Email: {clmg,ohzahata,kato}@is.uec.ac.jp. AbstractâIn vehicular ad ... hop broadcast protocol is required to disseminate traffic warning information to multiple ...
2012 IEEE Vehicular Networking Conference (VNC)
How to Broadcast Efficiently in Vehicular Ad Hoc Networks Without GPS Celimuge Wu, Satoshi Ohzahata, and Toshihiko Kato Graduate School of Information Systems University of Electro-Communications Chofu-shi, Tokyo, Japan Email: {clmg,ohzahata,kato}@is.uec.ac.jp Abstract—In vehicular ad hoc networks (VANETs), a multihop broadcast protocol is required to disseminate traffic warning information to multiple receivers. Reducing broadcast message overhead is important because it could directly affect the packet collision probability and MAC layer contention time at each node. There have been some broadcast protocols for VANETs. However, many of them assume the availability of the global positioning system. In this paper, we propose a broadcast protocol which does not require position information. The protocol uses twohop neighbor information to select relay nodes which are used to forward a packet. The proposed protocol also uses a joint redundancy and opportunistic transmission approach to recover a packet loss. We show the effectiveness of the proposed protocol by evaluating in a real vehicular ad hoc network.
I. I NTRODUCTION Vehicular ad hoc networks (VANETs) have been attracting interest for their potential role in intelligent transport systems. In a VANET, a multi-hop broadcast protocol is required for many applications such as a collision warning system. Due to various vehicle densities for different road segments, providing an efficient broadcast protocol is a well-known challenging problem. In a high-density network, the use of Flooding results in a large number of redundant transmissions which cause an increase of MAC layer contention time at each node. In a sparse network, in order to forward a packet to a multi-hop distance, it is important to eliminate packet loss at the relay nodes. There have been many broadcast protocols for VANETs. Since redundant broadcasts can cause a high packet collision probability and end-to-end delay, many protocols have been proposed to eliminate the redundant broadcasts. Wisitpongphan and Tonguz [1] have proposed three receiverbased broadcast schemes: weighted p-persistence, slotted 1persistence, and slotted p-persistence schemes. In these protocols, upon reception of a message, a receiver node determines whether rebroadcast or not by calculating a probability according to the distance from the sender node. Suriyapaiboonwattana et al. [2] have proposed a protocol which triggers a rebroadcast using an adaptive interval and an adaptive probability. Slavik and Mahgoub [3] have proposed a stochastic broadcast scheme in which all nodes rebroadcast the received message with a certain probability. Mylonas et al. [4] have proposed a Speed Adaptive Probabilistic Flooding algorithm to control the rebroadcast probability adaptively based on the
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vehicle velocity. Ma et al. [5] have proposed two receiveroriented approaches to broadcast emergency messages. However, in all the receiver-based protocols, each node determines whether to rebroadcast or not in an autonomous manner. Therefore, redundant broadcasts cannot be eliminated entirely. In some protocols, the sender node specifies the relay nodes. Generally, the selection of relay nodes is based on the information collected from exchanging hello messages among neighbor nodes. Sahoo et al. [6] have proposed BPAB, a Binary Partition Assisted emergency Broadcast protocol for VANETs. BPAB aims to use the most distant node in the intended direction to relay messages. However, in a fading channel, the use of the most distant node results in lost messages. Therefore, the link quality should be considered. In our previous work, we proposed FUZZBR [7], a fuzzy logic based broadcast protocol. FUZZBR chooses a relay node by considering inter-vehicle distance, vehicle movement and signal strength. However, all these solutions assume the availability of the global positioning system (GPS). Since position information is difficult to acquire in some roads, such as a tunnel, we consider the possibility of an efficient broadcast without GPS. An existing approach is to use multipoint relay (MPR) nodes to forward broadcast data packets [8]. However, MPR broadcast [8] does not consider node mobility as a factor in the relay node selection. As a result, a message can be lost at a relay node due to the vehicle movement. In our previous work, we have proposed a relay node selection which considers