Multi-Attribute Decision Making Routing Strategy for Interplanetary Communications

Trans. Emerging Tel. Tech.

Huang

et al. 2012

Because of the dynamic nature of delay tolerant networks (DTNs), many replication‐based routing schemes were proposed to increase the probability of delivery by making multiple copies of each message. In such schemes, one concern is how many replicas of a message should be distributed in the network. In this paper, we propose a routing scheme for DTNs, called adaptive spraying based on the intercontact time (ASBIT). The scheme is based on the idea of that each node dynamically chooses the right number of message copies disseminated to respond to the current conditions of the network. When forwarding, ASBIT selects the node with a higher centrality as the next hop, and utilises the multi‐attribute decision making theory for the division of the replication number between two nodes. Simulation results show that ASBIT achieves comparable delivery ratio and delivery delay while maintaining lower overhead compared with some well‐known routing schemes in sparse scenarios. Copyright © 2012 John Wiley & Sons, Ltd.

A MathClass-rel= {[a_{ij}]}_{2 MathClass-bin\times 3}

where a ij is the value of the attribute j for node i .Then normalise the matrix:

B MathClass-rel= [b_{ij}] MathClass-rel= ([], a_{ij} MathClass-bin∕ {MathClass-op\sum}_{i MathClass-rel= 1}^{2} a_{ij})

Each of the attribute is assigned a weight, such that

w MathClass-rel= w_{dc} MathClass-bin+ w_{s} MathClass-bin+ w_{fb} MathClass-rel= 1

where w dc , w s and w fb are weights for the representative attributes discussed previously, respectively.Obtain the weighted sum of the representative attributes for each node:

v_{i} MathClass-rel= {MathClass-op\sum}_{j MathClass-rel= 1}^{3} w_{j} b_{ij} MathClass-punc, i MathClass-rel= 1 MathClass-punc, 2

where w j is the weight for attribute j , namely w dc , w s or w fb .Finally, the division of the replication number between node a and node b is given by

\begin{matrix} aligned \\ right K_{a} & left = ⌈K \times \frac{v_{1}}{v_{1} + v_{2}}⌉ & right \\ right K_{b} & left = K - K_{a} \end{matrix}

where K i...…”

Section: Asbit Routing Schemementioning

confidence: 99%

“…Speed (s): Speed is the current speed of the node. Free buffer (fb): Free buffer is the ratio of the free space size to the maximum size of the buffer [43].…”

Section: Division Of the Replication Numbermentioning

confidence: 99%

“…When dividing the replication number between two nodes a and b, we apply the SAW algorithm in a stepwise manner as follows [18,43]:…”

Section: Division Of the Replication Numbermentioning

confidence: 99%

Section: Asbit Routing Schemementioning

confidence: 99%

See 2 more Smart Citations

Exploiting intercontact time for routing in delay tolerant networks

Luo

Trans. Emerging Tel. Tech.

Huang

et al. 2012

“…If the degree centrality of the uinfected node B with no copy is greater than or equal to the degree centrality of infected A, A will hand over some of its copies to B. The allocating algorithm utilizes Simple Additive Weighting (SAW) theory [23]. Table I introduces notations throughout the paper.…”

Section: A Overviewmentioning

confidence: 99%

Encounter-based routing in delay tolerant networks

2011 International Conference on Computational Problem-Solving (ICCP)

Luo

Qin

et al. 2011

Delay tolerant networks are intermittently connected. In this paper, we propose a DTN routing scheme EBR. The scheme is based on the idea of exploiting inter-contact time between mobile nodes to predict the number of nodes with no copy a node will encounter within the expected delay. In EBR, every node with message copies dynamically chooses the number of copies by itself. We evaluate the performance of EBR through various simulations. Simulation results show that EBR has good performance with low overhead in most scenarios.

Pairwise-Based Multi-Attribute Decision Making Approach for Wireless Network

IEEE/ACM Trans. Networking

Liu

et al. 2021

In the wireless network applications, such as routing decision, network selection, etc., the Multi-Attribute Decision Making (MADM) has been widely used. The MADM approach can address the multi-objective decision making issues effectively. However, when the parameters vary greatly, the traditional MADM algorithm is not effective anymore. To solve this problem, in this paper, we propose the pairwise-based MADM algorithm. In the PMADM, only two nodes' utilities are calculated and compared each time. The PMADM algorithm is much more accurate than the traditional MADM algorithm. Moreover, we also prove that our PMADM algorithm is sensitive to the parameters which vary seriously and in-sensitive to the parameters which change slightly. This property is better than that of the traditional MADM algorithm. Additionally, the PMADM algorithm is more stable than the traditional MADM algorithm. For reducing the computational complexity of the PMADM algorithm, we propose the low-complexity PMADM algorithm. For analyzing the computational complexity of the lPMADM algorithm, we propose the tree-based decomposing algorithm in this paper. The lPMADM algorithm has the same properties and performances as that of the PMADM algorithm; however, it is simpler than the PMADM algorithm. The simulation results show that the PMADM and lPMADM algorithms are much more effective than the traditional MADM algorithm.