On the Influence of Obstacle Shadowing in Evaluating VANET Safety Performance

Scott E. Carpenter Department of Computer Science North Carolina State University

Raleigh, NC, USA scarpen@ncsu.edu

Abstract— Wireless communication between vehicles enables

both safety applications, such as accident avoidance, and non-safety applications, such as traffic congestion alerts [1] with the intent of improving safety in driving conditions. Because the high costs of implementing limited test-bed environments constrain prototype testing, VANET researchers often turn instead to simulation toolsets from which a rich set of environmental scenarios are modeled. However, despite the availability of such tools, results are inconsistent. While VANET simulations allow investigators to select from a wide range of models for radio wave propagation, fading, and shadowing, they often fail to consider realistic road topologies and the presence of obstacles [2]. Safety performance assessments improve when models accurately reflect environmental conditions such as the fading effects of radio wave propagation through buildings and other obstacles.

The goal of this research is to show quantitatively how accurate, deterministic obstacle fading models impact the performance assessment of VANET safety applications.

An obstacle model and a fading model that uses it, along with a VANET simulation script, have been implemented and contributed to the open source ns-3 network simulation community. Results from simulation experiments that use these models show that deterministic obstacle fading greatly degrades Packet Delivery Ratio (PDR) and compares differently than stochastic fading models, such as Nakagami-m. Such network-level results can have dramatic impacts to safety performance assessment, such as awareness range. By considering the number of packets and time tolerance window, awareness probability and communications awareness range [3] can be used to assess application reliability, thereby providing comparative insight among safety application performance. Failing to account for the effects of obstacles in safety assessment can therefore inaccurately or even greatly overstate the performance of VANET safety applications. Including realistic obstacle fading in VANET simulation modeling improves VANET assessment and strengthens safety, thus supporting one of the primary goals of connected vehicle systems.

Keywords— VANET; obstacles; propagation loss; fading; shadowing; simulation; ns-3

I. INTRODUCTION Collisions among moving vehicles lead all causes of traffic

fatalities, injuries, and property damage [4]. By exchanging information using wireless communication, cars and trucks enable both safety applications, such as accident avoidance,

and non-safety applications, such as traffic congestion alerts [1]. The most promising technologies in the U.S. that will enable such a vehicular ad hoc network (VANET) are collectively referred to as Dedicated Short Range Communications (DSRC) and are estimated to prevent tens of thousands of automobile crashes every year [5]. Limited test-bed environments constrain VANET researchers, requiring them to rely on numerous available simulation frameworks that produce inconsistent results. While investigators may select from a wide range of models for radio wave propagation, the fading and shadowing models used are often entirely probabilistic and therefore fail to consider realistic road topologies and the presence of obstacles [2]. VANET modeling improves when a visibility scheme describes the topology as a configuration space and supports obstacle detection deterministically based on a realistic environmental representation. Model accuracy influences simulation results and directly impacts the performance assessment of VANET safety applications which differ by their communications requirements [6] [7].

By considering the number of packets and time tolerance window, awareness probability and communications awareness range [3] assess application reliability, thereby providing comparative insight among safety application performance.

The goal of this research is to show quantitatively how accurate, deterministic obstacle fading models impact the performance assessment of VANET safety applications. The following research questions (RQ) guide our investigations:

RQ1: Can fast fading and shadowing effects of obstacles, such as buildings, vehicles and trees, be modeled and efficiently simulated in ns-3 [8]?

Because model realism improves simulation results, a deterministically real fast fading and shadowing model that accounts for radio wave propagation through obstacles will improve the usefulness of existing detailed network simulation tools, such as ns-3.

RQ2: How does an obstacle model effect packet delivery ratio versus other VANET fading and shadowing models?

By implementing a deterministic obstacle fading model and comparing simulation results to other stochastic shadowing models, a performance characterization of the obstacle fading model can be made.

RQ3: What is the impact of a deterministic obstacle fading model to VANET application safety performance as compared to other fading and shadowing models?

Using simulation results to assess safety performance, the safety impact of a deterministic obstacle fading model can be quantifiably compared to other fading and shadowing models.

Our primary contributions include:

1) An obstacle model built using techniques from computational geometry, a fast-fading model that uses it, and a VANET simulation script were developed and contributed to the open source ns-3 network simulator community.

2) Simulation results of highway, residential, and downtown scenarios in the Raleigh, NC area compare quantitatively the effects of i) deterministic obstacle shadowing to ii) stochastic Nakagami-m fast fading and iii) no fading.

3) A resulting comparison that extends awareness range determination using simulation results and suggests the consequences of ignoring obstacles when evaluating VANET safety application performance.

4) Detailed analysis of results.

This paper is organized as follows: Section II provides background information and discusses related work and Section III describes the modeling approach and details the methodology. Section IV describes the experimental setup, results and analysis and Section V concludes the paper and discusses future work.

II. BACKGROUND AND RELATED WORK

A. DSRC Standards and Operations To improve driving safety and reduce traffic-related

fatalities, regulations will require vehicles to communicate cooperatively within a vehicular ad hoc network (VANET) using wireless technologies [9], thereby enabling both safety applications, such as accident avoidance, and non-safety applications, such as traffic congestion alerts [1]. The most promising technologies in the U.S. that will enable such a vehicular ad hoc network (VANET) are collectively referred to as Dedicated Short Range Communications (DSRC).

DSRC-related standards include IEEE Std. 802.11-2012 [10], IEEE Std. 1609/WAVE [11] [12] [13] [14] [15], and SAE J2735 Message Set Dictionary [16]. Figure 1 shows the DSRC reference model.

