In recent years, evaluation and development of the routing protocols in wireless sensor networks (WSNs) are very important and attractive research topic especially for monitoring applications. Because of, the difficulties of studying WSNs routing protocols in real implementation which takes a lot of time and it can be very expensive, using a suitable simulator become a common trend in such evaluation. This paper presents a systematic performance study of three routing protocols, Ad hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) protocols for WSNs by proposing a simulation model that targeted to the sensor networks with mobile sensor nodes and single sink as it is often seen in many monitoring applications such as military, agriculture, medical, transport, industry, etc to monitor physical environments. The performance study of WSNs routing protocols is analyzed by comparing important metrics like the end-to-end delay, total packets dropped, load, routing overhead, route discovery time, and number of hops per route in the Network under the same random waypoint mobility model for the three protocols. These routing protocols are implemented and simulated using OPNET Modeler simulator. Theoretical analysis and simulation results show that both AODV and DSR protocols have identical on demand behavior but with performance differentials resulted from the differences in protocol mechanics. In addition to, they are suffering from higher end to end delay compared to the Optimized Link State Routing (OLSR) protocol. The results obtained may be useful for implementation of wireless sensor networks for many monitoring and control applications.
Description Wide-area sensor infrastructures, remote sensors, RFIDs, phasor measurements, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. With the recent proliferation of smart-phones and similar GPS enabled mobile devices with several onboard sensors, collection of sensor data is no longer limited to scientific communities, but has reached general public. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to national or homeland security, critical infrastructures monitoring, smart grids, disaster preparedness and management, greenhouse emissions and climate change, and transportation. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or human-induced tactical decisions or strategic policy based on decision sciences and decision support systems.
Recently, the nuclear applications and services are entering in several domains of our life. It is a mandatory direction to protect the physical systems against any malicious sabotage or threat. Human activity detection and recognition techniques have a significant role in the physical protection systems. It enhances the protection regime through anomalous activity detection and authorized human-computer interaction applications. This paper is aimed to demonstrate a real-time system for moving object\human detection and recognition in order to protect the physical systems. It integrates computer vision with Internet of Things (IoT) technologies for detecting and recognizing the moving humans with their occupation times at the camera field. The proposed system can introduce more reliable and active intrusion detection scenarios with simple and low cost implementation techniques. It provides high flexibility for securing access to the monitoring information with storing for the historian purposes. Through an internet environment, the system can monitor the sensitive areas through detecting the unknown faces and their occupation times via ThingSpeak channels. The monitoring information is tracked through web based dashboards and ThingView mobile application. The system can launch automatically the alert notifications through SMS, and e-mail messages. These notifications are received whenever detecting an intruder face and exceeding the occupation times of human motion. The results provide effective features for implementing real-time and low cost applications even with low resolution cameras.
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