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Recently, advances in hardware such as CMOS camera nodes have led to the development of Visual Sensor Networks (VSNs) that process sensed data and transmit the useful information to the base station for completing subsequent tasks. Today, object detection and sending useful information to the base station for object recognition is emerged as an imp...
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Citations
... 5 This makes visual monitoring by wireless sensor networks challenging. 6 In any particular instance, a portion of or all camera-enabled detectors will collect visual information from the observed sector and send it over ad hoc wireless links to the network sink. 7 In fact, since redundant nodes can replace faulty nodes, sensing redundancy can be used to raise the achievable accessibility extent of sensor networks. ...
... Here, softmax loss function computation is split into two steps. Probability of normalization is determined in the first section using Equations (5) and (6). Losses are calculated in the second section according to Equation (7) x ...
Many monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target‐based monitoring in order to achieve availability.
... Many other solutions were also proposed to save energy that will not be detailed here, such as those found in [32,35,36]. They addressed the idea of extending the network lifetime from different approaches. ...
A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices.
... WSNs are usually employed in harsh and disaster environments for a long time [26,27]. In other words, limitation on the battery is another main challenge on wireless networks that influences their lifetime [28,29]. Therefore, it is essential to provide an energy-efficient data collection model on WSNs. ...
... The initial energy and transmission range of the wireless sensor nodes are considered 2 and 40 , respectively. For the accurate simulation, we considered CC2420 characteristics with transmission speed 250 / for sending/receiving data in each wireless sensor node [29]. Besides, εfs and εemp are 10pJ/bit/m2 and 0.0013pJ/bit/m4 all over the simulations, respectively. ...
Data collection on Wireless Sensor Networks (WSNs) is a significant challenge to satisfy the requirements of various applications. Providing an energy-efficient routing technique is the primary step in data collection over WSNs. The existing data collection techniques in the WSNs field struggle with the imbalance load distribution and the short lifetime of the network. This paper proposes a novel mechanism to select cluster-heads, cluster the wireless sensor nodes, and determine the optimal route from source nodes to the sink. We employ the genetic algorithm to solve the routing problem considering the hop-count of the cluster-heads to the sink, the number of each cluster member, residual energy of cluster-heads, and the number of cluster-heads connected to the sink as the fitness criteria. Our proposed mechanism uses a greedy approach to calculate the hop-count of each cluster-head to the sink for integrating the clustering and routing process on WSNs. The simulation results demonstrate that our proposed mechanism improves the energy consumption, the number of live nodes, and the lifetime of the network compared to other data collection approaches on WSNs.
... Another concern related to IoT systems is prolonging the lifetime. The lifetime of IoT is considered as the number of rounds between the system starting time and the round in which a certain percentage of initial devices are still alive [25] . For example, when 10% of live devices are assumed as IoT lifetime, our proposed mechanism outperforms the Fuzzy-C, Huris-C, Meta-C, and ELeach-C ones by 5.08, 2.79, 1.9 and 3.02 times, respectively. ...
Wireless communication on the Internet of Things (IoT) requires context-aware data transmission protocols. Developing an energy-efficient clustering mechanism is the primary challenge in data transmission over IoT. The existing approaches struggle with the short lifetime of IoT, imbalance load distribution, and high transmission delay. This paper proposes a novel cluster-head selection and clustering mechanism on IoT. It is composed of two main phases. The first phase selects the near-optimal cluster-heads using Artificial Bee Colony (ABC) algorithm. Performance criteria include the residual energy of the devices, the number of neighbors, Euclidean distance between devices and the sink, and Euclidean distance between each device and its neighbors. The principal objective of the second phase is to group devices into some clusters based on Euclidean distance between each cluster-head and its members, and the data volume generated by clusters. Simulation results verify that our mechanism improves energy consumption, lifetime, and transmission delay.
... To analyze the performance of the proposed mechanism, the dimensions of the monitoring environment are assumed 3400 m × 3400 m. To scatter network equipment all over the environment, we exploit the dataset, which is based on the real IoT in Santander [61] . Based on the information obtained from the dataset, the data model for devices is the FIWARE and their mobility model is the Small World in Motion (SWIM). ...
... For this reason, it can be argued that one of the most important challenges in the WSNs is the extreme energy restriction. 25 Since the efficiency of WSNs is extensively dependent on the network lifetime and its coverage, it is really necessary to consider the optimal algorithms for the object detection, the target tracking, the node clustering, and the data aggregation in such networks to reach the functional requirements such as reliability and being realtime. 26 As it mentioned, to meet the requirements of the applications of WSNs, each wireless sensor node collects the data from its FoV in the monitoring area and transmits the processed information or the raw data to the sink periodically or based on sink demand. ...
Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.
Closed-barrier coverage is a new coverage problem that requires sensors on the barrier to form a closed loop. Due to the features of end-to-end connection, constructing closed barriers is a challenging task. In a Hybrid Directional Sensor Network, sensors can both rotate and move, making it more difficult to schedule sensors to construct closed barriers. To address this challenge, we propose an optimization method for node deployment of closed-barrier coverage (CCOM). First, selecting multiple initiators transforms the closed-barrier coverage problem into a simple linear-barrier construction problem. Then, a weighted barrier graph is used to search the optimal barrier path. Finally, design a rotation strategy and moving plan for the sensors, fully utilizing their rotation and moving capabilities to achieve closed-barrier coverage with the minimum number of sensors and energy consumption. The simulation experiment results show that compared with other advanced methods, our proposed method has better performance in terms of barrier construction success rate, minimum required number of mobile sensors, moving distance, and network lifetime.
The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms’ computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition.
Since the world is rapidly moving towards a digitalized era, enterprises are obligated to transition from paper-based technologies to digital technologies that minimize human intervention. This transition has some challenges, including security, while enterprises are obligated to protect their data from cyber-attacks. Security challenges of Smart Manufacturing Execution Systems (SMES) are one of the most significant areas of this transition. Though many articles were published to support the research activities, there has not been any bibliometric analysis that specifies the research trends. This chapter aims to present a bibliographic analysis of the smart manufacturing execution systems related literature in the Web of Science (WoS) database between January 2010 and November 2020. This paper discusses the research activities and performs a detailed analysis by looking at the number of articles published, citations, institutions, research area, and authors. The analysis concluded that there are several significant impacts of research activities in Germany, China, and Italy compared to other countries.
