Routing and data diffusion in VANETs -- Routing mechanisms
Editor's Note: Wireless sensor networks lie at the heart of emerging applications in nearly every industry segment. In building these networks, designers contend with issues that encompass real-time communications, efficient high-bandwidth data exchange, multiple network topologies, selection of optimal routing strategies, and more. The book, Building Wireless Sensor Networks, offers detailed treatments on critical requirements and promising solutions in each of these areas and more.
This excerpt focuses on design challenges and methods associated with creating a vehicular ad hoc network (VANET). To share data as vehicles pass on roads or rest in parking areas, a VANET must contend with issues as varied as the physics of signal propagation, the fluid nature of data routing, and the security vulnerabilities associated with participation in an ad hoc network. Because of the changing nature of a VANET, designers need a broad understanding of these issues.
In this excerpt from the book, the authors offer an in-depth discussion that defines the nature of VANET challenges and discusses alternatives for their solution. Continuing the description of VANETs in part 1 and part 2, this installment of this series provides an in-depth discussion of key VANET routing mechanisms.
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Adapted from Building Wireless Sensor Networks, by Smain Femmam, Editor.
Chapter 3. Routing and data diffusion in vehicular ad hoc networks (Cont.)
By Frédéric Drouhin and Sébastien Bindel
This section presents routing protocols and their related mechanisms. In VANETs, vehicles have different velocities and are driven in different environments contributing to a decrease in the connectivity of the network. In such a situation, the design of routing protocols need to take into account these features. The first part of this section presents metrics used by routing protocols to assess the local links and thus the routing path. The second part details dedicated routing protocols for both V2V and for V2I infrastructures.
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Figure 3.6. Attenuation, shadowing, fast fading effect: power reception over distance with two ray ground, shadowing, Rice (K = 2) and Doppler model
3.3.1. Neighbor selection
Path selection relies on information provided by metric, given a “cost” to reach a neighbor. It can be related to the number of hops, the link quality, the bandwidth, the latency, etc. The computation of the path cost is then related to the cost of each local link composing it and four types of metrics can be distinguished according to [WAN 99]. The first one includes additive metrics, where the cost of the path is the sum of all costs of local links. The second one regroups multiplicative metrics, where the cost of the path is the multiplication of all costs of local links. The third group is the concave metric, where the cost of the path is the minimum cost of local links. The last group includes convex metrics, wherein the cost of the path is the maximum cost of local links. Owing to the high velocity of vehicles in VANET, most of the dedicated routing protocols use a combination of metrics in order to have an accurate assessment of the cost of the link. This section starts with a description of the most used metrics in VANET and details current implemented solutions.
188.8.131.52. Hop count metric
The traditional approach takes into account the number of hops to reach a destination. The ultimate benefit of such a technique is that it is user-friendly. This hop count number is currently coded on 8 bytes and incremented by one at each retransmission. Widely used by routing protocols dedicated to wired networks, such an approach becomes inefficient in wireless networks as pointed out by [DEC 03b]. Authors showed through a wireless sensor test, that the shortest paths often have fewer capacities than others and have opened a discussion on the link quality.
184.108.40.206. Link quality estimators
Link quality estimators (LQE) have been well designed for wireless sensor networks, and are also used in vehicular networks. [BAC 12] have classified these estimators into two categories. The first category, hardware-based, includes all estimators performing an assessment from information available at the physical layer. The second, software-based, regroups the rest of LQE running at upper layers, either on MAC or the IP layer.
220.127.116.11.1. Hardware LQE
Hardware-based estimators perform an assessment from information available at the physical layer. Assessments are provided by the receiver hardware without any additional computation costs, and are performed only by the receiver at each frame reception. In addition, the computed link quality can be assessed though the current traffic on the wireless channel without the need for any periodical broadcasts. In order to evaluate the reliability of an estimator, a good fitting with the Packet Reception Ratio (PRR) is required.
The first estimator, the received signal strength indicator (RSSI), gives the signal strength of the received packet. The signal-to-noise ratio (SNR) gives the difference between the pure received signal strength and the noise floor. The link quality indicator (LQI) only available in the IEEE 802.15 networks provides a link quality assessment based on the height symbols of the received packet. A second generation of hardware-based LQE has been designed based on detailed information on the decoding process related to the DSSS (direct-sequence spread spectrum) used in IEEE 802.15.4 networks. [HEI 12] have designed an estimator called CEPS, which relies on chip errors on the payload symbols to assess the PRR. As demonstrated by the authors, the correlation between the chip errors and the PRR can be approximated by a linear fit. Later, [SPU 13] suggested the BLITZ estimator, an improvement on the CEPS, by also considering chip errors related to the preamble. This improvement allows analysis of packet synchronization errors in order to obtain faster and more accurate information on the link quality.
As depicted in Figure 3.7(a), the correlation between the RSSI and the PRR is not easily deductible.
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Figure 3.7. PRR as a function of RSSI, SNR, LQI in WSN for 160 hours of data though 72 links got by [LIU 14]
However, [SRI 06] have demonstrated that over a RSSI threshold, the PRR is consistently high. This observation can be confirmed on the Figure 3.7(a), where the PRR is above 0.9 when the RSSI is below −85 dBm. Regarding the SNR, it provides a better correlation with the PRR than the RSSI; however, a simple observation is not sufficient to deduct the corresponding PRR. The same observation can be made for the LQI, even if the coefficient correlation is the highest. As a result, regardless of the estimator, a single reading is insufficient to determine the PRR. With CEPS and BLITZ, authors try to solve this deficiency. The two estimators present a better correlation with the PRR, and Blitz can provide an assessment as soon as a preamble is detected, even if the frame reception failed. The major drawback of such solutions is their implementation, since they require information from the decoding process only available in the chipset radio. That is why all experiments have been performed with a software radio.