Special issue on New Technologies, Mobility and Security - Final.pdf

DESIGN AND DEVELOPMENT OF A WIRELESS SENSOR MODEL FOR VEHICULAR AREA NETWORKS Umesh P, G.Varaprasad Department of Computer Science and Engineering, B.M.S. College of Engineering, Bangalore 5600 19, India. Email:drvaraprasad@gmail.com ABSTRACT Vehicular area network provides vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-person communications. Its aim is to increase the road safety and transport facility efficiency. It also provides a ubiquitous wireless environment for the end users. The vehicle area network is considered as one of the major applications for wireless networks. Here, each vehicle has a unique identifier and eavesdropper to accumulate the locations of vehicles. If the vehicle changes its pseudonym from time to time, then the longterm tracking is to be avoided. The proposed model automatically monitors the flow of vehicles and sends the data to the control room via gateway nodes. It uses number of sensors to reduce the traffic at important junctions while forwarding the vehicle from one place to another. Keywords: Vehicle adhoc network, sensor, control room, vehicle. NOMENCLATURES Val=Number of vehicles at sensor A P= Status of information at sensor A Z=Status of information at sensor B Q=Number of vehicles at sensor B Max=Maximum queue length is 200 Limit=Permissible vehicles between two sensors is 50 1. INTRODUCTION A Vehicle Adhoc Network(VANET) is a special type of mobile adhoc network[1], where all nodes are vehicles and move regularly at high speed. The VANET has unique requirements with respect to the applications, self organization and communication. It has been envisioned to be useful in many commercial applications[2]. For example, the VANET is also used to alert the drivers to avoid the traffic. It provides efficient routes while forwarding the vehicles from one node to another. It can also be used in propagation of the emergency warning information to the drivers to avoid the collisions[3]. In VANET, it uses sensor devices to monitor the network conditions such as vibration, pressure, motion, pollution, temperature and sound. Each sensor is capable of collecting valuable information and transmits the data to others[5]. These devices are very small, low cost and can be deployed in a large numbers in the network[1]. Failure of a single device does not affect the network performance. It is also possible to replace the broken device. The newly installed device should be detected with neighboring devices for communication. In mobile adhoc networks, the routing algorithms like proactive and reactive are used but proactive routing algorithms are not suitable for VANETs. Since, each MS keeps up-to-date information and consumes more amount of bandwidth. Generally, each MS has higher mobility and the topology will be changed frequently. The network performance depends on the mobility, density and load. The VANET is used for short-range wireless communication and has emerged as the preferred network design for quick transportation system. Federal Communications Commission has recently allocated 75MHz in 5.9GHz band for short range communication for vehicle-to-vehicle and vehicle-to-infrastructure communications. UbiCC Journal - Volume 3 1 This paper presents a design and development of wireless sensor model for VANETs to monitor the flow of vehicles and reduces the traffic over the network. Rest of the paper is arranged as follows. Section 2 presents some of the existing models. Proposed technique is discussed in section 3. Section 4 presents the simulation of proposed model. The results of this model are presented in section 5. Section 6 presents the conclusions and future research work. Figure 1. Sample model. 2. EXISTING WORK There are number of models used to monitor the traffic at different nodes. Each model has advantages and disadvantages. The conventional traffic methods are used to route the data packets based on the central administration principles. These models use loop-detectors and cameras to monitor the traffic. These devices are used to transmit the flow of vehicles data to the central room for taking necessary steps. But it is more expensive than non-conventional models. In fact, the performance of these systems is poor[6]. Advanced cruise-assistant-highway system helped to reduce the collisions[7]. It sends the traffic information to the drivers but it is a costly method. FleetNet[8] model uses built-in equipments with the sensors to monitor the vehicles. It is used in sending the emergency messages to the drivers over the networks. In[9], it measured end-to-end delay of a packet at local road. Greedy-perimeterstateless-routing algorithm is a location based protocol, which is presented in[10]. All the data packets are marked by the originator, and then transmitted to the destination location. Previous models are mainly focused on mobility and are used for small distance. 