Energy efficiency is one of the key aspects of IoT and Wireless Sensor Networks (WSNs) since the nodes of the network are running on battery power and the lifespan of the system is an important missioncritical parameter. In WSNs, where the energy consumption mainly depends on the radio interface and the transmission protocols, reliable packet forwarding from the source node to Base Station (BS) is crucial. In this paper, we focus on developing new routing algorithms which extend the lifespan of WSNs by achieving optimal energy balancing subject to the criterion that the packets must reach the BS with a predefined probability. We will propose novel two-hop and multi-hop routing algorithms to achieve this objective. The performance of the novel algorithms is compared with the LEACH routing protocol. Extensive simulations prove that the new routing methods are indeed energy efficient, and they are able to meet the predefined reliability criteria as well. The results given in this paper can contribute to reliable communication in IoT or WSN networks and result in lower energy consumption.

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.

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Efficient data collection is the core concept of implementing Industry4.0 on IoT platforms. This requires energy aware communication protocols for Wireless Sensor Networks (WSNs) where different functions, like sensing and processing on the IoT nodes must be supported by local battery power. Thus, energy aware network protocols, such as routing, became one of fundamental challenges in IoT data collection schemes.In our research, we have developed novel routing algorithms which guarantee minimum energy consumption data transfer which is achieved subject to pre-defined reliability constraints. We assume that data is transmitted in the form of packets and the routing algorithm identifies the paths over which the packets can reach the Base Station (BS) with minimum transmission energy, while the probability of successful packet transmission still exceeds a pre-defined reliability parameter. In this way, the longevity and the information throughput of the network is maximized and the low energy transmissions will considerably extend the lifetime of the IoT nodes. In this paper we propose a solution that maximizes the lifetime of the nodes.

A hazai és nemzetközi prostitúció és emberkereskedelem fontos forrásai lehetnek a szegregált lakókörnyezetek. A mélyszegénység és az elhanyagoló családi környezet „tökéletes” áldozatokat produkál az emberkereskedelemnek, így a szegregált lakókörnyezet, amennyire elszigeteltnek tűnik, annyira része a nemzetközi prostitúciónak. A prostituált beszervezése a jobb életre való csábítás különböző módozatain keresztül történik a szegregált lakókörnyezetben: ilyen például a loverboy metódus. Tapasztalataim szerint a szegregált lakókörnyezet főként Svájc és Németország prostituáltexportőre.