The cost of a large wireless sensor network consisting of hundreds of devices may only be justified if it is deployed for multiple monitoring tasks. Introducing multiple tasks to the system however raises concerns regarding resource contention and quality of service (QoS). For example, rapid response tasks may have strict requirements with regards to data delivery latency, whereas data gathering tasks may be more lenient. In order to address the differential QoS challenge, we use the random re-routing (RRR) wireless sensor network routing protocol to prioritise the packets of some tasks above those of others. We extend the RRR protocol beyond that found in the previous literature to improve performance, and in simulation demonstrate an acknowledgement mechanism for adapting the packet priorities assigned to tasks. 1 Index Terms-Wireless sensor networks, quality of service.
Abstract-We present a vision of an Intelligent Network in which users dynamically indicate their requests for services, and formulate needs in terms of Quality of Service (QoS) and price. Users can also monitor on-line the extent to which their requests are being satisfied. In turn the services will dynamically try to satisfy the user as best as they can, and inform the user of the level at which the requests are being satisfied, and at what cost. The network will provide guidelines and constraints to users and services, to avoid that they impede each others' progress. This intelligent and sensible dialogue between users, services and the network can proceed constantly based on mutual observation, network and user self-observation, and on-line adaptive and locally distributed feedback control which proceeds at the same speed as the traffic flows and events being controlled. We illustrate these concepts via an experimental testbed at Imperial College, based on the Cognitive Packet Network (CPN), that embodies some of these functionalities thanks to "smart packets" and reinforcement learning. At its edges, CPN is fully compatible with the IP protocol, while internally it offers routing that is dynamically modified using on on-line sensing and monitoring, based on users' QoS needs and overall network objectives.
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