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Next-Generation Wireless Networks Topic: Vehicular Networks Team 6 R99945051 洪晧瑜 R99922041 陳彥璋 R99922083 梁逸安 CARS: Context-Aware Rate Selection for Vehicular Networks P. Shankar , T. Nadeem , J. Rosca , L. Iftode , Proc. IEEE ICNP , Oct. 2008 , pp. 1 – 12. Introduction Rate Adaption IEEE 802.11 allows multiple transmission rate at the physical layer (PHY). Figure 1 shows the relationship between link quality and transmission rate. Higher data rates allow high quality links to transmit more data, but have a higher loss probability on low quality links. On the other hand, a low data rate is more resilient to low quality links, but fails to achieve a high throughput in a high quality link. Rate adaption is the problem of selecting the best transmission rate based on the real-time link quality, so as to obtain maximum throughput at all times. Link Quality The link quality is an important factor in rate adaption. However, the link quality changes rapidly due to fading, shadowing, and distance between two stations. In vehicular networks, when the two vehicles are stationary and close to each other, the link quality still oscillated by small-scale fading. In the case of a moving car, the large-scale path loss is much more significant than small-scale fading. Fig.1 Relationship between link quality and transmission rate. Key Challenges Rate adaption in vehicular networks faced the following three key challenges. 1. Rapid variation of the link quality. In addition to the fading effect, the mobility at vehicular speed mainly variates the link quality. So the transmission rate must adapt fast in order to be effective. 2. Few information in the estimation window during infrequent and burstytransmission. The usage of the estimation window is to record the recent transmission results of packet and then estimate the link quality. When a client periodically transmit packets in short bursts and then stay quiet, there are no recent samples available in the estimation window, causing anomalous behavior in rate adaption algorithms. 3. Distinguish losses due to environment from those due to hidden-station induced collision. A frame loss induced by environmental factors should trigger rate adaption to recover from the loss. However, a hidden-station induced loss should not trigger rate adaption, as reducing the transmission rate cannot solve the contention problem. Context-Aware Rate Selection Overview The core idea of CARS is to make use of context information from the application layer, in addition to the frame transmission statistics received from the lower layer. Figure 2 shows the overall system architecture of the CARS scheme. The context information used in CARS broadly consists of information about the environment that is available to the node. Vehicles can obtain the position and speed from a GPS device and periodically broadcast to the neighbors. Fig.2 The CARS system architecture Rate Adaption Algorithm Algorithm 1 describes the CARS algorithm.The key ideaof the CARS algorithm is to estimate the link quality usingboth context information as well as past history. The CARSrate selection algorithm estimates the packet error by meansof a weighted decision function involving two functions, ECand EH(line 4 of algorithm). The function ECuses thecontext information, transmission rate and packet length asinput parameters, and outputs the estimated packet error rate.The function EHuses an exponentially weightedmoving average (EWMA) of past frame transmission statisticsfor each bitrate.The algorithm calculates estimated throughput for each bitrateand selects the bitrate that it predicts will provide the mostthroughput. Context information is represented by the variable ctx. Theweight α determines whether to give preference to the contextinformation or to the EWMA. αis assigned based on thevehicle speed.More precisely, α = max(0,min(1, speed/S)). The authors select S = 30 (m/s) as the best value. N is the maximum number of retransmissions, andavg_retriescomputes the average number of retransmissions(line 5 of algorithm). ρis the weight that signifies the penaltygiven to unsuccessful packet transmission. The authors select ρ = 8 as the bestvalue. Performance Experimental Result Figure 3 shows that CARS consistently outperforms all the existing rate adaption algorithms at all distances. However, there is still a lot of scope for improvement compared with the supremum throughput. “Supremum throughput” means the maximum goodput possible at each distance range.Fig.3Measured good put averaged over distance for CARS compared with different state-of-the-art rate adaption algorithms. Fig.4 Packet distributions with selected data rates for Hotspot uploading scenarios. Fig.5 Packet distributions with selected data rates for Highway scenarios. Vehicle-to-Infrastructure Scenario(Simulation) In this scenario, all vehicles act as clients. The scenario consists of a road of length 5000 meters with multiple lanes. The base station is located at the middle of the road. As shown in figure4, CARS scheme exploits the available higher data rates better than the other schemes. Vehicle-to-Vehicle Scenario(Simulation) The authors describe the scenarios of Vehicle-to-Vehicle communication over a highway. Highway scenariosconsist of a bi-directional highway of 10Km length, whereeach direction has the same number of vehicles. As shown in figure 5, CARS manages to use higher data rates for transmission more optimally than both SampleRate and AARF. Conclusion In this paper, the authors showed why existing rate adaption