increased radio coverage and node mobility (here we call this Enhanced MPR Broadcast) [9]. However, Enhanced MPR Broadcast does not consider the fading feature of wireless channels. In a wireless channel, a node can receive a hello message from a neighbor which is at a distance where stable communication is impossible. If the neighbor node is selected as a relay node, the neighbor node loses the message with a high probability. Due to the lossy feature of wireless communication channels and various density of vehicles, an error recovery mechanism should be considered. The most common way to recover from a packet loss is to use retransmissions. Typically, a retransmission is triggered when the packet reception cannot be detected in the predefined retransmission time interval. The retransmissions increase the end-to-end delay when the
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retransmission time interval is small, and incurs unnecessary transmissions when the time interval is large. Therefore, an efficient approach is required to recover the packet loss with a short end-to-end delay. In this paper, we propose a VANET multi-hop broadcast protocol which is independent of position information. The protocol selects efficient relay nodes by exchanging hello messages among neighbors. For each relay node candidate, the protocol uses two-hop neighbor information to estimate the additional coverage and vehicle movement. The protocol uses hello message reception ratio to estimate the link quality between two neighbors. The protocol uses a fuzzy logic based approach to jointly consider the additional coverage, vehicle movement and link quality in the relay node selection. The protocol also uses a joint redundancy and opportunistic transmission approach to provide a high reliability even when the network is sparsely connected. The protocol can be tuned to use in different scenarios by modifying the fuzzy membership functions which are recorded in an external file. The protocol does not depend on specific modification on lower layers or hardware. Therefore, the protocol can provide a flexible and portable solution for multi-hop broadcast in vehicular ad hoc networks. We evaluate the proposed protocol by comparing with possible alternatives using a real VANET. The remainder of the paper is organized as follows. In section II, we give a detailed description of the proposed protocol. Next, we present experimental results in section III. Finally, we present our conclusions in Section IV. II. P ROPOSED P ROTOCOL : BR-NB A. Protocol design The proposed protocol, BR-NB (broadcast with neighbor information), specifies relay nodes to forward a packet. Before broadcasting a packet, a node attaches the addresses of the relay nodes to the packet. Upon reception of a packet, a node rebroadcasts the packet only if it is itself included in the relay node list. In the relay node selection, we consider inter-vehicle distance, vehicle movement and link quality. In order to provide a portable solution, BR-NB uses neighbor information to infer those information. In this paper, we use “neighbor node” to denote one-hop neighbor node, and use “two-hop (or 2-hop) neighbor node” to denote a node which cannot communicate directly but can communicate with the forwarding help of an other neighbor node. Vehicles exchange information through hello messages. Every vehicle inserts own ID, its one-hop neighbor information in hello messages. The one-hop neighbor information is acquired from the received hello messages. For each neighbor, the inter-vehicle distance is inferred from the number of twohop neighbors the neighbor could cover. Vehicle movement is inferred from the change of the number of two-hop neighbors the neighbor could cover. Link quality is inferred from the hello message reception ratio. Independent of position information, in BR-NB, each sender node uses neighbor information (1-hop and 2-hop) to calculate
relay node candidates set, and selects relay nodes using a fuzzy logic based method considering inter-vehicle distance, vehicle movement and link quality. BR-NB also uses a joint redundancy and opportunistic transmission approach to provide reliability. If the link to a relay node is weak, the sender node transmits a packet multiple times to provide a high reliability. An opportunistic transmission is used when a packet loss occurs at a relay node. B. Relay node selection by evaluating each neighbor based on fuzzy logic Upon reception of a hello message from a node x, node c evaluates the link status value F uzz(c, x) by using a fuzzy logic considering additional coverage factor, mobility factor and link quality factor. When a broadcast