Resilient responses to climate change: exploring organisational learning in networked development organisations

Drawing from the organisational learning and governance literature, this paper assesses four internationally networked governmental and non‐governmental organisations in the UK addressing climate change. We analyse how those concerned understand the climate change crisis, what mechanisms are put in place to address information flows, and what evidence there is of learning through sharing information between the organisational headquarters and their regional offices. The most striking finding is the evidence of learning that largely depends on ad‐hoc informal processes and shadow networks.

Quality time [QoS in 802.11e]

Wireless local area networks (WLANs) based on the IEEE 802.11 standard are now widespread. Most are used to provide access for mobile devices to a conventional wired infrastructure, and some are used where wires are not possible, forming an ad hoc network of their own. There are several varieties at the physical or radio layer (802.11, 802.11a, 802.11b, 802.11g), with each featuring different data rates, modulation schemes and transmission frequencies. However, all of them share a common medium access control (MAC) layer. As this is largely based on a contention approach, it does not allow prioritising of traffic or stations, so it cannot easily provide the quality of service (QoS) required by time-sensitive applications, such as voice or video transmission. In order to address this shortfall of the technology, the IEEE set up a task group that is aiming to enhance the MAC layer protocol so that it can provide QoS. The latest draft at the time of writing is Draft 11, dated October 2004. The article describes the yet-to-be-ratified 802.11e standard and is based on that draft.

Towards expressive rule induction on IP network event streams

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.