algorithms underutilize the wireless link capacity in vehicular environments, and introduced Context Aware Rate Selection (CARS), a novel scheme that uses context information from the environment to perform fast rate adaption in dynamic environments. Delay-bounded Routing in Vehicular Ad-hoc Networks Antonios Skordylis, Niki Trigoni , Oxford University Computing Laboratory ACM International Symposium on Mobile Ad hoc Networking and Computing, 2008 Introduction VANETs Vehicles are equipped with wireless transceivers that will enable them to communicate with each other and form a special class of wireless VANET-based applications The authors classify VANET-based applications into two categories: Figure 1 Model. Shaded circles indicate the Communication range 1) those that require broadcasting of information from one vehicle to many nearby vehicles 2) those that require the propagation of information hop-by-hop to a single destination Model (Figure 1). In the urban scenario the authors considerthe network consists of mobile nodes (vehicles) and a few stationary access points (APs) that do not provide full city coverage Objective The authors’ objective is to devise carry-and-forward algorithms that leverage knowledge of traffic statistics in an urban setting to enable timely delivery of messages from vehicles to stationary access points, whilst minimizing wireless transmissions and optimizing bandwidth utilization. Vehicles : Obtain geographical position from a GPS receiver. Get digital map(G(V,E)) and historical traffic statistics , including the average speed u and average vehicle density d. Can communicate with neighboring vehicles or APs within a range of 250 meters. APs: Infrastructure nodes whose absolute location in known to all vehicles. Message: tg :Represent message generation time. λ : Represent the time-to-live value(or the message delay threshold). Algorithm D-Greedy : Delay-bounded Greedy Forwarding The D-Greedy algorithm defines a forwarding strategy that assumes no knowledge of traffic information beyond node speed. Each vehicle maintains a neighbor list by periodically broadcasting beacons. A beacon message contains the unique vehicle identifier (id) and the length of the shortest path between the vehicle’s current location and the location of the closest AP (distToAP). Let distT oInt be the remaining length, until the next intersection, of the current street segment e on which the vehicle is traveling. D-Greedy computes the available delay budget : Del = = λ × distToInt/distToAP Then calculate the expected delay if the Data Muling strategy were to be used : DelDM = distToInt/u. If DelDM ≤ Del then the algorithm opts for the Data Muling strategy. Otherwise, the Multihop Forwarding strategy is chosen. D-MinCost : Delay-Bounded Minimum Cost Forwarding The D-MinCost algorithm leverages the knowledge of global traffic statistics, i.e. estimated values of average vehicle speed u and density d for all edges of the street graph G. Graph extension The authors convert the original directed graph G(V,E) that represents the street map to a new graph G’(V,E’), which contains the same set of vertices, and twice as many edges. For each directed edge e Є G that connects two vertices, they create a new sibling edge e’ Є G’ connecting the same two vertices. Figure 2 Replacing edge (d, a) in G by two sibling edges, one per strategy. Then the authors annotate each edge with two metrics: 1) cost (C), representing the number of message transmissions along the edge, and 2) delay (Del), denoting the time required to forward a message along the edge. For Data Muling strategy : CDM = 1, DelDM = l/u, (l : the length of the edge and u : the average vehicle speed) For Multihop Forwarding strategy : (if l > R and d ≥ l/R ) CMH = l/R, DelMH = CMH × q, (R : communication range, d : the average vehicle density for the edge, q : the time required for the node to check its neighbor list and identify the best next hop) Path selection D-MinCost utilizes the Delay Scaling Algorithm (DSA) to compute delay-constrained least cost paths from the vehicle’s location to all APs on the network. Then the AP that can be reached with the least cost, the exact min-cost path to that access point, and the strategy that should be followed at each edge can be obtained. EVALUATION Compared with Epidemic routing and MinDelay : D-Greedy, DMinCost and MinDelay exhibit very similar behavior, never falling behind Epidemic’s optimal values by more than 10%.( Figure 3) The rate of increase of bandwidth utilization for both D-Greedy and D-Mincost is significantly lower than that of MinDelay and Epidemic.( Figure 4) D-Greedy, DMinCost exhaust the available delay threshold, by delivering messages as late as possible, so delivering messages later than MinDelay and Epidemic. ( Figure 5) The messages that need to travel long distances to an access point make aggressive use of the Multihop Forwarding mode, while messages closer to an access point alternate between the two modes. ( Figure 6) Figure 3 Message Delivery Ratio Figure 5 Average delivery delay Figure 4: The total number of bytes sent Figure 6: Strategy chosen Conclusion In this paper, the authors proposed two packet forwarding schemes for urban vehicular networks, D-Greedy and D-MinCost, to route packets towards fixed infrastructure nodes. The algorithms leverage local and global traffic statistics to choose strategies that minimize communication cost and adhere to delay threshold. The schemes outperform Epidemic and MinDelay in terms of communication cost and achieving a similar delivery ratio to Epidemic.