- Open Access
Exploring efficient seamless handover in VANET systems using network dwell time
© Ghosh et al.; licensee Springer. 2014
- Received: 31 January 2014
- Accepted: 3 November 2014
- Published: 30 December 2014
Vehicular ad hoc networks are a long-term solution contributing significantly towards intelligent transport systems (ITS) in providing access to critical life-safety applications and services. Although vehicular ad hoc networks are attracting greater commercial interest, current research has not adequately captured the real-world constraints in vehicular ad hoc network handover techniques. Therefore, in order to have the best practice for vehicular ad hoc network services, it is necessary to have seamless connectivity for optimal coverage and ideal channel utilisation. Due to the high velocity of vehicles and smaller coverage distances, there are serious challenges in providing seamless handover from one roadside unit (RSU) to another. Though other research efforts have looked at many issues in vehicular ad hoc networks (VANETs), very few research work have looked at handover issues. Most literature assume that handover does not take a significant time and does not affect the overall VANET operation. In our previous work, we started to investigate these issues. This journal provides a more comprehensive analysis involving the beacon frequency, the size of beacon and the velocity of the vehicle. We used some of the concepts of Y-Comm architecture such as network dwell time (NDT), time before handover (TBH) and exit time (ET) to provide a framework to investigate handover issues. Further simulation studies were used to investigate the relation between beaconing, velocity and the network dwell time. Our results show that there is a need to understand the cumulative effect of beaconing in addition to the probability of successful reception as well as how these probability distributions are affected by the velocity of the vehicle. This provides more insight into how to support life critical applications using proactive handover techniques.
- Vehicle to infrastructure communication
- Proactive handover
- Network dwell time
The rapid growth of the number of cars on the roads has created a plethora of challenges for road traffic management authorities, such as traffic congestion, increasing number of accidents, and air pollution. Over the last decade, significant research efforts from both automotive industry and academia have been underway to accelerate the deployment of a wireless network based on short-range communications among moving vehicles (vehicle-to-vehicle, V2V) and roadside infrastructure (vehicle-to-infrastructure, V2I). This network is called a vehicular ad hoc network (VANET) and is characterised by high node speed, rapidly changing topologies and short connection lifetimes.
Several applications for VANETs have been categorised for road-safety, traffic efficiency, and infotainment applications (i.e. information and entertainment applications). The latter two can be typically referred to as non-safety applications; they aim to provide information and comfort/entertainment to travellers and have the great potential to increase the chances of success for VANETs and to accelerate their market penetration . Road traffic management for smart cities involves monitoring the actual traffic situation in real time (including volumes, speeds and incidents) and then controlling or influencing the flow using that information in order to reduce traffic congestion, deal efficiently with incidents and provide accurate and reliable traffic information and prediction to both drivers and authorities .
Beacons are used to discover and maintain neighbour relationships [3–5]. The European ITS VANET Protocol (EIVP) defines beacons as a cooperative awareness message (CAM) [6, 7]. Beacons also include a security component, and the size of a beacon is approximately 400 bytes long [3–5]. Beaconing can be used for reliability due to the lack of acknowledgements and reservation by means of RTS/CTS . Beacon messages are generated and issued periodically between the vehicle-to-vehicle (V2V) and vehicle to the roadside unit (RSU) i.e. (V2I) [6, 7]. The generation rate is the rate at which beacons are sent to the MAC for transmission. Since they are used to create a cooperative awareness, beacon generation rate should be in the order of several beacons per second to provide the system with accurate information about the close surroundings [3, 5, 8, 9]. Beacon frequency is the beacon generation rate which is denoted by (λ). Though some research efforts consider a fixed λ of 10 Hz , we motivate in  that generation rate adaptation as a network layer mechanism is one of the instruments to make beaconing more scalable. Increasing λ results in more beacons being sent and a higher temporal resolution. But this comes at the price of an increase in collision probability, especially in dense traffic. Hence, an adaptive beaconing is preferable .
In the next couple of years, it is evident that intelligent transport systems (ITSs) will entail the deployment of VANETs especially in smart cities. For this purpose, it is imperative not only to have a proper infrastructure with several RSUs being placed in a resourceful and cost-effective manner but also to serve the main purpose of ITS in order to have seamless connectivity for optimum coverage with ideal channel utilisation where vehicles are able to access applications and services quickly . The paradox of deployment issues are that, on the one hand, ITSs demand the deployment of the infrastructure in such a way that it supports seamless connectivity, but on the other hand, this comes at the cost of having many RSUs placed along the roadside leading to interference issues. Hence, in order to achieve seamless connectivity, the placement of RSUs within the general infrastructure needs to be fully investigated .
