Accumulative Feedback Adaptation Transmission Rate In Mobile Ad-hoc Networks Ashraf Al-Sharah and Sachin Shetty Department of Electrical and Computer Engineering, Tennessee State University, Nashville, TN, USA Email:
[email protected],
[email protected] Abstract—Mobile adhoc networks (MANET) are commonly used in a lot of applications in military and civilian domains. MANET are self-organized networks and depend on the ability of each node to optimize the transmission parameters to meet applications’ bandwidth requirements. MANET nodes need to adapt their transmission parameters depending on the channel status and network dynamics like variation in node density, traffic, and mobility. In this paper, we will present an accumulative feedback adaptation transmission rate scheme (AFAT) that ensures MANET nodes will be able to maximize the transmission rates to ensure applications’ bandwidth requirements are met. AFAT adopts a decentralized approach which involves communication of transmission rates between neighboring nodes. The knowledge of neighbor nodes’ transmission rates allows an individual node to adjust the rates accordingly. AFAT is scalable because the overhead only involves exchange of transmission rates between neighboring nodes. Simulation results confirm that MANET nodes adopting the AFAT scheme will converge to a transmission rate that will meet applications’ bandwidth requirements.
I. I NTRODUCTION Mobile ad-hoc networks (MANET) are deployed in many applications in military environments and civilian environments. With the ability of multi-rate radio, MANET nodes are expected to maximize network performance to meet bandwidth requirements of applications in these domains. Rate adaptation scheme is a popular scheme used to increase bandwidth availability. This scheme allows individual MANET nodes to optimize the data rate for the communication links with neighbors. Several rate adapatation schemes have been proposed in literature [6,10,11,12]. Though these schemes achieve rate adaptation, they either suffer from a central point of failure (information exchanged on control channel), lack of scalability (all nodes’ transmitting rates overwhelming resources), decentralized approaches which only use information available at each MANET node, and dependent on a centralized infrastructure (cannot trust network availability to remote site). In this paper we propose AFAT, that uses a dynamic rate adaptation scheme to improve network performance by only using information exchanged between neighboring nodes. AFAT adjusts the individual transmission rates based on the history of neighbors’ transmission rates. Each MANET node
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maintains a list of transmission rates for neighboring nodes during every time instant. Over the long run, the knowledge of neighbor’s network performance improves the ability of individual nodes to optimize the transmission rates and meet the application’s bandwidth requirements. II. RELATED WORK Research efforts on improving MANET performance either adopt a statically configured environment or dynamic environment which uses probability theory to characterize the network dynamics [7] [8] [9]. Rate adaptation schemes need to quickly respond to network dynamics. There is a need for a scheme that does not depend on a centralized infrastructure [2]. In [3], an auto rate fall-back mechanism adopts a trial and error approach to select the optimum modulation scheme. In [4], the authors propose a rate adaptation scheme for vehicular networks based on the contextual information, such as distance and relative velocity. It is a history-based approach and requires repetitive training before usage. It is designed specifically for vehicles traveling along known routes. Therefore, this scheme may not be suitable for dynamic mobile environments where routes are not known a priori and the condition is unpredictable [5]. Fig.1 provides a overview of rate adaptation schemes proposed in literature. The schemes are classified based on the following criteria: transmitter-based or receiver-based; SNRbased; window-based or frame-based. In transmitter-based schemes, the transmitter makes the rate selection decisions without using any feedback from the receiver. By comparison, in receiver-based schemes, the receiver monitors the channel quality, makes the rate selection for the next frame transmission, and feeds the selection back to the transmitter. III. SYSTEM MODEL To design an effective transmission rate adaptation scheme for mobile environments, it is critical to have a good understanding of the network dynamics. These types of schemes share transmission rate statistics and makes rate selection decisions at each iteration. The selection of an appropriate transmission rate which can be adjusted based on network dynamics is a challenging problem. We propose AFAT to
Fig. 3: Initial transmission rates and starting positions for nodes Each node will end up with a list of transmission rates from the neighbors. Let A represent successful transmission rates received from all neighbors which changes during every time slot. Fig. 1: Comparison of existing rate adaptation schemes address this issue by ensuring nodes compute transmission rates during every iteration based on the transmission rates used by the neighboring nodes.
