- Open Access
Hidden node aware routing method using high-sensitive sensing device for multi-hop wireless mesh network
© Parvin and Fujii; licensee Springer. 2011
- Received: 14 November 2010
- Accepted: 29 September 2011
- Published: 29 September 2011
Throughput maximization is one of the main challenges in multi-hop wireless mesh network (WMN). Throughput of the multi-hop WMN network seriously degrades due to the presence of the hidden node. In order to avoid this problem, we use a combination of the high-sensitive sensing function and beacon signalling at the routing. The purpose of this sensing function is used to avoid the hidden node during route formation in the self flow. This function is considered to construct a route from the source node to the destination node without any hidden node. In the proposed method, high-sensitive sensing device is utilized in both route selection and in the media access. The accuracy of our proposed method is verified by numerical analysis and by computer simulations. Simulation results show that our proposed method improves the network performance compared with the conventional systems which do not take account of the hidden node.
- Source Node
- Cognitive Radio
- Destination Node
- Hide Node
- Wireless Mesh Network
Therefore, in this paper we focus on the hidden node avoidance technique for the self flow interference. The aim of this paper is to select a route between the source node and the destination node that is protected from the hidden node of the self flow. This is accomplished using a high-sensitive sensing function in the route construction. In the proposed routing method, it is considered that every node utilizes high-sensitive sensing devices like the secondary terminal in the cognitive radio [20–22]. Every node senses the medium for selecting the route as well as for the medium access control. In the proposed routing method, we uses beacon signal to select the next hop node. The beacon signal is used for selecting the next hop node. First a node broadcast a Route Request (RREQ) packet. In the next frame, the same node transmits the beacon signal to inform all neighbor nodes about its presence. All the nodes that receive the beacon signal from that node relay the RREQ packets. The node will be selected as the next node of the route. Such operation is repeated from the source node until the RREQ packet arrives at the destination node. The destination node then sends the Route Reply (RREP) packet toward the source node. Since all nodes in the route can detect the beacon signal of its previous hop node, the route can be selected as to remove the self flow interference due to the hidden nodes.
Different types of routing metrics are proposed in the multi-hop WMN to find the best possible paths between the source and the destination node [6, 23–25]. In , the Expected Transmission Count (ETX) was proposed to minimize the expected total number of transmissions required to successfully deliver a packet over a wireless link. The Expected transmission time (ETT)  metric is an extension of ETX which considers Different link routes or capacities. ETT is the expected time to successfully transmit a packet at the MAC layer. The Airtime routing metrics specified in IEEE 802.11s  is based on the ETT with additional consideration given to channel access and the protocol overhead to reflect the amount of channel resources consumed by transmitting the data packets over a wireless link. Hop count is the traditional routing metric used in most of the common routing protocols like DSR  and AODV  designed for multi-hop wireless networks. It finds paths with the shortest number of hops. These metrics unfortunately fail to address directly the impact of the hidden node problem in WMN. This means the path selected by these metrics unable to remove the self flow interference in a flow due to the hidden node problem and causes frequent data collisions. Therefore, in this paper, we propose a routing method that selects a path without any hidden node. For this purpose we chose a node as a next node of the route that is not a hidden node using beacon signaling. The aim of the proposed routing method is to construct a route without any hidden node. The proposed routing method can mitigate the hidden node, no matter which routing metrics is used for the route selection. As the conventional routing protocol, AODV uses hop count metric to choose the shortest hop length path we also use hop count metric for path selection. However, the proposed routing scheme also works well if it use other routing metrics such as ETX and ETT for path selection. This is because most of the routing metrics does not concern about the hidden node collisions due to the self flow interference.
In the proposed routing method, spectrum sensing is considered to detect the beacon signal of the previous hop node. Several spectrum sensing methods have been studied [26, 27]. Energy detection is one of the very popular methods because of its simplicity and adequate performance . The sensing function of our proposed method is based on this energy detection method. This method detects unknown signals embedded in the noise by comparing the observed received signal power level with a threshold. After constructing the route, data transmission will be performed using the IEEE 802.11 DCF as the MAC protocol. The only change of the IEEE 802.11 DCF on the data transmitting period is just to change the carrier sensing level to the appropriate lower sensing level. With low sensing level, a node can detect the existence of a hidden node. On the other hand, with high sensing level, the node often miss the detection of the hidden node. Since the conventional wireless LAN uses CSMA/CA MAC protocol with high sensing level, the hidden node problem cannot be removed. The proposed method combines the beacon signal and the high-sensitive sensing function at routing to remove the self flow hidden node problem. During the route construction, beacon signaling is used to inform the nodes (that are not hidden node) the presence of previous hop node. In this way, our proposed route avoids the self flow hidden node collision in the multi-hop WMN. Hidden node collision between the multi flows is also minimized with appropriate low sensing level. Therefore, the hidden node problem is removed because all the nodes utilize a cognitive radio sensing technique for detecting the beacon signal of the hidden node. In the proposed routing method, a hidden node does not start its transmission as it senses the medium as busy. Thus the hidden node problem is removed during the routing method. So that it can avoid redundant packet collision or redundant transmission termination among self flow nodes.
