Many countries have deregulated their electricity retail markets to offer lower electricity charges to consumers. However, many consumers have not switched their suppliers after the deregulation, and electricity suppliers do not tend to reduce their charges intensely. This paper proposes an electricity market model and evolutionary game to analyze the behavior of consumers in electricity retail markets. Our model focuses on switching costs such as an effort at switching, costs in searching for other alternatives, and so on. The evolutionary game examines whether consumers choose a strategy involving exploration of new alternatives with the searching costs as “cooperators” or not. Simulation results demonstrate that the share of cooperators was not improved by simply giving rewards for cooperators as compensation for searching costs. Furthermore, the results also suggest that the degree of cooperators in a network among consumers has a vital role in increasing the share of cooperators and switching rate.

In communication networks, congestion control, routing, and multiple access schemes for scheduling transmissions are typically regulated by distributed algorithms. Engineers designed these algorithms using clever heuristics that they refined in the light of simulation results and experiments. Over the last two decades, a deeper understanding of these algorithms emerged through the work of researchers. This understanding has a real potential for improving the design of protocols for data centers, cloud computing, and even wireless networks. Since protocols tend to be standardized by engineers, it is important that they become familiar with the insights that emerged in research. We hope that this paper might appeal to practitioners and make the research results intuitive and useful. The methods that the paper describes may be useful for many other resource allocation problems such as in call centers, manufacturing lines, hospitals and the service industry.

Known an a criterion that solves the trade-off between fairness and efficiency, proportional fairness is well-studied in cellular networks in the Qualcomm High Data Rate System. In multi-hop wireless networks, proportional fairness is solved by maximizing the logarithmic aggregate utility function. However, this approach can deal with instantaneous rates only where long term fairness is to be targeted. In this case, cumulative rates are more suitable. This paper proposes a framework for multi-hop wireless networks to guarantee fairness of cumulative data rates. The framework can be extended to other kinds of fairness such as max-min fairness, and to more complex networks, multi-channel multi-radio wireless networks.

We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

This letter introduces a new fairness concept, namely proportional quasi-fairness and proves that the optimal end-to-end rate of a network utility maximization can be proportionally quasi-fair with a properly chosen network utility function for an arbitrary compact feasible set.

In most cases in wireless networks, a user has a two-way communication that consists of two sessions: uplink and downlink sessions, and its overall satisfaction to the communication depends on the quality of service of both sessions. However, in most previous approaches in wireless resource allocation, the satisfactions of a user for its uplink and downlink sessions are modeled separately and treated independently, which fails to accurately model user's overall satisfaction to its communication. Hence, in this paper we model user's overall satisfaction to its communication considering both its uplink and downlink sessions. To this end, we propose a novel concept for a utility function to model user's overall satisfaction to its communication, which is called a user-level utility function, considering user's satisfaction to uplink and downlink sessions jointly. To show the appropriateness of our approach, we apply our user-level utility functions to scheduling problems in TDMA wireless networks and show the performance improvement of our approach over the traditional approach that does not treat uplink and downlink sessions of a user jointly.

We use the network utility maximization (NUM) framework to create an efficient and fair medium access control (MAC) protocol for wireless networks. By adjusting the parameters in the utility objective functions of NUM problems, we control the tradeoff between efficiency and fairness of radio resource allocation through a rigorous and systematic design. In this paper, we propose a scheduling-based MAC protocol. Since it provides an upper-bound on the achievable performance, it establishes the optimality benchmarks for comparison with other algorithms in related work.