The advancement of wireless technology is affected by Spectrum scarcity and the overcrowding of free spectrum. Cognitive Radio Ad Hoc Networks (CRAHNs) have emerged as a possible solution to both the scarcity and overcrowding challenges of the spectrum. The CRAHNs ensure that the Secondary Users (SUs) do co-exist with Primary Users (PUs) in a non-interfering manner. The SUs access the licensed spectrum opportunistically when they are idle. CRAHNs have many use cases which include intermittent networks here referred to as intermittent CRAHNs (ICRAHNs). For example, the Military (MCRAHNs). MCRAHN is complex and characterized by a dynamic topology which is subject to frequent partitioning and route breakages due to attacks and destruction in combat. This study optimizes the routing protocols for intermittent networks such as the MCRAHNs. ICRAHN routing is a challenge due to the network’s intermittent attribute, which is subject to destruction in the case of MCRAHN which is characterized by frequent link breakages. The performance of the proposed routing scheme was evaluated through network simulations using the following metrics: throughput, and Routing Path delay, Node Relay delay, Spectrum Mobility delay. The simulation results show that the MAODV is the best-performing algorithm.
One-way ECS (Electric Car Sharing Service) is attracting attention as a new sustainable mobility option in urban areas. On the other hand, the vehicle uneven distribution problem occurs in one-way ECSs due to their usage patterns. In this paper, we propose a vehicle return prediction model for vehicle relocation to solve this problem. In the proposed method, two machine learning models are created to predict where and when a user will return a vehicle using static information such as departure time and location, and dynamic information such as the vehicle’s current location and direction of movement. The model is used to continually update the prediction results of vehicle returns during use, aiming for more accurate predictions. The proposed method has been evaluated using actual data from a one-way ECS and has achieved an accuracy of 0.93 for the prediction of stations to be returned. The method also achieved a MAE of 42.3 min and MAPE of 47
Utilizing edge and cloud computing to empower the profitability of manufacturing is drastically increasing in modern industries. As a result of that, several challenges have raised over the years that essentially require urgent attention. Among these, coping with different faults in edge and cloud computing and recovering from permanent and temporary faults became prominent issues to be solved. In this paper, we focus on the challenges of applying fault tolerance techniques on edge and cloud computing in the context of manufacturing and we investigate the current state of the proposed approaches by categorizing them into several groups. Moreover, we identify critical gaps in the research domain as open research directions.
In search by pattern in GPS trajectories, user draws a trajectory, the pattern query, and then receives a set of trajectories ranked by their similarity to the pattern query. We argue that when user draws a pattern query, an initial part of this query (prefix of chosen length) should have more weight than the rest of query. We assume that after receiving a set of similar trajectories, user can refine the pattern query in order to receive more relevant results. We give explanation of our approach by means of web search, where a user searches, for example, for “bratislava castle” and then adds a refinement to this query “opening hours”, where removing the initial part of query does not make sense, as search for “opening hour” alone would return irrelevant results. This idea has led us to considering pattern search that is weighted toward query prefix. We experimentally evaluate this approach, in our experimentation we apply the Geolife data set (Microsoft Research Asia).
We configure embedded devices with a smartphone via NFC using an open, platform independent protocol presented in this paper. A textual device specification defines the types of configuration values for a specific device and integrates the device into the configuration system. The specification needs to be provided by the embedded developer. It is translated into a C library that enables configuration value access, as well as blob that contains the compressed configuration metadata. A generic smartphone application interprets the metadata and configuration data read via NFC and allows the modification of the values according to the device specification encoded in the metadata. The modified configuration data can be stored, shared or transferred back to the embedded device. None of the configuration steps need an internet connection, which means data is kept private. Combined with the open protocol and the generic app, this ensures that embedded devices will not become obsolete through vendor decisions, as happens frequently with devices dependent on configuration via cloud services. Embedded developers only need to implement raw read and write binary access to an NFC storage device. The generated artifacts allow to transform that data into an easy-to-use data structure. A prototype system using a fully functional tool chain, a generic Android app and a single-board computer simulating an embedded device has been implemented and evaluated.
