Detecting movements of a target using face tracking in wireless sensor networks

Abstract—Target tracking is one of the key applications of wireless sensor networks (WSNs). Existing work mostly requires organizing groups of sensor nodes with measurements of a target’s movements or accurate distance measurements from the nodes to the target, and predicting those movements. These are, however, often difficult to accurately achieve in practice, especially in the case of unpredictable environments, sensor faults, etc. In this paper, we propose a new tracking framework, called FaceTrack, which employs the nodes of a spatial region surrounding a target, called a face. Instead of predicting the target location separately in a face, we estimate the target’s moving toward another face. We introduce an edge detection algorithm to generate each face further in such a way that the nodes can prepare ahead of the target’s moving, which greatly helps tracking the target in a timely fashion and recovering from special cases, e.g., sensor fault, loss of tracking. Also, we develop an optimal selection algorithm to select which sensors of faces to query and to forward the tracking data. Simulation results, compared with existing work, show that FaceTrack achieves better tracking accuracy and energy efficiency. We also validate its effectiveness via a proof-of-concept system of the Imote2 sensor platform. Index Terms—Wireless sensor networks, target tracking, sensor selection, edge detection, face tracking, fault tolerance Ç

Online planning for multi-robot active perception with self-organising maps

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons

A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP

Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances

Spatial networks with wireless applications

Many networks have nodes located in physical space, with links more common between closely spaced pairs of nodes. For example, the nodes could be wireless devices and links communication channels in a wireless mesh network. We describe recent work involving such networks, considering effects due to the geometry (convex,non-convex, and fractal), node distribution, distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Secure Wireless Avionics Intra-Communications the SCOTT approach

Paper presented at DecPS 2018 (held in conjunction with Ada-Europe 2018, 18-22 June, Lisbon, Portugal).This paper presents the objectives and architecture of the use case of secure wireless avionics intracommunications of the European Project SCOTT (secure connected trustable things). SCOTT aims to build trust of the Internet of Things (IoT) in industrial applications. SCOTT addresses multiple issues such as security, safety, privacy, and dependability across 5 industrial domains: automotive, aeronautics, railway, building and healthcare. The aeronautics use case focuses on the application for active flow control (AFC) based on dense wireless sensor and actuator networks (DWSANs). Topics about security, vulnerabilities and safety in the general field of wireless avionics intra-communications (WAICs) will be addressed. The paper presents preliminary conclusions of the vulnerabilities and security solutions across different entities and layers of the aeronautics IoT architecture.info:eu-repo/semantics/publishedVersio

An Event System Architecture for Scaling Scale-Resistant Services

Large organizations are deploying ever-increasing numbers of networked compute devices, from utilities installing smart controllers on electricity distribution cables, to the military giving PDAs to soldiers, to corporations putting PCs on the desks of employees. These computers are often far more capable than is needed to accomplish their primary task, whether it be guarding a circuit breaker, displaying a map, or running a word processor. These devices would be far more useful if they had some awareness of the world around them: a controller that resists tripping a switch, knowing that it would set off a cascade failure, a PDA that warns its owner of imminent danger, a PC that exchanges reports of suspicious network activity to its peers to identify stealthy computer crackers. In order to provide these higher-level services, the devices need a model of their environment. The controller needs a model of the distribution grid, the PDA needs a model of the battlespace, and the PC needs a model of the network and of normal network and user behavior. Unfortunately, not only might models such as these require substantial computational resources, but generating and updating them is even more demanding. Modelbuilding algorithms tend to be bad in three ways: requiring large amounts of CPU and memory to run, needing large amounts of data from the outside to stay up to date, and running so slowly that can't keep up with any fast changes in the environment that might occur. We can solve these problems by reducing the scope of the model to the immediate locale of the device, since reducing the size of the model makes the problem of model generation much more tractable. But such models are also much less useful, having no knowledge of the wider system. This thesis proposes a better solution to this problem called Level of Detail, after the computer graphics technique of the same name. Instead of simplifying the representation of distant objects, however, we simplify less-important data. Compute devices in the system receive streams of data that is a mixture of detailed data from devices that directly affect them and data summaries (aggregated data) from less directly influential devices. The degree to which the data is aggregated (i.e., how much it is reduced) is determined by calculating an influence metric between the target device and the remote device. The smart controller thus receives a continuous stream of raw data from the adjacent transformer, but only an occasional small status report summarizing all the equipment in a neighborhood in another part of the city. This thesis describes the data distribution system, the aggregation functions, and the influence metrics that can be used to implement such a system. I also describe my current towards establishing a test environment and validating the concepts, and describe the next steps in the research plan