routing so that it has less overhead and longer life of sensor nodes and the network. Although the overhead of the multi-copy routing network is large, by increasing the number of copies of message, the message can quickly reach to the destination nodes so as to improve the message submission rate and reduce the network delay. For the dynamic changes network structure, if we use the simple-copy to transfer the message, the worst case is that the nodes with messages may never meet with the destination nodes resulting in great message loss rate. So, multi-copy routing strategy has higher reliability than the simple-copy routing strategy. The main representative algorithms are as follows: direct transmission routing protocol [12, 13], first-time-connection routing protocol , random routing protocol , Spray and Wait routing protocol , Epidemic routing protocol , and RDAD routing protocol . Routing protocol based on data forwarding is mainly uses relevant knowledge to establish related model. Using the model to predict the probability of surrounding nodes reaching to the destination nodes, we can obtain the forwarding probability of the corresponding node and then choose the nodes with largest forwarding probability to relay messages. The main representative algorithms are as follows: PROPHET routing protocol , Spray and Focus routing protocol , and FPAD routing .
The nodes can communicate with each other and forward data when two nodes meet. But for DTN, some nodes considering their own resource usage  may refuse to the data delivery request from other nodes, or some nodes do not obey the rules of the relevant agreement. They take advantage of the defects existing in the agreement rules to obtain more resources than other nodes in the network [6, 7]. When there are selfish nodes in the delay-tolerant network, some nodes cannot deliver data successfully even if they meet. Node selfishness greatly hinders the normal communication between nodes and causes negative impact on the network performance and reduces the network connectivity constitutionally . Related research shows  that with 10% to 40% of selfish nodes, the network throughput may drop 16% to 32%. How to motivate nodes to deliver data and reduce the social selfish behavior of the nodes is of great significance .
3. Delay-tolerant algorithm based on the node selfishness
3.1. Problem description and model assumption
In the DTN, the relay nodes mostly use the ‘store-carry-forward’  approach to deliver data. So, in order to route data to their destination timely to achieve a higher success rate of transmission, we need to have as many nodes as possible in the connection detection process and detect the existing connections timely. Meanwhile, we minimize the number of messages of the relay carrying and erupting as possible to reduce the delay and the bandwidth costs.
3.2. Divide the activity area into grids
Numbering of the activity area (grids) of mobile nodes
3.3. Predict the next mobile location of the nodes based on first-order Markov process
The symbol w 1, w 2, w 3, ……, wn indicates the grid number of the mobile node H. In order to calculate each grid transition probability of active node, we define wi as wi,j, which indicates the node from location i to location j.
The symbol wp[jx] indicates the most possible grid of the next moment when the node now in the location j.
3.4. Estimate the credibility threshold R0 based on the node selfishness
From the expression formula of the selfishness of network, we know that the average credibility of the nodes in the network is higher and the corresponding selfishness of network is lower.
In the injection stage of the copies of message, it can have choice of injection according to the level of the credibility of the node itself. It can improve the later spread speed of the copies of message by choosing the nodes whose willing forwarding domain has a higher credibility to inject. If the copies of message are injected to the nodes which have lower credibility, in the later injection stage, the nodes cannot meet with the nodes injected for a long time to make the injected delay of the copies of message large. The worst case is that the node injects the copies of message to the node with credibility value 0 to result in the copy of message turning into invalid. This is because the copied message cannot be injected out through the node.To improve the injected efficiency of the copies of message in the network, we set up an injection threshold R0 to make the node with copies of message inject according to the threshold value. Thus, it only injects the corresponding copies of message to the nodes whose credibility exceed the threshold value R0 in its willing forwarding domain.
3.5. The delay-tolerant algorithm based on delay constraints
3.5.1. The main steps of the algorithm
Step 1: When the node is passing a static node, the node receives the data from corresponding static nodes.
Step 2: Look for the shortest grid path link to the sink node, implementation method showed in section 3.5.2.
Step 3: We judge whether there are the nodes within the communication scope in path link whose selfishness value is larger than R0. If there are, the nodes will transform data and delete information in itself, implementation method showed in section 3.4.
Step 4: If there are none, according to the Markov prediction method in section 3.3, we calculate the most possible movement track of the node to compare with link path to check whether there are coincident points. If there is coincident point, the node carrying message will move.
Step 5: If there are none, we check whether there are nodes whose selfishness value is larger than R0 within the communication scope of the nodes with message carried. If there are, the copies of message will be injected, the injection rules showed in section 3.5.3.
Step 6: If the above all cannot meet the conditions, we will move the node carrying message into the next operation.
Step 7: Until the information reaches to the sink node, the whole process is over.
