Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Applications of Context-Aware Systems in Enterprise Environments

In bring-your-own-device (BYOD) and corporate-owned, personally enabled (COPE) scenarios, employees’ devices store both enterprise and personal data, and have the ability to remotely access a secure enterprise network. While mobile devices enable users to access such resources in a pervasive manner, it also increases the risk of breaches for sensitive enterprise data as users may access the resources under insecure circumstances. That is, access authorizations may depend on the context in which the resources are accessed. In both scenarios, it is vital that the security of accessible enterprise content is preserved. In this work, we explore the use of contextual information to influence access control decisions within context-aware systems to ensure the security of sensitive enterprise data. We propose several context-aware systems that rely on a system of sensors in order to automatically adapt access to resources based on the security of users’ contexts. We investigate various types of mobile devices with varying embedded sensors, and leverage these technologies to extract contextual information from the environment. As a direct consequence, the technologies utilized determine the types of contextual access control policies that the context-aware systems are able to support and enforce. Specifically, the work proposes the use of devices pervaded in enterprise environments such as smartphones or WiFi access points to authenticate user positional information within indoor environments as well as user identities

On power line positioning systems

Power line infrastructure is available almost everywhere. Positioning systems aim to estimate where a device or target is. Consequently, there may be an opportunity to use power lines for positioning purposes. This survey article reports the different efforts, working principles, and possibilities for implementing positioning systems relying on power line infrastructure for power line positioning systems (PLPS). Since Power Line Communication (PLC) systems of different characteristics have been deployed to provide communication services using the existing mains, we also address how PLC systems may be employed to build positioning systems. Although some efforts exist, PLPS are still prospective and thus open to research and development, and we try to indicate the possible directions and potential applications for PLPS.European Commissio

A Context-Aware System to Secure Enterprise Content: Incorporating Reliability Specifiers

The sensors of a context-aware system extract contextual information from the environment and relay that information to higher-level processes of the system so to influence the system\u2019s control decisions. However, an adversary can maliciously influence such controls indirectly by manipulating the environment in which the sensors are monitoring, thereby granting privileges the adversary would otherwise not normally have. To address such context monitoring issues, we extend CASSEC by incorporating sentience-like constructs, which enable the emulation of \u201dconfidence\u201d, into our proximity-based access control model to grant the system the ability to make more inferable decisions based on the degree of reliability of extracted contextual information. In CASSEC 2.0, we evaluate our confidence constructs by implementing two new authentication mechanisms. Co-proximity authentication employs our time-based challenge-response protocol, which leverages Bluetooth Low Energy beacons as its underlying occupancy detection technology. Biometric authentication relies on the accelerometer and fingerprint sensors to measure behavioral and physiological user features to prevent unauthorized users from using an authorized user\u2019s device. We provide a feasibility study demonstrating how confidence constructs can improve the decision engine of context-aware access control systems

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