An Efficient Crowdsourcing Search Scheme in Vehicular Ad hoc Networks

Transcription

1 Int'l Conf. Wireless Networks ICWN' An Efficient Crowdsourcing Search Scheme in Vehicular Ad hoc Networks Chyi-Ren Dow, Duc-Binh Nguyen, Zi-How Lin and Shiow-Fen Hwang Department of Information Engineering and Computer Science Feng Chia University, Taichung, Taiwan {crdow, p39568, m12313, and sfhwang Abstract In recent years, Vehicular Ad-hoc Networks (VANETs) have become a popular research field. In addition, conditions happened surrounding our life often have the demand to be supported by other people, and these conditions often occur on the road. This study proposes an efficient crowdsourcing search scheme in VANETs to search objects and collect data. Our proposed mechanism is based on the virtual backbone construction in VANETs. The regional data exchange can be limited, and the packet transmissions can be reduced. We have established coordinator and header mechanisms to manage the information of region nodes, reduce the amount of packet transmission and improve searching efficiency. Experimental results show that our schemes can effectively provide assistance in terms of search efficiency and satisfactory ratio. Keywords: VANETs, crowdsourcing, search scheme. I. INTRODUCTION Vehicular Ad-hoc Networks are emerging network architecture using wireless network technologies on the intelligent transportation systems (ITS) for mobile information communications. There are many VANETs applications, such as cooperative monitoring of traffic load, prevention and warning of vehicle collision, and location based services (LBS) combined with the nearby area information, etc. In addition, a model for problem solving is also developed with the Internet. Jeff Howe, a reporter of the Wired magazine has created a whole new jargon Crowdsourcing in 26. Enterprise organizations solicit a large number of volunteers to help them to gather information, provide ideas and solve problems about technical through the Internet. Volunteers usually complete tasks in their spare time and charge a small reward or not, but there may be some opportunities to get more rewards in the future. This solution provides a new way of organizing labors, especially for the software industry and the service industry. For example, Wikipedia [2] is an Internet encyclopedia, and everyone can participate in online editing. In the past, when people got off a taxi and forgot something in the car, it is not only hard to find the lost property but also wasting time, especially when they do not know the taxi fleet and license plate number. Currently, some taxi fleets and radio stations provide services to help people finding their missing property online. However, it is not immediate when the messages are broadcasted until the driver finds the lost property and returns it. In recent years, with the development of the Internet and the popularity of smart phones, people began to help each other through exchanging messages over the network such as looking for an accident escape, animal abusing, bullying and other cases. These Internet users can assist in finding suspects as long as they have enough information. In fact, the concept of the crowdsourcing already exists in our daily life. Crowdsourcing mechanisms have been widely applied in many fields since created, and there were many applications developed with sensors on smart phones and tablets. However, in VANETs with crowdsourcing, how to choose the crowd to assist the task and communicate effectively to the demand for them, and how to make proper filtering and screening to ensure the quality and accuracy of the returned data by the crowd, are important issues about crowdsourcing. In addition, the concept of traditional practice of distribution of demand and recovery of data is similar to the broadcast or multicast schemes. It is easy to generate large amounts of streaming data. The influence is perhaps not obvious because there is enough bandwidth in the wired network architecture, but it is a serious problem in VANETs with limited bandwidth. If we want to provide applications with effective crowdsourcing, we need to reduce the time of request spread and data recovery. Both are closely related to the amount of packet transmission. Anycast [1] is a method of information spreading on the network. Its concept is that if there is anyone who can provide the required services, they will be accepted after the demand spread. This method not only can reduce the amount of packet transmission, but also can choose appropriate service providers. Thus, anycast is more suitable for applications in bandwidth limited wireless network infrastructure compared with the broadcast scheme. Considering these issues, the application and technology of taxi based VANETs with a group structure and high density to find and track the target in a city are worth a discussion among us. Therefore, we propose an efficient crowdsourcing search scheme in VANETs. It is a distributed searching system based on virtual backbone and geography information