data transmission is required, the node selects the node (as a relay node) which has the maximal fuzzy link status value from the relay node candidates. The calculation of relay node candidates will be described in II-G. 1) Why fuzzy logic?: In a vehicular ad hoc network, a wireless communication link can be vulnerable due to the vehicle distance, vehicle movement and channel fading. Therefore, in the link status evaluation, inter-vehicle distance, vehicle movement and link quality should be considered jointly. However, the three metrics are contradictory. If we select the farthest node as a relay node, it will minimize the number of relays. However, the relay node might lose the packet due to the low signal level. Moreover, due to node movement, the relay node might move out of the transmission range of the sender node. These conflicts are dependant on the vehicle movement, vehicle distribution and channel fading parameters. Therefore, the mathematical model of the optimal relay problem is complex to derive and a solution based on it would be too expensive for practical application. To acquire a flexible solution, we use a fuzzy logic based approach to solve this problem without deriving the mathematical model. Similar to human reasoning, fuzzy logic [10] can process approximate data by using non-numeric linguistic variables to express the facts. Fuzzy membership functions are used to represent the degrees of a numerical value belonging to predefined linguistic variables. Fuzzy rules are defined to conduct the final fuzzy value from the fuzzy degree. Defuzzification is used to convert the final fuzzy value to a numerical value. Since fuzzy membership functions and fuzzy rules can be tuned to satisfy the network environment, a fuzzy logic based system is flexible. 2) Procedure: The following is the procedure for calculating a fuzzy link status value for a neighbor node. (a) Calculation of multiple factors: Calculate an additional coverage factor, mobility Factor and link quality factor for the neighbor node (see II-C). (b) Fuzzification: Use predefined linguistic variables and membership functions to convert these factors to fuzzy values (see II-D). (c) Mapping and combination of IF/THEN rules: Map the fuzzy values to predefined IF/THEN rules and combine
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TABLE I N OTATION . MF ACF LQF ACR M Fi (c, x) M Fi−1 (c, x) AC(c, x) |A| N (x)
an exponential moving average to calculate the mobility factor as
mobility factor additional coverage factor link quality factor additional coverage rate current M F of node x for node c previous M F of node x for node c additional coverage of node x for node c number of elements in set A one-hop neighbor set of node x
M F (c, x) ← (1 − α)M Fi−1 (x) + αM Fi (x).
the rules to get the rank of the neighbor node as a fuzzy value (see II-E). (d) Defuzzification: Use a predefined output membership function and defuzzification method to convert the fuzzy output value to a numerical value (see II-F). C. Calculation of multiple factors Before we proceed further, we summarize the notation we use in this paper (as shown in Table I). 1) Calculating additional coverage factor from neighbor information: Upon reception of a hello message, a node calculates an additional coverage factor for the hello sender node. We use additional coverage of node x, AC(c, x), to denote the set of nodes which are one-hop neighbors of the node x but not one-hop neighbors of the current node c. Specifically, AC(c, x) is defined as AC(c, x) = N (c) ∩ N (x),
(1)
where N (x) and N (c) denote the one-hop neighbor set of node x, and one-hop neighbor set of the current node c respectively. We note here that node x belongs to N (c). Additional coverage factor ACF is calculated as |AC(c,x)| , AC(c, x) = φ ACF (c, x) = maxy∈N (c) |AC(c,y)| (2) 0, otherwise. We use ACF (c, x) to estimate the relative inter-node distance. The rationale behind this is that in most road topologies, especially in a straight road topology, the value of ACF is proportional to the inter-vehicle distance. The larger the distance between node c and x, the higher ACF (c, x). A higher ACF (c, x) means that the node x is more effective in terms of reducing the number of relays. 2) Calculating mobility factor from neighbor information: Upon reception of a hello message, a node calculates a mobility factor for the hello sender node. The mobility factor for node x at time i, M Fi (c, x), is calculated as |ACi (c,x)∩ACi−1 (c,x)| |ACi (c,x)∪ACi−1 (c,x)| , ACi (c,x)∪ACi−1 (c,x)=φ M Fi (c, x) = 0, otherwise. (3) where i and i − 1 indicates the current value and the previous value respectively. In order to eliminate some errors, we use