IEEE Std 802.11-2012 [10] incorporates modifications to the MAC and PHY layers (formerly known as IEEE 802.11p) intended to address the dynamic nature of potentially fast-moving vehicles. A primary enhancement allows a STA that is not a member of a Basic Service Set (BSS) to transmit data frames, allowing the PHY to operate “outside the context of a BSS,” (i.e., OCB) and thus defining a new type of 802.11 communications. DSRC achieves data rates from 3 to 27 Mbps [17], although the majority of testing in the U.S. utilizes the 6 Mbps configuration (i.e., Quadrature PSK with rate 1/2 coding) [18].

Fig. 1 – DSRC reference model.

While the 802.11 family is well-understood, 802.11 unicasting is not well-suited to the VANET environment [19]. The IEEE Std 802.11 supports safety beacons via broadcast transmissions (i.e., beaconcasting) using Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA). While sensing of the network delays a transmission which would otherwise cause a collision, CSMA/CA does not prevent all such collisions, especially in the hidden node scenario [20] in which one vehicle cannot sense that a transmission would collide with another nearby vehicle.

IEEE Std. 1609/WAVE provides further capabilities, such as: channel allocation and multi-channel access, priority queueing and channel routing, congestion control, security and privacy mechanisms, and an application programming interface (API) for messaging. WAVE supports IPv6-based data transfers and also non-IP-based traffic through the WAVE Short Message Protocol (WSMP). The design of IEEE 1609/WAVE supports a single control channel (CCH) and six service channels (SCH) that are defined in the 5.9 GHz range and typically occupy 10 MHz each. It is generally assumed that the CCH will be mostly dedicated to safety applications [17]. Sync intervals split the channel at a rate of 10 Hz, which CCH and SCH intervals then split equally, with a 4ms guard interval separating channel switches. Continuous channel access is also supported.

The WAVE standard addresses message priority using four different Access Classes (AC) per (CCH or SCH) channel, AC0 (lowest priority) to AC3 (highest priority), with the MAC layer maintaining separate queues and channel access for each AC [17]. The WAVE contention mechanism is similar to the one used in conventional Wireless Local Area Network (WLAN) and the IEEE 802.11e Enhanced Distributed Channel Access (EDCA) Quality of Service (QoS) enhancements [21].

Using the SAE J2735 DSRC Message Set Dictionary [16], the VANET applications are developed over the 1609/WAVE stack. Data elements that enable many safety applications are described in the most important message in the J2935 standard [17], the Basic Safety Message (BSM), which every vehicle has to broadcast at a nominal rate of 10 Hz. The BSM is

Transport TCP UDP

Data Link

Physical

ApplicationApplications

DSRC Message Set Dictionary SAE J2735

Man

agem

ent

1609

.3

WSM1609.3

Secu

rity

1609

.2Internet IPv6

LLC 802.2MAC 802.11

1609.4

PHY 802.11 (p)

divided into i) Part I mandatory elements, e.g., position, motion, braking status, and vehicle size and ii) Part II optional elements, e.g., vehicle events, path history and path prediction. Additionally, SAE J2735 provides guidelines on message prioritization among different message types. Applications may involve strictly vehicle-to-vehicle (V2V) messaging using an On-Board Unit (OBU), or may also involve the use of a Roadside Units (RSU) to support vehicle-to-infrastructure (V2I) communications. The Federal Communications Commission (FCC) defines four classes for DSRC device operations with desired communications zones ranging from 15 to 1000 meters with typical operation expected in the 400m range[22] [17]. While vehicles and infrastructure expect to communicate reliably over these ranges, radio-blocking obstacles and other interference that prevent message delivery challenge the safety effectiveness of all applications.

B. Safety Assessment and Metrics While IEEE Std 802.11 / WAVE PHY-level performance

has been extensively studied [21] [23] [24] and network performance is often assessed using throughput and end-to-end delay to quantify Quality of Service (QoS), these metrics generally express the Link and MAC layer effectiveness.

Differences in communications requirements (e.g., Transmission Mode, Update Rate, Allowable Latency, Data to be Transmitted and/or Received, and Maximum Required Range of Communications [6]:) support safety application performance comparison. For example, Cooperative Forward Collision Warning (CFCW), Emergency Electronic Brake Lights (EEBL), and Pre-Crash Sensing (PCS) differ in their update rate and communications range, with PCS requiring the highest update range (~ 20 Hz) over the shortest range (~50m), therefore giving it greater safety meaning to the driver.

Driving condition safety improvements from VANET technologies remain difficult to assess. Foremost, while numerous safety applications have been proposed, none are standardized [17]. Furthermore, prototype testing thus far has been constrained to limited test-bed environments where DSRC adoption rates have been relatively low, and even if comprehensive testing results were available, significant legal hurdles remain regarding risks and liabilities that could jeopardize successful deployment [25].

In a VANET, information dissemination of safety packets is critical, requiring a need to evaluate network-layer routing and delivery [26]; for accurate packet delivery we have to consider the packet delivery ratio (PDR) [27] and packet drop rate [28]. Additionally, routing overhead (i.e., number of routing bytes required by the routing protocol to construct and maintain its route [29]) and hop count from source to destination help assess routing performance [28].

Potential safety benefits of VANET applications can be assessed based on the estimated effectiveness of the application in terms of reduction of “crash-related factors” (e.g., functional years lost, vehicles crashed, and direct costs) [6]. While network level metrics have been historically important to the understanding and tuning of traditional Internet and Mobile Ad hoc Networking (MANET) communities [26], application performance depends instead on reliability metrics [26] [3]

[27]. Performance measurements considered are often network- (or packet-) level metrics (e.g., PDR and Average Per-Packet Latency) and/or application-level reliability metrics (e.g., Application-level T-Window Reliability [26]).