Though other research efforts have looked at many issues in VANET networks, very few papers have looked at handover issues. Most papers assume that handover does not take a significant time and does not affect overall VANET operation. The Y-Comm architecture  was developed to explore proactive handover issues in future mobile networks. It has introduced an advanced handover classification system as well as new concepts such as network dwell time (NDT), time before handover (TBH) and exit time (ET) . In, our previous work , we used NDT, TBH and ET  to analyse the handover issues in VANET systems. In this context, NDT is the time the vehicle spends in a RSU’s coverage range. The results showed that the real-time NDT (NDTr) measured using a simulation is not equal to the theoretical NDT or ideal NDT (NDTi) which is calculated using Y-Comm techniques in . The simulation was performed in OMNeT++  using the Veins framework. The results clearly showed that the NDT was affected by frequency of beaconing as well as the velocity of the vehicle.
The aim of this journal article is to further investigate the effect of these parameters on NDTr and its ability to reach the NDTi, the ideal NDT with different beacon sizes and the different velocities of a vehicle. This was done by looking at entrance and exit scenarios using the Veins framework simulation focusing on MAC and PHY layers in analysing the factors contributing to NDTr. This study points to the need to develop a more comprehensive set of equations that can be used to calculate NDT in VANET systems and hence help us to understand the main handover issues in VANETs.
The contributions of this journal are as follows:
To show the differences in the NDTi and NDTr based on the frequency and sizes of the beacon for two different velocities of a vehicle.
To show the orthogonal relation between the frequency and size of the beacon.
To investigate the relationship of cumulative probability distribution and its effect on measured NDT.
To investigate how the probability distribution of successful beacon reception changes with velocity.
To provide a step towards the modelling of a realistic NDT.
The rest of this journal article is structured as follows: Section 2 provides an overview of the related work in this area. Section 3 highlights on our previous work. Simulation background and setup are explained in Section 4. In Section 5, the simulation results and discussions have been critically evaluated. Section 6 demonstrates analysis which highlights the analytical approach towards the study. Finally, Sections 7 and 8 conclude this journal article and show the future work.
In , the author highlighted the importance of scalable beaconing and the fact that power control alone will not be sufficient if the requirement of the application has to be met. Hence, the rate at which beacons are generated must also be controlled. The author proposed an adaptive architecture and adaptive timing aspects of beacon generation.
In , the author proposed a distributed routing protocol and focuses on two kinds of handovers: inter RSU handover and intra RSU handover. The approximate location of the cars is found using the link quality based on received signal strength indicator (RSSI) from the timing advertisement from RSU.
In , a multi-technology seamless handover mechanism for vehicular networks is explored. The authors look at integrating other technologies like 3G to achieve seamless communication between the vehicle and the infrastructure without breaking an active session. Using extended mobility protocols of MIPv6 and PMIPv6, a test was performed to measure the handover latency for different bit rates between the same communication technology and between different communication technologies. Here, speeds of the vehicles considered were 50 and 60 Km/h respectively.
In , a time coordinated medium access control (MAC) protocol named WAVE point coordination function (WPCF) for vehicle to infrastructure (V2I) communication is investigated. The service disconnection time of various channel access techniques for V2I handover was shown. In order to reduce the handover delay and for a soft handover to happen, additional messages were added.
The work in , conducted a field experiment which extensively analysed the performance of V2I communication in an urban environment for an effective and reliable RSU deployment. The field testing was conducted in the city of Bologna with four key scenarios. Three urban scenarios and a highway scenario were considered. The communication performance was measured in terms of the packet delivery ratio (PDR) as a function of the distance of the onboard unit (OBU) to the communicating RSU. The author has presented the reliable connectivity range (RCR) and unreliable connectivity range (UCR) as the distance to the RSU up to which the experienced PDR is above 0.7 and below 0.1, respectively. The study was performed for two transmission power levels, i.e. 10 dBm and 20 dBm, and has shown that high transmission power levels can significantly increase the RCR and UCR distances. The study has also shown the effect of non-line-of-sight (NLOS), antenna heights, traffic and heavy vehicles. NLOS has shown a significant impact on the communication. The minimum and maximum reliable communication range distance was 400 m and 800 m (approx.), respectively.
