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KEYWORDSMultiple-valued logic, Quaternary logic, Modulo-n addition and multiplication, Galois addition and multiplication.

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I.  INTRODUCTIONToday's high end cars contain more than ten distributed audio and video ECUs such as visual sensors driver assistance cameras, DVD player, and audio sources such as FM and HD-radio systems. These in-vehicle devices are currently interconnected by different automotive specific network technologies such as Media Oriented System Transport (MOST), Controller Area Network (CAN), and Local Interconnect Network (LIN) [1] that provide limited transmission capacities. Point-to-point links realized by analogue Color Video Blanking Signal (CVBS) cables and Low-Voltage Differential Signaling (LVDS) wires are additionally used to transmit real-time video streams from driver assistance camera systems. The application of different network technologies and point-to-point links leads to an inflexible network architecture and a complex cable harness in the car which is expensive and requires a high validation and management effort. Due to the growing demand for new applications in the driver assistance and multimedia fields, the in-vehicle network will become even more complex and costly in the near future. Thus, traditional automotive network technologies are no longer suitable [1].Network systems in vehicles represent very high complex systems. Hence, topologies typically become very complex and the layout criticality is a major topic to be considered. Analysis of almost one hundred different topologies of vehicle manufactures worldwide led to the conclusion that less than 50 % of these layouts had been non-critical, if tolerances of all the parameters involved had been considered in their realistic worst-case scenario. Current vehicle network systems consist of various communication protocol networks such as CAN high speed, CAN low speed, LIN, FlexRay, MOST and others. The major challenge that network developers, dealing with the physical layer implementation, are facing is related to the signal integrity of the communication system. Meaning even if the logical set up and evaluation of the network is fine, the physics can make enormous problems and destroy the complete communication. Since each protocol has its own specification with respect to the physical layer implementation, all of them have their individual issues that the developer must take care of when creating the network.[2]. The paper is organized as follows. Section II gives an insight into the In-Vehicle Network. Section III explains the vehicle communication system. Section IV explains the different types of network topologies. Section V explains the CANoe tool. Section VI explains the CAPL language. Section VII explains the implementation. The paper is concluded in section VIII. II. IN-VEHICLE NETWORKAs automakers are incorporating more and more advanced features into vehicles, there is a growing need for enhanced processing power. S. Channon and P. Miller [3] estimate that the number of microprocessors per vehicle will increase exponentially and by the end of year 2010, the number of microprocessors in any high end vehicle will be 250 [4]. As...

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Rapid technological advances and innovations in the area of autonomous systems push the researchers towards autonomous networked systems with emphasis on Wireless Sensor Networks (WSNs). In WSN event-driven applications, it is critical to report the detected events in the area, resulting in sudden bursts of traffic due to occurrence of spatially-correlated or multiple events, causing loss of data. Also, nodes have very limited power due to hardware constraints. Packet losses and retransmissions resulting from congestion, cost precious energy and shorten the lifetime of sensor nodes. Till now, in WSNs, Congestion control techniques are based on detection of congestion and recovery, but they cannot eliminate or prevent the occurrence of congestion. Collision is a symptom of congestion in the wireless channel and can result in a timevariant channel capacity. Therefore, this research focuses on an efficient medium access control (MAC) technique to coordinate the access of nodes to the shared medium without interference. It uses the queue buffer length of the sensor nodes to estimate the congestion and then dynamically disseminates the traffic along with classifying them into different priority classes to provide a congestion-free routing path to the destination with improved Quality of Service (QoS)

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