3. PROPOSED METHOD The traffic-dot sensor is equipped with ATmega128L microprocessor, battery and magnetometer as shown in Figure 2. It senses the flow of vehicles and then transmits the data to the control room and neighbor devices. The control room keeps track of all the regular nodes. Figure 2. Traffic-dot device. Let us take two nodes ‘A’ and ‘B’. Both nodes exchange the flow of vehicles and same has been transmitted to the control room as shown in Figure 3. The traffic algorithm provides the sensing information from the node ‘A’ to the central room and ‘B’. If the number of vehicles crossed the road is less than Max, then the GREEN light is ‘ON’, This state is maintained till ‘A’ will receive the stop signal from ‘B’. The proposed model monitors flow of the vehicles and reduces the traffic at various places. It consists of regular nodes and a control room. The regular node is equipped with traffic-dot sensor as shown in Figure 1. The proposed system uses IEEE 802.15.4 protocol for communication. It provides low-bit-rate, lowcost, and less-power-consumption. This model controls the flow of vehicles with the help of regular nodes at every entry and exit points of the road using RED and GREEN signals. Figure 3. Proposed model. Traffic algorithm While(sensor_A_on) { Val=Read_sensor_A(); If(Y!=MAX) then Light_A(GREE); else 2 UbiCC Journal - Volume 3 { Send_B(complete); Z=Read_B(); while(Y==MAX&&Z!=complete) { Light_A(RED); Z=Read_B(); } Reset_sensor_A(); } } 4. SIMULATION Fig.5. Number of vehicles versus throughput. Figure 6 summarizes the packet delivery ratio. Based on the results, it concludes that up to traffic density of 5vehicles/km, the packet delivery ratios of two models are 93.56% and 98.35% respectively. At traffic density of 16vehicles/km per line, the packet delivery ratio of proposed model has decreased as compared to FleetNet model. If the traffic density is 25vehicles/km per line, then the proposed model delivers 95.86% of the data packets due to reactive algorithm principles. The proposed model considers an area of 7KmX7Km with a set of regular nodes deployed randomly over the network. The vehicle transmission range is 50m. The simulation consists of 10,000 nodes moving around a circular and square road of 6283m length with four lines. Here, it uses UMPS simulator to evaluate the network performance of two routing algorithms. Table I. Simulation parameters. Simulation time 2000s Topology size 7KmX7Km No. of nodes 1000 No.of clusters 10 No.of cluster heads 10 No. of malicious nodes 7 Transmission range 50m Routing protocol ZRP Frequency 2.4Ghz Channel capacity 2Mbps Traffic type CBR CBR packet size 512 bytes Simulator UMPS Total packets 30000 5. SIMULATION RESULTS Figure 6. Number of vehicles against packet delivery ratio. The average delay of a data packet is shown in Figure 7. The average delay of two models varies from 113ms to 1.10s. The FleetNet model has experienced more delay for all traffic densities. The route-discovery process will take long time in FleetNet model as compared to proposed model. If the traffic density is 25vehicles/km per line, then the proposed model takes only 0.19s. 6. CONCLUSIONS AND FUTURE WORK This simulation considers three performance metrics namely packet delivery ratio, average packet delay and throughput. From the results, it is noticed that the throughput of two models is increased if the number of vehicles is 6vehicles/km per line. The proposed model clearly outperforms for 25vehicles/km per line as compared to Fleet model. The connectivity in network is significantly better than that of small density traffic as shown in Figure 5. In urban areas, the VANET play an important role to provide transport facility efficiently. The performance evaluation is an important factor in