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Vehicle routing problem falls into NP hard problems. Researchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the usage of divide and conquer approach, the problem is divided into several parts that ensures finding the most suitable algorithms. In this special case, the combination of the genetic algorithm with the random restart hill climbing algorithm is used. The cluster first – route second algorithm provides that the approach will be attributed the necessary speed, from the usage of hill climbing algorithm. Furthermore, usage of genetic algorithm provides for bigger space that can be searched. For the selection step of the genetic algorithms, three types of distributions: uniform, log-normal and exponential were compared with fitness proportionate selection to further speed up the algorithm. The results show that the uniform distribution is the reasonable substitution, while the log-normal and the exponential distribution converge faster with a higher cost penalty.

Flexible Job Scheduling Problem presents a relaxation of the Job Scheduling Problem. It falls into the category of NP-hard problems. Our approach is to tackle the problem through usage of a coarse-grained genetic algorithm. Coarse-grained extends the genetic algorithm and thus increasing chances of more fitness landscape to be examined. Solutions are benchmarked with other approaches, through two of the most used datasets. Even though the results are less favourable than other approaches, in few cases our approach reaches the upper bound of the benchmarked solutions, thus impelling us to further investigate on the problem solution.

A shift in water monitoring approach from traditional grab sampling to novel wireless sensors is gaining in popularity not only among researchers but also in the market. These latest technologies readily enable numerous advantageous monitoring arrangements like remote, continuous, real-time, and spatially-dense and broad in coverage measurements, and identification of long-term trends of parameters of interest. Thus, a WSN system is implemented in a river in Kosovo as part of the InWaterSense project to monitor its water quality parameters. It is one of the first state of the art technology demonstration systems of its kind in the domain of water monitoring in developing countries like Kosovo. Water quality datasets are transmitted at pre-programmed intervals from sensing stations deployed in the river to the server at university via the GPRS network. Data is then made available through a portal to different target groups (policy-makers, water experts, and citizens). Moreover, the InWaterSense system behaves intelligently like staying in line with water quality regulatory standards.

The Internet of Things (IoT) vision is to connect uniquely identifiable devices that surround us to the Internet, which is best described through ontologies. Thereby, new emerging technologies such as wireless sensor networks (WSN) are recognized as an essential enabling component of the IoT today. Hence, given the increasing interest to provide linked sensor data through the Web either following the Semantic Web Enablement (SWE) standard or the Linked Data approach, there is a need to also explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture SEMDPA has been developed. It supports linking sensors and other devices, as well as people via a single web by mean of a device-person-activity (DPA) crossroad ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and Linked WSN data. SEMDPA could be easily extensible to capture semantics of input sensor data from other domains as well.

Association rule mining as descriptive data mining category aims to find interesting patterns on data. The quality of the patterns is measured with two metrics: confidence and support. Especially in fields dealing with sensitive data, such as healthcare, the resulting patterns should be novel and interesting. To achieve that, not only the quality of the data itself should be superior, but also other additional attributes added, do support the results. That should be achieved by using Semantic Web technologies and thus enriching data used with semantic relations between properties. A hypothesis suggests that especially tackling property relations, chain property being part of the current version of the W3C Web Ontology Language (OWL), will yield better rules. To validate the hypothesis, experiments were performed on raw data, then on an older version of OWL, which does not support the chain properties and finally on the current version of language involving chain properties. Results obtained suggest that the latter produces novel rules with strong confidence and support, not encountered in former two experiments.

The Internet of Things (IoT) vision is connecting uniquely identifiable devices to the internet, best described through ontologies. Furthermore, new emerging technologies such as wireless sensor networks (WSN) are recognized as essential enabling component of the IoT today. Hence, the interest is to provide linked sensor data through the web either following the semantic web enablement (SWE) standard or the linked data approach. Likewise, a need exists to explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture has been developed. It supports linking sensors, other devices and people via a single web by mean of a device-person-activity (DPA) ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and linked WSN data. The architecture could be easily extensible to capture semantics of input sensor data from other domains as well.

Wireless sensor networks (WSNs) are aiding water quality monitoring with support for real-time and remote quality measurements in terrain. Environmental monitoring portals receiving data from sensors have been a practice since a while among researchers. However, the Web portal introduced here is essentially an integrated portal since it supports modeling and management of both, the observational stream data on water quality coming from wireless sensors – dynamic data, as well as of the data describing the WSN itself, its devices and the corresponding site allocation data – static data. Access is given to a wide range of individuals, from water experts to WSN engineers, to general public. Experts' module infers statistics about water parameters given the experts' data and rules. The portal is further distinguished for its level of scalability: it allows adding with ease new components, like add certain new regulatory documents for water quality, and directly compare data measured by sensors with corresponding quality standards. The aim is to enrich the portal with semantics in future.

In this paper, in order to tackle one of the dozens of the challenging optimization problems in aviation industry, we introduce an algorithm for planning the sequence of arrivals and departures of aircraft in a runway. The algorithm is based in Iterated Local Search (ILS) algorithm and uses two effective operators, namely swap and shift, for exploring the neighborhood of current running solution. The experiments that are conducted with real instances show that this approach achieves better results than the First Come First Served approach, while computing the results in relatively short period of time, which is in order of milliseconds.

Internet of Things (IoT) applications by means of wireless sensor networks (WSN) produce large amounts of raw data. These data might formally be defined by following a semantic IoT model that covers data, meta-data, as well as their relations, or might simply be stored in a database without any formal specification. In both cases, using association rules as a data mining technique may result into inferring interesting relations between data and/or metadata. In this paper we argue that the context has not been used extensively for added value to the mining process. Therefore, we propose a different approach when it comes to association rule mining by enriching it with a context-aware ontology. The approach is demonstrated by hand of an application to WSNs for water quality monitoring. Initially, new ontology, its concepts and relationships are introduced to model water quality monitoring through mobile sensors. Consequently, the ontology is populated with quality data generated by sensors, and enriched afterwards with context. Finally, the evaluation results of our approach of including context ontology in the mining process are promising: new association rules have been derived, providing thus new knowledge not inferable when applying association rule mining simply over raw data.

University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice better than the multi thread algorithm.