IV. ACCUMULATIVE FEEDBACK ADAPTATION TRANSMISSION RATE In this section, we present the details of the design and implementation of AFAT. A. Adaptation Transmission Rate AFAT will be deployed on the MANET nodes. Our model does not need a centralized infrastructure to coordinate the exchange of messages. The first step for each node is to build a list of transmission rates for neighboring nodes. max
n \
Ti(input)
(1)
i=1
where Ti is the transmission rate represented by a matrix of n*n which each node will build for each given time, and input represents incoming rates from other nodes. where Fig. 2: Model: Accumulative feedback adaptation transmission rate in MANET Fig. 2 shows the model of the MANET scenario consisting of 3 nodes. Each node generates its own transmission rate, receives neighboring nodes’ transmission rates, and generates the new transmission rate for the next time slot. We consider that all of the nodes start at the same location with the same initial transmission rate. Fig. 3 shows the initial starting point and initial transmission rates for the nodes. During every time slot, nodes transmit their rates to the neighbors while they move towards the ending point. Based on the feedback from the neighbors, each node will use the AFAT scheme to compute transmission rates for the next time slot. Without loss of generality, we consider three nodes in the model: N1, N2, N3. Each node generates a transmission rate at each given time, t. For example, the transmission rate from N1 to N2 is T1,2 and from N2 to N1 is T2,1 and so on.
Tmin ≤ Ti ≤ Tmax
(2)
where all of the transmission rates (T) will be 0 ≤ Ti ≤ 1
(3)
For passing the rates between nodes, we will use the expression of Txj and Tjx for all nodes where Txj and Tjx are the outgoing and the incoming rates between nodes, where: T (xi) = max
n \
Tij
(4)
i=1
and n
T (yi) = max
\ i=1
where
Tji
(5)
Tmin ≤ Tij, Tji ≤ Tmax
(6)
We implemented a feedback mechanism to ensure neighboring nodes are informed about the successful transmission rate. Specifically, we create a table for the successful transmission rates to be stored in each node of the network. Each node will exchange the transmission rates with its neighbors. The transmission rates received from the neighbors will be stored locally and immediately exchanged with other neighbors. The local storage is important in case the messages exchanged between neighbors is lost or corrupt. If the node does not receive a transmission rate from the neighbor, the node will use the transmission rate received in the previous exchange. So for updating the transmission rates: T will be the updated table of transmission rates where T (updatetable) =
n \
Tji −
i=1
n \
Tij
(8)
We implemented the AFAT algorithm in Matlab. In this section, we provide the simulation experimental results based on the Matlab based simulation environment.
After all tables are updated then the final decision table will be updated using: n \ i=1
Tji −
n \
Tij
(10)
A. Simulation Setup The simulation scenario consists of five nodes, N1, N2, N3, N4, N5. Each node will run the algorithm independently and communicate the successful transmission rates to its neighbors. Each node will have n-1 incoming rates and n-1 outgoing rates. Over a period of time, there will be many messages exchanged between the neighbors. During every time slot, each node will use the AFAT algorithm to find the optimal local maximum transmission rate.
(11)
B. Results Fig. 4 illustrates the initial transmission rates for the five nodes in our simulation setup.
(9)
i=1
Note that when n \ i=1
Tji −
n \
Tij ≤ 0
i=1
which leads to T≤0
In algorithm1, we propose a distributed AFAT algorithm in mobile ad-hoc networks. The main component of the algorithm is implemented in steps 2-5. Every node implements the algorithm until it reaches the ending point. Each node is initialized with the same transmission rates to ensure fairness. At step 2, the nodes exchange the transmission rates. Txi denotes the incoming transmission rates and Txj denotes the outgoing transmission rates. At step 3, each node will update the transmission table based on the provided formula. At step 4, after finding the transmission rates, now each node needs to find the best transmission rate by using the argmax function to ensure the optimal Txi and Txj rates. In step 5, the updated transmission table is used in the next time slot. V. SIMULATION AND NUMERICAL RESULTS
where
T (f inalupdatetable) = argmax
(12)
(7)
i=1
T≥0
argmaxf (x) = x | ∨y : f (y) ≤ f (x)
This situation occurs when either the node is out of range or the message is lost. Algorithm 1 : Accumulative Feedback Adaptation Transmission Rate (1) Begin: for n = 0 to infinity (2) Find transmission rates Txi, Txj (3) Update transmission table n n=o input(j) − output(i) (4) Maximize argmax n T ,T xj i=0 xi (5) Update table T(xi+1), T(xj+1) (6) End Definition: arg max stands for the argument of the maximum, that is to say, the set of points of the given argument for which the given function attains its maximum value. The arg max is defined by
Fig. 4: Initial transmission rates values for the network members Fig. 5 illustrates the transmission rates that nodes generate using the AFAT algorithm. We observe the incoming and outgoing transmission rate for one node at a specific instant time. The node will use the information in the table to choose the successful transmission rate to be used with other nodes. The successful transmission rates will be used to create the
final local maximum table for each node and those rates will be use for communication between nodes which is illustrated in Fig. 6.
