A Probabilistic Analysis of Path Duration Using Routing Protocol in VANETs

Abstract

In recent years, various routing metrics such as throughput, end-to-end delay, packet delivery ratio, path duration, and so forth have been used to evaluate the performance of routing protocols in VANETs. Among these routing metrics, path duration is one of the most influential metrics. Highly mobile vehicles cause frequent topology change in vehicular network environment that ultimately affects the path duration. In this paper, we have derived a mathematical model to estimate path duration using border node-based most forward progress within radius (B-MFR), a position based routing protocol. The mathematical model for estimation of path duration consists of probability of finding next-hop node in forwarding region, estimation of expected number of hops, probability distribution of velocity of nodes, and link duration between each intermediate pair of nodes. The analytical results for the path duration estimation model have been obtained using MATLAB. The model for path duration estimation has been simulated in NS2. Each of the analytical results has been verified through respective simulation results. The result analysis clearly reveals that path duration increases with the increase in transmission range and node density and decreases with the increase in the number of hops in the path and velocity of the nodes.

Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

“…(2) for = 1 to sr // Generating CRV (4) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (5) endfor (6) for = 1 to sr // Generating OV (7) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (8) generate CRK vector (VIC, IC, RC, CC) randomly and assign weight according to ranks (9) calculate ST using (2) (10) calculate ERT using (3) (11) endfor (12) Partition ( , , , V ) into sub-networks as = ⋃ =1 (13) for = 1 to (14) Divide time horizon into time seeds as = 1, + 2, + 3, ⋅ ⋅ ⋅ + , (15) for each time seed , (16) dr = rand(0 − ) (17) for = 1 to dr // Generating Customer Request Vector for Dynamic Requests (18) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (19) endfor (20) for = 1 to dr // Generating Customer Order Vector for Dynamic Requests (21) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (22) generate CRK vector (VIC, IC, RC, CC) and assign weight according to ranks (23) calculate ST using (2) (24) calculate ERT using (3) (25) endfor (26) = 0 (27) Generate position ( ) and velocity ( ) for th particle in th generation from COV (28) while (| best ( ) − best ( − 1) < |) do (29) = + 1 (30) for each particle ( ( ), ( )) of the search space (31) evaluate fitness using objective function (5) best ( ) = ( ) (34) endfor (35) best ( ) = best 1 ( ) (36) for = 2 to number of particles in the swarm (37) if (Fitt[ ( best ( ), ( ))] > Fitt[ ( best ( ), ( ))]) (38) best ( ) = best ( ) (39) endfor (40) endwhile (41) store best ( ) for th time seed (42) endfor (43) store the set of best ( ) for th partition (44) endfor Algorithm 1: TS-PSO.…”

“…The other two important considerations of the problem are request vector and order vector. As soon as a customer enters in the system, he/she sends request information to the central depot via VANETs communication [22][23][24][25][26][27][28][29]. Each customer request is a vector of length six as illustrated in Figure 3.…”

Analysis and Design of Functional Device for Vehicular Cloud Computing

“…Real-time data transmission response was the key feature of DSRC in a rapidly changing environment. Data dissemination was one of most critical issues for vehicular traffic environments [28]. These real-time data applications required complex computations and brought challenges in implementing applications for vehicle devices [29,30].…”

Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach

“…The communicated video can be related to driving safety; for example, an accident ahead, or pedestrians or animals crossing the road [11,12]. It can also be related to onboard communications, such as Vehicle-to-Vehicle (V2V) or Vehicle-to-Office (V2O) video conferencing [13,14]. On-board infotainment can also offer advertisements provided by supermarkets or shopping malls along the road utilizing roadside unit Internet of Things (IoT) environments [15,16].…”