Packet Delivery Ratio PDRnet(d) is the percentage of broadcast packets that are successfully received by all vehicles within each transmitting vehicle’s coverage range, d [26] [27]. Receipt failures degrade PDR due to concurrent transmissions, hidden terminal problems and the impact from channel fading [27]. PDR is derived [27] with respect to two aspects:

1) PDRC(x), the impact of collisions due to concurrent transmissions, and

2) PDRF(x), the impact of channel fading and path loss.

Taken together, it can be shown [27] that:

𝑃𝐷𝑅𝑛𝑒𝑡(𝑑) = 𝑃𝐷𝑅𝐶(𝑑) ∙ 𝑃𝐷𝑅𝐹(𝑑) (1)

Application-level T-window reliability, Papp(d), is the probability of receiving at least one packet out of multiple packets from a broadcasting vehicle at distances smaller than or equal to d, within a time interval, T. Papp(d) describes the application-level reliability for safety applications, whereas PDRnet(d) describes the wireless communication reliability at the packet level [26]. Application-level reliability can be expressed in terms of network-level reliability as given in (2) [26]:

𝑃𝑎𝑝𝑝(𝑑) = 1 − (1 − 𝑃𝐷𝑅𝑛𝑒𝑡(𝑑))𝑇𝑡 , (2)

where t is the beaconcasting interval and τ is the tolerance time window.

Generalizing, the awareness probability, PA(x, n, TTol), as given in (3), is the probability of receiving at least n packets in the tolerance time window, TTol [27] [3], where Ps(x) = PDRnet(x); awareness probability equals application-level T-window reliability when n = 1.

𝑃𝐴(𝑥,𝑛,𝑇𝑇𝑜𝑙) = ∑ ��𝑇𝑇𝑜𝑙𝜏�

𝑘�

�𝑇𝑇𝑜𝑙𝜏�

𝑘=𝑛 𝑃𝑠(𝑥)𝑘(1 − 𝑃𝑠(𝑥))�𝑇𝑇𝑜𝑙𝜏�−𝑘 (3)

By considering the number of packets and time tolerance windows, awareness probability can be applied to different safety applications in order to assess application reliability. However, while an awareness probability can be calculated analytically using network-level PDR, confusion remains over how to best use this application-level metric to evaluate application performance, as there are no standards that define suitable numbers for messages received or time tolerance windows and the requirements are hard to capture.

The authors of [3] provide a methodology that extends the use of awareness probability to instead determine the awareness range, RA(PA)=d, which is the maximum distance, d, at which a threshold awareness probability, PA, is met or exceeded. By first fixing an awareness probability threshold (i.e., PA = 90%, 95%, 99.99%), the network-level PDR necessary to achieve PA can be determined and the effective awareness range of the PDR can further be calculated. The awareness range can then be compared to the vehicular operating conditions (e.g., braking distance, expected

communications range, etc.) to determine if such conditions can be met. Awareness range describes whether or not there is sufficient distance and time for a safety application to support user responses that maintain safe operating conditions and is therefore conceptually much closer to a suitable measure of application-level performance.

C. Simulation Numerous available simulation frameworks assist VANET

researchers by providing toolsets with supporting models from which a rich set of environmental scenarios are simulated. Often treated as separate architectural modules, simulators address mobility, networking, and radio propagation [30], while some tight coupling among these primary modules may be found in some over-arching VANET simulators [31]. For a good review of VANET simulator toolsets and models, see [31]. However, despite the availability of such tools, results are inconsistent. For example, when simulating the same environment and protocol, one investigation of VANET mobility generators shows performance deviations among them, making simulation results “unconvincing and inconclusive [31].” Indeed, the simulation requirements of VANET environments presents numerous challenges to the VANET researcher, such as “constrained road topology, multi-path fading and roadside obstacles, traffic flow models, trip models, varying vehicular speed and mobility, traffic lights, traffic congestion, drivers’ behavior, etc. [31]” The level of capability for addressing these challenges varies among current simulators. Most-concerning to environmental accuracy, VANET simulations often fail to consider realistic road topologies and the presence of obstacles [2]. Figure 2 shows a classification of representative mobility generators and VANET and network simulators.

Fig. 2 – Representative VANET simulation tools. Adapted

from: [31].

Because obstacles not only constrain vehicular movement but also interfere with radio transmissions, accurate environmental representation of obstacles is absolutely required in urban scenarios to benefit VANET simulation, although less critical in highway scenarios [30]. The authors of [32] found that signal propagation varies especially between line-of-sight (LOS) and obstructed-line-of-sight (OLOS). The authors of [33] found similar effects occur when radio waves propagate “Around-the-Corner” (ATC).

Due to the costs of vehicles, communications equipment, and road systems access and control, establishing a vehicular

ad hoc network (VANET) test environment remains mostly cost-prohibitive in academic research, although collaborative organizations have developed some limited connected-vehicle test beds [34]. While simulation tools thus remain a highly useful tool set to the VANET modeler, realism continues to present challenges within the environmental models including: vehicular mobility, city-scape, and radio wave propagation loss and shadowing. Radio wave attenuation is often modeled deterministically based mainly on inter-vehicular line-of-sight (LOS) distance, while some modelers improve upon this with stochastic modeling intended to account for radio wave shadowing, which may be further characterized by environmental differences, such as open highway versus urban settings. Recent models allow shadowing to be modeled more deterministically when interference includes radio wave propagation through buildings [33] [2] [35]. However, availability remains limited for such models within non-proprietary network simulation tools. For example, within OMNeT++ [36], the Simple Obstacle Model [33] is only available through Veins [37], making it useful only to vehicular network modeling. Within ns-3 [8], a buildings model exists, but limits buildings to cubes with linearly placed x- and y- coordinates and is implemented mainly for LTE modeling only.