Fig. 8: Network members are moving from starting point toward to the ending point
Fig. 5: Transmission rates at instant time for one node with the other network members
nodes exchange transmission rates with neighbors and areable to achieve the highest possible transmission rate when they reach the ending point. For future work, we would like to investigate the performance of AFAT under jamming attacks. Jamming attacks can affect the possibility of nodes achieving the highest possible transmission rates because messages exchanged between neighboring nodes will be subject to losses. Depending on the severity of the jamming attack, it is quite possible that the nodes could be isolated from its neighbors for a brief period of time and can only depend on the history of transmission rates to make a decision. VII. ACKNOWLEDGMENT
Fig. 6: Final transmission rates for the nodes Fig. 7 illustrates the time it takes for the nodes to reach the highest possible transmission rate.
Fig. 7: Time taken by the five nodes to reach highest possible transmission rates Fig. 8 captures the movement of the nodes from the starting point to the ending point. The figure illustrates the collaboration between the nodes to ensure the transmission rates are optimal during every time slot. VI. CONCLUSION In this paper, we propose AFAT, a dynamic rate adaptation scheme which ensures MANET nodes achieve the highest possible transmission rate as the nodes move towards the ending point. The simulation results show that the network
This work was performed with support from the BoeingTennessee State University contract, TBC-TSU-GTA-1. We would like to thank Mr. Jarrett Datcher and Mr. Ryan Hammond for their support and guidance in this project. R EFERENCES [1] Thomas S. Messerges, ohnas Cukier, Tom A.M. Kevenaar, Larry Puhl, Rene truik, Ed Callaway, A Security Design for a General Purpose, SelfOrganizing, Multihop Ad Hoc Wireless Network 1st ACM Workshop Security of Ad Hoc and Sensor Networks Fairfax, Virginia 2003 [2] Joint Power and Rate Adaptation in Ad Hoc Networks Based on Coupled Interference [3] P. Chevillat, et al., Dynamic data rate and transmit power adjustment in IEEE 802.11 wireless LANs, International Journal of Wireless Information Networks, vol. 12, 2005. [4] P. Shankar, T. Nadeem, J. Rosca, and L. Iftode, CARS: Context aware rate selection for vehicular networks, in IEEE ICNP08. [5] Practical Rate Adaptation in Mobile Environments Xi Chen, Prateek Gangwal and aji Qiao Iowa State University, Ames, IA 50011 leon6827, prateek,
[email protected] [6] Jamming Attack Detection and Rate Adaptation Scheme for IEEE 802.11 Multi-hop Tactical Networks Kwangsung Ju and Kwangsue Chung Department of Communications Engineering Kwangwoon University, Seoul, Korea
[email protected],
[email protected] [7] J. Zhang, K. Tan, J. Zhao, H. Wu, and Y. Zhang, A Practical SNRGuided Rate Adaptation, Proc. IEEE INFOCOM, 2008. [8] Onoe Rate Control, URL:www.madwifi.org/browser/trunk/ath-rate/onoe, year = 2011 [9] H. Jung, K. Cho, Y. Seok, T. Kwo, and Y. Choi, RARA: Rate Adaptation Using Rate-Adaptive Acknowledgment for IEEE 802.11 WLANs, Proc. IEEE Consumer Comm. and Networking Conf. (CCNC 08), pp. 62-66, 2008 [10] X. Li, N. Mitton, D. Simplot-Ryl,”Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks.” Networking 2011, May 2011, Valencia, Spain. pp.147-159, 2011
[11] A. Fu, P. Sadeghi, and M.l Mdard, ”Dynamic Rate Adaptation for Improved Throughput and Delay in Wireless Network Coded Broadcast” IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 22, NO. 6, DECEMBER 2014 [12] R. Fritzsche, P. Rost, and G. P. Fettweis”Robust Rate Adaptation and Proportional Fair Scheduling with Imperfect CSI” DOI 10.1109/TWC.2015.2420564, IEEE Transactions on Wireless Communications