3.5.2. Grid link
By the simple mathematical knowledge, we know that the linear distance between two points is the shortest, while in the two-dimensional plane grid, the diagonal distance is the shortest. So, the shortest grid link can be converted into the number of displacement grids between diagonal and axes x and y.
The symbol Num d is the number of grids from grid W i to grid W 00 . The two symbols |wi,x|, |wi,y| are the absolute values of the x,y coordinates of the grid i.
The symbol Num dx and Num dy is defined as the number of girds of the x,y coordinates from grid W i to grid W 00 .
3.5.3. The rules of copy injection
The message carrier S will judge whether the credibility value R i is greater than R0 when it meets the node set Sum i in the domain of willing to forward. If R i ≥ R0, the node will inject the copies of message to the node i. Instead, it will not. We need to quantify the delay conditions when injected. So, we introduce two time parameters on message: T s and Tcur. The symbol T s indicates the effective time delay of message M. In other words, it is the prescribed target delay of message M depending on the need to delay of the application. Tcur indicates the time that it has been used. We consider it valid only when message M is passed to the target node in the time of T s . When the time used by message M exceeds T s , the message M will be considered invalid and nodes can automatically delete the copies of message.
For the value T s , we can add a field in the packet header of message M. When the message is produced, the application layer will set value to the time field to be used when it judges the time constraints at the subsequent message transmission process, while for the value Tcur, we can directly obtain it by message timestamps.
4. The simulation experiment
In the simulation experiment, the first step is to use the software SETDEST in the NS-2 to simulate the motion model. We should set the size of the area of the node movement, the speed value of the node movement, the number of nodes in the area of movement and the simulation running time. The second step is that CBRGEN is used to produce the packets delivered between the nodes. Finally, the results of the three parameters can be drawn out: the message delivery rate, the message delay and the network overhead.
The successful delivery rate of message - The theoretical value of the successful delivery rate of message is 100%. The successful delivery rate of message indicates the radio of the amount of receiving information of the destination node and the amount of sending information of the source node, in a certain time limit.
The message delay - The message delay is the average time of the message delivered successfully from the source node to the destination node.
The overhead of message delivered - The number of the copies of message reflects the network resource consumption of the message in the transfer process. Firstly, the copy of message needs to take up the corresponding cache space to consume the storage resource. Secondly, the message needs energy when transferred to consume energy resource. Lastly, the message takes up the corresponding communication channel when transferred to consume the network bandwidth. So, the number of copies of message can reflect the network resource overhead. The cost cannot be estimated directly, but it is usually related to the number of the copies of message in the network. If there are more copies of message in the network, the link cost will be higher. Conversely, the link cost is lower. So, the number of copies of message can reflect the link cost of the message in the transfer process.
4.1. Node density impact on the performance of the algorithm
From the simulation results, all of the algorithms show that the message delivery rate is improving along with the corresponding increase in the number of nodes in the network. With the number of nodes in the network up to a certain number, the message delivery rate is no longer to grow along with the increase of the number of nodes. For Epidemic, its message delivery rate is the highest initially and the growth rate of its message delivery rate is largest along with the increase in the number of nodes. But if the number of nodes reaches up to 60, the message delivery rate begins to fall. For several other routing protocols, along with the growth in the number of nodes, its message delivery rate continues to increase. When the number of nodes is up to the peak, the rate is unchanged basically. The DTSNS algorithm proposed in this paper compared with Epidemic algorithm has lower message delivery rate at the beginning process. But, the message delivery rate of DTSNS algorithm has larger rate when the number of nodes reaches up to 80. It indicates that DTSNS algorithm is suitable for large-scale wireless sensor network. DTSNS algorithm compared with other two algorithms has a higher message delivery rate.
From Figure 4, we can obtain that with the increase of the number of nodes, the corresponding message transmission delay will reduce, and the delay will no longer continue to reduce when the number of nodes is more than a certain value. When the number of nodes in the network is more than 60, the message transmission delay of Epidemic algorithm will increase instead. And for several other algorithms, the message transmission delay will remain unchanged. The delay value of DTSNS algorithm proposed in this paper remains unchanged all the time, because of the control of the number of copies of message. For DTSNS algorithm, with the increase of the number of nodes in the network, the number of copies of message is not to continue to increase. As a result, this algorithm has higher message delivery rate and lower message transmission delay.
