Pedestrian flow is an important indicator of public places, since it can provide more potential economic benefits. Pedestrian flow prediction is developed to help the decisionmaking for the operators (such as shopping center owner). Furthermore, the operators aperiodically arrange some events to attract the nearby pedestrians, such as the sales promotions, and we term this kind of events as business event. Moreover, their placement will affect the distributions of the pedestrian flows. In this paper, we investigate the influence of the business events on the pedestrian flows. Then, we propose an Attraction Based Matrix Factorization model, called ABMF, to efficiently predict the pedestrian flow with business events and enable operators to formulate candidate solutions. The experimental results show the superiority of our prediction method compared with other state-of-the-art prediction techniques.

To improve the experience of various network applications, dynamic rate allocation is an essential issue in recent ISP networks. The emergence of software-defined networking (SDN) and the OpenFlow specification makes dynamic rate allocation in ISP networks efficient. The allocation could locally run on a home network gateway (edge switch) under SDN, but such local range of rate allocation reduces the overall fairness and performance in the whole network. However, under a global range, the request of rate allocation from a small number of hosts will cause all switches on the entire network to participate. This is termed as cascading problem, which causes a high cost for re-allocating the rates with the global range of switches. In this paper, we investigate the cascading problem for dynamic rate allocation with SDN, and discuss the tradeoff between the performance and cost for the range of rate allocation. We propose a Rate Allocation algorithm with Limited Range (RALR) in SDN, and discuss it for dynamic rate allocation by the theory of Lyapunov drift. Our intensive simulations verify the performance of the proposed strategy of rate allocation in SDN.

Software Defined Networks (SDN) have been proposed for use in applications of the Internet of Things (IoT), termed as software-defined IoT (SD-IoT) network, because of the popularity and capability of mobile devices being used for networking in relatively large areas. However, a single controller in SDN has a limited request-processing capability, so a distributed control plane with multiple physical controllers has been used to achieve scalability and reliability for supporting the IoT applications. Accordingly, the data plane of an SDN is partitioned into multiple domains, and each controller just takes over one. When considering both delays and loads of requests to the controllers, a partitioning problem arises. It is required to consider the distributions of flow paths, since inter-domain flow paths will create an extra load of requests to the controllers. In this paper, we investigate the partitioning problem in SD-IoT networks. Since uploading sensing data through the IoT gateways are non-uniform, we utilize a hypergraph to model the relationship between the spatial events and the gateways in IoT for urban sensing. We propose a Partitioning Algorithm for Software-defined IoT Network (PASIN) to partition the SDN by considering both delays and loads of requests from the flow paths. Our extensional simulations verify the effectiveness of our proposed approach.