(4)
In here, α is set to 0.7 because that is the best value for many cases according to our experimental results. As shown in (4), the change of the additional coverage is used to estimate the relative movement of a neighbor node. When a neighbor vehicle is moving in the same velocity as the current vehicle, the change of the additional coverage is small due to the relatively static neighbor set. 3) Calculating link quality factor from neighbor information: A node maintains a counter for each neighbor to calculate the number of hello messages received. Since the hello messages are sent with a predefined time interval (1 sec.), each node can calculate the reception ratio of the hello messages. This ratio can be used to estimate the quality of the link to the neighbor. The hello reception ratio is updated for each 10 seconds interval as HRRi (c, x) =
Number of received hello messages at c from x Number of hello message sent at x (5)
The link quality factor, LQF , is calculated as LQF (c, x) ← (1 − α)LQFi−1 (x) + α × HRRi (c, x). (6) D. Fuzzification The process of converting a numerical value to a fuzzy value using a fuzzy membership function is called “fuzzification.” The fuzzy membership functions of additional coverage factor, mobility factor and link quality factor are defined in Fig. 1. A node uses the ACF membership function to calculate what degree additional coverage factor belongs to {Large, Medium, Small}. Similarly, the sender node also calculates what degree the mobility factor belongs to {Slow, Medium, Fast} and what degree the link quality factor belongs to {Good, Medium, Bad}. Small Medium 1
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Fuzzy membership functions (Left: ACF, Middle: MF, Right: LQF)
E. Mapping and combination of IF/THEN rules Based on the fuzzy values of additional coverage factor, mobility factor and link quality factor, a node uses the IF/THEN rules (as defined in Table II) to calculate the rank of the neighbor. The linguistic variables of the rank are defined as {Perfect, Good, Acceptable, NotAcceptable, Bad, VeryBad}. For example, in Table II, Rule1 is expressed as follows.
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TABLE II RULE BASE Coverage Large Large Large Large Large Large Large Large Large Medium Medium Medium Medium Medium Medium Medium Medium Medium Small Small Small Small Small Small Small Small Small
Rule1 Rule2 Rule3 Rule4 Rule5 Rule6 Rule7 Rule8 Rule9 Rule10 Rule11 Rule12 Rule13 Rule14 Rule15 Rule16 Rule17 Rule18 Rule19 Rule20 Rule21 Rule22 Rule23 Rule24 Rule25 Rule26 Rule27
Mobility Slow Slow Slow Medium Medium Medium Fast Fast Fast Slow Slow Slow Medium Medium Medium Fast Fast Fast Slow Slow Slow Medium Medium Medium Fast Fast Fast
Link quality Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad Good Medium Bad
function forms a shape as shown in Fig. 2. The centroid of this shape is used for deriving the final numerical value. The x coordinate of the centroid is the defuzzified value F uzz(c, x), which shows the evaluation value of the neighbor node.
Rank Perfect Good NotAcceptable Good Acceptable Bad NotAcceptable Bad VeryBad Good Acceptable Bad Acceptable NotAcceptable Bad Bad Bad VeryBad NotAcceptable Bad VeryBad Bad Bad VeryBad Bad VeryBad VeryBad
G. How many relay nodes should be selected: calculation of forwarding candidates BR-NB uses a subset of neighbor nodes to relay messages. As mentioned above, BR-NB utilizes a fuzzy logic based approach to select relay nodes from relay node candidates. In order to describe how BR-NB utilizes neighbor information, we introduce “reliable two-hop neighbor set”, “target set” and “relay node candidates” as shown in Fig. 3.
Fig. 3.
IF Additional Coverage is Large, Mobility is Slow, and Link Quality is Good THEN Rank is Perfect. Since there can be multiple rules applying at the same time, we use the Min-Max method to combine their evaluation results. In the Min-Max method, for each rule, the minimal value of the antecedent is used as the final degree. When combining different rules, the maximal value of the consequents is used (similar to Ref. [7]). F. Defuzzification 1
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Defuzzification is the process of converting the membership degrees to a numeric value based on an output membership function. The output membership function is defined as in Fig. 2. Here we use the Center of Gravity (COG) method to defuzzify the fuzzy result. For example, if the degree for Rank {Acceptable} is 0.25, the degree for Rank {Good} is 0.5 and the degree for Rank {Perfect} is 0.5, the consequent result
“Target set” and “relay node candidates” for node S.
