J. Sens. Actuator Netw.2014, 3(2), 95-112; doi:10.3390/jsan3020095 - published online 11 April 2014 Show/Hide Abstract
Abstract: In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.
J. Sens. Actuator Netw.2014, 3(1), 81-94; doi:10.3390/jsan3010081 - published online 4 March 2014 Show/Hide Abstract
Abstract: The ensuring reliability of wireless sensor networks (WSN) is one of most important problems to be solved. In this article, the influence of the security and communication factors in the reliability of Wireless Sensor Networks was analyzed. Balancing security against performance in WSN is another issue to be solved. These factors should be considered during security analysis of quality of protection of realized protocol. In the article, we analyze wireless sensor network where hierarchical topologies is implemented with high performance routing sensors that forward big amount of data. We present the experiment results which were performed by high-performance Imote2 sensor platform and TinyOS operating system.
J. Sens. Actuator Netw.2014, 3(1), 64-78; doi:10.3390/jsan3010064 - published online 20 February 2014 Show/Hide Abstract
Abstract: Asset monitoring, specifically infrastructure monitoring such as water distribution pipelines, is becoming increasingly critical for utility owners who face new challenges due to an aging network. In the UK alone, during the period of 2009–2010, approximately 3281 mega litres (106) of water were wasted due to failure or leaks in water pipelines. Various techniques can be used for the monitoring of water distribution networks. This paper presents the design, development and testing of a smart wireless sensor network for leak detection in water pipelines, based on the measurement of relative indirect pressure changes in plastic pipes. Power consumption of the sensor nodes is minimised to 2.2 mW based on one measurement every 6 h in order to prolong the lifetime of the network and increase the sensor nodes’ compatibility with current levels of power available by energy harvesting methods and long life batteries. A novel pressure sensing method is investigated for its performance and capabilities by both laboratory and field trials. The sensors were capable of measuring pressure changes due to leaks. These pressure profiles can also be used to locate the leaks.
J. Sens. Actuator Netw.2014, 3(1), 44-63; doi:10.3390/jsan3010044 - published online 3 January 2014 Show/Hide Abstract
Abstract: Step counting-based dead-reckoning has been widely accepted as a cheap and effective solution for indoor pedestrian tracking using a hand-held device equipped with motion sensors. To compensate for the accumulating error in a dead-reckoning tracking system, extra techniques are always fused together to form a hybrid system. In this paper, we first propose a map matching (MM) enhanced particle filter (PF) as a robust localization solution, in which MM utilizes the corridor information to calibrate the step direction estimation and PF is applied to filter out impossible locations. To overcome the dependency on manually input corridor information in the MM algorithm, as well as the computational complexity in combining two such algorithms, an improved PF is proposed. By better modelling of the location error, the improved PF calibrates the location estimation, as well as step direction estimation when the map information is available, while keeping the computational complexity the same as the original PF. Experimental results show that in a quite dense map constraint environment with corridors, the proposed methods have similar accuracy, but outperform the original PF in terms of accuracy. When only partial map constraints are applied to simulate a new testbed, the improved PF obtains the most robust and accurate results. Therefore, the improved PF is the recommended DR solution, which is adaptive to various indoor environments.
J. Sens. Actuator Netw.2014, 3(1), 26-43; doi:10.3390/jsan3010026 - published online 2 January 2014 Show/Hide Abstract
Abstract: The paper presents a novel methodology for the control management of a swarm of autonomous vehicles. The vehicles, or agents, may have different skills, and be employed for different missions. The methodology is based on the definition of descriptor functions that model the capabilities of the single agent and each task or mission. The swarm motion is controlled by minimizing a suitable norm of the error between agents’ descriptor functions and other descriptor functions which models the entire mission. The validity of the proposed technique is tested via numerical simulation, using different task assignment scenarios.