III. OBSTACLE MODELS

A. Radio Propagation Models A Radio Propagation Model (RPM) handles the effects of

signal attenuation due to distance, multipath signal fading due to reflectors, and shadowing as radio waves move through free space and obstacles, such as buildings. Modeling that uses empirical data to evaluate a vehicle-to-vehicle communications channel shows that path loss is primarily influenced by:

i) LOS signal attenuation,

ii) fast fading effects such as strong ground reflection, and

iii) slow fading from random scatters and shadowing effects [38].

Many different RPMs have been proposed for VANET, ranging in their design complexity from simpler models such as unit-disk up through more complex ones that specifically address the interference that obstacles cause.

The simplest model, often used in VANET simulation, is the unit-disk model, in which vehicles can communicate with each other if they are within a threshold distance and cannot communicate otherwise [39] [40]. Another commonly simulated RPM in VANET is the two-ray model, which takes into account signal reflection from the road surface and sufficiently represents path loss in IVC [41].

The authors of [39] conclude that the largely used unit disc model fails to realistically model a communication channel, while parameters of simplistic models like log normal can be adjusted to match the corresponding system metrics of more complex and hard to implement obstacle based models. For (small-scale) fast fading models, various stochastic distributions have been proposed, including Rice, Rayleigh, Nakagami-m, lognormal and Weibull distributions [39] [42].

VANETSIMULATORS

NETWORKSIMULATORS

MOBILITYGENERATORS

TRaNS NCTUns GrooveNet MobiREAL

ns-3 OMNeT++ ns-2 SNS GlobMoSim JiST/SWANS GTNetS

SUMO MOVE CitiMob FreeSim STRAW NetStream

Veins

Channel models commonly used for multipath and shadow fading can be normalized by the general gamma distribution, as in (4) [43]:

𝑓𝑔𝑎𝑚𝑚𝑎(𝑟;𝛼,𝛽, 𝑣) = 2𝑣𝑟2𝑣𝛼−1

(β/α)𝛼Γ(𝛼)𝑒−

𝛼𝑟2𝑣β , (4)

where α is the fading parameter, β is the power-scaling parameter, v is the shape parameter, and Γ(α) is the gamma function. Distribution special cases include Nakagami-m (v=1), Weibull (α=1), and Rayleigh (v=α=1), while the lognormal distribution is a limiting case (α →∞,v→0).

The Nakagami-m fading model determines signal power reception probabilistically dependent on model parameters that simulate fading levels and may be described as given by (5):

𝑓𝑁𝑎𝑘𝑎𝑔𝑎𝑚𝑖−𝑚(𝑟;𝑚,𝛺) = 2𝑚𝑚𝑟2𝑚−1

Ω𝑚Γ(𝑚)𝑒−

𝑚𝑟2Ω , (5)

where m is the Nakagami parameter (i.e., shape parameter), Γ(m) is the gamma function, and Ω is the average power of multipath scatter field, which controls the spread of the distribution. The Nakagami-m distribution in (5) can be calculated using the general gamma distribution of (4) by setting v = 1, α=m, and β=Ω/m.

Stochastic models determine the physical parameters of the vehicular channel in a completely probabilistic manner without considering underlying geometry [39]. Stochastic communications modeling could therefore deviate severely from realistic behavior, negatively impacting simulations of transmission-critical safety applications [33].

Obstacles play a part in radio signal propagation, with various modeling approaches [44] [39] [45] [46] being undertaken and differing in their treatment of distance-based attenuation and additional fading effects. While sophisticated methods like ray-tracing remain impractical for evaluating typically large-scale VANETs [32] [39], hybrid solutions that differentiates between LOS and non-LOS conditions have been proposed [32] [47] [35] [46].

Because radio waves can physically penetrate one or more buildings, models based on direct LOS alone are insufficient, leading the authors of [33] to collect empirical results showing the effects of building shadowing in urban environments. Gathering measurements using IEEE 802.11p devices, the authors of [33] present a realistic, yet computationally inexpensive path loss simulation model that uses the number of obstacle walls and distance between two vehicles to estimate the effect that buildings and other obstacles have on the radio communications.

B. Obstacle Modeling VANET modeling improves when a visibility scheme

describes the topology as a configuration space and supports obstacle detection. Most visibility schemes divide the configuration space into sub-areas by some criteria. For example, the Manhattan grid model assumes that all vehicles move only in streets arranged as a Manhattan-style grid and treats non-street areas as buildings. However, Manhattan layouts are uncommon in real scenarios [48]. The authors of [35] extend the areas of each roadway segment to include a

proximity area to include locations within one half-width of the roadway. Figure 3 shows an example urban downtown scenario (Raleigh, NC USA) simulated in the Simulator for Urban Mobility (SUMO) [49], an open source macroscopic traffic simulation package [50], using buildings data from OpenStreetMap (OSM) [51].

Fig. 3 – A downtown Raleigh, NC simulation scenario,

in SUMO. Buildings (light shading) may significantly attenuate radio signal propagation between vehicles, affecting VANET application safety performance.

Current approaches to detect obstacles within a configuration space [48] [2] [44] employ different algorithms with varying complexity. Many come from computational geometry techniques including intersection problems [45] [33] and binary space partitions (BSP) [52] [33] that are applied to model vehicles and/or buildings as obstacles.

C. Methodology This section elaborates the research methodology in terms

of each request question (RQ).

RQ1: Can fast fading and shadowing effects of obstacles, such as buildings, vehicles and trees, be modeled and efficiently simulated in ns-3?

An obstacle fading model was developed for the ns-3 network simulation toolset that uses computational geometry techniques (e.g., an obstacle model) to determine NLOS distances traversed and number of segment intersections (i.e., walls penetrated) in roadway scenarios using building footprint information from OSM. The models are tested using a VANET simulation script that was also developed for ns-3, and all code (obstacle model, obstacle fading model, and VANET simulation script) have been contributed to the ns-3 community.

Three environments differentiate between obstacle densities and illustrate the problem:

i) an open highway setting,

ii) a residential neighborhood, and

iii) an urban downtown scenario.