Algorithm 1 Select relay nodes 1: if (The current node is the source node) then 2: “Target set” ← “reliable 2-hop neighbor set”. 3: “Relay node candidates” ← “all neighbors”. 4: while (“Target set” != φ) do 5: Get the node which has the maximal fuzzy link status value from “Relay node candidates”. 6: Set the node as a relay node. 7: “Target set” ← “Target set” - “the relay node’s neighbors”. 8: end while 9: else 10: “Target set” ← “reliable 2-hop neighbor set” - “the upstream node’s neighbors”. 11: “Relay node candidates” ← “all neighbors” - “the upstream node’s neighbors”. 12: while (“Target set” != φ) do 13: Get the node which has the maximal fuzzy link status value from “Relay node candidates”. 14: Set the node as a relay node. 15: “Target set” ← “Target set” - “the relay node’s neighbors”. 16: end while 17: end if Each node maintains a “reliable two-hop neighbor set”. For a node S, a two-hop neighbor (n2) is added to the “reliable two-hop neighbor set” if there exists a neighbor node (n1) satisfies
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LQF (S, n1) ≥ LQFT H && LQF (n1, n2) ≥ LQFT H (7)
where LQFT H is the predefined threshold value for the link quality factor. In BR-NB, we set LQFT H to 0.7. “Target set” is the set of neighbors which all selected relay node should cover. As shown in Algorithm 1, upon reception of a broadcast data message, a node (if the node is specified as a relay node) calculates the “Target set” by subtracting the hello sender’s one-hop neighbor from own reliable 2hop neighbor set. The node then updates the “relay node candidates”. If the node is the source node (the broadcast data message generator), the “Relay node candidates” are its neighbors (see Fig. 3). If the node is a forwarder node, the node has to subtract the upstream node’s neighbors from its neighbors to get the “Relay node candidates”. The node then selects the relay node which has the maximal fuzzy link status value (see II-B) from its “relay node candidates”. Once a relay node is selected, the node then updates the target by subtracting the selected relay node’s neighbors. If the “Target set” is still not null, the node selects the next relay node using the same method. This procedure is repeated until the all nodes in the “Target set” can be covered by the selected relay nodes. In this way, the messages can be broadcasted to all intended receivers in the network. H. Joint redundancy and opportunistic transmission approach to recover packet losses In order to improve the packet reception ratio, the proposed protocol uses two types of loss-recovery approaches: redundancy based approach and opportunistic transmission approach. Before sending a broadcast data packet, a sender node c checks the link quality factor (LQF ) of the relay node x whether satisfies LQF (c, x) > LQFT H
a tunnel. BR-NB is easy to implement because it does not require any special function (for example, RSSI indicator) of wireless devices. BR-NB can be easily tuned to use in different networks by changing the fuzzy membership functions and fuzzy rules. Therefore, BR-NB is portable and flexible. III. E XPERIMENTAL R ESULTS We used a real-world vehicular ad hoc network to evaluate the performance of BR-NB. BR-NB was implemented in Ubuntu 10.04. As shown in Fig. 4, we used 10 cars (each attached with 1 laptop computer) to generate the VANET. All cars ran towards the same direction but in two lanes with different velocity levels. The road was straight (with length of around 4 Km), and there was no traffic light on the road. All cars ran towards the same direction starting from two different lanes with different velocity levels (the maximum allowable velocity of the slower lane was 30 Km/h). The cars in the faster lane (5 cars in total) outran all cars in the slower lane and went back to the slower lane. Each car went to the end of the road and turned back. Each laptop ran at ad-hoc mode and communicated with each other using 2.4GHz wireless radio. We evaluated the protocol with various vehicle velocities (the maximum allowable velocity of the faster lane). For each case, we used the average value for 10 minutes experiments. The heading car sent broadcast packets with an interval of 1 packet per second. All other cars were intended receivers. The proposed protocol was compared with Flooding, MPR [8], EMPR [9] and EMPR-ReTr. In EMPR-ReTr, a sender node retransmits a packet when the corresponding ACK packet does not arrive in 50ms time interval. The maximum allowable number for retransmissions was 2. In the following results, the error bars indicate the 95% confidence intervals.