The rest of the paper is organized as follows. In Section 2 we present a brief overview of the background. The proposed method is described in Section 3 and the network model and the analysis of the proposed method is explained in Section 4. The performance evaluation through simulation is present in the Section 5. Finally, we conclude the paper in Section 6.
2.1 Hidden node problem
In this section we explain the proposed method using a simple graph model. The detail explanation of our proposed method also explained in this section with example.
3.1 Graph model
v(i): Set of neighbors of the node
v*(i): Set of nodes within the sensing range of the node
h(i): Set of hidden nodes of the node
Here, i is the hop number and N i is the i th hop candidates node of the route.
When a source node has a data packet to transmit to a destination, it checks the routing table for the destination entry. If the route is unknown it generates a RREQ packet and broadcasts to its neighbor nodes. Each RREQ packet contains an ID, source and destination IP addresses, sequence number, hop count, and time out field. The ID field uniquely identifies each RREQ packet and the sequence number indicates the freshness of the packets. The hop count represents the path length between the source and the destination. The time out field indicates the time duration, during which each intermediate node waits for sensing the beacon of the previous hop node. When an intermediate node receives RREQ packet, it checks the source IP and ID pair. If any intermediate node receives two RREQ packets with the same source and ID pair then it will drop the duplicate RREQ packet. If the node receives multiple RREQ from different nodes, it forwards the first received RREQ and drops the others RREQs. After receiving the RREQ packet, the intermediate node senses the spectrum to detect the beacon of the previous hop node. If it cannot detect the beacon signal within the time out field duration it drops the RREQ. The RREQ packet is rebroadcast by the intermediate node if the node can detect the beacon signal and increment the hop count. The intermediate nodes also create and preserve a reverse route to the source node for a certain interval of time. There may be several RREQ packets finally arriving at the destination node along different paths. The route selection is made at the destination node. The destination node can use a routing metric to select the best route between the source and the destination node. Many routing metrics are proposed for this purpose. The proposed routing method will avoid the hidden node, no matter which routing metrics it uses for the route selection. In this paper, we use hop count routing metric to select a route. However, the proposed routing method can also perform well with other routing metrics such as ETX and ETT. In order to evaluate the numerical analysis, we use the hop count metric for the path selection. It simply chooses the route with minimum hop count. The destination node then generates a RREP packet, which contains the route record in RREQ and sends back to the source node via the reverse path.
3.2 Route construction example
In this section first the successful transmission probability is derived. The next hop selection of the proposed routing method and the convention routing method (AODV) is calculated. Finally, we calculate the throughput performance of the proposed routing method and the conventional method.
4.1 Propagation model
4.2 Network model
In this paper, we make some assumptions:
Nodes are randomly distributed on a 2-D plane according to the Poisson distribution with density μ. In an area A, the probability of there being N, stations is:(5)
We assume all the stations in the network use fixed transmit power. We also assume the transmission range d tx and the interference range di are equal for all nodes.
Packet generation follows the Poisson distribution with density λ p /s.
The receiver can decode the packet correctly if the Signal to interference and noise ratio (SINR) at the Receiver exceeds the minimum required SINR:(6)
where Pi is the interference power and noise is the background noise.
4.3 Successful transmission probability
where H and P are the time for the packet header (PHY and MAC headers) and the payload, respectively, and θ is the physical slot time.
4.4 Next hop selection
4.4.1 Proposed routing
4.4.2 Conventional routing
where Payload is the packet payload size and rate is the data rate of the network.
In this section, we evaluate the performance of the proposed routing method using analysis and computer simulation. Furthermore, we compare it with the conventional AODV routing method.
5.1 Simulation set up
Required SINR (data packet)
Path loss exponent
Network throughput It is defined as the amount of packets received successfully by the destination per unit time (in Mb/s).
Collision probability The ratio of the total number transmission failures over the total number of transmission attempts.
Network delay It is defined as the total time taken by the destination node to receive the packet successfully sent from the source node. It consists of two parts: route establishment delay and data transmission delay. Route establishment delay means the time required to transmit the RREQ from the source node to the destination node. Data transmission delay is the time that the packet spends in the wireless medium.