2 196 Int'l Conf. Wireless Networks ICWN'15 in VANETs. It uses vehicles on the backbone to play the role of crowd, and apportions the searching tasks to the crowd through crowdsourcing in VANETs. The information of vehicles is integrated and exchanged with headers via coordinators in a geographic grid [9]. When there are searching demands generated, our system will ask the coordinators first, and then forward the packet to headers and request vehicles to join the searching task. II. RELATED WORK The topic of data searching has been engaged in network research. Lakas et al. [8] proposed a hybrid cooperative through cooperating vehicles using the store-and-forward technique to share collected information. Noguchi et al. [14] proposed a location-aware service discovery scheme. It spreads service discovery messages to nodes inside a geographical area with IPv6 multicast. Some research schemes [6], [13] improved AODV to propose methods to optimize the route of discovery. Lo et al. [12] enabled vehicles to cooperatively aid the source node to discover the location of the destination node without the support of location services. The geographic load balancing routing, namely GLRV is deployed to provide a virtual backbone. GLRV can increase delivery ratio and reduce the transmission latency in hybrid VANETs architecture. How to process the distribution of a vast number of information from different locations is an important issue. Data aggregation technique aims to solve redundant or distributed data problem to improve the communication efficiency. Tal et al. [16] analyzed various solutions by using Fuzzy Logic in data aggregation schemes is suitable to get benefits in the development process of traffic systems that relies on these schemes. Zhang et al. [19] presented a hierarchical data aggregation scheme to reduce the transmitting of the redundant data which resulted from multisource data collecting and multi-path data transmitting. Traditional data aggregation methods usually rely on a fixed routing structure to ensure data to be aggregated. However, they cannot be used in highly mobile vehicle environments. Catch-Up [17] dynamically changed the forwarding speed of nearby reports so that they had a better chance to meet each other and be aggregated together. Dietzel et al. [4] proposed a generic model to describe and classify the proposed schemes. DA2RF [18] is an infrastructure-free data aggregation scheme by restricting forwarders to limit the number of forwarders in VANETs. In this way, transmission collisions can be avoided as much as possible. With the increasing popularity of smart phones in recent years, there are many studies using crowdsourcing techniques, including the sensing data collection, transportation issues, data accuracy, and other interesting applications. CrowdITS system [1] used the smart phones to collect sensing data without additional sensors and communication equipment and make improvements to traffic by collection of traffic information. CrowdOut [2] is based on contributions made by mobile users equipped with smart phones. It allows users to report traffic offense in real time and to map them on a city plan. There are systems sought volunteers to mine the disaster sensing dead space by crowdsourcing [3] and create a noise map in the urban environment through mobile phones with crowdsourcing [7]. Huang et al. [5] presented selection methods of automatic sensor based on crowdsourcing models for unattended acoustic sensor selection. Furthermore, passengers can also be an effective statistical evaluation of the traveling road conditions through the triaxial sensors in the phone [15]. Liu et al. [11] combined people and mobile sensing devices into a live wireless sensor. It uses this combination to remedy the traditional sensor blind, and also conforms the concept of crowdsourcing. III. THE PROPOSED SEARCHING METHOD In this section, we introduce our proposed searching mechanisms, including virtual backbone establishment of the tree searching architecture and the design of the crowdsourcing anycast query spreading mechanism. Then, we formulate crowdsourcing data filtering mechanisms. A. The Virtual Backbone of the Tree Searching Architecture Traffic could change very often with complex road structure in urban environments as time goes by different distances, directions and speed of the vehicle will cause the network topology changes and affect the stability of the chain. If the city roads are divided by geographic grids to establish a backbone tree, we can limit the area of data exchange of information and reduce unnecessary traffic packets to achieve fast and stable data transmission. Exchanging and maintaining the data table between important nodes in the backbone can simplify data storage and management. The data can be found more quickly, and the searching time can also be reduced with precise management. The vehicle stays in each geographic grid longest will be elected as a header to manage the information of other vehicles in the grid to reduce the packet number of switched data. Normally, the vehicle closest to the center of the grid will be elected as a coordinator. As shown in Figure 1,, where and are coordinates of the center, and are coordinates of the vehicles in the same grid. is the range of transmission to consider a vehicle as the header in the grid. As shown in Figure 1, we calculate the distance between each vehicle and the center, the header of the grid is D1 because it has the smallest distance. Headers within grids regularly gather information of vehicles in the same grid. As shown in Figure 2, the coordinator within the grid gathers information from adjacent grids in order to accelerate the speed of searching, but it will generate a lot of queries and return packets when there are too many coordinators. Therefore, development an appropriate number of coordinators is an important issue. As shown in Figure 3, considering the degree of branching of the tree and beginning with the smallest number of grids. If the degree of the header within the grid is greater than 2, it will be elected as the coordinator. Therefore, this step may lead to a coordinator

3 Int'l Conf. Wireless Networks ICWN' having too many nearby coordinators. For example, coordinator within grids 22 and 54 are superfluous, because there are too many coordinators. In order to avoid collecting unnecessary information from other paths, a coordinator is created every three hops. However, the coordinator cannot be established if it is located in the leaves of the tree (grids 2 and 58) to avoid excessive control overhead and reduce excess coordinators. Because grids 55 and 58 collect information on the same area, they will result in a large amount of duplicated information and redundant of transmission. B. The Crowdsourcing Anycast Query Spreading Mechanism Figure 1 Grid Header Election Figure 4 The Reply Table Figure 5 The Query Packet Figure 2 Coordinator Collection Figure 3 Coordinator Establishment The header or the coordinator within each grid maintains a reply table as shown in Figure 4 which contains detailed data fields of each informed object. Vehicles will periodically reply the information of board objects (such as lost and sensor data, etc.) to headers within grids, and forward the data to coordinators for management. When there is a task (object searching or data collection, etc.), our system will generate query packets as shown in Figure 5 to send to the nearby coordinator. These query packets contain the purpose of task (lost, target searching, data collection, etc.), content (including target characteristics, time, location, etc.). The coordinator receiving the query packet will be compared with the reply table, and then return to the source node. If the task needs to cooperate for finding the target, the header within the grid near the target area asks the vehicle within the grid for joining the task after receiving the query packet. When any vehicle responses content, the task starts. When the task is completed, the data will be returned by the header and coordinator. If there is no matching information, the coordinator passes the query packets to other coordinators through the adjacent headers within the grids, and they compare with the reply table. In order to maintain the

