Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Bananas are the number one selling produce item in the grocery store. On average, bananas account for 6% of produce department sales and 1% of total grocery store sales. According to The Packer’s “2010 Fresh Trends”, 88% of consumers in all categories purchase bananas. Also, 94% of consumers in the study purchased bananas within the last twelve months. Over the last decade, fuel prices have increased to a point where logistics and shipping have become more important than ever to the banana industry. This logistics challenge is compounded because there are no bananas grown in the United States and the fruit has to be shipped from around the world. Fuel is used at high rates via the ocean cargo and trucking shipments to meet yearly demand. To manage these logistical challenges, this thesis analyzes the optimal shipping route for bananas arriving to the west coast from Central and South America to various markets using a transshipment model. The goal of the transshipment model estimates the supply chain that creates the lowest cost. Through analysis of fuel, trucking, and shipping markets, the model makes the optimal decision regarding transportation routing. The model is limited to transportation costs only. However, items such as fruit costs and other additional up charges could be analyzed.

The pervasiveness and operational autonomy of mesh-based wireless sensor networks (WSNs) make them an ideal candidate in offering sustained monitoring functions at reasonable cost over a wide area. To extend the functional lifetime of battery-operated sensor nodes, stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. This work proposes a cross-layer organizational approach based on sleep scheduling, called Sense-Sleep Trees (SS-Trees), that aims to harmonize the various engineering issues and provides a method to extend monitoring capabilities and operational lifetime of mesh-based WSNs engaged in wide-area surveillance applications. Various practical considerations such as sensing coverage requirements, duty cycling, transmission range assignment, data messaging, and protocol signalling are incorporated to demonstrate and evaluate the feasibility of the proposed design approach.

The pervasiveness and operational autonomy of mesh-based wireless sensor networks (WSNs) make them an ideal candidate in offering sustained monitoring functions at reasonable cost over a wide area. To extend the functional lifetime of battery-operated sensor nodes, stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. This work proposes a cross-layer organizational approach based on sleep scheduling, called Sense-Sleep Trees (SS-Trees), that aims to harmonize the various engineering issues and provides a method to extend monitoring capabilities and operational lifetime of mesh-based WSNs engaged in wide-area surveillance applications. Various practical considerations such as sensing coverage requirements, duty cycling, transmission range assignment, data messaging, and protocol signalling are incorporated to demonstrate and evaluate the feasibility of the proposed design approach.