Street and building data derives from an open source, OpenStreetMap [51]. Multiple experiments inject 50 to 250 vehicles (in steps of 50) into each scenario to model vehicular mobility. Using SUMO, randomly-placed vehicles move realistically and follow routes that intend to obey expected traffic laws, lane changing, and traffic light signaling. Vehicles vary speed and braking using common car-following models, such as the Krauss Model [53]. Generated ns-2 movement files capture the resulting mobility traces.

Modeling assumes a 100% DSRC equipage penetration rate and all vehicles use a single 802.11p continuous access 10 MHz channel operating in the 5.9 GHz range to broadcast a 200-byte BSM 10 times per second using WiFi and WAVE models from a network simulator, ns-3.

As radio waves propagate through space (e.g., directly through free space, reflected off of surfaces, and/or through objects), the power density diminishes as a result of what is commonly referred to as path loss. The transmitter and receiver power relates to path loss as follows: when the power at the input (i.e., transmitter) and output (i.e., receiver) are represented as 𝑃𝑖 and 𝑃𝑜, respectively, then the power ratio, R, is given in (6):

𝑅 = 𝑃𝑖𝑃𝑜

(6)

Path loss is often expressed in units of decibel (i.e., dB) as in (7):

𝑋[𝑑𝐵] = 10𝑙𝑜𝑔(𝑋), (7)

where X represents a value to be converted to dB, log(∙) represents the base-10 logarithm, and X[dB] is the converted value. In all equations that follow, path loss terms (i.e., Lx) are in dB.

The path loss in dB, L, is defined as the power ratio, R, in decibels, which is calculated by substituting (6) into (7) to arrive at (8):

𝐿 = 10log (𝑃𝑖𝑃𝑜

) (8)

Total path loss is modeled as the cumulative effects of:

i) signal attenuation due to distance (i.e., propagation loss),

ii) fast fading multipath such as ground reflections, and

iii) slow fading from shadowing due to buildings and other obstacles.

Therefore, total path loss, Lt, can be expressed as:

𝐿𝑡 = 𝐿𝑝 + 𝐿𝑓 + 𝐿𝑠 , (9)

where Lp represents path loss due to propagation, Lf represents path loss due to fading such as multipath interference, and Ls represents path loss due to shadowing such as obstacles. Each of the terms in (9) is now considered.

Path loss due to propagation (i.e., the Lp term in (9)) is simulated in all experiments using the two-ray ground propagation loss model, thusly following the works of [33] and

[37]. The relation between received power after propagation loss, Pr,prop_loss, and initial transmission power, Pt, as a function of distance, d, can be approximated using the two-ray ground propagation model as given in (10), where Gt and Gr are the transmitter and receiver gains, respectively, and hb and hm are the heights of the receiver and transmitter, respectively:

𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠 = 𝑃𝑡𝐺𝑡𝐺𝑟ℎ𝑏2ℎ𝑚2

𝑑4 (10)

By rewriting (10), the power ratio, Rp, as in (6) can be stated as (11):

𝑅𝑝 = 𝑃𝑡𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

= 𝑑4

ℎ𝑏2ℎ𝑚2 𝐺𝑡𝐺𝑟

(11)

Two-ray ground path loss, Lp,2-ray, can be calculated by substituting (11) and (6) into (8) and simplifying as in (12-15):

𝐿𝑝,2−𝑟𝑎𝑦 = 10𝑙𝑜𝑔 (𝑅𝑝) (12)

= 10𝑙𝑜𝑔( 𝑑4

ℎ𝑏2ℎ𝑚2 𝐺𝑡𝐺𝑟

) (13)

= 10𝑙𝑜𝑔(𝑑4) − 10𝑙𝑜𝑔(ℎ𝑏2ℎ𝑚2 𝐺𝑡 (14)

= 40𝑙𝑜𝑔 (𝑑) − 10𝑙𝑜𝑔(ℎ𝑏2ℎ𝑚2 𝐺𝑡𝐺𝑟) (15)

Modeled assuming Nakagami-m fading, fast fading path loss (i.e., the Lf term in (9)), Lf,Nak, is expressed as (16):

𝐿𝑓,𝑁𝑎𝑘 = 𝐼𝑓,𝑁𝑎𝑘𝐿𝑁𝑎𝑘 (16)

where If,Nak is the fading loss indicator function (i.e., 1 = include Nakagami-m fading, and 0 = exclude) and LNak is the resulting path loss resulting from the Nakagami-m distribution in (5). Fast fading further reduces power received after propagation loss. Therefore, the fast fading output power, Po = Pr,Nak, can be calculated for an input power, Pi = Pr,prop_loss, as given in (17):

𝑃𝑟,𝑁𝑎𝑘 = 𝑓𝑔𝑎𝑚𝑚𝑎 �𝑚𝑁𝑎𝑘,𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑚𝑁𝑎𝑘, 1� (17)

where mNak is the Nakagami-m shape parameter and the Nakagami distribution is calculated using the gamma distribution (4) with v = 1, α=mNak, and β=Pr,prop_loss/mNak.

