text: We present our Zero Energy Network'' (ZEN) protocol stack for energy harvesting wireless sensor networks applications. The novelty in our work is @math fold: (1) Energy harvesting aware fully featured MAC layer. Carrier sensing, Backoff algorithms, ARQ, RTS CTS mechanisms, Adaptive Duty Cycling are either auto configurable or available as tunable parameters to match the available energy (b) Energy harvesting aware Routing Protocol. The multi-hop network establishes routes to the base station using a modified version of AODVjr routing protocol assisted by energy predictions. (c) Application of a time series called Holt-Winters'' for predicting the incoming energy. (d) A distributed smart application running over the ZEN stack which utilizes a multi parameter optimized perturbation technique to optimally use the available energy. The application is capable of programming the ZEN stack in an energy efficient manner. The energy harvested distributed smart application runs on a realistic solar energy trace with a three year seasonality database. We implement a smart application, capable of modifying itself to suit its own as well as the network's energy level. Our analytical results show a close match with the measurements conducted over EHWSN testbed.
  @cite_18: The energy-efficiency usage is relative to each layer of wireless sensor networks. In order to improve the ZigBee mesh routing protocol for energy-efficiency usage, we propose a routing algorithm combining AODVjr with the node residual energy. To reduce the energy consumption of bottleneck nodes and extend network lifetime, the improved routing algorithm aims to build a new balancing method between the energy-aware routing metric and the shortest path. Based on the energy model of CC2430 RF transceiver, the implemented routing protocol is simulated using NS2 (Network Simulator version 2). Simulation result shows that this improved protocol can extend the network lifetime very efficiently.
  @cite_26: We consider the problem of periodic task scheduling in sensor nodes powered with energy harvesters. In particular, we focus on systems with stochastic energy sources such as solar panels, and we present two energy-aware scheduling algorithms that reduce the likelihood of task violations. Our algorithms, called Smooth to Average Method (STAM) and Smooth to Full Utilization (STFU), are static schedulers that do not require prescience of the incoming energy to operate effectively.
  @cite_4: Neural network predictor is widely applicable in intelligent forecast. However, it encounters the crucial problem that the predicted values always cannot achieve the satisfactory results because the generalization capability in neural network predictor can't perform extrapolation well. Therefore, this study introduced an integrated predictor to compare with neural network predictor utilized for the applications of non-periodic short-term time series forecast. This proposed predictor in fact is an integrated model, combing a grey prediction model and a cumulative least squared linear prediction model, with the technique of automatically compensating a possible overestimated predicted value by a potential damped predicted value around those predicting points having locally extreme high or low value. The verification of this study also experiments successfully in the stock price indexes forecast, and the results out of integrated predictor achieved the best accuracy on the predicted stock price indexes compared with Back-Propagation neural network predictor, Box-Jenkins, and Holt-Winters smoothing.
  @cite_8: Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.
  @cite_2: This paper, bases on the trend and seasonal fluctuation of China's monthly railway freight, uses ARIMA model and Holt-Winters model to forecast. After comparison of forecasting results, a final freight result from January to December of 2010 is generated. After verification, the predict results of ARIMA model and Holt-Winters model, with errors lower than 4 , are preferable to be used of railway freight forecast.
  @cite_12: A kind of combination forecasting model based on Holt-Winters method and the evaluation model of customer credit are proposed in this paper, utilizing the time series data of demand. The Holt-Winters model is used to identify the pattern which contained in data set, while the customer-credit evaluation model is used to analyze the intent of ordering, which can reduce the uncertainty of prediction. Thus a favorable result can be got by the combination of these two values of forecasting.
summary: Accurate prediction of the energy is an important factor for energy neutral operations. Time series based energy predictions are commonly used for predicting solar energy. Exponential Weighted Moving Average (EWMA) is commonly applied for future energy predictions @cite_8 , @cite_18 and assigns weights to data in the time series, assigning lower weights to older data and giving importance to more recently acquired data. Holt-Winters (HW) time series is widely used in stock markets trend analysis, production planning, healthcare decisions on staffing & purchasing, demand forecasting @cite_12 and for predicting share prices where forecast value takes trend and seasonal variation into account @cite_4 , @cite_2 . In @cite_26 a three state markov chain model is used to predict the solar energy source and their transition probabilities are restricted for day time only. Energy samples from a simulation model are also generated from the model.