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Wireless Sensor Networks enable future IoT revolution, smart cities and detecting, classifying and tracking Military threats

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. The self-powered nodes are equipped with sensors of light, sound, heat, pressure, e.t.c, communicating components and data processing that gather information or detect special events and send the data to the base station to be processed.

The emergence of small, low-cost, and low-power sensor technologies like microelectromechanical system (MEMS) sensors, on-board signal processing and wireless communication all integrated on a single chip (system on a chip (SoC)), has stimulated great interests in the utilization of Wireless sensor networks in a wide variety of critical applications.

Wireless Sensor Networks (WSN) are being used in many industrial and civilian application areas including health care, utilities, and remote monitoring. In Smart home concepts they can gather information from environment to provide custom behaviors for a given individual; monitoring temperature, flow level, and pressure parameters in industrial processes like supervisory control and data acquisition; provide real time automatic traffic data collection to assist in alleviating traffic congestion; sensors mounted on heavy duty bridges, within concrete and composite materials and big buildings can enable condition based maintenance of these assets; assist agriculture by providing information regarding soil degradation and water scarcity; supporting mobility while monitoring vital body functions in hospital and home care.

Recently Wilmington police used ShotSpotter technology to pick up the sound of the gunfire and leading them to right block location where they found a car containing the body of a 33-year-old man who was the victim of a homicide. “This narrows the search down quite a bit and directs our officers pretty precisely and accurately as to where to go,” said Wilmington Police Captain Jim Varrone.

The wireless sensor Networks are also of great practical importance to military in surveillance missions as they allow monitoring of border and  other critical areas  to provide early warning of hostile events and targets while removing the risk to human personnel. Military applications of Wireless sensor networks include detection, classification, and determining the direction of movement of intruding personnel and vehicles. They are also useful in detection, classification and accurate localization of battlefield acoustic transient events, namely gunshots, RPG, and artillery fires, especially in urban environments.  They can also useful  for detection of Nuclear, Biological and chemical (NBC) agents.

DARPA had developed “Shooter Localization” technology under its Network Embedded Systems Technology (NEST) program. Ad-hoc wireless network of cheap acoustic sensors were used to accurately locate enemy shooters. Nodes detect shockwave and muzzle blast and send back their data to the base station and Base station then determines shooter location. Fast and accurate enemy shooter localization are key in reducing friendly casualties and neutralizing enemy combatants. The Performance parameters were Average accuracy of 1 meter and Latency of 2 seconds.

China launched a national research project on WSN, supported by the prestigious National 973 Program of China, in September 2006.China is experiencing rapid economic growth, but is also facing serious social and environmental problems, such as safety threats in the mining industry, environmental pollution, and transportation congestion. The advance of wireless sensor networks (WSNs) has made it possible to deploy low-cost networked sensors to address many huge challenges.  The project adopts an application-driven methodology and aims to address the real-world critical problems facing Chinese society

 

Wireless Sensor Networks to play major part in IoT revolution

WSN shall also play major part in another revolution that is in IoT although other communication techniques are also used in IoT.  The future billions of Internet of Things (IoT) devices  shall be deployed ‘everywhere’ and to be accessed ‘any time’ from ‘anywhere’,  anything from large buildings, industrial plants, planes, cars, machines, any kind of goods. WSN technology  shall also be employed in smart cities for applications in smart grid, smart water, intelligent transportation systems, and smart homes.

Defence and Security Applications

Elta Systems have developed a modular network of autonomous distributed sensors including seismic, acoustic, electro-optical sensors and miniature ground surveillance radars. Each sensor, includes a sensitive microphone, for acoustic detection, a geophone picking up seismic vibration from nearby movement, a GPS receiver, communications transceiver and low-power controller and signal processor. The sensor can pick up moving heavy vehicles (such as tanks) from a distance of 500 meters and walking humans from 50 meters.

The Army Research Laboratory (ARL) has conducted experiments using acoustic sensor arrays suspended below tethered aerostats to detect and localize transient signals from mortars, artillery, and small arms fire. The airborne acoustic sensor array calculates an azimuth and elevation to the originating transient, and immediately cues a collocated imager to capture the remaining activity at the site of the acoustic transient.  The aerostat array’s advantage over ground systems is that it is not as affected by diffraction and reflection from man-made structures, trees, or terrain, and has direct line-of-sight to most events.

