Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most...Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users' demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users' dynamic demand. It is noteworthy that both users' original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.展开更多
Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and...Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.展开更多
基金supported by the National Key Research and Development Plan of China under Grant No.2016YFB0800301the Fund of Science and Technology on Communication Networks Laboratory under Grant No.KX162600024Youth Innovation Promotion Association CAS under Grant No.2016394
文摘Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users' demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users' dynamic demand. It is noteworthy that both users' original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.
基金supported by the National High Technology Research and Development Program(863)of China(No.2015AA016101)the National Natural Science Fund(No.61300184)Beijing Nova Program(No.Z151100000315078)
文摘Information centric networking(ICN) is a new network architecture that is centred on accessing content. It aims to solve some of the problems associated with IP networks, increasing content distribution capability and improving users' experience. To analyse the requests' patterns and fully utilize the universal cached contents, a novel intelligent resources management system is proposed, which enables effi cient cache resource allocation in real time, based on changing user demand patterns. The system is composed of two parts. The fi rst part is a fi ne-grain traffi c estimation algorithm called Temporal Poisson traffi c prediction(TP2) that aims at analysing the traffi c pattern(or aggregated user requests' demands) for different contents. The second part is a collaborative cache placement algorithm that is based on traffic estimated by TP2. The experimental results show that TP2 has better performance than other comparable traffi c prediction algorithms and the proposed intelligent system can increase the utilization of cache resources and improve the network capacity.
