These priorities are static and do not account for network load or vehicle's context severity. In this paper, we propose a novel opportunistic service reprioritization ...
Context Severity Based Opportunistic Service Reprioritization for IEEE 802.11p VANETs Mohammad A. Salahuddin, Ala Al-Fuqaha, †Frederic Jacquelin and †Yohan Shim Department of Computer Science, Western Michigan University, Kalamazoo, MI, USA {mohammad.salahuddin, ala.al-fuqaha}@wmich.edu †
Scenaria, Inc., Plymouth, MI, USA {frederic.jacquelin, yohan.shim}@scenaria.com Abstract—IEEE 802.11p Wireless Access for Vehicular Environments (WAVE) is the approved communication protocol for Vehicular Ad–hoc Networks (VANETs) and Intelligent Transportation System (ITS) applications. WAVE offers service differentiation by prioritizing packets based on an application’s requested QoS. These priorities are static and do not account for network load or vehicle’s context severity. In this paper, we propose a novel opportunistic service reprioritization (OSR) technique for IEEE 802.11p (WAVE). It dynamically promotes and/or demotes vehicle’s load to different access categories, by taking into account the vehicle’s context severity and network link layer bounds. We show the feasibility of our approach by formulating the opportunistic service reprioritization technique as a Linear Programming (LP) problem and solve it to guarantee optimal QoS with respect to severity for all access categories. We compare our opportunistic service reprioritization technique with WAVE and show significant improvement. The weighted average delay in OSR outperforms classical WAVE, on average by 90%. Index Terms—Context Severity, IEEE 802.11p, Opportunistic Service Reprioritization, Service Differentiation, VANETs, Vehicular Ad hoc Networks, WAVE, Wireless Access in Vehicular Environment
I
I. INTRODUCTION
EEE 802.11p enables Wireless Access for Vehicular Environments (WAVE) [1] in Vehicular Ad hoc Networks (VANET) and Intelligent Transportation Systems (ITS). A VANET consists of mobile and fixed nodes, such as the vehicle nodes and the infrastructure nodes, like the road–side equipment (RSE). VANET vehicles have an on–board unit (OBU) that comprises of positioning systems, processing units and radio transceivers. Communication in VANETs is decomposed into vehicle– to–vehicle (V2V) and vehicle–to–infrastructure (V2I) communication, where vehicles communicate with each other and with road–side equipment (RSE) and infrastructure, respectively. Various applications, such as, safety, efficiency
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and infotainment, have been developed specifically for ITS and its users. ITS applications are categorized into different classes. WAVE provides the low latency V2V and V2I communication necessary in this high speed, mobile environment. WAVE has been designed to utilize the categorization of the ITS applications, so that it can provide different QoS, with respect to delay, to these applications. WAVE provides this service differentiation by manipulating the medium access control (MAC) parameters to prioritize the load and queue it into different access categories, which provide the different delays. Higher priority access categories provide lower delay. However, this static load assignment to access categories does not account for network load or vehicle’s context severity. Moreover, in high traffic and/or high vehicle density scenarios WAVE incurs significant delay degradation [2]. Authors in [3] have identified maximum acceptable delays for time–critical applications, and the delay degradation in congested scenarios of WAVE may exceed these limits, compromising safety. We propose a novel scheme to dynamically reprioritize the load for the access categories based on network load and the context severity of vehicles. A technique that reflects on the network load, when assigning load to access categories, can opportunistically assign load from lower priority access categories to higher priority access categories. A significant contribution of our work includes, bounding the delay of the access categories so that we can guarantee QoS, with respect to delay for load on the different access categories. This is a major limitation in WAVE [4]. Furthermore, our proposed scheme has been carefully designed to ensure that load from higher severity vehicles is given priority and assigned to higher priority access categories, while improving the QoS for all vehicles in the network. It is important to note that we are proposing an enhancement to WAVE rather than suggesting a new MAC protocol for WAVE. In our opportunistic service reprioritization (OSR) technique, vehicles are context aware and can infer a context severity metric. The context severity metric accounts for various vehicle parameters, such as, speed, acceleration,
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directional stability, etc. Vehicles send their context severity metric and load decomposition, with respect to access categories, to an aggregator RSE. The RSE finds the optimal assignment of load redistribution to access categories that improves the QoS, with respect to delay, for all vehicles, while prioritizing load from higher severity vehicles. This load decomposition and assignment is returned to the vehicles as the new operating parameters. Now, vehicles reprioritize their load based on the new operating parameters received from RSE, which account for network load and context severity. Figure 1, illustrates how our opportunistic service reprioritization scheme can be conceptualized for a vehicle . OSR reroutes load after the channel routing and before the MAC contention and transmission attempts. It depicts the promotion and/or demotion of a vehicle’s load from an access category, based on the new operating parameters received denotes the load that was from the RSE. For example, for vehicle , based on classical initially assigned to WAVE operating parameters. However, our OSR can opportunistically promote or demote this load partially or completely to other access categories. For example, is the load that is opportunistically promoted from to based on the vehicle’s context severity metric and the network load. MAC Contention
AC1 AC2 AC3
II. RELATED WORK
Transmission Attempt
Channel Routing (CCH/SCH)
Traffic Reprioritization
It is intuitive that the context severity of a vehicle should be an integral component of WAVE service differentiation and should dictate the QoS with respect to delay for the vehicle. This will improve the performance of time critical safety applications. Furthermore, the network load should be leveraged, to dynamically reprioritize the traffic and maximize the utilization of higher priority access categories and their lower delays, while meeting delay bounds. In this paper, we will show that these parameters along with our opportunistic service reprioritization scheme can guarantee bounded delay for higher priority access categories, giving higher context severity vehicles higher priority, while improving QoS with respect to delay for all vehicles. Our results will show that OSR outperforms WAVE with respect to delay, while accounting for essential vehicular network parameters of network load and context severity, which are missing in WAVE. The rest of the paper is organized as follows. In Section II, we present an overview of related work in the area of service differentiation and delay in WAVE. In Section III, we define a linear programming problem to model our opportunistic service reprioritization technique and show applicability and feasibility of our approach. Section IV discusses our results and compares them with classical WAVE, to show significant improvement of our OSR over classical WAVE. In Section V, we summarize our work and give future research direction.
AC4
IEEE 802.11p MAC Fig. 1. Our Opportunistic Service Reprioritization for WAVE.
Our OSR scheme has been critically designed to leverage demotion of load from a higher priority access category to a lower priority access category, while striving to improve the QoS for all vehicles in the network. We will show that demotion of load, opportunistically and with respect to vehicle context severity, can still improve QoS, with respect to delay, for all vehicles in the network, while outperforming classical WAVE. Therefore, we propose an innovative opportunistic service reprioritization technique that not only complements WAVE but enhances it to account for two essential parameters; network load and context severity of vehicles.
Numerous studies ([5], [6], [7], [8]) are underway to devise new and ingenious medium access control (MAC) layer protocols that are more suitable, efficient, or secure for WAVE. However, our proposed opportunistic service reprioritization technique complements and enhances the existing MAC layer protocol in classical WAVE, which has been established as the standard communication protocol for vehicular environments. Amadeo et al. [4] have devised an innovative MAC layer protocol that enhances the ability of WAVE to provide better QoS for non–safety applications. While [9] develops enhancements to WAVE to reduce the delay for safety applications. Our OSR strives to improve the QoS for all categories and applications, while prioritizing load from higher context severity vehicle applications. Furthermore, [4] uses the position of vehicles to ensure effective utilization of the resource, while we use the contextual information inferred from the position of a vehicle in our approach. However, both approaches bound the delay, unlike the unbounded delay of classical WAVE, which compromises time sensitive applications. Chrysostomou et al. [10] designed a dynamic tuning of contention window parameters to offer service differentiation in WAVE and improve the QoS with respect to throughput. The authors in [11] also design adaptations of the WAVE MAC protocol by using dynamic contention windows and vehicle mobility parameter of average speed to provide service differentiation. Others [12] have analyzed and evaluated the MAC layer parameters for WAVE.
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However, OSR uses the standard WAVE contention window and MAC layer parameters. It uses more complex vehicle mobility parameters, such as acceleration and directional stability, to conjure a context severity metric for vehicles in the network. OSR provides service differentiation based on the context severity metric and network load.
Subject to
A. Problem Given a set of vehicles with context , ,…, and traffic loads , ,…, , ,…, severities and access categories ( ) each with a delay bounded . Find an optimal assignment of vehicle loads to by access categories, such that, it improves the quality of service (QoS) with respect to context severity for all vehicles, while . meeting delay bounds We formulate this with continuous variables as a linear programming (LP) problem. In the sections below, we define the variables and present the formulation along with its discussion. B. Definitions Number of Number of vehicles V Upper bound on delay for
,
,
,…,
from
,
≡
,…,
≡
is the load promoted
is the load demoted from
to Delay for
(3)
1,
∀ ,1
,
1
1
1
is the mean number of retries and
D. Formulation Objective
(4) (5)
is the mean
1 Δ ∑
(7)
1 Δ ∑ where, the inter–arrival time is inversely proportional to the load on the , that is, sum of load on from all , and transmission delay Δ vehicles ∑
. The mean delay per retry is inversely
proportional to the rate of backoff 1
∀ , 1 ∀ ,
∑ 1
(2)
1
to ,
1
E. Discussion The objective of the LP formulation is to minimize the severitydelay product for all vehicles across all access categories. This will improve the QoS with respect to severity for all vehicles. The opportunistic nature of our service differentiation and load reprioritization technique is captured by the minimax objective in the formulation. This goal entails a constant effort to balance the delay across all access categories to achieve optimal severitydelay product for all vehicles and access categories. Therefore, it is inherent in our design to first efficiently utilize access categories by assigning high severity load to high priority (i.e. low delay) access category and second to opportunistically assign load to them to achieve equilibrium in delay. This improves the QoS for all vehicles. The delay incurred by traffic in each access category is due to contention at the medium access control ( ) layer. We compute the delay as defined in Equation (6), which has evolved from [13]. 1 (6)
C. Variables
(1)
delay per retry. The mean number of retries is inversely proportional to the probability of finding the channel idle. This probability is
Rate of backoff for Load from for Context severity of
∀1
Where,
Transmission delay,
Δ
∀1
III. OPPORTUNISTIC SERVICE REPRIORITIZATION AS A LINEAR PROGRAMMING PROBLEM In this section, we will define a mathematical model for our opportunistic service reprioritization technique to complement WAVE. First, we will give an unambiguous definition of the problem. Then, we will model opportunistic service reprioritization as a linear programming problem. The solution to the linear programming problem will not only guarantee optimal results but also demonstrate the feasibility of our approach. The optimal results will also serve as a benchmark for real–time heuristics of our technique. However, implementation of real–time heuristics for OSR is beyond the scope of this paper.
∀ , 1 2
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which is given as (8)
is the average contention and arbitration inter–frame space ( )
where, window size for is [14] for
Substituting (7) and (8) into (6), we get, 1 Δ ∑ .
.
