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Information communication technology related vehicle services need to support location and the transmission of communication and traffic information between vehicles, or between vehicles and infrastructure. In particular, the technology for the measurement of the accurate location of a vehicle is dependent on location-determination technology like Global Positioning System, and this technology is very important for vehicle driving and location services. If, however, a vehicle is in a Global Positioning System radio-shadow area, neither a Global Positioning System nor a Differential Global Positioning System can accurately measure the corresponding location because of a high error rate caused by the shadowing intervention. Even an Inertial Measurement Unit could provide inaccurate location data due to sensor drift faults around corners and traffic-road speed dumps. Vehicles, therefore, need an absolute location to prevent the provision of inaccurate vehicle-location data that is due to radio-shadow areas and relational Inertial Measurement Unit positions. To achieve this, we assume that vehicle-to-infrastructure communication is possible between a vehicle and roadside unit in Vehicular Ad hoc Networks. We used iBeacon at the roadside unit and revised its Universally Unique Identifier so that it generates absolute Global Positioning System location data; that is, moving vehicles can receive absolute Global Positioning System data from the roadside unit-based iBeacon. We compared the proposed method with current Global Positioning System and Inertial Measurement Unit systems for the following two cases: one with a radio-shadow area and one without. We proved that the proposed method generates location data that are more accurate than those of the other methods.

Wireless sensor networks (WSNs) are self-organizing networks in which sensor nodes with limited resource are scattered in an area of interest to gather information. WSNs need to have effective node's energy management methods for stable and seamless communication. As one of a number of good technical solutions, a clustering technique has been issued and proposed among researchers for reducing energy consumption in WSNs. Also, it can prevent the problem of data duplication by the sensor nodes. Generally, to reduce WSNs' energy consumption as much, cluster heads (CHs) are selected dynamically based on cluster rotation mechanism. However, the CH that is already previously selected could not be selected again unless the round process is over even though the node has more energy than others. Following this fact, in WSNs, there is a kind of irregular energy consumption status among sensor nodes because of CHs' overhead energy usages. To solve this problem, in WSN networks, any sensor node should be a candidate to be CH without any exception even if the node is already chosen before. Therefore, in this paper, we will establish and propose an energy balanced CH selection mechanism and the distribution of sensor node's energy consumption in WSNs for equal and stable energy management.

The data transmission of sensor nodes which detect events might be affected by their neighbor nodes located in their communication range. Thus, we analyze the energy consumption of sensor networks as a function of the number of cluster heads considering the above two options, multihop-based communication and the case where a neighbor node is in the transmission range for communicating data. This helps us to elect the number of energy-efficient cluster heads in a more practical manner. Also, we determine the effect of electing the number of cluster heads by restricting the local cluster size.

This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection). The handoff trigger is decided based on the received signal strength (RSS). To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service), and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.

The Internet of Medical Things (IoMT) facilitates patients with all-time-connected medical devices through cost-effective solutions and a feeling of comfort with round-the-clock hospital support. The patients who cannot visit hospitals for routine checkups prefer the usage of IoMT devices. The healthcare facilities also rely on the real-time statistics of IoMT machines and diagnose problems and their solutions within a small interval of time. This could only be possible when a robust convergence of technologies is used with IoMT devices. This review study discusses how we may apply the convergence of IoMT devices with trending technologies and focuses on delivering a new dimension of novel IoMT usage through broad multi-homing dense networks. It also discusses various applied machine learning algorithms available in the healthcare industry. It compares a variety of disease predictions and the accuracy of the equipment and decision recommendations. The review study also discusses various IoMT convergence open challenges and opportunities through the labor industry healthcare system.

The increasing energy consumption is a legacy of the fast improvement of ICT (Information and Communication Technology). It is also contrary to the current energy conservation and emission reduction concept. There are several reasons for high energy consumption. Among them, we find that the increase in base station density of the 5G heterogeneous network (5G HetNets) is prominent. We present a micro base station deployment strategy in 5G HetNets for obtaining high energy efficiency. It optimizes target values as are trade-offs at different user distribution probabilities to improve adaptation to different user distribution scenarios. An energy deployment algorithm based on high efficiency for micro base stations is considered as jointly optimizing micro base station’s number, deployment location, and power configuration. Simulation results show that the proposed combinatorial optimization strategy effectively improves the system energy efficiency compared to the deployment method, regardless of the power factor. Moreover, it verifies that the micro base station power affects the energy efficiency in HetNets.

AbstractText-to-SQL is the problem of converting a user question into an SQL query, when the question and database are given. In this article, we present a neural network approach called RYANSQL (Recursively Yielding Annotation Network for SQL) to solve complex Text-to-SQL tasks for cross-domain databases. Statement Position Code (SPC) is defined to transform a nested SQL query into a set of non-nested SELECT statements; a sketch-based slot-filling approach is proposed to synthesize each SELECT statement for its corresponding SPC. Additionally, two input manipulation methods are presented to improve generation performance further. RYANSQL achieved competitive result of 58.2% accuracy on the challenging Spider benchmark. At the time of submission (April 2020), RYANSQL v2, a variant of original RYANSQL, is positioned at 3rd place among all systems and 1st place among the systems not using database content with 60.6% exact matching accuracy. The source code is available at https://github.com/kakaoenterprise/RYANSQL.