The use of wireless sensor network (WSN) for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, while the second one is based on the sets similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixedrate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.

Nowadays, keeping a strong and good health is one of the main concern of the general public or governments. The Internet of Things (IoT) has been emerged as an efficient solution to build smart healthcare systems deployed either at hospitals or in-home. Such networks rely on biomedical sensors which are used in electronics-based medical equipment to remotely collect vital signs of patients (pressure, temperature, hart rate, oxygen saturation etc.). Generally, these biosensors are implemented on or inside the patient’s body and take three types of record data such as numerical, images and videos. However, the big data collected by various biomedical sensors along with the need of emergency detection, the limited sensor energies, and the prediction of the progress of patient situation are the major challenges for heath-based IoT applications. In order to overcome these challenges, we propose, in this paper, an efficient sensor-based data analytics for real-time patient monitoring and assessment to help both hospital and medical staff. The proposed mechanism consists in three phases: Emergency detection, adapting sensing frequency and real time prediction of patient situation. Through simulations on real health data, we show the effectiveness of our mechanism compared to other exiting techniques.

Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers’ focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems.

Limited battery power and high transmission energy consumption in wireless sensor networks make in‐network aggregation and prediction a challenging area for researchers. The most energy consumable operation is transmitting data by a sensor node, comparing it with the energy consumption of in‐network computation which is negligible. The energy trade‐off between communication and computation provides applications benefit when processing the data at the network side rather than simply transmitting sensor data. In this study, the authors consider a cluster‐based technique with which data is sent periodically from sensor nodes to their appropriate cluster‐heads (CH). The proposed technique manages energy efficiency in periodic sensor network and it consists of two phases: ‘aggregation phase and adaptation phase’. The aggregation phase is used to find similarities between data (measurements captured during a period p ) in order to eliminate redundancy from raw data, thus reducing the amount of data‐sets sent to the CH. The adaptation phase provides sensors the ability to identify duplicate data‐sets captured among successive periods, using the sets‐similarity joins functions. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. Results show that the proposed technique is effective in term of energy consumption and quality of data.

Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients’ classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.