(9)
(10)
, instead it is demoted to first, in an effort demoted to to offer the next best QoS. Therefore, the new load on is the sum of the loads from for , the load promoted and demoted to , less the and , respectively. load promoted and demoted to based on this new Constraint (1) captures the delay for redistributed load and Equation (10).
is directly proportional to the load on Therefore, delay the access category and inversely proportional to rate of backoff . Table I ([1], [14]) delineates the WAVE parameters used to compute . It is evident from Table I, that for 4. This implies that the same when compared load incurs a lower delay if assigned to , and so on. Thus, a lower index access category has to higher priority than a higher index access category. WAVE service differentiation is accomplished due to the difference in rate of backoff for the different . TABLE I WAVE QOS PARAMETERS Parameter
3 6 9 13 µs
AC1
AC2
3, 7
,
7, 15
,
15, 1023
,
15, 1023
AC4
AC3
Fig. 2. Promotion and demotion links to redistribute load amongst
.
IV. RESULTS
32 µs ,
for by . It is Constraint (2) bounds the delay to ensure QoS with important to carefully choose , that is, the respect to delay and priorities. Note that lowest priority is unbounded to accommodate load, which overflows from higher priority . Constraints (3), (4) and (5) are used for correct redistribution of load amongst the access categories.
Value 2
Our service differentiation technique augments WAVE’s service differentiation by reprioritizing packets based on the context severity of vehicles. The fundamental fact that lower index access categories incur lower delay implies that load from higher severity vehicles should be assigned to access categories with lower indices. We achieve this by employing severitydelay products that have to be minimized. Since the severity metric is constant for each vehicle , to minimize a high severitydelay product, the delay has to be minimized. This implies that load from low priority access categories with the high delay, should be pushed to higher priority access categories, with the lower delay, to minimize the severitydelay product. To enable the loads to be moved from one access category to another, we deploy a series of links between each access category. This conceptualizes the promotion or demotion of load between access categories, as illustrated in Figure 2, where is the load promoted or and to , respectively. demoted from The links have been carefully designed to systematically redistribute the load amongst the access categories, such first promotes or demotes to its neighbor in the chain that load is not directly as illustrated in Figure 2. For example,
In this section, we will compare our opportunistic service reprioritization technique to WAVE and show that it significantly outperforms classical WAVE and accounts for context severity of vehicles. We use typical parameters presented in Table I and II [15] for evaluation of OSR and WAVE. We used lp_solve [16] for optimization. In our scenarios, we uniformly distributed vehicles across four classes of context severity. Each vehicle generated the same total load that was randomly distributed in the four access categories. TABLE II NETWORK PARAMETERS Parameter
Value 400 bytes 27 Mbps 10 packets/sec 100 ms 200 ms 300 ms
Figure 3, illustrates the significant benefit of OSR when compared to classical WAVE. We show that the weighted average delay of OSR is significantly smaller than WAVE. OSR has a two–fold benefit. First, higher severity vehicles are guaranteed best QoS with respect to delay and secondly, lower severity vehicles are opportunistically assigned higher priority access categories without compromising QoS, thus improving
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the QoS with respect to delay for all vehicles. Furthermore, as the number of vehicles increase, the load increases, resulting in higher delay. However, the rate of increase in delay is higher in WAVE when compared to OSR. Furthermore, it shows the scalability of OSR with large number of vehicles. We show that, on average, OSR outperforms classical WAVE, with respect to, weighted average delay by 90%. Weighted Average Delay, OSR Weighted Average Delay, WAVE
600
100% 90%
500
Load Distribution (%)
Weighted Average Delay (ms)
700
vehicles first to higher priority , as in Figure 5. In doing so, we improve the QoS with respect to delay for all vehicles and access categories. It is important to note that to achieve optimal load assignment with respect to severity, we may promote lower severity vehicle load to higher priority access category since it achieves the minimum severitydelay product while maximizing the load assignment to an access category.
400 300 200 100 20
40
60
80 100 120 140 160 180 200 Number of Vehicles
40%
S2
30%
S1
20%
AC2 AC3 Access Category
AC4
Fig. 5. Load decomposition, with respect to severity, in the access categories.