4.2. Node cache impact on the performance of the algorithm
From Figure 5, we know that the message delivery rate of each algorithm increases with the growth in the buffer capacity of nodes. Epidemic algorithm is most easily affected by the buffer capacity of nodes. As the buffer capacity of nodes is small, the rate of message delivered is relatively low but the rate of message delivered is more than that of three other protocols with the growth in the buffer capacity of nodes. For DTSNS algorithm proposed in this paper, its message delivery rate is the largest when the buffer capacity is small. And its message delivery rate is only lower than that of Epidemic algorithm when the buffer capacity is large. It can be seen from Figure 6 that the result of message transmission delay is just opposite. Above all, it can show how each algorithm depends on network resource. Epidemic algorithm depends largely on the network resource compared with others. In other words, with sufficient network resource its performance is best; otherwise, its performance is worst. For DTSNS algorithm, no matter the case of the buffer capacity, whether big or small, it still has good performance.
4.3. Selfishness of network impact on the performance of the algorithm
From Figure 7, the message delivery rate of each algorithm drops sharply with the increase in the selfishness of network. In comparison, the performance of DTSNS routing protocol is the best, while the performance of Spray and Wait routing protocol is the worst. From Figure 8, with the growth in selfishness of network, the message transmission delay of each algorithm continues to increase. With the growth in social selfishness of network, the average credibility of the corresponding nodes will reduce and the number of the nodes willing to forward data for other nodes will be less. Thus, it reduces the network connectivity. Because of reducing the network connectivity, for all algorithms, it will reduce their message delivery rate and increase their message transmission delay. For Epidemic algorithm using the strategy of flooding, the message delivery rate is relatively lower compared with other routing algorithms. For Spray and Wait algorithm and Spray and Focus algorithm, at the stage of spreading the copies, if the copies of message are injected to the nodes with the small credibility, then it will seriously hinder the spread of the copies in the network. Due to the number of the actual valid message reduced, the message delivery rate will be very low. For DTSNS routing protocol, at the stage of the injection of copies of message, we adopt the injection scheme based on node credibility threshold to make copies of message effectively injected.
4.4. The number of copies of message produced by different algorithms
From Figure 9, the number of copies of all algorithms is increasing with the passage of time, but for Epidemic algorithm, its copies of message spread the fastest, presenting exponential growth. For Spray and Wait algorithm and Spray and Focus algorithm, they have the limit for the maximum number of copies of message in the network. With the number of copies of message growing to a certain stage, the value will be a constant. For DTSNS routing protocol, its number of copies of message is controlled by the delay constraints to self adapt to have the smallest number of copies. More copies of message consume more network resource seriously. This is because the copies of message take up the node cache and need a large amount of energy and network bandwidth at the forwarding process. For DTSN which has limited network resource, the overhead is unbearable. So, though Epidemic algorithm has good performance, it is rarely used in practical application.
With very broad application prospects, DTSN appeal to a large number of researchers to conduct study. The resource of DTSN is limited and intermittently connected to make DTSN difficult. It is of great significance that we can reduce the transmission delay and communication overhead on the basis of ensuring the message delivery rate and, in the case of the network nodes, with social selfishness. We propose a routing delay-tolerant algorithm based on selfishness of nodes in this paper. Firstly, we divide the activity area into grids. Secondly, we predict the next mobile location of the nodes based on the Markov process and estimate the credibility of the nodes based on the node selfishness in order to reduce the network communication overhead and improve message delivery success rate. Then, we put forward the delay-tolerant algorithm based on delay constraints. Lastly, the simulation results show that the new algorithm, which is compared with Spray and Focus, Spray and Wait, and Epidemic, is of higher level of data-submitting rate and lower latency and resource consumption of the messages in the large-scale network.
This work is supported by Chongqing education science Project of China in 2013, Hunan Provincial Science and Technology Program of China (No. 2013GK3082),National Basic Research Program of China (Research On Holographic Measurement of Intra-domain, 973 Program, 2009CB320505/G1999032707), the “Research Fund of the Ministry of Education - China CMC” (Key Technology of CM-OSS2.0 Integrated Network Management, MCM20123041), and the Key Science and Technology Program of Chongqing (Research on Network Analysis Techniques, CSTC2013yykfA40003).
