An animation-and-chirplet based approach to intruder classification using PIR sensing

Upadrashta, Raviteja; Choubisa, Tarun; Aswath, V. S.; Praneeth, A.; Prabhu, A.; Raman, S Ganapathi; Gracious, Tony; Kumar, Prashant; Kowshik, Sripad S.; Iyer, Madhuri Sheethala; Prabhakar, T. V.

doi:10.1109/issnip.2015.7106914

Cited by 10 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lone packet traffic model is realistic for sensor networks that carry low duty cycle measurements, or just carry an occasional alarm packet. For example, recently there has been an effort to design passive infra-red (PIR) sensor platforms that can detect intrusion of a human or animal, and also can classify whether the intruder is a human or an animal ( [15]). The data rate generated by such a platform deployed in a forest will be very low.…”

Section: Traffic Modelmentioning

confidence: 99%

See 1 more Smart Citation

Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network Along a Line

Chattopadhyay¹,

Coupechoux²,

Kumar³

2016

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (the explore-forward approach). We consider a very light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure asyou-go approach in terms of network cost. Next, we propose two learning algorithms for the explore-forward approach, based on Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical model-free algorithms for deployment over large regions. Finally, we demonstrate the end-to-end traffic carrying capability of such networks via field deployment.

show abstract

Section: Traffic Modelmentioning

confidence: 99%

“…where the inequality in (15) follows from the fact that π * (ξ out , ξ relay ) is an optimal policy for (ξ out , ξ relay ), and the inequality in (16) follows from the fact that π * (ξ out +κ, ξ relay ) is an optimal policy for (ξ out + κ, ξ relay ).…”

Section: Appendix B Explore Forward Deploymentmentioning

confidence: 99%

Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network Along a Line

Chattopadhyay¹,

Coupechoux²,

Kumar³

2016

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

show abstract

“…UpadrashtaRaviteja et al 2015 [15] established a Passive Infrared (PIR) sensor to detect animal intrusion detection arrangement, which was built in-house and takes the shape of a sensor-tower platform (STP). The conventional signals are modelled.…”

Section: Animal Intrusion Detection System Using Neural Networkmentioning

confidence: 99%

A Literature Research Review on Animal Intrusion Detection and Repellent Systems

Kumar¹,

Neelaveni²,

P³

et al. 2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

View full text Add to dashboard Cite

Conflicts between humans and animals have become a big issue in agriculture and the forest zone, putting human lives in jeopardy and causing a huge loss of resources. A wireless sensor-based animal intrusion warning system can overcome these issues, delivering an automatic alert note to the owner and forest authority. The Sensor will detect the animal's movement, and the camera will capture the image. The captured image will be classified using image processing techniques. The Global System for Mobile communications module will send an alarm notification SMS to the forest department or the owner. As a result, the immediate alert information takes corrective decision depending on the type of incursion. This research is being used to learn about the many imaging methods, sensor technologies, and research sets that can be utilized to prevent humans and animals from conflict.

show abstract

“…The lone packet traffic model is realistic for WSNs carrying low duty cycle measurements, or just an occasional alarm packet. For example, recently developed passive infra-red (PIR) sensor platforms can detect and classify human or animal intrusion ( [30]); such sensors deployed in a forest generate very low data. The paper [6, Section 3.2] uses 1.1% duty cycle for a multi-hop WSN for wildlife monitoring; the sensors gather data from RFID collars tied the animals, and generate light traffic.…”

Section: Traffic Modelmentioning

confidence: 99%

Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks Along a Line

Chattopadhyay

Ghosh

Kumar

2018

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

We are motivated by the need, in emergency situations, for impromptu (or "as-you-go") deployment of multihop wireless networks, by human agents or robots (e.g., unmanned aerial vehicles (UAVs)); the agent moves along a line, makes wireless link quality measurements at regular intervals, and makes on-line placement decisions using these measurements. As a first step we have formulated such deployment along a line as a sequential decision problem. In our earlier work, reported in [1], we proposed two possible deployment approaches: (i) the pure as-you-go approach where the deployment agent can only move forward, and (ii) the explore-forward approach where the deployment agent explores a few successive steps and then selects the best relay placement location among them. The latter was shown to provide better performance (in terms of network cost, network performance and power expenditure), but at the expense of more measurements and deployment time, which makes explore-forward impractical for quick deployment by an energy constrained agent such as a UAV. Further, since in emergency situations the terrain would be unknown, the deployment algorithm should not require a-priori knowledge of the parameters of the wireless propagation model. In [1] we, therefore, developed learning algorithms for the explore-forward approach.The current paper fills in an important gap by providing deploy-and-learn algorithms for the pure as-you-go approach. We formulate the sequential relay deployment problem as an average cost Markov decision process (MDP), which trades off among power consumption, link outage probabilities, and the number of relay nodes in the deployed network. While the pure as-you-go deployment problem was previously formulated as a discounted cost MDP (see [1]), the discounted cost MDP formulation was not amenable for learning algorithms that are proposed in this paper. In this paper, first we show structural results for the optimal policy corresponding to the average cost MDP, and provide new insights into the optimal policy. Next, by exploiting the special structure of the average cost optimality equation and by using the theory of asynchronous stochastic approximation (in single and two timescale), we develop two learning algorithms that asymptotically converge to the set of optimal policies as deployment progresses. Numerical results show reasonably fast speed of convergence, and hence the model-free algorithms can be useful for practical, fast deployment of emergency wireless networks.Index Terms-Wireless networks, impromptu network deployment, as-you-go relay placement, relay placement by UAV, Markov decision process, stochastic approximation. !

show abstract

An animation-and-chirplet based approach to intruder classification using PIR sensing

Cited by 10 publications

References 13 publications

Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network Along a Line

Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network Along a Line

A Literature Research Review on Animal Intrusion Detection and Repellent Systems

Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks Along a Line

Contact Info

Product

Resources

About