Throughput, Bit-Cost, Network State Information: Tradeoffs in Cooperative CSMA Protocols

In wireless local area networks, spatially varying channel conditions result in a severe performance discrepancy between different nodes in the uplink, depending on their position. Both throughput and energy expense are affected. Cooperative protocols were proposed to mitigate these discrepancies. However, additional network state information (NSI) from other nodes is needed to enable cooperation. The aim of this work is to assess how NSI and the degree of cooperation affect throughput and energy expenses. To this end, a CSMA protocol called fairMAC is defined, which allows to adjust the amount of NSI at the nodes and the degree of cooperation among the nodes in a distributed manner. By analyzing the data obtained by Monte Carlo simulations with varying protocol parameters for fairMAC, two fundamental tradeoffs are identified: First, more cooperation leads to higher throughput, but also increases energy expenses. Second, using more than one helper increases throughput and decreases energy expenses, however, more NSI has to be acquired by the nodes in the network. The obtained insights are used to increase the lifetime of a network. While full cooperation shortens the lifetime compared to no cooperation at all, lifetime can be increased by over 25% with partial cooperation.Comment: Some typos in v1 were corrected, to be presented at ISWCS 2010 in York, United Kingdo

Monocentric analysis of restricting the budget share of housing alone or with transportation

Considering the prolonged rise of energy price and the still elevated housing prices, the policy to limit the share of housing expenses in the households' budget, so as to secure their solvability, has been criticized. Supposedly, it induces people to get farther from the city center in search for cheaper housing prices, but with subsequent increased transportation costs that are often disregarded during the house search process. Therefore, to improve the well-being of households, it has been advocated to set a constraint on the share of both housing and transportation expenditure. The paper is purported to analyze and compare the effects of the two policies in terms of: 1. Well-being of the households; 2. Land-use: city size and density curve; 3. Solvability of the households; 4. Transportation costs. The analysis is carried out within the classical monocentric model of urban economics. After setting a general analysis, an applied model is specified to capture the effects of each policy in straightforward formulae. It is shown that constraining housing expenses may increase the well-being of households. Besides, both policies prove effective in reducing urban sprawl and hereby energy consumption. Thus the choice of the optimal policy will depend on the local authority's objectives.monocentric model, urban economics, housing expenses, transportation expenses, housing policy

SINGLE VERSUS MULTIPLE OBJECTIVE RECREATION TRIPS: A SPLIT-SAMPLE MULTI-SITE ANALYSIS

A random utility model of the choice over trip duration on multiple objective recreation trips is developed. We explore several methods for allocating trip expenses to estimate the welfare of single and multiple-objective trips. Preliminary results suggest that traditional methods for handling travel costs are inadequate in a multiple-objective setting.Resource /Energy Economics and Policy,

LIHEAP Stabilizes Family Housing and Protects Children's Health

The heating and cooling season presents special challenges for our nation's low-income families. With limited resources, many are challenged to manage the seasonal spike in utility expenses, facing decisions about whether to pay the rent, keep the lights and heat on, or buy enough groceries to get through the end of the month. We know that each of these decisions will have significant implications for the health of their youngest children. Unfortunately, these tough choices are all too common this winter as the nation experiences increases in energy prices, unusually cold weather, and continued high unemployment

Energy-aware dynamic pricing model for cloud environments

Energy consumption is a critical operational cost for Cloud providers. However, as commercial providers typically use fixed pricing schemes that are oblivious about the energy costs of running virtual machines, clients are not charged according to their actual energy impact. Some works have proposed energy-aware cost models that are able to capture each client’s real energy usage. However, those models cannot be naturally used for pricing Cloud services, as the energy cost is calculated after the termination of the service, and it depends on decisions taken by the provider, such as the actual placement of the client’s virtual machines. For those reasons, a client cannot estimate in advance how much it will pay. This paper presents a pricing model for virtualized Cloud providers that dynamically derives the energy costs per allocation unit and per work unit for each time period. They account for the energy costs of the provider’s static and dynamic energy consumption by sharing out them according to the virtual resource allocation and the real resource usage of running virtual machines for the corresponding time period. Newly arrived clients during that period can use these costs as a baseline to calculate their expenses in advance as a function of the number of requested allocation and work units. Our results show that providers can get comparable revenue to traditional pricing schemes, while offering to the clients more proportional prices than fixed-price models.Peer ReviewedPostprint (author's final draft