 Remote Battlefield Sensor System (REMBASS) and  Improved Remote Battlefield Sensor System (IREMBASS)

The Remotely Monitorer Battlefield Sensor System (REMBASS) and Improved REMBASS (I-REMBASS) contain passive sensors that, once emplaced, can be unattended for up to 30 days. The sensors are normally in an idle mode with very low power dissipation. When a target comes into detection range, the sensors note a change in the ambient energy level (seismic/acoustic, thermal, and/or magnetic), and are activated. The sensors identify the target (as a person or a tracked or wheeled vehicle), format this information into short digital messages, and transmit the messages to a monitoring device (either the SMS, the PMS or M/P).  Messages are demodulated, decoded, displayed, and recorded to provide a time-phased record of intruder activity at the SMS.

I-REMBASS is a downsized version of the originally fielded REMBASS.

This system complements other manned/unmanned surveillance systems such as ground surveillance radar, unmanned aerial vehicles, and night observation devices. The system provides division, brigade, and battalion commanders with information from beyond the forward line of own troops (FLOT), and enhances rear area protection. It can be deployed anywhere in the world in a tactical environment in support of reconnaissance, surveillance, and target acquisition (RSTA) operations. The system consists of eleven major components: a passive infrared (IR) sensor, magnetic (MAG) sensor, seismic/acoustic (SA) sensor, radio repeater. Sensor Monitoring Set (SMS), radio frequency monitor (referred to as portable monitoring set (PMS)) , code programmer, antenna group, power supply, mounting rack, and Sensor Signal Simulator (SSS).

 

Army Aviation Research, Development and Engineering Center (AMRDEC)

The Army Aviation Research, Development and Engineering Center (AMRDEC) has developed “FireFly” acoustic sensor with sophisticated algorithms that allow it to classify threats, weather from small arms fire, heavy machine gun or rockets artillery and mortar as well as geo-locate the threat with a high degree of spatial accuracy. The Firefly system was initially deployed to Afghanistan in May 2012, to support a fires detection requirement.

AMRDEC and ARL, have jointly developed The Serenity payload that includes the FireFly acoustic sensor coupled with an electro optical array to provide 360-degree hemispherical surveillance coverage. In addition to small arms detection, the Serenity Payload is also equipped to detect mortar detonation and rocket fire. The systems are increasing the security of deployed Soldiers on a forward operating base. The payload, has been deployed to theater in the Middle East with additional deployments planned for other areas of the world.

A Line in the Sand: A Wireless Sensor Network for Target Detection, Classification, and Tracking

Ohio State University researchers A. Arora, P. Dutta, S. Bapat, and others have studied the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets.

“We consider a surveillance application scenario called “A Line in the Sand.” The objective of this scenario is to identify a breach along a perimeter or within a region. The intruding object, or target, may be an unarmed person, a soldier carrying a ferrous weapon, or a vehicle.

The three fundamental user requirements of this application are target detection, classification, and tracking. The system user specifies several QoS parameters that affect how well the system detects, classifies, and tracks targets. In addition to these QoS parameters, the user defines the area or border to be protected.”

“The authors deployed 90 sensor motes with metal object detection capabilities. They used a combination of magnetometer and micro-power impulse radar sensors to detect and classify moving metallic objects, such as armed vehicles and tanks. The sensor nodes self-form into a network, and once an object passes through the network, nodes collaborate together to classify the passing object as a metallic object or nonmetallic object. ”

Collaborative signal processing enables the system to simultaneously achieve better sensitivity and noise rejection, by averaging across time and space, than is possible with an individual node which averages only across time.

The concept used in this project is the “influence field,” which can be defined as the number of sensors that hear an object. Moreover, the proposed system tries to capture the shape of the influence for detection, classification, and tracking. “The main contribution of our work is that it demonstrates, through a proof of concept system implementation, that it is possible to discriminate between multiple object classes using a network of binary sensors.”

