Pervasive surveillance-agent system based on wireless sensor networks: design and deployment

Nowadays, proliferation of embedded systems is enhancing the possibilities of gathering information by using wireless sensor networks (WSNs). Flexibility and ease of installation make these kinds of pervasive networks suitable for security and surveillance environments. Moreover, the risk for humans to be exposed to these functions is minimized when using these networks. In this paper, a virtual perimeter surveillance agent, which has been designed to detect any person crossing an invisible barrier around a marked perimeter and send an alarm notification to the security staff, is presented. This agent works in a state of 'low power consumption' until there is a crossing on the perimeter. In our approach, the 'intelligence' of the agent has been distributed by using mobile nodes in order to discern the cause of the event of presence. This feature contributes to saving both processing resources and power consumption since the required code that detects presence is the only system installed. The research work described in this paper illustrates our experience in the development of a surveillance system using WNSs for a practical application as well as its evaluation in real-world deployments. This mechanism plays an important role in providing confidence in ensuring safety to our environment

Intelligent Sensors Security

The paper is focused on the security issues of sensors provided with processors and software and used for high-risk applications. Common IT related threats may cause serious consequences for sensor system users. To improve their robustness, sensor systems should be developed in a restricted way that would provide them with assurance. One assurance creation methodology is Common Criteria (ISO/IEC 15408) used for IT products and systems. The paper begins with a primer on the Common Criteria, and then a general security model of the intelligent sensor as an IT product is discussed. The paper presents how the security problem of the intelligent sensor is defined and solved. The contribution of the paper is to provide Common Criteria (CC) related security design patterns and to improve the effectiveness of the sensor development process

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

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Deep Reinforcement Learning based Collision Avoidance in UAV Environment

Unmanned Aerial Vehicles (UAVs) have recently attracted both academia and industry representatives due to their utilization in tremendous emerging applications. Most UAV applications adopt Visual Line of Sight (VLOS) due to ongoing regulations. There is a consensus between industry for extending UAVs’ commercial operations to cover the urban and populated area controlled airspace Beyond VLOS (BVLOS). There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges related to UAVs’ autonomy for detecting and avoiding static and mobile objects. An intelligent component should either be deployed onboard the UAV or at a Multi-Access Edge Computing (MEC) that can read the gathered data from different UAV’s sensors, process them, and then make the right decision to detect and avoid the physical collision. The sensing data should be collected using various sensors but not limited to Lidar, depth camera, video, or ultrasonic. This paper proposes probabilistic and Deep Reinforcement Learning (DRL)-based algorithms for avoiding collisions while saving energy consumption. The proposed algorithms can be either run on top of the UAV or at the MEC according to the UAV capacity and the task overhead. We have designed and developed our algorithms to work for any environment without a need for any prior knowledge. The proposed solutions have been evaluated in a harsh environment that consists of many UAVs moving randomly in a small area without any correlation. The obtained results demonstrated the efficiency of these solutions for avoiding the collision while saving energy consumption in familiar and unfamiliar environments.This work has been partially funded by the Spanish national project TRUE-5G (PID2019-108713RB-C53)