VANET. It is also noticed that the proposed model has shown the better results in terms of packet delivery ratio, average 3 UbiCC Journal - Volume 3 delay and throughput. In this work, transmission range and parameters are fixed. However, it also observed that low transmission range will not guarantee the connectivity among all nodes to ensure effective communication. Figure 7. Number of vehicles against delay. REFERENCES 1. J. Beutel M. Dyer, L. Meier, “Scalable Topology Control for Deployment-Sensor Networks”, In Proc. of International Conference Information Processing in Sensor Networks, pp. 359-363(2005). J. Ding S. Y. Cheung, and P. Varaiya “Signal Processing of Sensor Node Data for Vehicle Detection”, In Proc. of IEEE ITS, pp. 70-75(2004). AutoNet: Adhoc Peer-to-Peer Information Technology for Traffic Networks www.its.uci.edu/ monally/mgmautonet.htm C. Li K. Ikeuchi, M. Sakauchi, “Acquisition of Traffic Information using Video Camera with 2DSpatiotemporal Image Transformation Technique”, In Proc. of IEEE ITS, pp. 634-638(1999). D. McErlean, S. Narayanan, “Distributed Detection and Tracking in Sensor Networks”, In Proc. of ASILOMAR, pp. 1174-1178(2002). Z. Sun, G. Bebis, and R. Miller, “On-road Vehicle Detection Using Optical Sensors: A Review”, In Proc. of IEEE ITS, pp. 585590(2004). 7. O. Sidla, L. Paletta, and C. Janner, “Vehicle Recognition for Highway Lane Safety”, In Proc. of IEEE ITS, pp. 531-536(2004). 8. Wilfried E, “FleetNet Applications for InterVehicle Communication”, IEEE Intelligent Vehicles Symposium, pp. 162-167(2003). 9. Tatsuaki. O, Kazuya. M, Shoji F, Susumu Matsui, “Performance Measurement of Mobile Ad Hoc Network for Application to Internet-ITS”, In Proc. of International Symposium on Applications and Internet, pp. 83-87(2004). 10. J. P. Singh, Nicholas B, “Proposal and Demonstration of Link Connectivity Assessment Based Enhancements to Routing in Mobile Adhoc Networks”, IEEE Vehicular Technology Conference, vol. 15, pp. 2834-2838(2003). Author’s information Umesh P obtained B.E Degree in Electronics and Communication Engineering from Visveswaraiah Technological University, Belgaum in 2004. Currently he is doing M.Tech Degree in Computer Science and Engineering in B.M.S. College of Engineering, Bangalore. His areas of interests are embedded system, wireless communications. G.Varaprasad received B.Tech Degree in Computer Science and Engineering from Sri Venkateswara University, Tirupati in 1999 and M.Tech Degree in Computer Science and Engineering from B.M.S. College of Engineering, Bangalore in 2001 and PhD Degree in Computer Networks from Anna University, Chennai in 2004 and worked as a Postdoctoral fellow at Indian Institute of Science, Bangalore in 2005. Currently, he is working as an Asst. Professor in B.M.S. College of Engineering, Bangalore. His areas of interests are wireless communications and sensor network. 2. 3. 4. 5. 6. UbiCC Journal - Volume 3 4 A GAME THEORETIC POWER CONTROL APPROACH FOR MIMO MC-DS/CDMA SYSTEMS V.Nagarajan and P.Dananjayan † Department of Electronics and Communication Engineering, Pondicherry Engineering College, Pondicherry -605014, India nagarajanece31@rediffmail.com,pdananjayan@rediffmail.com † † Corresponding author ABSTRACT A major challenge to enhance the performance of multiuser multiple-input multiple-output (MIMO) multi-carrier direct sequence code division multiple access (MC -DS/ CDMA) system relies on the effective multiple access interference suppression. In this work a novel distributed non cooperative power control game with pricing (NPGP) is considered for utilizing the system resource more efficiently. The ratio of throughput versus power is referred to as the utility function which should be maximized by combating the multiple access interference (MAI). Simulation results show that the propounded scheme achieves significant performance improvement, compared with the conventional system without NPGP. Keywords: Game theory, power control, pricing, MIMO, MC-DS/CDMA. 1 INTRODUCTION The enormous growth of wireless services during the last decade gives rise to the need for a bandwidth efficient modulation technique that can reliably transmit high data rates. As multi carrier technique combine good bandwidth efficiency with an immunity to channel dispersion, these techniques have received considerable attention. To able to support multiple