Simulation study of routing protocols in wireless sensor networks

Wireless sensor networks, a distributed network of sensor nodes perform critical tasks in many application areas such as target tracking in military applications, detection of catastrophic events, environment monitoring, health applications etc. The routing protocols developed for these distributed sensor networks need to be energy efficient and scalable. To create a better understanding of the performance of various routing protocols proposed it is very important to perform a detailed analysis of them. Network simulators enable us to study the performance and behavior of these protocols on various network topologies. Many Sensor Network frameworks were developed to explore both the networking issues and the distributed computing aspects of wireless sensor networks. The current work of simulation study of routing protocols is done on SensorSimulator, a discrete event simulation framework developed at Sensor Networks Research Laboratory, LSU and on a popular event driven network simulator ns2 developed at UC Berkeley. SensorSimulator is a discrete event simulation framework for sensor networks built over OMNeT++ (Objective Modular Network Test-bed in C++). This framework allows the user to debug and test software for distributed sensor networks. SensorSimulator allows developers and researchers in the area of Sensor Networks to investigate topological, phenomenological, networking, robustness and scaling issues, to explore arbitrary algorithms for distributed sensors, and to defeat those algorithms through simulated failure. The framework has modules for all the layers of a Sensor Network Protocol stack. This thesis is focused on the simulation and performance evaluation of various routing protocols on SensorSimulator and ns2. The performance of the simulator is validated with a comparative study of Directed Diffusion Routing Protocol on both ns2 and SensorSimulator. Then the simulations are done to evaluate the performance of Optimized Broadcast Protocols for Sensor Networks, Efficient Coordination Protocol for Wireless Sensor Networks on SensorSimulator. Also a performance study of Random Asynchronous Wakeup protocol for Sensor Networks is done on ns2

Incremental Trust in Grid Computing

This paper describes a comparative simulation study of some incremental trust and reputation algorithms for handling behavioural trust in large distributed systems, such as those based on the Grid paradigm. Two types of reputation algorithm (based on discrete and Bayesian evaluation of ratings) and two ways of combining direct trust and reputation (discrete combination and combination based on fuzzy logic) are considered. The various combinations of these methods are evaluated from the point of view of their ability to respond to changes in behaviour and the ease with which suitable parameters for the algorithms can be found in the context of Grid computing systems.

Preserving message integrity in dynamic process migration

Processor and network management have a great impact on the performance of Distributed Memory Parallel Computers. Dynamic Process Migration allows load balancing and communication balancing at execution time. Managing the communications involving the migrating process is one of the problems that Dynamic Process Migration implies. To study this problem, which we have called the Message Integrity Problem, six algorithms have been analysed. These algorithms have been studied by sequential simulation, and have also been implemented in a parallel machine for different user process patterns in the presence of dynamic migration. To compare the algorithms, different performance parameters have been considered. The results obtained have given preliminary information about the algorithms behaviour, and have allowed us to perform an initial comparative evaluation among them

Preserving message integrity in dynamic process migration

Processor and network management have a great impact on the performance of Distributed Memory Parallel Computers. Dynamic Process Migration allows load balancing and communication balancing at execution time. Managing the communications involving the migrating process is one of the problems that Dynamic Process Migration implies. To study this problem, which we have called the Message Integrity Problem, six algorithms have been analysed. These algorithms have been studied by sequential simulation, and have also been implemented in a parallel machine for different user process patterns in the presence of dynamic migration. To compare the algorithms, different performance parameters have been considered. The results obtained have given preliminary information about the algorithms behaviour, and have allowed us to perform an initial comparative evaluation among them.Facultad de Informátic

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time