A decentralized batch-based group key management potocol for mobile Internet of Thngs (DBGK).

It is anticipated that constrained devices in the Internet of Things (IoT) will often operate in groups to achieve collective monitoring or management tasks. For sensitive and mission-critical sensing tasks, securing multicast applications is therefore highly desirable. To secure group communications, several group key management protocols have been introduced. However, the majority of the proposed solutions are not adapted to the IoT and its strong processing, storage, and energy constraints. In this context, we introduce a novel decentralized and batch-based group key management protocol to secure multicast communications. Our protocol is simple and it reduces the rekeying overhead triggered by membership changes in dynamic and mobile groups and guarantees both backward and forward secrecy. To assess our protocol, we conduct a detailed analysis with respect to its communcation and storage costs. This analysis is validated through simulation to highlight energy gains. The obtained results show that our protocol outperforms its peers with respect to keying overhead and the mobility of members.

Is China's fifth-largest inland lake to dry-up? Incorporated hydrological and satellite-based methods for forecasting Hulun Lake water levels

Hulun Lake, China’s fifth-largest inland lake, experienced severe declines in water level in the period of 2000-2010. This has prompted concerns whether the lake is drying up gradually. A multi-million US dollar engineering project to construct a water channel to transfer part of the river flow from a nearby river to maintain the water level was completed in August 2010. This study aimed to advance the understanding of the key processes controlling the lake water level variation over the last five decades, as well as investigate the impact of the river transfer engineering project on the water level. A water balance model was developed to investigate the lake water level variations over the last five decades, using hydrological and climatic data as well as satellite-based measurements and results from land surface modelling. The investigation reveals that the severe reduction of river discharge (- 364±64 mm/yr, ~70% of the five-decade average) into the lake was the key factor behind the decline of the lake water level between 2000 and 2010. The decline of river discharge was due to the reduction of total runoff from the lake watershed. This was a result of the reduction of soil moisture due to the decrease of precipitation (-49±45 mm/yr) over this period. The water budget calculation suggests that the groundwater component from the surrounding lake area as well as surface run off from the un-gauged area surrounding the lake contributed ~ net 210 Mm3/yr (equivalent to ~ 100 mm/yr) water inflows into the lake. The results also show that the water diversion project did prevent a further water level decline of over 0.5 m by the end of 2012. Overall, the monthly water balance model gave an excellent prediction of the lake water level fluctuation over the last five decades and can be a useful tool to manage lake water resources in the future.