4 198 Int'l Conf. Wireless Networks ICWN'15 effectiveness of the searching mechanism, we have developed the threshold of searching time to avoid searching too long or endless searching. We will terminate searching and return results if time is over. Our crowdsourcing search mechanism is listed as follows: (1) Coordinators regularly update the reply form. (2) Source node generates a query packet, and delivers to coordinators for comparing data. In case of the data is matched, the coordinator returns to the source node. (3) If the data is not matching, the query packet will be passed to other coordinators for comparing data by neighbor headers. (4) After receive the query packet, the header of each neighbor will ask other vehicles if willing to assist or not. (5) The task starts when any vehicle responses content. (6) When the task completes, messages will be responded to the source node via headers and coordinators. (7) If searching time exceeds T search, the task will be terminated. C. The Filtering Mechanism Although we have designed the query packet format and reply mechanism to provide the format of returned data. However, getting the number of requirements from the return data is also a problem. Hence we need to formulate the filtering mechanism to deal with these situations. If we allow the transmitted of data to be collected together, it would cause a considerable burden for the network. Thus, we must filter redundant information through hierarchical steps to reduce traffic and leave useful information. We divide data filtering into three types. For the first type, the searching condition given from the source node to the coordinator is precision and the searching condition given from the coordinator to the header is fuzzy. By this way, we can reduce the difficulty of searching by vehicles and increase the number of data. The coordinator can filter those data returned by headers before passing to the source node. The second type) where conditions given from the source node, and the coordinator are both precise. It increases the difficulty of searching by vehicles but reduces the amount of data and reduces the load of networks. The third type represents the conditions given from the source node and the coordinators are both fuzzy, the data returned by vehicles will not be filtered by the coordinator, and the source node can select the information which one does it wants. When the new beginning data is received, our mechanisms allow the same information received within the time threshold T for maintaining accuracy and reliability of the information. The number of repetitions will be recorded into the reply table and form a credit value. The higher credit value represents the higher reliability of the data. Each cumulative data will get a survival time, T life after time T, and it is not allowed to accept the same data. When the time T life ends, the extra data will be discarded or replaced by new data. IV. EXPERIMENTAL RESULTS This section is carried to evaluate our proposed crowdsourcing search scheme. We use the version 2.35 of Network Simulator 2 (NS-2) as our simulator. The simulation vehicular movement mode is generated by Simulation of Urban Mobility (SUMO). As shown in Figure 6, we compare the control overhead with the time change. At the beginning, since the purpose of our method is to establish the backbone, the amount of control packets required will be greater than AODV and Geogrid. However, once the backbone structure and information forms contents are completed, the needed amount of control overhead will gradually stabilize. Control Overhead Success Rate Time (sec) Figure 6 Control Overhead vs. Time Distance (m) Figure 7 Successful Rate vs. Distance Ours AODV Geogrid AODV Geogrid Ours As shown in Figure 7, the success rate is compared to the change in distance. The range for distance is 5 to 25. The success rate of AODV decreases as the distance increases, because AODV always starts a new search. Therefore, the success rate will decrease when the distance increases. On the other hand, when Geogrid perform a search, every point is looking for the nearest coordinator for inquiries. Thus, the success rate obtained will display a stable status. In our method, every coordinator works together to maintain and

5 Int'l Conf. Wireless Networks ICWN' exchange the reply tables. This reduces the search time, which also raises the level of efficiency. In comparison, Geogrid can find the target more effectively. V. CONCLUSIONS In this study, we propose an effective crowdsourcing search scheme in VANETs. It can reduce the number of transmission packets through the establishment of coordinator and header, and provide crowdsourcing search of the query instead of blind searching. The proposed scheme not only improves the results of discovery, but also reduces dissemination time and reply time. Design of the table information can help to exchange all reply discoveries. As the future work, we intend to implement our crowdsourcing search scheme. However, not everyone is willing to participate in crowdsourcing tasks. Maybe we can combine incentives to promote the wishes of people, providing them with virtual or real currency. Furthermore, we will consider more methods for both data aggregation and data filtering in order to achieve more perfect results. ACKNOWLEDGMENT The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract No E REFERENCES [1] K. Ali, D. A. Yaseen, A. Ejaz, T. Javed and H. S. Hassanein, CrowdITS: Crowdsourcing in Intelligent Transportation Systems, Wireless Communications and Networking Conference, Paris, pp , Apr [2] E. Aubry, T. Silverston, A. Lahmadi and O. Festor, CrowdOut: A Mobile Crowdsourcing Service for Road Safety in Digital Cities, Pervasive Computing and Communications Workshops, Budapest, pp , Mar [3] E. T. Chu, Y. L. Chen, J. Y. Lin and J. W. S. Liu, Crowdsourcing Support System for Disaster Surveillance and Response, Symposium on Wireless Personal Multimedia Communications, Taipei, pp , Sep [4] S. Dietzel, J. Petit, F. Kargl and B. 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