Energy-aware Context Estimation for IoT Networks

Abstract Next generation network (e.g., 5G networks) are increasingly confronted with demands to deal with the huge data and energy problems. Huge data and energy problems are of vital importance in the Internet of Things (IoT) network. This dissertation includes two sub-works which could lend supports to the solutions to huge data and energy problems. As coming down to the feasibilities of the huge data and energy problems, we may substantiate them to the two sub-works which are context estimation and energy-aware. The first sub-work we bend our mind to is the context estimation, whereas the second one is the network optimization with the ability of the energy-aware. We simulate our proposed frameworks on the current mature networks, say, IEEE 802.11 DCF network and wireless sensor network, due to these two networks have been playing the most important roles on the wireless network evolution process toward the IoT. In the first work, we proposes a particle filter framework to perform an online estimation of the unsaturated buffers of the stations in the IEEE 802.11 DCF network. Using this framework, an access point can adapt flow control to its serving stations and configure related parameters dynamically, thus improving the system throughput and reducing the packet latency. Current research analyzing the unsaturated condition in the IEEE 802.11 DCF network is based on the steady-state model, whereas this proposed method is devoted to the dynamic estimation for the probability distribution of the unsaturated buffer in the stations, in either homogeneous or heterogeneous networks. This study also employs theoretical support from the Bayesian Inference to the particle-filtering algorithm. The estimation accuracy and effectiveness were evaluated via Root Mean Square Error and time complexity. Furthermore, we considered different network loads and the convergence speeds in our analysis. Our analysis demonstrated that the dynamic estimation scheme we are proposing has a greater awareness of the traffic changes in the varying wireless networks, when compared to the traditional static traffic model. On the other hand, we also develop a new statistical decision making framework to select the optimal subset of wireless sensors to activate sensors, while meeting various Quality of Service (QoS) criteria specified by users queries. The sensor nodes are powered solely by energy harvested from the environment and should be activated in an efficient and economical manner based on the available battery energy, which may not be directly observed by the decision maker. Our decision making framework consists of two aspects: the first is the estimation of the current available battery levels of each of the sensors; and the second is a sensor selection policy. The energy estimation step is based on the Cumulative Energy Harvesting Process which is carried out over the collaborative wireless sensor networks. The sensor selection policy is based on the estimated battery levels and uses the Cross-Entropy method which efficiently solves the resulting combinatorial problem to select the sensor set with the long lifetime. This work exhibits the long lifetime of the network for accommodating the large data network in the future. We also investigate the effect of various parameters, which provides insights into the robustness and effectiveness of our framework under different operational conditions.