Sabita Maharjan

Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach

Demand Response Management (DRM) is a key component in the smart grid to effectively reduce power generation costs and user bills. However, it has been an open issue to address the DRM problem in a network of multiple utility companies and consumers where every entity is concerned about maximizing its own benefit. In this paper, we propose a Stackelberg game between utility companies and end-users to maximize the revenue of each utility company and the payoff of each user. We derive analytical results for the Stackelberg equilibrium of the game and prove that a unique solution exists. We develop a distributed algorithm which converges to the equilibrium with only local information available for both utility companies and end-users. Though DRM helps to facilitate the reliability of power supply, the smart grid can be succeptible to privacy and security issues because of communication links between the utility companies and the consumers. We study the impact of an attacker who can manipulate the price information from the utility companies. We also propose a scheme based on the concept of shared reserve power to improve the grid reliability and ensure its dependability.

Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid

Smart grid is widely considered to be the next generation of power grid, where power generation, management, transmission, distribution, and utilization are fully upgraded to improve agility, reliability, efficiency, security, economy, and environmental friendliness. Demand response management (DRM) is recognized as a control unit of the smart grid, with the attempt to balance the real-time load as well as to shift the peak-hour load. Communications are critical to the accuracy and optimality of DRM, and hence at the core of the control performance of the smart grid. In this paper, we introduce cognitive radio into the smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, we formulate the sensing-performance tradeoff problem between better control performance and lower communication cost, paving the way towards a green smart grid. The impact of the communication outage on the control performance of DRM is also analyzed, which reduces the profit of power provider and the social welfare of the smart grid, although it may not always decrease the profit of power consumer. By employing the energy detector, we prove that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate our theoretical analysis.

Economic Approaches for Cognitive Radio Networks: A Survey

Efficient resource allocation is one of the key concerns of implementing cognitive radio networks. Game theory has been extensively used to study the strategic interactions between primary and secondary users for effective resource allocation. The concept of spectrum trading has introduced a new direction for the coexistence of primary and secondary users through economic benefits to primary users. The use of price theory and market theory from economics has played a vital role to facilitate economic models for spectrum trading. So, it is important to understand the feasibility of using economic approaches as well as to realize the technical challenges associated with them for implementation of cognitive radio networks. With this motivation, we present an extensive summary of the related work that use economic approaches such as game theory and/or price theory/market theory to model the behavior of primary and secondary users for spectrum sharing and discuss the associated issues. We also propose some open directions for future research on economic aspects of spectrum sharing in cognitive radio networks.

Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

Demand Response Management in the Smart Grid in a Large Population Regime

In this paper, we introduce a hierarchical system model that captures the decision making processes involved in a network of multiple providers and a large number of consumers in the smart grid, incorporating multiple processes from power generation to market activities and to power consumption. We establish a Stackelberg game between providers and end users, where the providers behave as leaders maximizing their profit and end users act as the followers maximizing their individual welfare. We obtain closed-form expressions for the Stackelberg equilibrium of the game and prove that a unique equilibrium solution exists. In the large population regime, we show that a higher number of providers help to improve profits for the providers. This is inline with the goal of facilitating multiple distributed power generation units, one of the main design considerations in the smart grid. We further prove that there exist a unique number of providers that maximize their profits, and develop an iterative and distributed algorithm to obtain it. Finally, we provide numerical examples to illustrate the solutions and to corroborate the results.

Cooperative Resource Management in Cloud-Enabled Vehicular Networks

Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications.

Social-aware energy harvesting device-to-device communications in 5G networks

With ever increasing demands for local area services in 5G cellular networks, solutions are necessary to deliver local area data in a spectrum- and energy-efficient manner. In this article we propose a new cellular communication architecture that integrates energy harvesting technologies and social networking characteristics into D2D communications for local data dissemination. The proposed architecture includes three domains: the physical domain, the energy domain, and the social domain. Specifically, in the physical domain, D2D communications enable two nearby users to communicate with each other directly. In the energy domain, devices harvest energy from renewable energy sources. In the social domain, D2D users form social networks exhibiting stable social structures and relations. Then we mainly focus on the efficient local data dissemination issues in the proposed architecture and propose two social-aware energy harvesting D2D communication schemes (device relay and device multicast). Illustrative results demonstrate significant spectrum and energy efficiency enhancement for local data dissemination in 5G cellular networks.

Sensing-delay tradeoff for communication in cognitive radio enabled smart grid

Smart grid is widely considered to be the next generation of power grid, which will be integrated with information feedback communication to improve agility, reliability, efficiency and security. One of the key foundations of smart grid is timely access to meter data via reliable communication infrastructure. In this paper, we introduce cognitive radio into home area networks of smart grid, whose framework and result can be trivially extended to neighborhood and wide area networks. By means of spectrum sensing and channel switching techniques, smart meters can decide to transmit meter data either on the original unlicensed channel or the additional licensed channel, in order to improve reliability and timeliness. We find optimal sensing time to reduce packet loss rate and delay, under the constraint that the PU is sufficiently protected. We formulate the sensing-delay tradeoff problem and prove that it has unique optimal sensing time which yields the minimum delay. Simulation results are provided, which match very well with the theoretical results.

Social Computing for Mobile Big Data

Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing.

Toward secure energy harvesting cooperative networks

The concept of energy harvesting cooperative networks is an emerging technology that has very high potential for a large variety of applications. However, energy transfer capability may lead to unprecedented security challenges. In this article, we study energy security issues and the solutions in energy harvesting networks. We first identify typical energy related attacks and then propose defense solutions against these attacks. We also carry out security analysis and performance analysis to evaluate our proposed solutions. Simulation results have shown that the proposed defense solutions are effective and efficient.