Substituting (17) into (6), where Pi = Pr,prop_loss and Po = Pr,Nak, leads to (18):

𝑅𝑓 = 𝑃𝑖𝑃𝑜

=𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑃𝑟,𝑁𝑎𝑘=

𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑓𝑔𝑎𝑚𝑚𝑎�𝑚𝑁𝑎𝑘,𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑚𝑁𝑎𝑘,1�

(18)

Further substituting (18) and (6) into (8) results in (19-20):

𝐿𝑁𝑎𝑘 = 10 𝑙𝑜𝑔�𝑅𝑓� (19)

= 10 𝑙𝑜𝑔 �𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑓𝑔𝑎𝑚𝑚𝑎�𝑚𝑁𝐴𝑘,𝑃𝑟,𝑝𝑟𝑜𝑝_𝑙𝑜𝑠𝑠

𝑚𝑁𝑎𝑘,1�� (20)

Slow fading shadowing effects of obstacles (i.e., Ls of (9)) are modeled using the Simple Obstacle model of [33], in which obstacle shadowing path loss, Ls,o, is dependent on both per-wall-attenuation and per-meter-attenuation as given generally in (21):

𝐿𝑠,𝑜 = 𝐼𝑠(𝛼𝑛 + 𝛽𝑚𝑜), (21)

where Is is the shadowing loss indicator function (i.e., 1 = include obstacle fading / shadowing and 0 = exclude), α is the attenuation per wall, in dB, n is the number of walls penetrated, β is the attenuation per meter, in dB, and mo is the distance, in meters, traveled through obstacles.

Prior to the development and contribution to the code base of our obstacle shadowing model, a deterministic obstacle shadowing model did not previously exist in ns-3. In the obstacle model, two-dimensional polygons represent obstacle boundaries which have attenuation-per-wall and attenuation-per-meter propagation loss attributes. Using the obstacle model, an obstacle-aware shadowing model leverages the Computational Geometry Algorithms Library (CGAL) [54] to count the number of walls as obstacle intersections, calculate the distance traveled through obstacles as interior intersection lengths and implement deterministically the shadowing effects of wireless transmissions for obstacle line of sight (OLOS) pathways. For performance optimizations, obstacle locations are stored in a binary space partition (BSP) and inter-vehicle obstacle obstructions are cached. Figure 4 gives the pseudo-code for the algorithm that determines for two vehicles (i.e., points) the number of obstacle walls penetrated and total distance traveled through obstacles

Fig. 4 – Pseudo-code for Algorithm

GETOBSTRUCTEDDISTANCEBETWEEN that determines the number of obstacle wall intersections and obstructed

distance between two points.

Substituting (15), (16), (20), and (21) into (9), the total path loss, Lt, is expressed as (22) and (23):

𝐿𝑡 = 𝐿𝑝,2−𝑟𝑎𝑦 + 𝐿𝑓,𝑁𝑎𝑘 + 𝐿𝑠,𝑜 (22)

= 40 𝑙𝑜𝑔(𝑑) − 10 𝑙𝑜𝑔(𝐺𝑡𝐺𝑟ℎ𝑏2ℎ𝑚2 ) +

𝐼𝑓,𝑁𝑎𝑘 �10 𝑙𝑜𝑔�𝑃𝑟,𝑝𝑟𝑜𝑝𝑙𝑜𝑠𝑠

𝑓𝑔𝑎𝑚𝑚𝑎 �𝑚𝑁𝑎𝑘,𝑃𝑟,𝑝𝑟𝑜𝑝𝑙𝑜𝑠𝑠𝑚𝑁𝑎𝑘

, 1��� +

𝐼𝑠(𝛼𝑛 + 𝛽𝑚𝑜) (23)

A VANET simulation script previously completed by the author and contributed to ns-3 conducts the overall VANET simulation. For each simulation, the mobility trace files produced by SUMO simulations play back for 200 seconds of

network simulation time, during which network characteristics are modeled in ns-3.

RQ2: How does an obstacle model effect packet delivery ratio versus other VANET fading and shadowing models?

Developed and implemented in the ns-3 network simulator, the models in (9) assess the impact of fading and shadowing in a VANET, and experiments quantitatively compare them. For example, identical experiments are repeated and results compared using different path loss models for:

i) two-ray ground propagation loss only,

ii) two-ray ground propagation loss and Nakagami-m fast fading, and

iii) two-ray ground propagation loss and obstacle shadowing.

RQ3: What is the impact to VANET application safety performance of a deterministic obstacle fading model, as compared to other fading and shadowing models?

Simulation trials capture comparative evaluation metrics, such as packet delivery ratio (PDR), after which each experiment computes an analytical safety performance assessment, extending the approach in [3]. Using a target safety performance threshold of 95%, we compute the safety communications range for 5, 7, and 9 out of 10 messages per second, leading to a quantitatively comparative assessment of the highway, residential, and urban scenarios.

IV. EXPERIMENTAL SETUP AND RESULTS

A. Experimental Setup To evaluate our methodology, simulation experiments

assess safety communications awareness range as vehicular and obstacle densities vary. The open highway, residential neighborhood, and urban downtown environments use the OSM topographic data [51] for the surrounding Raleigh, NC, USA area. These three scenarios provide different topologies and densities of roadway networks, and obstacles such as buildings and foliage. Figure 5 shows representative satellite imagery views from GoogleEarth™ and corresponding vehicle and building/obstacle-only simulation views in SUMO. The open highway scenario is selected from an interstate area near the Raleigh-Durham International Airport (RDU) that includes a few traffic-light-controlled off-interstate areas and buildings, although the densities of both are much less than the residential and urban downtown scenarios.

Routes within each environment are randomly selected and vary from 50 to 250 in steps of 50. Each vehicle traverses a minimum 1.2 km route with source and destination edges chosen randomly such that traffic originates with greater probability on the fringe of the road network and follows longer, wider roads. Vehicle-route start times are staggered in 0.01s increments. Vehicles are assumed to enter the scenario from external locations using a reasonable lane and speed and thereafter obey traffic light signals (TLS) and car-following rules according to the Krauss model [53]. Vehicular simulation experiments executed in SUMO 0.19 [49] generate corresponding ns-2 movement files for later playback in the ns-

Algorithm GETOBSTRUCTEDDISTANCEBETWEEN (p1, p2, B) Input. Locations, p1 and p2, of two vehicles and a pre-determined binary search partition (BSP), B,

of obstacles. Output. The total obstructed distance, mo, and the number of obstacle edge intersections, n. 1. mo ← 0; n ← 0 2. Initialize a maximum range, r, the distance from either point p1 or p2 to an obstacle center-

point, that is used to filter the set of obstacles to the subset which are sufficiently nearby for calculation purposes (i.e., for optimization, exclude far-away obstacles).