Figure 6, compares the delay of classical WAVE and OSR for 1000 vehicles, with respect to maximum tolerable delays for ITS applications. For example, authors in [15] identify the maximum tolerable delay for safety applications, like intersection collision avoidance, to be 100 ms. 100000
OSR Delay WAVE Delay Tmax AC1 (100 ms) Tmax AC2 (200 ms) Tmax AC3 (300 ms)
10000
OSR Initial
Delay (ms)
Load Distribution (%)
S3
50%
AC1
Figures 4 and 5 illustrate the opportunistic load redistribution effect of OSR with 200 vehicles and four distinct classes of severity, S1, S2, S3 and S4, in decreasing order of severity, respectively. It is evident from these results, that our proposed approach pushes load to higher priority in access categories by assigning them greater load, as in Figure 4.
80%
S4
60%
0%
Fig. 3. OSR outperforms WAVE and has a smaller rate of increase in delay with respect to number of vehicles.
90%
70%
10%
0
100%
80%
70% 60% 50% 40%
1000 100 10
30% 20%
1
10%
AC1
0% AC1
AC2 AC3 Access Category
AC4
AC2 AC3 Access Category
AC4
Fig. 6. QoS with respect to delay bounds on access categories.
Fig. 4. OSR load redistribution amongst the access categories.
The load was redistributed amongst the access categories to minimize the severitydelay product for all vehicles and access categories. This is achieved at delay equilibrium, while abiding by delay bounds, and assigning higher severity
Therefore, if we bound delay on by 100, then our OSR technique guarantees this delay for . However, classical WAVE exceeds this bound, degrading the performance of time critical safety applications for . Similar degradation of service is visible for . Note, to meet the delay bound, load is distributed via promotion and/or
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demotion to achieve the optimal load assignment to access categories with respect to context severity and delay. We illustrate the significance of dynamic load redistribution in Figure 7. Assume that has a high load volume. Our OSR will significantly outperform classical WAVE since it can dynamically redistribute load, demoting it to other lower priority access categories. Classical WAVE has static priorities and access category assignment, which results in degradation of delay. It can also be seen that delay is bounded by from below, as from Equation (10). 120
OSR Delay WAVE Delay T max AC1
Delay (ms)
100
technique but also show promising enhancements for classical WAVE. It also provides a benchmark for the development of future real–time heuristics and extensive simulation of the OSR technique. Our future work also includes scrutinizing the parameters that would ensure an effective context severity metric, and designing a fuzzy inference system for them. This would be encompassed in a detailed architecture and framework for the OSR technique, including software and/or hardware tradeoff, performance and complexity analysis. Thorough simulation analysis would be conducted to compare the performance of applications w.r.t QoS and time, against vehicle density and context severity. REFERENCES [1]
80 60 40
[2]
20
[3]
0 AC1
AC2 AC3 Access Category
[4]
AC4
[5]
Fig. 7. Delay equilibrium in OSR compared to service deterioration in classical WAVE.
V. CONCLUSION
[6]
In this paper, we instigate the use of two essential vehicular network parameters, specifically, network load and context severity of vehicles, to devise an innovative technique to dynamically reprioritize load in access categories and achieve significant improvement in delay over classical WAVE. Our opportunistic service reprioritization technique has multi–fold benefits over classical WAVE. First, it prioritizes load based on context severity of vehicles in the network. It is evident that higher severity vehicles should be given better QoS, with respect to delay, than lower severity vehicles. Second, OSR fully utilizes higher priority access categories and their inherent lower delays by accounting for network load. This utilization is due to the dynamic and opportunistic reprioritization of load to higher priority access categories. Third, OSR guarantees bounded delay for the first three access categories, whereas, WAVE significantly deteriorates in service in high vehicle density and/or large load scenarios, compromising the integrity of time critical safety applications. This technique not only achieves a lower latency than classical WAVE but also improves the QoS, with respect to delay, for all vehicles in the network. Our methodology entails formulating OSR technique as a linear programming problem, to show its applicability and feasibility. We solve the problem optimally and prove our claims. These results not only show the feasibility of our
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