Utilization Of A Large-Scale Wireless Sensor Network For Intrusion Detection And Border Surveillance

To control the border more effectively, countries may deploy a detection system that enables real-time surveillance of border integrity. Events such as border crossings need to be monitored in real time so that any border entries can be noted by border security forces and destinations marked for apprehension. Wireless Sensor Networks (WSNs) are promising for border security surveillance because they enable enforcement teams to monitor events in the physical environment. In this work, probabilistic models have been presented to investigate senor development schemes while considering the environmental factors that affect the sensor performance. Simulation studies have been carried out using the OPNET to verify the theoretical analysis and to find an optimal node deployment scheme that is robust and efficient by incorporating geographical coordination in the design. Measures such as adding camera and range-extended antenna to each node have been investigated to improve the system performance. A prototype WSN based surveillance system has been developed to verify the proposed approach

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

The In-Transit Vigilant Covering Tour Problem of Routing Unmanned Ground Vehicles

The routing of unmanned ground vehicles for the surveillance and protection of key installations is modeled as a new variant of the Covering Tour Problem (CTP). The CTP structure provides both the routing and target sensing components of the installation protection problem. Our variant is called the in-transit Vigilant Covering Tour Problem (VCTP) and considers not only the vertex cover but also the additional edge coverage capability of the unmanned ground vehicle while sensing in-transit between vertices. The VCTP is formulated as a Traveling Salesman Problem (TSP) with a dual set covering structure involving vertices and edges. An empirical study compares the performance of the VCTP against the CTP on test problems modified from standard benchmark TSP problems to apply to the VCTP. The VCTP performed generally better with shorter tour lengths but at higher computational cost

Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

It is common to implicitly assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is itself a major challenge. We address the problem of learning to look around: if a visual agent has the ability to voluntarily acquire new views to observe its environment, how can it learn efficient exploratory behaviors to acquire informative observations? We propose a reinforcement learning solution, where the agent is rewarded for actions that reduce its uncertainty about the unobserved portions of its environment. Based on this principle, we develop a recurrent neural network-based approach to perform active completion of panoramic natural scenes and 3D object shapes. Crucially, the learned policies are not tied to any recognition task nor to the particular semantic content seen during training. As a result, 1) the learned "look around" behavior is relevant even for new tasks in unseen environments, and 2) training data acquisition involves no manual labeling. Through tests in diverse settings, we demonstrate that our approach learns useful generic policies that transfer to new unseen tasks and environments. Completion episodes are shown at https://goo.gl/BgWX3W

Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Leveraging UAVs for 6G Networks

Advancing towards 6G networks emphasizes integrating communication with sensing functionalities, promising unparalleled connectivity, efficiency, and intelligence in forthcoming networks. In this context, uncrewed aerial vehicles (UAVs) emerge as pivotal assets, offering versatile solutions for both communication and sensing tasks. Leveraging their mobility and flexibility, optimizing the UAV deployment or trajectory can enhance the network performance to meet the new demands.Anticipating next-generation 6G networks, traditional cellular architectures' constraints have led to the exploration of new network topologies, including cell-free architectures. These architectures abandon the concept of cell, allowing users to connect to multiple base stations and mitigating the effects of cellular boundaries for fairer scenarios. Combining cell-free architectures with UAVs offers substantial performance gains by leveraging UAV adaptability for dynamic coverage and capacity optimization. To fully leverage this potential, we propose a comprehensive framework for cell-free UAV networks. Initially, UAVs operate as flying base stations within a framework of perfect fronthaul connectivity. This paradigm is extended to accommodate wireless fronthaul scenarios, prompting UAVs to function as flying relays instead of flying base stations.Moreover, UAVs hold significant potential beyond their role in communication. Equipped with sensors and video cameras, UAVs can serve a dual purpose, enabling efficient data collection and sensing tasks. One critical application is wildfire tracking, addressing the pressing need for early detection and monitoring of wildfires. With the escalating frequency and intensity of wildfires globally, efficient wildfire tracking has become imperative for mitigating their devastating impact. Integrating the strengths of cell-free UAV networks with artificial intelligence, our aim is to optimize UAV trajectories to achieve two primary objectives: (i) cover the fire perimeter with cameras and (ii) ensure reliable transmission of captured images to the network. This design significantly enhances resilience, allowing UAVs to transmit images even if certain base stations are compromised by fire incidents. However, the complexity of the overall problem presents a challenge, leading to the utilization of reinforcement learning in this scenario.In addition to the aforementioned applications in cell-free networks and wildfire tracking, this dissertation also explores similar scenarios with cellular connectivity. This includes exploring the integration of communication, sensing, and data collection functionalities withintraditional cellular networks. These methodologies, which involve optimizing trajectories via traditional techniques or more sophisticated such as reinforcement learning, contribute to enhancing the efficiency and reliability of cellular networks as well

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A Sleep-Scheduling-Based Cross-Layer Design Approach for Application-Specific Wireless Sensor Networks

The pervasiveness and operational autonomy of mesh-based wireless sensor networks (WSNs) make them an ideal candidate in offering sustained monitoring functions at reasonable cost over a wide area. To extend the functional lifetime of battery-operated sensor nodes, stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. This work proposes a cross-layer organizational approach based on sleep scheduling, called Sense-Sleep Trees (SS-Trees), that aims to harmonize the various engineering issues and provides a method to extend monitoring capabilities and operational lifetime of mesh-based WSNs engaged in wide-area surveillance applications. Various practical considerations such as sensing coverage requirements, duty cycling, transmission range assignment, data messaging, and protocol signalling are incorporated to demonstrate and evaluate the feasibility of the proposed design approach