In , the author has referred and criticised the works of [18–20] where a predictive or proactive handover approach is proposed for 802.11 networks. Here, the author has proposed a proactive polling mechanism where the information about the approaching vehicles are forwarded to the next RSU, where the vehicles become part of the communication schedule even before entering the next RSU’s transmission range. The next RSU starts polling early enough to account for a 20% increase in average speed over the distance between the two access points. When a vehicle experiences a significant decrease in average speed or leaves the highway entirely, the RSU cannot continue sending out proactive polling messages indefinitely. Hence, a decrease in average speed of 20% is allowed. The polling by the next RSU stops after this threshold. A new RSU after this should go through the regular connection setup process.
The work in , proposed a seamless handover scheme based on proactive caching of data packets. Here, when an OBU is about to leave the coverage area of an RSU, the buffered packets will be forwarded to the entire candidate RSUs. The new RSU which is one of the candidates will transmit the buffered packets to the OBU and a message is sent to the rest of the candidate RSUs to discard the cached packets.
In , a new architecture called the MYHand architecture for providing extended information in next generation network (NGN) scenarios is detailed. By using the IEEE 802.21 protocol basic schema  and part of the Y-Comm architecture , MYHand improves the handover managed by mobile devices (user centric management). A scenario with three access providers and a mobile user walking through the avenue was simulated by using network simulator 2 (NS2).
The work in  proposes a proactive handover policy using a simple mathematical model. Proactive handover facilitates minimise disruption due to service degradation or packet loss during handover by signalling to the higher layers that a handover is about to happen. This work shows how the NDT and time before vertical handover (TBVH) are calculated in heterogeneous environments. TBVH is the time a mobile node has got to hit the circle for handover given the velocity and direction. The paper analysed various vertical handovers (WLAN-3G, 3GWLAN) in their work.
In , a seamless proactive vertical handover algorithm was proposed which took into account the users preferences, network conditions, velocity of the mobile station and application requirements for selecting a candidate network for handover which is stable. The proposed algorithm calculates the residence time in a candidate network which has already been proposed and highlighted in . This shows the importance of NDT in achieving a proactive handover.
To the best of our knowledge, no work has considered the importance of lower layers (i.e. PHY and MAC) in achieving an effective proactive handover.
Data exchange range is the region where the actual data transmission takes place.
Time before handover is the region where the OBU gets ready for handover.
Time to handover is the region where the actual handover takes place.
The work considered a very basic setup where there was no interference or other sources of noise, no effects of buildings and no traffic density issues in order to concentrate on the effect of beaconing and velocity of the vehicle on the network dwell time. Here, beacon size was kept constant at 656 bits.
Comparison of network dwell time from simulation with theoretical calculation
λ= 1 Hz
λ= 5 Hz
λ= 10 Hz
λ= 20 Hz
λ= 40 Hz
0 to 108 km/h
Minimum overlapping needed for a soft handover
Minimum overlapping needed
λ= 1 Hz
λ= 5 Hz
λ= 10 Hz
0 to 108 km/h
For the simulation experiments, the discrete event simulation environment OMNeT++  is used in conjunction with the Veins framework [26, 27]. This is a mobility simulation framework for wireless and mobile networks. A beaconing model using IEEE 802.11p was implemented in Veins framework by . All the PHY and MAC properties used in the IEEE 802.11p simulation model conform to [28, 29].
4.1 Simulation scenario
RSU configuration parameters
100, 300, 500, 723, 1,574 bytes
OBU configuration parameters
10 m/s, 30 m/s
(36 km/h), (108 km/h)
OBU receiver sensitivity
Beacon sizes of 100, 300, 500 and 723 bytes have been used in  for 6 Mbps packet error ratio experiment. This result was used in the development of Veins framework in 6 Mbps packet error rate modelling. Beacon size of 1,574 bytes was also used in an experimental study . Further, this result was used in the development of Veins framework in 18 Mbps packet error rate modelling. Hence, we have considered these sizes of beacon to conduct our study.
4.2 Calculation of reception power
where sat → minimum signal attenuation threshold.
4.3 Calculation of detection range
where Λ→ wavelength = (speedoflight/carrierfrequency), p Max → maximum transmission power possible, α → minimum path loss coefficient, sat → minimum signal attenuation threshold and minRecvPow → minimum power level to be able to physically receive a signal.
