SURVEILLANCE SYSTEM BASED ON QUADRANTS AS SUPPORT IN URBAN POPULATIONS

El propósito de este trabajo de investigación es el desarrollo de un sistema con capacidad de procesar los datos obtenidos en tiempo real por parte de ciudadanos, los cuales ofrecen control de riesgo y de protección de privacidad y seguridad, para las comunidades urbanas. Para tal propósito, se diseñó una arquitectura capaz de gestionar las peticiones de los ciudadanos y generar las respuestas por parte del cuadrante más cercano a la solicitud. Además, el sistema tiene la capacidad de generar recomendaciones asociadas a los sitios que generen riesgo a las personas que lo visitan. Los resultados de las pruebas del sistema demostraron una mejora significativamente en los tiempos de respuesta de la Policía frente a las solicitudes de los ciudadanos. Este trabajo de investigación se desarrolló bajo los parámetros del Plan Nacional de Vigilancia Comunitaria por Cuadrantes (PNVCC) de la Policía Nacional de Colombia.PALABRAS CLAVES: Computación urbana, computación ubicua, GPS, QR-Code, sistemas de recomendación, teoría de colas.The purpose of this research is to develop a system capable of processing the data in real time by citizens, which offer risk control and privacy protection and security for urban communities. For this purpose architecture capable of managing the requests of citizens and generate responses from the nearest quadrant design application. The system also has the ability to generate recommendations associated with sites that create risk to people who visit. The results of the system tests showed significantly improved response times of the Police against citizen’s requests. This research was developed under the parameters of the National Plan for Community Surveillance by Quadrants (PNVCC) of the National Police of Colombia.KEYWORDS: Urban Computing, Urban Security, ubiquitous computing, GPS, QR-Code, recommender system, queuing theory

Social Cost Guarantees in Smart Route Guidance

We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms

Task allocation in the Internet of Things

The last few years have been involved by the technological revolution represented by the Internet of Things (IoT). The IoT vision aims to interconnect devices with different capabilities such as sensors, actuators, Radio Frequency Identification (RFID) tags, smart objects (e.g. smartphones), and servers, within the same heterogeneous network. The aim is to enable the network objects to dynamically cooperate and make their resources available, in order to reach a goal, i.e. the execution of one or more applications assigned to the network. As known since its invention, the Internet interconnects nodes with dissimilar characteristics without central authorities by introducing some simple yet effective protocols that allow for nodes' interoperability so that information is successfully exchanged and services are provided by servers to clients and among peers. Fortunately, the same happens among objects in the IoT so that interoperability is assured and the data sensed by objects distributed and connected to the physical world is now available for the benefit of the human users. The realization of the IoT paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. However, to reach this goal, it's important to consider some key features that characterize many IoT objects: i) available nodes' resources (electrical energy, memory, processing, node capability to perform a given task) are often limited. This is the case, for example, of battery powered nodes, which have limited energy amounts. ii) sensors may provide information that is not unique but can be generated by set of different objects which for example are capable to sense the same physical measure of the same geographical. iii) the number of nodes in the IoT is quickly overcoming the number of hosts in the 'traditional' Internet and most of these have a low reliability due mostly to the mobility and energy. This entails for a new paradigm of communication according to which objects coordinate with the other objects in groups and provide a unified service to the external world (the application that requires the service), with the intent to distribute the load of the requested services according to specific community defined rules, which could be: lifetime extension, QoS (Quality of Service) maximization, reward maximization, or others. It is evident that an appropriate coordination of objects' resources utilization would consistently improve their performance. This foreword is necessary to introduce this thesis, which is defined as follows. Task allocation in the IoT: given the IoT paradigm and the requirements of IoT applications, the nodes involved in the execution of the same application should cooperate to reach the optimal allocation of tasks among them. They should execute tasks to reach the global application target and to satisfy the relevant requirements while optimizing the network performance in terms of resources used. This issue should be continuously addressed to dynamically adapt the system to changes in terms of application requirements and network topolog

Adaptive traffic management for secure and efficient emergency services in smart cities

Rapid increase in number of vehicles on the roads as well as growing size of cities have led to a plethora of challenges for road traffic management authorities such as traffic congestion, accidents and air pollution. The work presented in this paper focuses on the particular problem of traffic management for emergency services, for which a delay of few minutes may cause human lives risks as well as financial losses. The goal is to reduce the latency of emergency services for vehicles such as ambulances and police cars, with minimum unnecessary disruption to the regular traffic, and preventing potential misuses. To this end, we propose to design a framework in which the Traffic Management System (TMS) may adapt by dynamically adjusting traffic lights, changing related driving policies, recommending behavior change to drivers, and applying essential security controls. The choice of an adaptation depends on the emergency severity level announced by the emergency vehicle(s). The severity level may need to be verified by corresponding authorities to preserve security measures. We discuss the details of our proposed framework and the potential challenges in the pape