5.2 Appropriate sensing level
5.3 Results and discussions
5.3.1 Impact on network throughput
We first study the impact of single flow on throughput. Figure 11b depicts the impact on the network throughput for a single flow. In this case, R is varied from 100 to 1,000 m between the source and the destination pair. The number of data packet is fixed to 200. The proposed method uses the appropriate sensing level -92 dBm and the conventional sensing level -62 dBm. According to Figure 11b, we can see that with small distance like 100-200 m, the throughput is almost the same for both the proposed system and the conventional system. The reason behind this is, direct communication can be established from the source to the destination with small distance and both systems do not have any hidden node i.e., the performance of both the proposed method and the conventional method is the same with the small distance between the source and the destination. However, the proposed method performs better than the conventional method with increasing distance due to the presence of hidden node. The proposed method also performs better even if it uses the conventional higher sensing level, -62 dBm. In the conventional system AODV does not consider the existence of the hidden node during the routing process and the media access CSMA/CA MAC protocol uses high sensing level of, -62 dBm so that it can not avoid hidden node problem.
In order to examine the impact of traffic load on the throughput we change the packet generation rate from 200 to 1,000 packets/s. The traffic flow is set to 4. The throughput under different traffic load is shown in the Figure 12b. The throughput of the proposed method with appropriate sensing level -92 dBm and conventional sensing level -62 dBm keeps increasing with traffic loads. This is because with high traffic load our proposed method does not have collisions due to hidden node. However, the throughput of the conventional method decreases as the traffic load increases. The reason for this is the collisions due to the high traffic load can not overcome.
5.3.2 Impact on collision probability
Beacon signal is used in our proposed method during the route construction. Every node transmits beacon signal after the transmission of the RREQ packet. For high density network there is some probability of beacon collision. In order to investigate the effect of the network density on the beacon signal, the probability of the beacon collision is calculated by varying the number of the multi-hop flow is shown in Figure 15b. In this case, the traffic generation rate is fixed to 200 packets/s for each flow. The number of relay node is 200. The source node and the destination node are randomly generated within the simulation area. We assume all pairs start routing phase simultaneously and we check the collision of beacon signal. It is observed from Figure 15b, the probability of beacon collision is very low. Because the duration of the beacon packet is very small and the beacon packet takes priority over the data packet transmission. Thus the beacons are rarely collide.
5.3.3 Impact on network delay
In this paper, we present a novel routing method using a high-sensitive sensing function for a multi-hop wireless mesh network. Using the sensing function, all nodes sense the medium of the interfered nodes before constructing the route. Beacon signal is used to avoid the existence of a self flow hidden node. During the route construction, all nodes sense the beacon of its previous hop nodes. The next node of the route is selected depending on this beacon signal sensing result. In this way, the proposed method choose a node as the next hop node for the route which is not a hidden node. Thus the constructed route in this way is a hidden node free route. Due to this sensing technique, the hidden node does not start its transmission if its previous hop node is busy. Using appropriate lower sensing level high end-to-end network throughput is achieved. We use the hop count routing metric for numerical analysis. However, the proposed method also performs well with other routing metrics such as ETX and ETT checking with computer simulation. From the computer simulation, it is confirmed that the proposed routing method improves the network throughput as well as reduce the probability of collision with ETX and ETT routing metrics. Our numerical and simulation results agree quite well. It is concluded that the network throughput has been significantly improved due to the absence of the hidden node. This is because, the proposed method can avoid the hidden node problem by combining the sensing criteria and beacon signal during the route construction. It can be confirmed that the proposed routing method achieves better performance compared with the conventional method, because the proposed system can avoid the hidden node problem. The numerical analysis of the proposed routing method with routing metrics ETX and ETT is our future works. Moreover, we will evaluate the performance of the proposed routing method with other routing metric like Airtime in the future.
This work is partially funded by Japanese Ministry of International Affairs and Communications under Strategic Information and Communication R&D Promotion Program (SCOPE).
