Continuous objects detection and tracking in wireless sensor networks

Sheltami, Tarek R.; Khan, Shehryar; Shakshuki, Elhadi M.; Menshawi, Menshawi K.

doi:10.1007/s12652-016-0380-5

Cited by 15 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method avoids transmitting redundant information and reduces communication cost. In [32], authors proposed a new algorithm named continuous object detection and tracking. The energy consumption is mainly reduced by decreasing the number of boundary nodes and uploading information.…”

Section: B Methods For Detecting Continuous Object Boundarymentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

View full text Add to dashboard Cite

Internet of Things (IoT) has been widely used to facilitate environmental perception, where detecting the boundary regions for continuous objects with energy-efficient manner is a challenge to be explored to support domain applications. This paper proposes a novel approach for continuous objects boundary prediction and detection in IoT sensing network, which is called Cloud Model-IoT Sensing Network Collaborative (CM-IoTSNC). Specifically, when an event is potentially occurred, the atmospheric dynamic diffusion model deployed on the cloud is adopted to predict gas diffusion trend in ideal and complex environments, moreover prediction results are transmitted to the IoT devices in the real-time fashion for scheduling security plans in advance. One-hop neighbor nodes are activated by abnormal nodes to determine a more accurate boundary region. Compared our technique with two traditional methods, namely Wireless Sensor Monitoring and Activating One-hop Neighbor Nodes, experimental results show that our method has a good performance in reducing the energy consumption and prolonging the lifetime of the network. INDEX TERMS Boundary detection, continuous objects, IoT sensing networks, energy efficiency, gas dynamic diffusion model.

show abstract

Section: B Methods For Detecting Continuous Object Boundarymentioning

confidence: 99%

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…6, 60% of researchers have used cluster-based networks are used by 60% of the researchers for object tracking. Because of the advantages, it has over the rest such scalability, energy efficient and adaptable; hence, a single, multiple, discrete or continuous object can be tracked using this topology [117]. Also, because continuous objects expand in shape and size, which require many faces to track and monitor, face-based network, would be efficient in case of tracking a discrete object.…”

Section: Analysis and Comparisonmentioning

confidence: 99%

“…In Fig. 10, shows the number of published articles that are energy efficient, cluster-based network has the highest number of papers that are energy efficient [117], [121], [122], [56], [87], [106] followed by the tree-based networks [123], [124]. The methods are energy efficient only in situations where the target speed is constant.…”

Section: Fig 9 Object Recoverymentioning

confidence: 99%

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adam

Anisi

Ali

2020

Future Generation Computer Systems

View full text Add to dashboard Cite

The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smatter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction. Highlights One of the most interesting applications of wireless sensor networks in smart cities would be detecting and monitoring or tracking of objects based on object tracking sensor networks (OTSN).  The review delineates the advantages as well as the weakness of existing OTSN and conduct a comparative view.  Furthermore, research challenges in OTSNs and existing methods are investigated with a focal point of energy constraints, object detection, object speed, tracking accuracy, sensor node collaboration, data aggregation and object recovery position estimation in WSN and smart cities.  Open research issues that required further improvement are summarized.

show abstract

“…Due to their nature and characteristics of changing size and shape, they pose new challenges for the detection algorithm. A new data structure and method are proposed to solve this problem [ 21 ]. The new data structure reduces the communication cost of the overall algorithm without compromising the accuracy, and the new method achieves good performance in simulation.…”

Section: Related Studiesmentioning

confidence: 99%

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Zou

et al. 2017

Sensors

View full text Add to dashboard Cite

Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

show abstract

Continuous objects detection and tracking in wireless sensor networks

Cited by 15 publications

References 36 publications

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

Energy-Efficient Boundary Detection of Continuous Objects in Internet of Things Sensing Networks

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

Contact Info

Product

Resources

About