users, the multicarrier transmission technique can be combined with a CDMA scheme. In tandem the demand for wireless services increases, efficient use of resources has gained a significant importance. Ever increasing need for wireless systems to provide high data transmission rates need a system which performs well under severe fading conditions. Though MIMO MC-DS /CDMA seem to be an excellent candidate for high data rate communication, its performance is limited by multiple access interference (MAI) and near-far effect. The power control algorithm plays a significant role in combating this effect. Compared with single antenna MC-DS /CDMA, MIMO MCDS /CDMA exhibits better performance, but it has the traditional impairment as the single carrier system [1,2]. Hence the performance of a MIMO MC-DS /CDMA consequently lies in the area of interference suppression and power control in multi user scenario. Recently, an alternative approach to the power control problem in wireless systems based on an economic model has been proposed [3]. In [3] game theoretic approach is employed to study the power control in the multi user scenario for the proposed model. It is a powerful tool in modeling interactions between self-interested users and predicting their choice of strategies. Each player in the game maximizes some function of utility in a distributed fashion [3, 4]. The game settles at Nash equilibrium if one exists. Since users act selfishly, the equilibrium point is not necessarily the best operating point from a social point of view. To circumvent this, pricing the system resources appears to be a powerful tool for achieving a more socially desirable result [2,3]. In the MC-DS/CDMA, raising one’s power not only increase their signal-to interference-and–noise ratio (SINR), but also increases the interference observed by other users, thereby declining their SINR, each tend to increase their own power levels, thereby reaching the Nash equilibrium. To overcome this situation a distributed game theoretic power control algorithm to provide efficient use of the radio resources in CDMA system has been established [4,5]. The power control problem in multi-user MIMO CDMA system, using game theory framework has been proposed in [2,6] is considered in this work. A new utility functions for the NPG by using singular value decomposition (SVD) is proposed to solve the problem. The new utility functions, which are based on MIMO MC-DS /CDMA system for wireless data, refer to the spectral efficiency and power efficiency is UbiCC Journal - Volume 3 5 considered. The utility functions also reflect to the quality of service (QoS) that the data users get, where utility is defined as the ratio of throughput to transmit power. Then Nash equilibrium and the performance of the power control games in a single cell MIMO MC- DS/ CDMA system is considered which seems to be an ideal solution to use the system resource more efficiently. The paper is organized as follows. Section 2 explains MIMO MC –DS/ CDMA system and the utility function of the power control game. Section 3 shows the two NMPCGs for the MIMO MC– DS/CDMA system. Section 4 discusses the existence and uniqueness of the games and the algorithm to reach the Nash equilibrium. Simulation results are given and discussed in section 5. Finally, Section 6 draws the conclusion. 2 MIMO MC –DS/CDMA SYSTEM AND UTILITY FUNCTIONS The uplink of a single cell N-users MIMO MC- DS/ CDMA system with feedback is considered for our analysis. Each user is assumed to have Mt transmit antennas and the base station is equipped with Mt x Mr antennas. Each antenna is Subchannel. capable of transmitting 1x Mr subcarriers and processing gain G are considered. In this system, the user's bit stream is demultiplexed among several transmitting antennas, each of which transmits an independently modulated signal, simultaneously and in the same frequency band. The base station receives these signal components by an antenna array whose sensor outputs are processed such that the original data stream can be recovered. Assume that the channel state information (CSI) is perfectly known to receiver, and the transmitter can get the CSI through feedback. Assume H, which is the channel matrix of user i can be decomposed using SVD is given in Eq. (1). H i =U i iV i = m in M t ,M r k =1 convenience. Since each antenna can accommodates