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We propose the design and development of an energy-efficient Risk Notification Message Dissemination Protocol (RNMDP) for vehicular ad hoc networks (VANETs). RNMDP propagates Risk Notification Messages (RNMs) from their location of origin (called the Risk Zone) to vehicles approaching the Risk Zone. RNMDP assumes each node is aware of its current location in the network. The protocol works as follows: A RNM is broadcast in the neighborhood of the Risk Zone. A node receiving the RNM from another node waits for a Rebroadcast-Wait-Time before deciding to rebroadcast the message. The Rebroadcast-Wait-Time for a node is modeled based on the ratio of the distance between the node and the immediate sender of the RNM and the direction of movement of the node. Priority for rebroadcast is given for nodes farthest away from the sender and traveling towards the Risk Zone. Nodes that are traveling in lanes in direction away from the Risk Zone are also considered for rebroadcast, albeit with a larger Rebroadcast-Wait-Time. During the Rebroadcast-Wait-Time, if a node hears the same RNM again rebroadcast in the neighborhood, then the node stops from further broadcasting the message. If a node does not hear the RNM in its neighborhood during the RebroadcastWait-Time, the node broadcasts the message in its neighborhood. A RNM is considered to have been delivered to all the vehicles in the road, if the message reaches the Target Zone. The performance of RNMDP has been compared with that of the commonly used flooding strategy through extensive simulations conducted for highway networks with different number of lanes and lane density. Simulation results indicate that with a slightly larger delay (i.e., no more than 35% of the delay incurred for flooding), RNMDP can achieve the same message delivery ratio attained by flooding, but at a relatively much lower energy loss compared to flooding., Comment: 15 pages, 15 figures

We propose a two-layer mobile sink and static sensor network (MSSSN) architecture for large scale wireless sensor networks. The top layer is a mobile ad hoc network of resource-rich sink nodes while the bottom layer is a network of static resource-constrained sensor nodes. The MSSSN architecture can be implemented at a lower cost with the currently available IEEE 802.11 devices that only use a single halfduplex transceiver. Each sink node is assigned a particular region to monitor and collect data. A sink node moves to the vicinity of the sensor nodes (within a few hops) to collect data. The collected data is exchanged with peer mobile sinks. Thus, the MSSSN architecture provides scalability, extends sensor lifetime by letting them operate with limited transmission range and provides connectivity between isolated regions of sensor nodes. In order to provide fault tolerance, more than one mobile sink could be collecting data from a given region or a mobile sink could collect data from more than one region. In the later half of the paper, we discuss several open research issues that need to be addressed while implementing the MSSSN architecture., Comment: 11 pages, 1 figure

The objective of this research is to integrate an RFID (Radio Frequency Identification) reader into a Wireless Sensor Network (WSN) to authorize or keep track of people carrying RFID tags. The objective was accomplished by integrating hardware and software. The hardware consisted of two WSN nodes - the RFID node connected to one of the WSN nodes, and a computer connected to the other WSN node. For the RFID equipment, we used the SM130-EK kit, which included the RFID reader and the RFID tags; and for the WSN, we used the Synapse Network Evaluation kit, which included the two sensor nodes. The software consisted of a program module developed in Python to control the microprocessors of the nodes; and a database controlled by a simple program to manage the tag IDs of people wearing them. The WSN and RFID nodes were connected through I2C interfacing. Also, the work of sending commands to the RFID node, to make it read a tag and send it back to the computer, was accomplished by the Python code developed which also controls the data signals. At the computer, the received tag ID is evaluated with other existing tag IDs on the database, to check if that tag has authorization or not to be in the covered area. Our research has the potential of being adapted for use with secure real-time access control applications involving WSN and RFID technologies.