Abstract-For locating a set of stationary devices, algorithms such as MDS-MAP have been favoured by the sensor network community. This is generally because of their low computational complexity. Whilst comparisons for complexity and performance have been done for other algorithms, non-linear regression (NLR) has been neglected. The authors find that it is not much more expensive than MDS-MAP, yet can yield significantly better accuracy for sensor network localisation.
Sensor and actuator networks are often installed in buildings for energy-related applications such as lighting and climate control. Such systems require metadata about the deployed hardware (e.g. which room each is in, what the function of each room is) in order to operate effectively. In this paper we present methods to automatically determine such metadata, in particular the room connectivity graph (i.e., which rooms share a doorway/interior window). Crucially, our method works with just one sensor unit per room, does not require special placement of any of the sensors, and can therefore work on data from existing widely-deployed applications (such as burglar alarms). We apply this method to a 30-day data set from single per-room sensor units deployed in two residential homes in the United Kingdom. Room connectivity is determined based on: spillover of artificial light between rooms; occupancy detections due to movement between rooms; and a fusion of the two. The fusion of both techniques is shown to work better than either technique alone, with a 93% true positive rate and 0.5% false positive rate (aggregate across both houses), and a convergence time of under a week.
Broadcast algorithms are a fundamental building block of a number of ad-hoc protocols and mobile applications. Broadcast primitives in ad-hoc wireless networks should ideally be lightweight and use passive data to determine whether to retransmit a message. They must also deliver messages with a high probability while tolerating adverse network conditions. This paper looks at the particular problem of heterogeneous topologies, in which some regions of an ad-hoc network are critical to the propagation of messages. Traditional broadcast protocols do not perform well in these topologies, while others require complex data structures, some form of training or convergence, or some active route discovery and maintenance. To alleviate these limitations, this paper explores three new lightweight mechanisms that use passive retransmission data to try to recognise a node's importance within a wireless network. By combining these three mechanisms, we construct a family of protocols based on the previously published PAMPA algorithm. Our preliminary evaluation shows that one of these variants is particularly promising, presenting higher delivery ratios in adverse conditions for a small communication overhead.
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