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149 CONCLUDING REMARKS Current advances in the eld of wireless ad hoc networks show that intervehicle communication (IVC) based on vehicular ad hoc networks (VANET) is a feasible approach that has a competitive edge over other technologies such as cellular networks with respect to several aspects: low latency for emergency warnings, high reliability due to link redundancy, and low cost of usage due to the lack of dependency on infrastructure and the use of unlicensed frequency hands These features are ideal for the applications envisioned for the Intelligent Transportation Systems (ITS) VANETs can support vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communication, which will enable vehicular safety applications including collision warning as well as other ITS applications like local traf c information for routing, high-speed tolling, and mobile infotainment Some of the properties that distinguish VANETs from general ad hoc networks can affect their design signi cantly.

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minus<int> intSub; ires = intSub( ival1, ival2 ); dres = BinaryFunc( minus<double>(), dval1, dval2 );

multiplies<complex> complexMultiplies; cres = complexMultiplies( cval1, cval2 ); dres = BinaryFunc( multiplies<double>(), dval1, dval2 );

divides<int> intDivides; ires = intDivides( ival1, ival2 ); dres = BinaryFunc( divides<double>(), dval1, dval2 );

modulus<int> IntModulus; Ires = IntModulus( Ival1, Ival2 ); ires = BinaryFunc( modulus<int>(), ival2, ival1 );

P(i)

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It is expected, at early stages of market introduction, that only a small fraction of vehicles will support communication capabilities Such conditions will have a signi cant impact on information propagation due to network partitioning Therefore, message dissemination have to rely on data buffering, repeated broadcast, and opportunistic forwarding Also due to the partitioned, highly dynamic nature of these networks, it is dif cult to maintain a global view of the network Moreover, in many ITS applications, the destination vehicles are not known in advance Thus, routing based on geographic multicast is more desirable, where only knowledge about immediate neighbors is retained The target area for message propagation is application-dependent and is determined by the level of interest in the message VANETs also exhibit a rapid phase change from free- ow to congested networks due to traf c jams.

negate<int> intNegate; ires = intNegate( ires ); Ires = UnaryFunc( negate<int>(), Ival1 );

The Relational Function Objects The predefined relational function objects support equality, inequality, greater than, greater than or equal, less than, and less than or equal

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equal_to<string> stringEqual; sres = stringEqual( sval1, sval2 ); ires = count_if( svecbegin(), svecend(), equal_to<string>(), sval1 );

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Extensive research has focused on the impact of vehicle density on network connectivity, transmission range, capacity, and dissemination of messages by assuming a homogeneous distribution of vehicles Yet, little has been done to investigate the effect of nonhomogeneous distribution of vehicles, due to traf c jams and road constraints, on these factors The VANET research community is increasingly aware of the limitations of the traditional mobility models These limitations are manifested in the lack of representing interactions among vehicles and de ning a relationship between node density and speed There are several recent attempts to develop realistic vehicle mobility models based on car-following models or use off-the-shelf traf c simulators to generate realistic vehicle movement for VANET research Since many application of VANETs aim to change the drivers behavior in response to traf c-related messages, an ideal VANET simulator should include a mobility model that responds to such messages..

not_equal_to<complex> complexNotEqual; cres = complexNotEqual( cval1, cval2 ); ires = count_if( svecbegin(), svecend(), not_equal_to<string>(), sval1 );

greater<int> intGreater; ires = intGreater( ival1, ival2 ); ires = count_if( svecbegin(), svecend(), greater<string>(), sval1 );

greater_equal<double> doubleGreaterEqual; dres = doubleGreaterEqual( dval1, dval2 ); ires = count_if( svecbegin(), svecend(), greater_equal<string>(), sval1 );

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less_equal<int> intLessEqual; ires = intLessEqual( ival1, ival2 ); ires = count_if( svecbegin(), svecend(),

14.10 EXERCISES 1. Derive Eq. (14.5). 2. Equation (14.5) was derived with the assumption that all vehicles are equipped with wireless transceivers and are willing to participate in the network. Remove this restriction by assuming that only a fraction of vehicles participate in the network and derive lower-bound MTR in terms of the total vehicle density. 3. Algorithm 1 relies on estimating the vehicle local density as shown in reference 25. Study the reference and provide a list of proposed enhancements to existing vehicular ad hoc protocols using local density estimates. 4. Discuss the parameters that have to be considered for routing in a VANET. 5. Figure 14.4 might be used to render a policy for the transmission power of a wireless unit. Identify the variables and any derivations from those variables which will lead to a power control strategy for VANET units. 6. Common ad hoc mobility models, such as the Random Waypoint model, do not have a provision for node density. What are the conditions in which it is possible to use these mobility models to generate vehicle movements 7. The NaSch model [20, 21] generates an accurate traf c behavior at the macroscopic level. However, the model s xed car size, low vehicle speed resolution, and simplistic acceleration model reduce its accuracy at the microscopic level. Identify the model s limitations and suggest ways to enhance the model to re ect more accurate microscopic car movement.

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