A survey of wireless technologies coexistence in WBAN: analysis and open research issues

Wireless Body Area Network (WBAN) is the most convenient, cost-effective, accurate, and non-invasive technology for e-health monitoring. The performance of WBAN may be disturbed when coexisting with other wireless networks. Accordingly, this paper provides a comprehensive study and in-depth analysis of coexistence issues and interference mitigation solutions in WBAN technologies. A thorough survey of state-of-the art research in WBAN coexistence issues is conducted. The survey classified, discussed, and compared the studies according to the parameters used to analyze the coexistence problem. Solutions suggested by the studies are then classified according to the followed techniques and concomitant shortcomings are identified. Moreover, the coexistence problem in WBAN technologies is mathematically analyzed and formulas are derived for the probability of successful channel access for different wireless technologies with the coexistence of an interfering network. Finally, extensive simulations are conducted using OPNET with several real-life scenarios to evaluate the impact of coexistence interference on different WBAN technologies. In particular, three main WBAN wireless technologies are considered: IEEE 802.15.6, IEEE 802.15.4, and low-power WiFi. The mathematical analysis and the simulation results are discussed and the impact of interfering network on the different wireless technologies is compared and analyzed. The results show that an interfering network (e.g., standard WiFi) has an impact on the performance of WBAN and may disrupt its operation. In addition, using low-power WiFi for WBANs is investigated and proved to be a feasible option compared to other wireless technologies.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.