sub carriers, the total throughput will be the summation of the throughput of individual carrier. In order to solve the power control problem in the MIMO MC –DS/ CDMA system, a marginal utility function which is expressed in Eq (3) is established. um = T / P i i i min Mt ,Mr N 1 L log M 1 BER 2 k ,i k ,i k=1 S=1 { } ( ( )) = min Mt ,Mr N 1 P ,i k k=1 S=1 { } (3) The power control utility function is given in Eq (4) min Mt ,Mr N 1 L log Mk ,i 1 2BER k ,i 2 k=1 S=1 { } ( ( )) u = i min Mt ,Mr N 1 P ,i k k=1 S=1 min Mt ,Mr N 1 log Mk ,i f k ,i 2 k=1 N=1 min Mt ,Mr N 1 P ,i k k=1 S=1 { } { } ( ) = (4) { } where, f( k,i) =(1-2BER(( k,i))L is called efficiency function. The frame successive rate (FSR) is approximated by, f( ,i), which closely follows the behaviour of the probability of correct reception while producing FSR equals zero at Pi =0. The pricing mechanism was introduced into the CDMA non-cooperative power control game [4]. By using the pricing mechanism, the power control game was more efficient. A new utility function with pricing in MIMO MC- DS/ CDMA power control game is developed. It is expressed in Eq. (5) min Mt ,Mr N 1 { } U i ( k ) i ( k )V i ( k ) M r×I (1) where M t×I U i( k ) and unitary Vi( k ) are vectors, and respectively, and i ( k ) are the singular values of Hi. The peruser attainable normalized throughput, in bit per second Hertz, of MIMO MC- DS /CDMA system is the sum of the normalized throughputs of the min (Mt, Mr) decoupled sub channels. Then the normalized throughput of ith user is given in Eq (2). min Mt ,Mr T = i k=1 { } u c i = k =1 S =1 log Mk ,i f 2 ( k ,i ) tP i P i { } min Mt ,Mr N 1 L log Mk ,i 1 BER ,i Tk ,i = 2 k k=1 S=1 { } ( ( )) (2) where k,i is to represent the SINR of ith user in kth sub channel, which is using sth sub carrier for min Mt ,Mr N 1 P = k =1 P ,i i k S =1 { } (5) where Pi is the total transmitting power of the ith UbiCC Journal - Volume 3 6 user, and t is a positive scalar. This proposed utility function, which gives attention to both spectral efficiency and power efficiency, are based on MlMO MC- DS/ CDMA system. . 3. NON COOPERATIVE MIMO POWER CONTROL GAME Let G = N ,{ Ai},{Ui (.)} no user may gain by unilaterally deviating Nash equilibrium. Hence, Nash equilibrium is a stable operating point because no user has any incentive to change strategy [3]. The Nash equilibrium of proposed NMCPGs are given in sec 4.1 and 4.2. 4.1. The NMCPG, GI, G2 are supermodular games with appropriate strategy space Ai = P i , Pi denote the non respectively [8,9]. Consider the game G1 first. cooperative MlMO power control game (NMCPG) where N = {l, 2... N} is the index set for the mobile users currently in the cell. The ith user select a total transmit power strategy Pi, such that Pi Ai where Ai, denotes the strategy space of ith user. Let the vector P =( P1,........, PN ) denote the outcome of the game in terms of the selected power levels of all users, and P-i, denotes the vector consisting of elements of P other than the ith element. The strategy space of all the users excluding the ith user is denoted A-i. According to the analysis, two NMCPGs are established. These games have the same player space and strategy space, but different utility functions The game G1 is given by, min Mt ,Mr N 1 log M f 2 k ,i k ,i k=1 S=1 P i in t f k,i uli 1 m M ,Mr N 1 = 2 f k,i log2Mk,i k,i P P i k= S= 1 1 i k,i { } ( ) ( ) ( ) ( ) 2f (8) 2u min Mt,Mr N=1 1 li log2 Mk,i = 2 P Pj P i k=1 S=1 i { } ( k,i ) ( ) 2 k,i ( k,i ) k,i Pj (9) 2 { } G1 = max U1i( P ,P i ) = i P Ai i ( ) (6) If f ( k ,i ) u li for 0 , it can be concluded that Pi P j ( 2 k ,i ) all jKi. Assume there exists a P-i such that 00 for all Pj Aj, j " i. Then the Nash equilibrium is unique and general updating algorithm converges monotonically to an equilibrium whose convergence holds for any initial policy in the strategy space. It can be concluded that each of our NMCPGs has unique Nash equilibrium point and then the asynchronous power control algorithm, we considered in this work, converges to a unique Nash equilibrium point. In this algorithm UbiCC Journal - Volume 3 7 users update their transmission powers in the same manner as in [2].Assume user i updates its transmission power at time instances in the set Ti ={ti1 ti2 …..}, with tik< tik+1 and ti0 for all i € N. Let T={t1,T2,…} where T=T1 # T2 # …… # TN with tk