- Li X, Liu L, Hu X: Research on delay/disruption tolerant network. Research and Development of computer 2009, 46(8):1270-1277.Google Scholar
- Spyropoulos T, Rais RN, Turletti T, Obraczka K, Vasilakos A: Routing for disruption tolerant networks: taxonomy and design. Wireless networks 2010, 16(8):2349-2370. 10.1007/s11276-010-0276-9View ArticleGoogle Scholar
- Vasilakos A, Zhang Y, Spyropoulos TV: Delay Tolerant Networks: Protocols and Applications. Boca Raton: CRC Press; 2012.Google Scholar
- Dvir A, Vasilakos A: Backpressure-based routing protocol for DTNs. ACM SIGCOMM Computer Communication Review 2010, 40(4):405-406. 10.1145/1851275.1851233View ArticleGoogle Scholar
- Yang K: Research on the Key Techniques of Data Transmission in Delay-tolerant Mobile Sensor Network. Beijing: Beijing University of Posts and Telecommunications; 2012.Google Scholar
- Campos-Naňez E, Garcia A: A game theoretic framework for power control in wireless sensor network. IEEE Transaction on Computers 2008, 56(3):552-561.Google Scholar
- Jaramillo JJ, Srikant R: Distributed and adaptive reputation mechanism for wireless ad-hoc networks. In Proceedings of the 13th annual ACM international conference on Mobile computing and networking. Montreal; 2007. 9–14 SeptGoogle Scholar
- Jain S, Fall K, Patra R: Routing in a delay tolerant network. In Proceedings of ACM SIGCOMM, vol 34. New York: ACM Press; 2004:145-158.Google Scholar
- Spyropoulos T, Psounis K, Raghavendra CS: Single-copy routing in intermittently connected mobile networks. In Proceedings of IEEE Conference of Sensor and Ad Hoc Communications and Networks. Santa Clara; 2004. 4–7 OctoberGoogle Scholar
- Vahdat A, Becker D: Epidemic routing for partiallyconnected ad hoc networks. Technical report: Duke University; 2000.Google Scholar
- Widmer J, Le Boudec J-Y: Network coding for efficient communication in extreme networks. In Proceedings of the 2005 ACM SIGCOMM. Philadelphia; 2005. 26 AugustGoogle Scholar
- Wang Y, Wu H: Delay/Fault-Tolerant mobile sensor network (DFT-MSN): a new paradigm for pervasive information gathering. IEEE Transaction on Mobile Computing 2006, 6(8):1021-1034.Google Scholar
- Abdulla M, Simon R: The impact of the mobility model on delay tolerant networking performance analysis. In Proceedings of Annual Simulation Symposium. Norfolk; 2007. 26–28 MarchGoogle Scholar
- Spyropoulos T, Psounis K, Raghavendra C: Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking. Philadelphia; 2005. 26 AugustGoogle Scholar
- Xu FL, Liu M, Gong HG, Chen GH, Li JP, Zhu JQ: Relative distance-aware data delivery scheme for delay tolerant mobile sensor networks. Journal of Software 2010, 21(3):490-504. 10.3724/SP.J.1001.2010.03459View ArticleGoogle Scholar
- Lindgren A, Doria A, Schelen O: Probabilistic routing in intermittently connected networks. SIGMOBILE Mobile Computing Communications Review 2003, 7(3):19-20. 10.1145/961268.961272View ArticleGoogle Scholar
- Psounis SK, Raghavendra CS: Spray and Focus: efficient mobility - assisted routing for heterogeneous and correlated mobility. In Proceedings of the Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. White Plains; 2007. 19–23 MarchGoogle Scholar
- Liu T, Peng J, Yang J: Data delivery for heterogeneous delay tolerant mobile sensor networks based on forwarding probability. Journal of Software 2013, 24(2):215-229.View ArticleGoogle Scholar
- Ramanathan R, Redi J: A brief overview of ad hoc networks: challenges and directions. IEEE Communications Magazine 2002, 40(6):48-53. 10.1109/MCOM.2002.1007408View ArticleGoogle Scholar
- Li Q, Zhu S, Cao G: Routing in socially selfish Delay Tolerant Networks. In 2010 Proceedings of IEEE INFOCOM. San Diego, CA; 2010. 14–19 MarchGoogle Scholar
- Li Y, Hui P, Jin D, Su L, Zeng L: Evaluating the impact of social selfishness on the epidemic routing in delay tolerant networks. IEEE Communications Letters 2010, 14(11):1026-1028.View ArticleGoogle Scholar
- Zhu Y, Xu B, Shi X, Wang Y: A survey of social-based routing in delay tolerant networks: positive and negative social effects. IEEE Communications Surveys and Tutorials 2013, 15(1):387-401.View ArticleGoogle Scholar
- Jain S, Demmer M, Patra R, Fall K: Using Redundancy to Cope with Failures in a Delay Tolerant Network. ACM SIGCOMM Computer and Communication Review 2005, 35: 109-120. 10.1145/1090191.1080106View ArticleGoogle Scholar
- Almasaeid HM: Data Delivery in Fragmented Wireless Sensor Networks Using Mobile Agents. New York: ACM; 2007.View ArticleGoogle 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.