- Akyildiz IF, Wang X, Wang W: Wireless mesh networks: a surveys. IEEE Comput Netw 2005, 47(4):445-487.MATHView ArticleGoogle Scholar
- Bruno R, Conti M, Gregori E: Mesh networks: commodity multihop ad hoc networks. IEEE Commun Mag 2005, 43(3):123-131.View ArticleGoogle Scholar
- Zhang Y, Jijun L, Honglin H: Wireless Mesh Networking. Auerbach publication, Boca Raton; 2007.Google Scholar
- Gast MS: 802.11 Wireless Networks: The definitive Guide. O'Reilly & Associate, USA; 2002.Google Scholar
- Johnson DB, Maltz DA: Dynamic Source Routing in Ad Hoc Wireless Networks. In Proceedings of Mobile Computing. Volume chapt. 5. Edited by: Imielinski T, Korth H. Kluwer Academic Publishers, Dordrecht; 1996:153-181.View ArticleGoogle Scholar
- Perkins C, Royer EM, Das SR: Ad Hoc On-Demand Distance Vector (AODV) routing. IETF RFC 3561 2003.Google Scholar
- Khurana S, Kahol A, Jayasumana AP: Effect of Hidden Terminal On The Performance of IEEE 802.11 MAC Protocol. Proceedings of IEEE LCN'98 1998, 12-22.Google Scholar
- Hadzi-Velkov Z, Gavrilovska L: Performance of the IEEE 802.11 Wireless LANs Under Influence Of Hidden Node. Proceedings of IEEE PWCS 1999, 221-225.Google Scholar
- ANSI/IEEE Std 802.11: Part II: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. 1999.Google Scholar
- Xu K, Gerla M, Bae S: Effectiveness of RTS/CTS handshake in IEEE 802.11 based adhoc networks. Ad Hoc Netw J 2003, 1: 107-123.View ArticleGoogle Scholar
- Gupta P, Kumar PR: The capacity of wireless networks. IEEE Trans Inform Theory 2000, 46(2):388-404.MATHMathSciNetView ArticleGoogle Scholar
- Deng J, Liang B, Varshney PK: Tuning the Carrier Sensing Range of IEEE 802.11 MAC. Proceedings of IEEE Globecom'04 2004, 5: 2987-2991.Google Scholar
- Hekmat R, Mieghem PV: Interference in Wireless Multi-hop Adhoc Networks and its Effect on Network Capacity. Proceedings of Med-hoc-Net 2002.Google Scholar
- Zhu J, Guo X, Yang LL, Conner WS: Leveraging Spatial Reuse in 802.11 Mesh Networks with Enhanced Physical Carrier Sensing. Proceedings of IEEE ICC 2004.Google Scholar
- Yang X, Vaidya NH: On the Physical Carrier Sense in Wireless Ad Hoc Networks. Proceedings of IEEE Infocom 2005.Google Scholar
- Zhai H, Fang Y: Physical Carrier Sensing and Spatial Reuse in Multirate and Multihop Wireless Ad Hoc Networks. Proceedings of IEEE Infocom 2006.Google Scholar
- Kim TS, Lim H, Hou JC: Improving Spatial Reuse Through Tuning Transmit Power, Carrier Sense Threshold, and Data Rate in Multihop Wireless Networks. Proceedings of ACM MobiCom 2006.Google Scholar
- Zeng Z, Yang Y, Hou JC: How Physical Carrier Sense Affects System Throughput in IEEE 802.11 Wireless Networks. Proceedings of IEEE Infocom 2008.Google Scholar
- Fuemmeler J, Vaidya N, Veeravalli VV: Selecting the Transmit Powers and the Carrier Sensing Thresholds for CSMA Protocols. Proceedings of Wicon 2006, 1321-1329.Google Scholar
- Hossain E, Bhargava VK: Cognitive Wireless Communication Networks. Springer, Berlin; 2007.View ArticleGoogle Scholar
- Cabric D, Mishra SM, Brodersen RW: Implementaion issues in spectrum sensing for cognitive radios. Proc Signals Syst Comput 2004, 2: 772-776.Google Scholar
- Mitra J, Maguire GQ Jr: Cognitive radio: making software radios more personal. Proc IEEE Pers Commun 1999, 6(4):13-18.View ArticleGoogle Scholar
- De Couto D, Aguayo D, Bicket J, Morris R: A High-Throughput Path Metric for Multi-Hop Wireless Routing. Proceedings of ACM MobiCom 2003.Google Scholar
- Padhye J, Drave R, Zill B: Comparison of routing metrics for static multi hop wireless networks. Proc ACM SIGCOM 2004.Google Scholar
- Wireless Medium Access Control (MAC) and physical layer (PHY) specifications: Amendment: ESS Mesh networking IEEE P802.11s/D1.00 2006.Google Scholar
- Uchiyama H, Umebayashi K, Kamiya Y, Suzuki Y, Fujii T, Ono F, Sakaguchi K: Study on Cooperative Sensing in Cognitive Radio Based Ad-Hoc Network. Proceedings of IEEE Pimrc 2007.Google Scholar
- Urkowitz H: Energy Detection of unknown deterministic signals. Proc IEEE 1967, 55(4):523-531.View ArticleGoogle Scholar
- Tobagi F, Kleinroack L: Packet Switching in radio channels: Part II--The Hidden Terminal Problem in carrier Sense Multiple-Access and the Busy-Tone Solution. IEEE Trans Commun 1975, 23: 1417-1433.MATHView ArticleGoogle Scholar
- Bianchi G: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J Sel Area Commun 2000, 18(3):535-547.View ArticleGoogle Scholar
- Cali F, Conti M, Gregori E: Dynamic tuning of the IEEE 802.11 Protocol to Achieve a Theoretical Throughput Limit. IEEE/ACM Trans Netw 2000, 8(6):785-799.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.