Journal of Ambient Intelligence and Smart Environments 

About: Journal of Ambient Intelligence and Smart Environments is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Computer science & Smart environment. It has an ISSN identifier of 1876-1364. Over the lifetime, 310 publications have been published receiving 3038 citations.

Internet of things for smart agriculture: Technologies, practices and future direction

Abstract: The advent of Internet of Things (IoT) has shown a new direction of innovative research in agricultural domain. Being at nascent stage, IoT needs to be widely experimented so as to get widely applied in various agricultural applications. In this paper, I review various potential IoT applications, and the specific issues and challenges associated with IoT deployment for improved farming. To focus on the specific requirements the devices, and wireless communication technologies associated with IoT in agricultural and farming applications are analyzed comprehensively. Investigations are made on those sensor enabled IoT systems that provide intelligent and smart services towards smart agriculture. Various case studies are presented to explore the existing IoT based solutions performed by various organizations and individuals and categories according to their deployment parameters. Related difficulties in these solutions, while identifying the factors for improvement and future road map of work using the IoT are also highlighted.

The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0

Abstract: Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on CyberPhysical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies relevant research challenges and opportunities to shape the future of intelligent manufacturing environments.

Human Activity Recognition using Multisensor Data Fusion based on Reservoir Computing

Abstract: Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost.

Capacitive Proximity Sensing in Smart Environments

Abstract: To create applications for smart environments we can select from a huge variety of sensors that measure environmental parameters or detect activities of different actors within the premises. Capacitive proximity sensors use weak electric fields to recognize conductive objects, such as the human body. They can be unobtrusively applied or even provide information when hidden from view. In the past years various research groups have used this sensor category to create singular applications in this domain. On the following pages we discuss the application of capacitive proximity sensors in smart environments, establishing a classification in comparison to other sensor technologies. We give a detailed overview of the background of this sensing technology and identify specific application domains. Based on existing systems from literature and a number of prototypes we have created in the past years we can specify benefits and limitations of this technology and give a set of guidelines to researchers that are considering this technology in their smart environment applications.

Sensors, vision and networks: From video surveillance to activity recognition and health monitoring

Abstract: This paper presents an overview of the state of the art of three different fields with the shared characteristics of making use of a network of sensors, with the possible application of computer vision, signal processing, and machine learning algorithms. Namely, the paper first reports the state of the art and possible future directions for Intelligent Video Surveillance (IVS) applications, by recaping the history of the field in terms of hardware and algorithmic progresses. Then, the existing technologies of Wireless Sensor Networks (WSNs) are compared and described. Their applications to human activity recognition (HAR), both from a single or multiple sensors perspectives, are described and classified, followed by the current research trends and challenges. Finally, recent advances on camera-based health monitoring (including vision-based Ambient Assisted Living and patient monitoring, and camera-based physiological measurements) are described in full details, with the challenges faced.