Energy-Efficient Surveillance System Using Wireless Sensor Networks

Researchers Tian He, Sudha Krishnamurthy and others at the University of Virginia and Carnegie-Mellon University have developed an energy-efficient WSN system based on a MICA2 platform with 70 MICA2 motes along a 280-feet-long perimeter. “The general objective of such an application is to alert the military command and control unit in advance to the occurrence of events of interest in hostile regions. The event of interest for our work is the presence of moving vehicles in the deployed region.”

Authors list several application requirements that must be satisfied to make this system useful in practice:

Longevity: Due to the confidential nature of the mission and the inaccessibility of the hostile territory, it may not be possible to manually replenish the energy of the power constrained sensor devices during the course of the mission, Hence, the application requires energy-aware schemes that can extend the lifetime of the sensor devices, so that they remain available for the duration of the mission.

Adjustable Sensitivity: The system should have an adjustable sensitivity to accommodate different kinds of environments and security requirements. In critical missions, a high degree of sensitivity is desired to capture all potential targets even at expense of possible false alarms. In other case, we want to decrease the sensitivity of the system, maintaining a low probability of false alarms in order to avoid inappropriate actions and unnecessary power dissipation.

Stealthiness: It is crucial for military surveillance systems to have a very low possibility of being detected and intercepted. Miniaturization makes sensor devices hard to detect physically; however, RF signals can be easily intercepted if sensor devices actively communicate during the surveillance stage. A zero communication exposure is desired in the absence of significant events.

Effectiveness: The precision in the location estimate and the latency in reporting an event are the metrics that determine the effectiveness of a surveillance system. Accuracy and latency are normally considered important metrics of tracking performance.
Time synchronization and localization are important for a surveillance application because the collaborative detection and tracking process relies on the spatio-temporal correlation between the tracking reports sent by multiple motes. The time synchronization module is responsible for synchronizing the local clocks of the motes with the clock of the base station. The localization module is responsible for ensuring that each mote is aware of its location. The routing component establishes routes through which the motes exchange information with each other and the base station

This work was supported by the DAPRPA IXO offices under the NEST project.

DARPA’s SensIT: Sensor Information Technology For the Warfighter

DARPA acted as a pioneer in the new wave of sensor network research by launching an initiative research program called SensIT whose primary goal was the creation of a new class of software for distributed microsensors. The program has two key thrusts: a) development of novel networking methods for ad hoc deployable microsensors, and b) leveraging the distributed computing paradigm for extraction of right and timely information from a sensor field, including detection, classification, and tracking of targets.

 

The program has five tasks: Fixed Networking, Fixed-Mobile Networking, Collaborative Signal and Information Processing, Query/Tasking, and Integration.

Fixed Networking

Networked microsensors for the military must have many desirable characteristics. First, the networking algorithms and protocols should support ad hoc networking that can scale to large numbers. Second, networking must support rapid self-assembly without any manual intervention for configuration. Third, networking must be adaptive to the environment, as nodes or links may be dead on arrival or links may suffer outages due to fading. Fourth, networking must support the primary operational and technical requirements that drive system parameters, such as low latency and high energy efficiency. Sensor networking must be resource efficient, and support incremental deployment. Finally, networking must be survivable, secure, and have low probability of detection by others when the sensors are deployed.

Fixed-Mobile Inter-Networking

SensIT research aims to enable seamless interaction between forward-deployed, ad hoc sensors and fixed devices, and networks in rear areas. Sensors on mobile platforms might be located on UAVs, robots, ground vehicles, and even troop uniforms. Relays and other power-rich resources such as UAVs and mobile command posts will be integral elements of a fixed-mobile network

Collaborative Signal and Information Processing

Fusion A key feature of a sensor network is collaborative processing among nodes. Incorporating inputs from multiple sources increases the signal to noise ratio, i.e. the accuracy of useful target information. High node density enables dense spatial sampling and data sharing. This data must be fused, whereby the data from multiple nodes is combined to detect, classify, and track targets. The information must be exfiltrated to users or dynamic query points.

Querying and Tasking

User interaction with sensors is handled primarily through tasking of sensor operations and queries. In a sense a sensor field is a database, as sensor devices collect, process, and store data and information.

Integration

A SensIT goal includes the integration of all the software from the above tasks, through well-defined APIs, and demonstration, in lab and in field, of new capabilities of microsensor networks.

 

 References and Resources

 

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