Based on the simulation parameters as shown in Tables 3 and 4, the detection range is calculated in the simulation. The outcome from the formula suggests 907.84256 metres, i.e. the radius (R) of the coverage. For this reason, all the mathematical calculations in our work has considered 908 metres (approx.) as the radius of the coverage.
4.4 Calculation of successful packet reception in simulation
For each beacon received at the PHY layer, a PacketOk number is computed which is a packet reception ratio. This number is computed based on bit error rate (BER) and length of beacon. This computed double number is compared against a randomly generated double number ranging between 0 and 1. If the computed number is less than the randomly generated number, then that respective beacon is dropped at the PHY layer, reason assumed that there is an error in the packet.
and L → length of the packet.
In Figure 4, from the graph, we can observe that as the vehicle is heading towards the RSU, the packet reception probability increases and at a point reaches 1 which means there is no possibility of error in the packet. In other words, we can say that the region where the P = 1 is a very reliable communication region. This is the time from T3 to T4 which has been shown in the Figure 3.
5.1 Why NDT?
where μ ml → mobility leave rate from Equation 5, R H → handover radius and Vmax → maximum velocity of the vehicle
5.2 Further investigation into PHY layer in relation to beacon size
6.1 Cumulative probability calculations
In order to investigate the effect of beacon frequency, we also need to look at the cumulative probability of a successful packet reception, in addition to calculating the probability of a successful packet reception for an individual packet at a given time ‘t’. Since we know the single packet reception probability using Equation 8 from the simulation, the cumulative probability can be calculated.
where, P N is greater than PN-1….=1.
Since P is increasing because the vehicle is moving towards the RSU, hence the cumulative probability reaches ‘1’ long before infinity and therefore affects the successful reception of the beacon. This analysis applies when the vehicle enters the network.
For the exit scenario P the probability of the successful reception decreases as we move away from the RSU; hence, 1 - P is increasing. Once the vehicle does not hear the beacon after the period T, the inverse of the beacon frequency, it immediately hands over to the next RSU. Our results consider the effect of the cumulative probability on entrance and exit regions of RSU coverage.
6.2 Cumulative probability effect on beacon frequency
6.3 The change in probability of successful beacon reception (Δ P)
6.3.1 The change (Δ P) at entry
Δ P is significant because the SNR changes more rapidly with the increased velocity of the vehicle. Hence, Δ P increases significantly as the velocity of the vehicle increases. Where, P N is the probability of packet reception of an individual packet ‘N’ and ‘N - 1’ is the previous packet. Δ P is calculated until P reaches 1.
6.3.2 The change (Δ P) at exit
where P N is the probability of packet reception of an individual packet ‘N’ and ‘N + 1’ is the next packet. Δ P is calculated until P reaches 0.
The simulation experiments were conducted to analyse the change in P with respect to different velocities and different beacon frequencies. Due to vast amounts of results collected from the simulation, therefore, these results are available on request. These results clearly show the effect of size of beacon, velocity of vehicle and frequency of beacon. If a formula is being modelled based on these results, then for a given velocity of vehicle and for a given beacon size and frequency, the rate of change of P can be calculated using the modelled formula. With this rate of change being known the P and CP at any point can be calculated, which in turn can be used to predict the NDTr more accurately.
In this journal article, we have investigated the effect of beaconing on network dwell time using cumulative probability and individual successful beacon reception. This work has proved that the size of the beacon directly affects the individual packet reception probability (i.e. the value of P changes), especially the probability distribution of the first packet P1 and hence affects both single reception probability as well as the cumulative reception probability. However, the frequency of the beacon only affects the cumulative probability, and hence, this shows that the effect of beacon size and beacon frequency is orthogonal to each other with regard to the network dwell time. In addition, the rate of change of the probability, i.e. (Δ P) is affected by the velocity of the vehicle and the velocity affects both the cumulative probability and the probability of successful reception. Hence, though the size and frequency of the beacon have orthogonal effects, the velocity of the vehicle affects both of these parameters. This work therefore significantly enhances our attempt to build a full-blown analytical model that encompasses all layers in an attempt to provide seamless handover.
In the long term, we are seeking to develop a comprehensive framework that includes types of modulation being used as well as traffic density in order to handle seamless handover in both urban and motorway contexts.
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