The Internet of Things (IoT), has significantly impacted several industry sectors in recent years, particularly those that deal with air, soil, and water quality. With the growing diversity of sensors and their processing and communication capabilities, a number of methods have been put up for creating systems that continuously gather environmental data. These data can be further processed for long-term analysis or quick response. Every solution that has been put forth presents a static system, meaning that the sensors are positioned in fixed, designer-determined locations. Our work presents a dynamic and autonomous system in which sensors are free to travel within a given area and, by utilizing intelligent algorithms and distributed systems techniques, they determine the optimal way to employ in order to monitor the target environment by combining intelligent algorithms and distributed systems techniques.

This work proposes a dynamic and flexible approach to guide traffic congestion based on anonymous messages delivered by the vehicles in the proximity of a crossroad. The system uses real-time traffic information to determine the state of the traffic flows at a signalized intersection in order to schedule the traffic lights efficiently. A vehicle, modeled as automaton, securely disseminate and exchange its internal state with a trusted traffic authority in a wireless communication. A roadside unit, installed at the road intersection, collects the traffic information, verifies the information, and uses a phase-based control algorithm to optimize the amount of the vehicles entering the intersection. The new approach has been evaluated through simulation under light and heavy traffic conditions, and the results compared with fixed-time traffic signal control and Webster's method.

Keyword queries offer a convenient alternative to traditional SQL in querying relational databases with large, often unknown, schemas and instances. The challenge in answering such queries is to discover their intended semantics, construct the SQL queries that describe them and used them to retrieve the respective tuples. Existing approaches typically rely on indices built a-priori on the database content. This seriously limits their applicability if a-priori access to the database content is not possible. Examples include the on-line databases accessed through web interface, or the sources in information integration systems that operate behind wrappers with specific query capabilities. Furthermore, existing literature has not studied to its full extend the inter-dependencies across the ways the different keywords are mapped into the database values and schema elements. In this work, we describe a novel technique for translating keyword queries into SQL based on the Munkres (a.k.a. Hungarian) algorithm. Our approach not only tackles the above two limitations, but it offers significant improvements in the identification of the semantically meaningful SQL queries that describe the intended keyword query semantics. We provide details of the technique implementation and an extensive experimental evaluation.

We propose the demonstration of Keymantic , a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.