Exploring efficient seamless handover in VANET systems using 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 [1]. 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 [2].

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 [8]. 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 [10], we motivate in [8] 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 [5].

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 [2]. 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 [8].

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 [11] 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 (T_BH) and exit time (ET) [12]. In, our previous work [2], we used NDT, T_BH and ET [8] 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 [12]. The simulation was performed in OMNeT++ [13] 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:

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 [4], 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 [14], 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 [15], 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 [9], 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 [16], 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 [17], 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 [21], 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 [22], 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 [23] and part of the Y-Comm architecture [24], 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 [12] 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 [25], 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 [12]. 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.

In our previous work [2], we presented the various challenges in providing a ubiquitous communication in VANET systems using roadside units. The concepts of Y-Comm like NDT, time to handover (T_EH) and ET were introduced and have shown the importance of those concepts in achieving a seamless communication. In the context of VANET systems, NDT is the time the mobile unit spent under the coverage of a given RSU; T_EH is defined as the time the mobile unit takes to handover to the next RSU; ET is the time the mobile unit spends in an RSU before it must be handover to another RSU.In Figure 1, the diagram demonstrates the following: • Detection range is the region where both the vehicle’s receiver sensitivity threshold and the SNIR are met for the payload. Vehicles within this range of the transmitting RSU are able to decode packets.

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.

For the simulation experiments, the discrete event simulation environment OMNeT++ [13] 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 [26]. All the PHY and MAC properties used in the IEEE 802.11p simulation model conform to [28, 29].

4.1 Simulation scenario

A stationary node (i.e. RSU) is placed as shown in Figure 2. Another mobile node (i.e. vehicle) is made to run over the range of the RSU for collecting various values for our study with two different velocities (10 m/s and 30 m/s). To understand and model a concept like NDT which no other work has ever considered, we first have to start with a simple scenario. We have considered a very basic setup where there are no interferences, no effects of buildings or any traffic density. Hence concentrating on the effects of beaconing, size of beacons and velocity of the vehicle on NDT. This will allow us to understand the key factors before studying more complex scenarios. A scenario as similar to Figure 2 is created.

During simulation, the RSU broadcasts the beacon with different λ and with different beacon sizes for two different velocities. An overview of the different parameter values used is shown in Tables 3 and 4. Note that these parameters contain the EDCA default values [6–8]. The remaining parameters are set according to the default values used by the Veins framework [26].

Parameter	Values
Transmission power	20 mW
Bit rate	18 Mbps
Sensitivity	-94.0 dBm
Thermal noise	-110.0 dBm
Header length	11 bytes
Beacon length	100, 300, 500, 723, 1,574 bytes
Send data	False

Parameter	Values
Speed	10 m/s, 30 m/s
	(36 km/h), (108 km/h)
Channel bandwidth	10 MHz
OBU receiver sensitivity	-94.0 dBm

Beacon sizes of 100, 300, 500 and 723 bytes have been used in [30] 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 [31]. 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.

4.4 Calculation of successful packet reception in simulation

In Figure 3, T1 and T2 is the time when the first packet at PHY and MAC layers are received, respectively. Between T3 and T4 is the region where the packet is always successfully received, i.e. where probability (P) of successful packet reception is ‘1’. T5 and T6 is the time when the last packet at MAC and PHY layers are received. All the packets between T1 to T2 and T5 to T6 are lost due to bit errors. Figure 3 also shows that the reliable communication starts only when the packet reaches the MAC layer. The reason and the way the packets are dropped by the simulation in the PHY layer is explained below.In the simulation, T2 is the time where the actual communication starts and we know that we receive packet at T1, but these received packets are discarded due to bit errors; hence, the question which has to be asked here is, can this time T2 be determined given that the vehicle receives the first packet in PHY layer at time T1? To analyse this effect, we further carefully investigate in the calculation of the successful packet reception. Figure 3 shows that the communication starts only when the packet reaches the MAC layer.The graph, shown in Figure 4, has been simulated in OMNeT++ using the Veins framework. The graph shows the PacketOk and random double number. This simulation is carried on with one RSU and one vehicle moving at 30 m/s as shown in the scenario. The beacon with a size of 100 bytes with a beacon generation frequency of 1 Hz is broadcasted by the RSU.

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.

The lower (red) line is the randomly generated double number and the upper (blue) line is the computed PacketOk. The PacketOk number below the randomly generated number curve is assumed as error and has been dropped at the PHY layer. Packet delivery ratio in Veins for 18 Mbps bit rate is calculated using the below formula which has been modelled using [31].

$\text{PDR}\Rightarrow {\left[1-1.5\mathit{\text{erfc}}\left(0.45\sqrt{\text{SNR}}\right)\right]}^L,$

(3)

where

$\text{Signal to noise ratio (SNR)}=10^{{\text{SNR}}_{\mathit{\text{dB}}}/10}$

(4)

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.

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.

Using the results of this journal article, we will develop a complete analytical framework which will be incorporated into the handover policy management mechanisms in mobile devices which will allow proactive seamless handover in both urban and motorway context as depicted in Figure 23. This will involve realistic techniques of estimating Δ P, P, CP and NDTr.

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.