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Buildings have been an important energy consuming sector, and inefficient controlling of lights can result in wastage of energy in buildings. The aim of the study is to reduce energy consumption by implementing a smart lighting system that integrates sensor technologies, a distributed wireless sensor network (WSN) using ZigBee protocol, and illumination control rules. A sensing module consists of occupancy sensors, including passive infrared (PIR) sensors and microwave Doppler sensors, an ambient light sensor, and lighting control rules. The dimming level of each luminaire is controlled by rules taking into consideration occupancy and daylight harvesting. The performance of the proposed system is evaluated in two scenarios, a metro station and an office room, and the average energy savings are about 45% and 36%, respectively. The effects of different factors on energy savings are analyzed, including people flow density, weather, desired illuminance, and the number of people in a room. Experimental results demonstrate the robustness of the proposed system and its ability to save energy consumption. The study can benefit the development of intelligent and sustainable buildings.

IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.

Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology.

In a distributed wireless sensor network (DWSN), sensors continuously perceive the environment, collect data, and transmit it to remote users through the network so as to realize real-time monitoring of the environment or specific targets. However, given the openness of wireless channels and the sensitivity of collecting data, designing a robust user authentication protocol to ensure the legitimacy of user and sensors in such DWSN environments faces serious challenges. Most of the current authentication schemes fail to meet some important and often overlooked security features, such as resisting physical impersonation attack, resisting smartcard loss attack, and providing forward secrecy. In this work, we put forward a practically secure two-factor authentication scheme using a physically unclonable function to prevent a physical impersonation attack and sensor node capture attack, utilize Chebyshev chaotic mapping to provide forward secrecy, and improve the efficiency and security of session key negotiation. Furthermore, we use the fuzzy verifier technique to prevent attackers from offline guessing attacks to resist smartcard loss attacks. In addition, a BAN logic proof and heuristic security analysis show that the scheme achieves mutual authentication and key agreement as well as prevents known attacks. A comparative analysis with state-of-the-art schemes shows that the proposal not only achieves desired security features but also maintains better efficiency.

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented.

Distributed Wireless Sensor Networks (DWSNs) comprised of set of sensor nodes that are geographically distributed in harsh environments. However, a centralized WSN does not consider the network scalability and also leads to high energy consumption. Due to these open issues, distributed computing approach is considered since it resolves scalability issue and several paths are established for data transmission. To achieve higher energy efficiency, scalability and security, in this paper we propose a distributed protocol called Secure Coronas-Based Zone Clustering and Routing (SC-ZCR). The proposed SC-ZCR aim at addressing the number of issues in DWSN and it support for long-term deployment. In SC-ZCR, we will pursue several processes including Zone Clustering, Energy Efficient Routing and Data Encryption and Security Verification. Zone clustering is carried out using Adaptive Neuro-Fuzzy System, where we consider four parameters: node angle, distance between sensor node to the sink node, node residual energy and belief value. Belief value of each sensor node is computed using Principal Component Analysis. Then energy efficient routing is established by Q-Hop Routing Protocol, which finds optimum and shortest path using Whale Optimization Algorithm. For data packets encryption, RC6 is used and then security level of data packets are verified using potential weight factor δ , which is computed using key size K , block size B , and number of rounds R . Experiments conducted using NS3.26 simulator and the simulation result show that the proposed SC-ZCR outperforms in terms of Coverage Ratio, Residual Energy, Network Lifetime, Delay, Packet Drop Rate, and Security Strength.

Sensor networks are often part of a cyber-physical system. A large-scale sensor network often involves big data collection and data fusion. The agent technology has drawn much attention in wireless sensor networks (WSNs) to perform data fusion and energy balancing. Existing multi-agent itinerary algorithms are either time-consuming or too complicated in practice. In this paper, we design a routing itinerary planning scheme for the multi-agent itinerary problem by constructing the spanning tree of WSN nodes. First, we build a multi-agent-based distributed WSN (DWSN) model and energy consumption model. Second, we present a novel routing itinerary algorithm named DMAIP, which can group all the sensor nodes into multiple itineraries for agents. We also extend DMAIP and design DMAIP-E, which can avoid long-distance transmission in DMAIP. Our evaluation results demonstrate that our algorithms are better in terms of life cycle and energy consumption than the existing DWSN data collecting schemes.

The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

In recent years, there has been significant growth in mobile wireless sensor networks (WSNs), yet prevailing research has primarily focused on 2D planar deployments, overlooking the importance of three-dimensional (3D) coverage in various applications. This oversight leads to ineffective data gathering due to incomplete area coverage and network connectivity. In previous researches (Boufares et al. in: 2018 31 IEEE/ACS 15th international conference on computer systems and applications (AICCSA), Aqaba, Jordan, pp 1–8, 2018, in: 13th international wireless communications and mobile computing conference (IWCMC), Valencia, pp 1628–1633, 2017, in: IEEE wireless communications and mobile computing conference (IWCMC), Dubrovnik, Croatia, pp 563–568, 2015a, in: the 4th international conference on performance evaluation and modeling in wired and wireless networks (PEMWN), Hammamet, Tunisia, pp 103–108, 2015b), we proposed 3D mobile autonomous redeployment strategies based on the Virtual Forces Algorithm, tailored for diverse configurations: 3D volume applications such as smart homes or agriculture, 3D flat surfaces like snow monitoring, and 3D terrain surfaces like volcano monitoring. Our approach ensured complete coverage and connectivity in these scenarios. Moreover, energy efficiency emerges as a critical concern, given the autonomous and mobile nature of sensor nodes operating on finite battery power. Hence, in this paper, we provide an overview of our previous results, highlighting the efficacy of our 3D mobile autonomous redeployment strategies across various configurations. Subsequently, we delve into an in-depth analysis of the energy consumption associated with the different proposed contributions. Building upon these insights, we propose an energy harvesting approach aimed at extending the operational lifespan of mobile 3D WSNs, thus ensuring sustained functionality in diverse real-world applications.Through these contributions, we address critical challenges and pave the way for improved performance in modern sensor network applications.