We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation.

Tracking movement of mobile nodes has received significant scientific and commercial interest, but long term tracking of resource-constrained mobile nodes remains challenging due to the high energy consumption of satellite receivers. Cooperative position tracking has been proposed for energy efficiency, however, all the cooperative schemes use opportunistic cooperation and optimize for either energy or accuracy. Considering the existence of a reasonably stable group of mobile nodes like animals, birds, and mobile assets, we propose a cluster-based cooperative tracking algorithm, where cluster head centrally coordinates resource usage among cluster members. Variants of this strategy include the use of a cooperative Kalman filter with and without inertial sensor inputs to estimate nodes' positions. We use the Boid flocking algorithm to generate group position movements in 3D and perform various experiments to compare the energy and position accuracy tradeoff of our proposed scheme with individual-based tracking and existing cooperative schemes. We perform the experiments for fixed periodic GPS sampling and dynamic GPS sampling triggered by node position error uncertainty tolerance limit. Experiments results show that in periodic sampling scheme cooperative tracking yields more than one-quarter reduction in energy consumption and more than one-third improvement in position accuracy over individual-based tracking, however, results for dynamic sampling scheme are comparable with existing cooperative scheme.

We consider the problem of tracking a group of mobile nodes, which have limited computational and energy resources, using noisy RSSI measurements and position estimates available within the group. Existing solutions such as cluster-based GPS duty-cycling, individual tracking, and multilateration-based localization and tracking can only partially deal with the challenges of dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. To efficiently cope with these challenges, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the group size and resource availability into consideration and determines the best solution at any particular time instance. We consider a clustering approach where a cluster head assigns the task of GPS activation and coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes simulated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to 20% in comparison with the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using around 50% less energy.