3. Create a bounding box, b, for p1 and p2 and extend in all direction by r. 4. Get the set of potential obstacles, O. O ← the range search of b within B. 5. For every obstacle o ∈O, do: 6. if the distance from p1 or p2 to the obstacle center is within range, r, then 7. for each edge s ∈o, 8. if s intersects a ray from p1 to p2 9. n ←n + 1 10. save the min. and max. distances from {p1, p2} to the intersection pt. 11. mo ←mo + distance between min., max. values in step 10 (i.e., obstructed distance) 12. return mo and n

3 network simulator. Figure 6 shows the process flow for the experimental setup.

(a)

(b)

Fig. 5 – GoogleEarth™ view (a) and SUMO view (b) of the open highway scenario near Raleigh, NC USA.

To assess the impact of obstacle-awareness on safety performance, IVC simulation of IEEE WAVE and 802.11p devices use the vanet-routing-compare script, introduced in ns-3. For each scenario, vehicles travel random routes captured in the vehicular mobility files generated using SUMO for 200 seconds of simulation time. While all vehicles begin their routes within the first few seconds of the start of simulation time, very few (i.e., less than 10%) reach their destination before the simulation time ends. All vehicles transmit a 200-byte BSM at 10 Hz using transmission power of 20 dBm and are assumed to be GPS time-synchronized with uniformly distributed accuracy of 1-10 µs. Continuous-channel access to a single 10 MHz channel in the 5.9 GHz range is assumed using IEEE 802.11p and IEEE 1609/WAVE standards as implemented in ns-3. Network simulation parameters are summarized in Table 1.

Fig. 6 – Process flow for experimental setup.

Table 1 – Network simulation parameters.

Effectiveness is measured by comparing the packet delivery ratio (PDR) of the actually received packets and the expected packets, within a coverage radius of the transmitter (e.g., 50m, 100m, 200m, 300m, 400m, 500m, 600m, 800m, 1000m, and 1500m). Safety performance is evaluated by equating the node reception probability (NRP) to PDR and analytically evaluating the awareness probability and ultimately the awareness range.

B. Results and Discussion Simulations were conducted in ns-3 of open highway,

residential neighborhood, and urban downtown VANET settings with 50-250 vehicles traveling random routes. Propagation loss was modeled using the two-ray ground model, with additional fast fading effects modeled using the obstacle fading model of this research which is compared to the stochastic Nakagami-m fading model as well as results without any fading effects. For each configuration, 30 trials were executed and resulting PDR results were captured from simulation for transmission ranges of 50 – 1500 m that are assumed to be normally distributed and were statistically combined to produce means and 95th percentile confidence interval bounds.

Open Street Map SUMO

Raleigh.osm

Raleigh_buildings.osm

netconvert

Road network file

Urban highway settings

randomTrip.py

Routes

polyconvert

Obstacles (xml file)

SUMO

Simulation results (Floating Car Data file)

traceExporter2.py

ns-2 mobility file

ns-3

Simulation configuration

VANET simulation results

www.openstreetmap.org

Parameter Value UnitsBSM size 200 bytesBSM rate 10 Hz

Transmit power 20 dBmFrequency 5.9 GHz

Channel bandwidth 10 MHzChannel access 802.11p OCB

Tx range 50 - 2000 mSync time accuracy 1-10 us (uniform)

Encoding OFDMRate 6 Mbps

Propagation loss model

Two-ray ground

Simulation time 200 s

PDR is the expected long-term average probability of receiving one packet and decreases when:

i) distance increases (due to increased propagation path loss),

ii) vehicle density increases (due to increased collisions),

iii) fading effects are included (due to increased path loss), and

iv) obstacle density increases (due to increased shadowing path loss).

Figure 7 charts the average PDR for the highway scenario and shows that PDR decreases as transmission range increases and is further affected by fading. The PDR for obstacle shadowing in the 200-600m range shows a tendency towards limited faded effects, while PDR otherwise implies a stronger fading effect. This is explained by more frequent unimpeded communications that do not exhibit fading effects along the open highway road segments (i.e. 200-600m), as in Fig. 5(b), resulting in the higher chance of successful safety exchanges.

Fig. 7 – PDR for the open highway scenario with 250

vehicles, for three different fading models as a function of the distance from the transmitter.

C. Safety Awareness In both the residential and downtown scenarios (figures

omitted due to space limitations) PDR drops off rapidly for short (i.e., < 100m) ranges for both the no fading and Nakagami-m fading models. This is because higher intersection densities of vehicles simultaneously emitting safety messages can saturate the channel with resulting transmission collisions degrading PDR. However, if obstacles sufficiently reduce power such that some messages are blocked from receipt, then the lower localized amount of background noise increases the signal-to-noise ratio and improves the chance of reception for those unobstructed signals that receivers can sense. In effect, obstacles increase spatial reuse. Similar to the effects experienced by party guests that relocate to more quiet places to continue conversation, often placing walls between themselves and other, louder side-conversations, so to do obstacles such as building block inter-vehicle communications thus improving localized PDR, an effect

called The Dinner Party Effect. However, while obstacle shadowing can therefore improve PDR and ultimately awareness range, there is also a downside in that messages that are prevented from reaching recipients could jeopardize safety.

(a)

(b)

(c)

Fig. 8 – Awareness ranges for receiving n out of 10 safety beacons per second for the open highway scenario and

different fading models for (a) n = 5, (b) n = 7, and (c) n = 9.

