Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

A. Zamboni, F. Bezzo, N. Shah Spatially explicit static model for the strategic design of future bioethanol production systems. 2. Multiobjective environmental optimization.

In developing an optimization framework to assist in the design process of biofuel systems, the economic effectiveness of the supply network should not be adopted as the sole criterion to focus on. In fact, there has recently been growing attention in including environmental concerns at the strategic level of supply chain management. In this part 2, the spatially explicit multi-echelon mixed integer linear program (MILP) modeling framework described in part 1 of this work has been extended by including environmental issues along with the traditional economic ones within a more comprehensive multi-objective optimization tool. The economics have been assessed by means of supply chain analysis techniques, focusing on biomass cultivation site locations, ethanol production capacity assignment and facilities location, as well as transport system optimization. The environmental performance of the system has been evaluated in terms of greenhouse gas (GHG) emissions, by adopting a well-to-tank (WTT) approach to consider the supply network operating impact on global warming over the entire life cycle. The strategic design tool as developed has been applied and solved in assessing the emerging corn-based Italian ethanol system. The resulting outcomes demonstrate the valuable support that the model may provide in formulating a welladvised strategic policy to promote the market penetration as well as to reduce the social and environmental impacts of biomass-based fuels

A comprehensive approach to the design of ethanol supply chains including carbon trading effects

The optimal design of biofuels production systems is a key component in the analysis of the environmental and economic performance of new sustainable transport systems. In this paper a general mixed integer linear programming modelling framework is developed to assess the design and planning of a multiperiod and multi-echelon bioethanol upstream supply chain under market uncertainty. The optimisation design process of biofuels production systems aims at selecting the best biomass and technologies options among several alternatives according to economic and environmental (global warming potential) performance. A key feature in the proposed approach is the acknowledgement of an economic value to the overall GHG emissions, which is implemented through an emissions allowances trading scheme. The future Italian biomass-based ethanol production is adopted as a case study. Results show the effectiveness of the model as a decision making-tool to steer long-term decisions and investments

European supply chains for carbon capture, transport and sequestration, with uncertainties in geological storage capacity: Insights from economic optimisation

Carbon capture and storage is widely recognised as a promising technology for decarbonising the energy and industrial sector. An integrated assessment of technological options is required for effective deployment of large-scale infrastructures between the nodes of production and sequestration of CO2. Additionally, design challenges due to uncertainties in the effective storage availability of sequestration basins must be tackled for the optimal planning of long-lived infrastructure. The objective of this study is to quantify the financial risks arising from geological uncertainties in European supply chain networks, whilst also providing a tool for minimising storage risk exposure. For this purpose, a methodological approach utilising mixed integer linear optimisation is developed and subsequent analysis demonstrates that risks arising from geological volumes are negligible compared to the overall network costs (always <1% of total cost) although they may be significant locally. The model shows that a slight increase in transport (+11%) and sequestration (+5%) costs is required to obtain a resilient supply chain, but the overall investment is substantially unchanged (max. +0.2%) with respect to a risk-neutral network. It is shown that risks in storage capacities can be minimised via careful design of the network, through distributing the investment for storage across Europe, and incorporating operational flexibility

An approach to optimize multi-enterprise biofuel supply chains including Nash equilibrium models

An increasing concern in the supply chain management field is the determination of policies aiming at improving the performance of the whole system while preserving an adequate reward for each partaker. The work presented here deals with this critical issue applying the Nash game theory to the development of bioenergy systems. The supply chain planning problem was formulated as a Mixed Integer Linear Programming (MILP) model using a linearized Nash-type objective function. The approach was demonstrated through a case study concerning the bioethanol production in Northern Italy. Result show significant improvements in the mechanisms of transfer price formation towards a fair profit allocation between partakers in bioenergy systems