(8)
or not. In here, LQFT H is the same as the one defined in (7). We call a link is strong if (7) is satisfied. If the constraint is not satisfied, the sender node transmits the packet twice. In this way, the message can be delivered to the next relay node with a short delay. A data packet also can be lost when the link quality factor satisfies (7). Therefore, in here, we also use an opportunistic transmission approach to recover packet losses. When a node overhears a transmission from the upstream node but does not overhear any transmissions from the downstream relay node in the predefined time interval ReT rT H , the node relays the packet on the behalf of the selected relay node. Note that, this happens only when the node is strongly connected to the selected relay node. This is to avoid unnecessary transmissions. With the joint use of redundant transmissions and opportunistic transmissions, the proposed protocol can achieve a high packet reception ratio. I. Portability and flexibility BR-NB uses neighbor information to infer inter-vehicle distance, vehicle movement and link quality. BR-NB does not require position information which is difficult to acquire in
Fig. 4.
Experiment using a real-world vehicular ad hoc network.
A. Packet Delivery ratio Fig. 5 shows the packet delivery ratio for various node velocities. BR-NB shows a significant advantage over other protocols. This is because BR-NB considers additional coverage, vehicle movement and link quality in the relay node selection. In the Flooding, each vehicle rebroadcasts a packet upon the first reception. When the vehicle velocity increases, the inter-vehicle distance increases, resulting in the average packet loss ratio at the neighbor vehicles increases. Without any retransmission mechanism, Flooding shows a performance
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C. End-to-End delay In a real-world experiment, there could be time differences between different laptop computers. In order to show the results more clearly with the existence of time synchronization errors, we show the reduced delay of BR-NB as compared with the EMPR-ReTr here. 18
Reduced Delay for Each Hop (ms)
degradation with the increase of the vehicle velocity. MPR cannot provide a good performance because it always chooses the most distant node as a relay node. EMPR shows a little higher packet delivery ratio due to the mobility consideration. EMPR-ReTr shows a higher packet delivery ratio as compared with EMPR due to the retransmissions. However, when intervehicle distance is high (the network is relatively sparse), the retransmissions do not work well when the relay node is not selected properly.
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B. Number of messages Fig. 6 shows the number of messages for various node velocities. These messages include the data packets and ACK messages. EMPR-ReTr shows the highest number of messages because of the ACK messages which are used for triggering retransmissions. From this result, we know that the end-toend retransmission approach is not a good solution, especially when the network density is high. Since BR-NB does not use ACK messages, the protocol shows a lower message overhead.
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Fig. 7 shows the average reduced delay for each packet each hop. As shown in the figure, BR-NB is effective in terms of end-to-end delay especially when the vehicle velocity is high. When the vehicle velocity becomes higher, the intervehicle distance becomes larger, resulting in the reception ratio at the relay nodes becomes lower. This increases the number of retransmissions in EMPR-ReTr. As a result, as compared with EMPR-Retr, BR-NB is more effective in term of end-to-end delay when the vehicle velocity is high. IV. C ONCLUSIONS We proposed BR-NB, a portable and flexible multi-hop broadcast protocol for vehicular ad hoc networks. BR-NB infers inter-vehicle distance, vehicle movement and link quality by exchanging neighbor information with hello messages. BR-NB employs a fuzzy logic based approach to jointly consider these metrics to provide a flexible solution. BR-NB is easy to implement because it does not require position information or any specific hardware support. Through realworld experiments, we confirmed the advantage of BR-NB over other existing alternatives.
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ACKNOWLEDGMENT
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This work was supported by JSPS KAKENHI Grant Number 23700072.
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Maximum Allowable Velocity (Km/h) Fig. 6.
Packet delivery ratio for various vehicle velocities.
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