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Fig. 9 –The difference between awareness range (ΔRA) for (a) open highway, (b) residential neighborhood, and (c)

urban downtown scenarios.

Using PDR from the simulation results, awareness probability (PA) is computed as in (3) for receiving n = 5, 7, and 9 out of 10 messages per second. Awareness range (RA) is then determined analytically from PA for each fading scenario and compared among them. As seen in the results for the highway scenario (i.e., Fig. 8(a-c)), awareness range decreases as application performance requirements (i.e., n) increase. Furthermore, the tendency for the awareness range in open highway to behave more like the no fading model is more obvious when the awareness range is closer to 400m (i.e., Fig.

8(a) and (b)) than when the awareness range is less than 200m (i.e., Fig. 8(c)) and obstacle shadowing results are more in line with the Nakagami-m fading model. This follows from the tendency for PDR in the 200-600m range for this scenario to exhibit minimal fading effects.

Lastly, the difference between awareness range (RA) for obstacle fading and no fading, ΔRA, is shown and plotted in Fig. 9. Here, negative numbers mean that obstacle fading gives an awareness range less than the awareness range resulting when no fading is assumed. In the highway results (i.e., Fig. 9(a)), the lack of buildings results in only limited reduction in awareness range. When there are no obstacles and low vehicular density, awareness range is considerably shorter (e.g., 300 - 350m) in the residential neighborhood results (i.e., Fig. 9(b)) and the urban downtown scenario (i.e., Fig. 9(c)).

D. Performance

Fig. 10 –Clock-time (minutes) of the sum of times to

simulate 100 vehicles in the highway, residential, and downtown scenarios for different fading models.

Simulations were conducted using the equipment from the High Performance Computing services at North Carolina State University [55]. As shown in Fig. 10, although the average times indicate that the expected overhead of using the deterministic obstacle shadowing model is greater than the stochastic Nakgami-m fading model, the confidence intervals imply that the simulation performance of the obstacle shadowing model in on the order of using the Nakagami-m fading model.

V. CONCLUSIONS AND FUTURE WORK

A. Conclusions Safety performance assessments improve when models

accurately reflect environmental conditions such as the fading effects of radio wave propagation through buildings and other obstacles. The effective awareness range decreases as building density increases; building-rich environments such as urban downtown show shorter awareness ranges as compared to more open expanses of highway settings. Furthermore, as VANET safety applications require higher awareness probabilities and more messages received per time window, the awareness range of the application decreases. Thus, to maintain safety-application limits PDR must remain sufficiently high and

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packet reception must overcome propagation and fading effects. In fact, obstacle fading effects from buildings in a downtown scenario can significantly reduce awareness range, especially for VANET application with high safety requirements. Results based on stochastic Nakagami-m fading fail to realistically differentiate between highway, residential, and urban settings and differ from the deterministic results obtained using the obstacle fading model.

An obstacle model and a fading model that uses it, along with a VANET simulation script, have been implemented and contributed to the open source ns-3 network simulator community. The models are shown to execute efficiently with simulation overhead on the order of the stochastic Nakagami-m fading model. Results generated from simulation experiments that use these models show that deterministic obstacle fading can greatly degrade PDR and compare differently than stochastic Nakagami-m fading. Such network-level results can have dramatic impacts to safety performance assessment, such as awareness range. Failing to account for the effects of obstacles in safety assessment can therefore inaccurately or even greatly overstate the safety performance of VANET safety applications. Including realistic obstacle fading in VANET simulation modeling improves VANET safety assessment and strengthens safety, thus supporting one of the primary goals of connected vehicle systems.

B. Future Work Although our framework for simulating in ns-3 an IEEE

802.11-enabled VANET shows that the influence of obstacle shadowing can significantly influence safety performance, actions that further improve safety awareness range can be pursued.

Firstly, evaluations can be conducted to assess the potential safety improvement of using RSUs and other infrastructure to act as information relays in especially dangerous roadway situations, such as uncontrolled and urban intersections, and dangerous “Dead Man’s Curve” scenarios. Simulation scenarios thus far have included only beaconcasting vehicles and could therefore benefit by including road side units (RSU) that collect and disseminate application-specific data. SAE Std J2735 envisions the Signal Time and Phase (SPAT) application [16], which relies on an RSU to coordinate vehicular movements through intersections. While message receipt is obviously critical to the coordination efforts for intersection movement, the impact to safety of obstacles nearby the intersection has not been fully studies with respect to infrastructure usage.

Secondly, the absence of infrastructure presents challenges to safety information spread. SAE Std J2735 describes the Stop-Sign Movement Assist application [16] to assist vehicles in infrastructure-less, uncontrolled intersections. Such scenarios may benefit when vehicles act as relays to propagate the safety messages of other vehicles. While many geo-routing protocols have been proposed for VANET, the US standards do not require them. Therefore, it is possible that safety may be improved by using obstacle awareness proactively to more efficiently disseminate regionalized safety information. By reducing the potential for link failure, long-lived routes can be established a priori.

Thirdly, in the presence of saturated channels, congestion mitigation algorithms may be further explored to compare mitigation techniques of adaptive data rate against other more common proposals. While congestion control mitigation is not part of the current standards, research is active towards recommendations. Current algorithms under evaluation leverage adaptive power and transmission intervals that act by avoiding and/or quieting certain safety information message transmissions. The lack of information exchanges by reducing safety message transmission rates may jeopardize safety, especially in obstacle-dense environments.

Fourthly, inter-vehicle communication data collection and processing techniques could produce (and update) safety-awareness regional maps so that accurate communication-challenged areas could be disseminated subsequently to vehicles. For example, information describing the (especially limited) communications potential of a region being approached (i.e., a forthcoming intersection, or dangerous curve) may be downloaded in advance to a vehicle, similar to the downloading of GPS data, or construction zone awareness as already envisioned by VANET applications..

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