Several works recently focus on monitoring air quality of critical areas using sensors attached to buses. They aim to monitor the maximum number of critical areas using a limited number of sensors. In practice, we may want to have information for all critical areas. We work on the problem of covering all the areas using the minimum number of sensors in this work. We show that, even when the bus routes are not pre-defined, the problem is NP-hard and is significantly harder than the problem of the previous works. Then, we develop two algorithms for the case that the routes are pre-defined. Those algorithms include a fixed parameter tractability and a 2-approximation algorithm for a special case of the problem. Our experiment results show that, although we usually give the similar number of sensors as the algorithm in the previous works, our algorithms have a shorter computation time than the classical greedy algorithm.

The use of mobile unmanned aerial vehicles (UAV) to collect data from wireless sensor networks has attracted great attention recently. It is often necessary to relate the stream of sensed data to the deployed location of the data producing sensor nodes. This paper addresses the problem of localizing these nodes using a mobile UAV that is unaware of its own location. This, in contrast to existing works, does not assume a GPS equipped UAV. Each sensor node receives beacon packets transmitted by the UAV at random positions and records an RSSI vector. The L1norm distance of two RSSI vectors, which is theoretically proved to be linearly related to the distance between two nodes, is used for ranging. An existing location estimator is then applied to localize target nodes. Extensive simulations in different environments validate high localization accuracy of the proposed algorithm in order of few meters even under noisy channel conditions.

We propose a new approach towards RSS (Received Signal Strength) based wireless localisation for scenarios where, instead of absolute positioning of an object, only the information whether an object is inside or outside of a specific area is required. This is motivated through a number of applications including, but not limited to, a) security: detecting whether an object is removed from a secure location, b) wireless sensor networks: detecting sensor movements outside of a network area, and c) computational behaviour analytics: detecting customers leaving a retail store. The result of such detection systems can naturally be utilised in building a higher level contextual understanding of a system or user behaviours. We use a supervised learning method to overcome issues related to RSS based localisation systems including multipath fading, shadowing, and incorrect model parameters (as in unsupervised methods). Moreover, to reduce the cost of collecting training data, we employ a detection method called One- Class SVM (OC-SVM) which requires only one class of data (positive data, or target class data) for training. We derive a mathematical approximation of accuracy which utilises the characteristics of wireless signals as well as OC-SVM. Based on this we then propose a novel mathematical formula to find optimal placement of devices. This enables us to optimize the placement without performing any costly experiments or simulations. We validate our proposed mathematical framework based on simulated and real experiments.

We propose a new approach towards wireless localisation related to scenarios where the device positions are known a priori, however the device IDs are not, and therefore need to be matched using RF methods. We propose a maximum-likelihood based matching algorithm called MLMatch for resolving this problem based on measured RSSI values. Since the search space of node-to-positions permutations grows factorially with the number of target devices, an Integer Linear Programming formulation is therefore utilised to reduce computation time. In addition, we analyse the stability of the algorithm with respect to different fading models and other wireless propagation parameters such as pathloss. Finally, we report on experiments performed indoors and outdoors using up to 33 wireless devices in order to validate our results.

We propose new approaches toward wireless localization of devices belonging to the Internet of Things (IoT), specifically related to scenarios where the device positions are known a priori, however, the device IDs are not. These positions and device IDs therefore need to be matched using radio frequency positioning methods, which are more time and cost efficient as compared to manual installation. Immediate examples of real world applications include but are not limited to smart lighting and heating. We propose maximum-likelihood matching algorithms called MLMatch and MLMatch3D for resolving this problem based on measured received signal strength indicator values. Since the search space of node-to-position permutations grows factorially with the number of target devices, we propose several searching methods including mixed integer programming, linear programming relaxation to reduce computation time. The MLMatch3D algorithm further addresses the problem whereby nodes are located at multiple rooms and/or floors of a building. This algorithm first utilizes a graph partitioning method to determine in which room a node is located, followed by MLMatch for finding room specific positions corresponding to each node. In addition, this paper analyzes the stability of these algorithms with respect to different wireless fading models as well as compares the performance of these algorithms in various environments via numerical simulations. Finally, we report on experiments performed indoors and outdoors using up to 33 wireless devices in order to demonstrate the problem and validate our results.

For data sets retrieved from wireless sensors to be insightful, it is often of paramount importance that the data be accurate and also location stamped. This paper describes a maximum-likelihood based multihop localization algorithm called kHopLoc for use in wireless sensor networks that is strong in both isotropic and anisotropic network deployment regions. During an initial training phase, a Monte Carlo simulation is utilized to produce multihop connection density functions. Then, sensor node locations are estimated by maximizing local likelihood functions of the hop counts to anchor nodes. Compared to other multihop localization algorithms, the proposed kHopLoc algorithm achieves higher accuracy in varying network configurations and connection link-models.