An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network

Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energy-efficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an exampl

A comparison of software platforms for Wireless Sensor Networks: MANTIS, TinyOS and ZigBee

Wireless sensor networks are characterized by very tight code size and power constraints, and by a lack of well-established standard software development platforms such as Posix. In this paper, we present a comparative study between a few fairly different such platforms, namely MANTIS, TinyOS and ZigBee, when considering them from the application developer's perspective, i.e. by focusing mostly on functional aspects, rather than on performance or code size. In other words, we compare both the tasking model used by these platforms and the API libraries they offer. Sensor network applications are basically event based, so most of the software platforms are also built on considering event handling mechanism, however some use a more traditional thread based model. In this paper, we consider implementations of a simple generic application in MAN- TIS, TinyOS and the Ember ZigBee development framework, with the goal of depicting major differences between these platforms, and suggesting a programming style aimed at maximizing portability between them

Smart Sensing System for Real-time Automatic Traffic Analysis of Highway Rest Areas

State transportation agency spends millions of dollars annually to maintain and improve the service provided to the drivers in the highway rest areas. In order to collect traffic data in real-time, Researchers can use the vehicle data in the rest areas. Therefore, it is helpful immensely to update the existing safety policies in the rest areas. Transportation agencies don\u2019t have any automated systems to perform \u201cautomatic\u201d and \u201creal-time\u201d vehicle identification and classification in the highway rest areas. Motivated by a dire need to enhance and modernize the transportation system, the author proposes an advanced modular system that will integrate a smart sensor to extract a rest area traffic pattern in real-time. Currently, Caltrans collects traffic data from Automated Vehicle Classification (AVC) stations and also manual census collected in the specific locations. However, this technology is too expensive, time consuming, and disruptive; therefore it has not been used widely in many different locations. In recent years, There have been many significant improvements in MEMS sensors domain with respect to size, cost and accuracy. Moreover, extreme miniaturization of RF transceivers and low power micro-controllers have motivated researchers to develop small and low power sensors and radio equipped modules. These sensors are gradually replacing traditional wired sensor systems. These modules which are often called \u201csensor mote\u201d (size of a quarter) communicate with other sensor nodes and build an intelligent network of sensors. Because of the miniaturization and low power consumption, these sensor motes are extremely efficient due to their low power budget. The authors propose a wireless MEMS sensor based automatic vehicle classification and identification system for highways rest areas. The author's developed Automatic Vehicle Classification and Identification (AVCI) system consists of two parts, AVCI sensor nodes containing magneto-resistive and accelerometer sensors. These sensors calculate speed and axles respectively. The next part, the system proposes a Access Point (AP) which collects data from sensor motes and calculate speed, axles counts and then it classifies the collected data based on Federal Highway Administration (FHWA) 13-categories Scheme-F[5]. The AP includes a RF transceiver to communicate with the sensor motes and also a GPRS (General Packet Radio Service) shield to transmit aggregated traffic data to the county or regional traffic data collection center

A Model-Based Approach for Bridging Virtual and Physical Sensor Nodes in a Hybrid Simulation Framework

The Model Based Design (MBD) approach is a popular trend to speed up application development of embedded systems, which uses high-level abstractions to capture functional requirements in an executable manner, and which automates implementation code generation. Wireless Sensor Networks (WSNs) are an emerging very promising application area for embedded systems. However, there is a lack of tools in this area, which would allow an application developer to model a WSN application by using high level abstractions, simulate it mapped to a multi-node scenario for functional analysis, and finally use the refined model to automatically generate code for different WSN platforms. Motivated by this idea, in this paper we present a hybrid simulation framework that not only follows the MBD approach for WSN application development, but also interconnects a simulated sub-network with a physical sub-network and then allows one to co-simulate them, which is also known as Hardware-In-the-Loop (HIL) simulation