Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Design a secure IoT Architecture using Smart Wireless Networks

The Internet of Things (IOT) is a revolution in the technology world, and this field is continuously evolving. It has made life easier for people by providing consumers with more efficient and effective resources in faster and more convenient ways. The Internet of Things is one of the most exciting fields for the future by 2030. 90% of the planet will be connected and all devices in homes and businesses around us will be connected to the Internet making it more vulnerable to violations of privacy and protection. Due to the complexity of its environment, security and privacy are the most critical issues relevant to IoT. Without the reliable security of the devices, they will lose their importance and efficiency. Moreover, the security violation will outweigh any of its benefits. In this paper, an overview of various layered IoT architectures, a review of common security attacks from the perspective of the layer, and the best techniques against these attacks are provided. Moreover, an enhanced layered IoT architecture is proposed, which will be protected against several security attacks

Assessing and augmenting SCADA cyber security: a survey of techniques

SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

Survivability Strategies for Emerging Wireless Networks With Data Mining Techniques: a Case Study With NetLogo and RapidMiner

[EN] Emerging wireless networks have brought Internet and communications to more users and areas. Some of the most relevant emerging wireless technologies are Worldwide Interoperability for Microwave Access, Long-Term Evolution Advanced, and ad hoc and mesh networks. An open challenge is to ensure the reliability and robustness of these networks when individual components fail. The survivability and performance of these networks can be especially relevant when emergencies arise in rural areas, for example supporting communications during a medical emergency. This can be done by anticipating failures and finding alternative solutions. This paper proposes using big data analytics techniques, such as decision trees for detecting nodes that are likely to fail, and so avoid them when routing traffic. This can improve the survivability and performance of networks. The current approach is illustrated with an agent based simulator of wireless networks developed with NetLogo and data mining processes designed with RapidMiner. According to the simulated experimentation, the current approach reduced the communication failures by 51.6% when incorporating rule induction for predicting the most reliable routes.This work was supported in part by the research project Construccion de un framework para agilizar el desarrollo de aplicaciones moviles en el a mbito de la salud through the University of Zaragoza and Foundation Ibercaja under Grant JIUZ-2017-TEC-03, in part by the Universidad de Zaragoza, in part by the Fundacion Bancaria Ibercaja, in part by the Fundacion CAI in the Programa Ibercaja-CAI de Estancias de Investigacion under Grant IT1/18, in part by the program Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores through the Spanish Ministry of Education, Culture and Sport under Grant CAS17/00005, in part by the Desarrollo Colaborativo de Soluciones AAL through the Spanish Ministry of Economy and Competitiveness under Grant TIN2014-57028-R, in part by the Organismo Autonomo Programas Educativos Europeos under Grant 2013-1-CZ1-GRU06-14277, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the project under Grant TIN2017-84802-C2-1-P.García-Magariño, I.; Gray, G.; Lacuesta Gilabert, R.; Lloret, J. (2018). Survivability Strategies for Emerging Wireless Networks With Data Mining Techniques: a Case Study With NetLogo and RapidMiner. IEEE Access. 6:27958-27970. https://doi.org/10.1109/ACCESS.2018.2825954S2795827970

Threats and Challenges for Security Measures on the Internet of Things

[Purpose] The Internet of Things (IoT) has grown rapidly in the past few years and billions of devices are connected to the IoT network for collecting and sharing data globally for various applications. Due to the billions of connected devices, there is a potential risk of data loss, identity theft, device manipulation, trust issues, falsification of data, network/server manipulation, and various impacts in the application of IoT platforms. The IoT-enabled devices are growing rapidly day by day leading to amplify the threats to the reliability of the network. [Methodology/Approach/design] The research work aims to push the present state of the art by identifying privacy and security requirements that IoT is presently needed. [Findings] Various existing solutions for security in IoT and their limitations are addressed. Security issues such as trust based privacy policies for context-awareness, efficient holistic frameworks, and lightweight strategy for system resource constraints are identified. [Practical Implications] The technological age of IoT will be relying on a large number of devices is forecast to expand substantially. Although many of the technology-related privacy and security challenges exist, developers and researchers need to work in collaboration to resolve those threats, as they have accomplished with several other related technologies

A secure and intelligent framework for vehicle health monitoring exploiting big-data analytics

This is an accepted manuscript of an article published by IEEE in IEEE Transactions on Intelligent Transportation Systems on 04/01/2022. Available online: https://doi.org/10.1109/TITS.2021.3138255 The accepted version of the publication may differ from the final published version.The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the current perspective. In this case, the Internet of Everything (IoE) is an emerging concept that can play an essential role in the automotive industry by integrating the stakeholders, process, data, and things via networked connections. But the unavailability of intelligent features leads to negligence about proper maintenance of vehicle vulnerable parts, reckless driving and severe accident, lack of instructive driving, and improper decision, which incurred extra expenses for maintenance besides hindering national economic growth. For this, we proposed a conceptual framework for a central VHMS exploiting IoE-driven Multi-Layer Heterogeneous Networks (HetNet) and a machine learning technique to oversee individual vehicle health conditions, notify the respective owner driver real-timely and store the information for further necessary action. This article transparently portrayed an overview of central VHMS and proposed the taxonomy to achieve such an objective. Subsequently, we unveiled the framework for central VHMS, IoE-driven Multi-tire HetNet, with a secure and trustworthy data collection and analytics system. Finally, anticipating this proposition’s outcome is immense in the automotive sector. It may motivate the researcher to develop a central intelligent and secure vehicular condition diagnostic system to move this sector towards Industry 4.0.The authors would like to thank University Malaysia Pahang for providing the laboratory facilities and financial support under the University FLAGSHIP Research Grants (Project number RDU192203), International Matching Grant (No. RDU192704), and Postgraduate Research Scheme Grant (No. PGRS200325)

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput