OR-Tree: An Optimized Spatial Tree Index for Flash-Memory Storage Systems

The Location Based Services (LBS) seem to be the next revolution on small computing handheld devices in terms of location aware advertising, security alerts, news updates, disaster management, geo-fencing, buddy-findings, gaming, criminal investigations, turn-by-turn navigation and so on. In today’s scenario there is an explosion of technologies to communicate with mobile, connected devices and sensors. In this paper we are presenting a literature survey of LBS that includes the architecture of the LBS ecosystem, the key market players, and the latest trends in LBS development. Finally, the broad research areas such as location determination techniques, geo-sensor networks, and location based natural queries, location privacy and authorization, geo-social networks, LBS QoS, and Location Based Recommender Systems (LBRS) have been studied and presented briefly.

HARMO-DATA is an ongoing project, funded by EU in the framework of the INTERREG V-A Italy-Slovenia 2014-2020 Programme. It involves different stakeholders, target groups and end-users in three regions: Friuli-Venezia-Giulia (Italy), Veneto (Italy) and Slovenia. The main purpose of this project is to develop common solutions for more efficient cross-border spatial data management – by harmonizing the existing spatial data, implementing a cross-border spatial data platform, and developing a common protocol for the harmonization of territorial data. It will provide an instrument to define the specific obligations and rights of the involved parts – in terms of data harmonization, exchange, use and maintenance. Five pilot case studies were identified by the project partners – in cooperation with public and private end-users, and additional stakeholders. The core use cases of the project relate to spatial data search, view and download, and the harmonization model for spatial datasets applies the INSPIRE data specifications. A joined common spatial data platform was established as an extension of the existing search-view-download platforms (metadata systems), upgraded and improved to better enable open data access by users from both Italy and Slovenia. The common spatial HARMO-DATA data platform, as well as, a joint protocol for cross-border spatial data harmonization, have been formalized in an official bilateral agreement.

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.

This article represents a dynamic grid system (DGS), a privacy grid system defined by the user. This is the primary all-encompassing secure and spatial data satisfying basic essential necessities for confidentiality-securing snapshot and location-based services (LBSs). First, secure and spatial data are responsible for achieving simple matching operation using a semi-trusted third party. The semi-trusted third party has no information about the location of the user. Second, under the defined adversary model, we can provide a secured snapshot and uninterrupted location-based services. Third, not beyond the proximity of the user???s area, the communication cost does not rely on others ideal confidentiality location; it depends on the number of pertinent salient activities. Fourth, despite these things, it has only been targeted on the range and our system that can effectively support different spatial queries without altering the algorithms that are kept running by the semi-reliable third parties and the database servers, given that the spatial query is abstracted into spatial regions within the desired search area. The experimental assessment shows a more efficient approach towards the dynamic grid structure than the progressive confidentiality technique for uninterrupted location-based services. We offer a dual spatial data transformation and encryption scheme in which encrypted requests are executed completely on the encrypted database by the provider, and the user gets encrypted results. To attain services found on their location, location-based services want users to consistently report their locations to a potentially unreliable server which may open them to security risks. Lamentably, there were many restrictions in the existing confidentiality-securing methods for location-based services, such as the trustworthy third party requirement, confidentiality restrictions and high overhead communication.

Multi-view group representation learning for location-aware group recommendation

Exploiting location-related behaviors without the GPS data on smartphones

Flash memory is a non-volatile storage device that can retain its contents when the power is switched off. Generally, it is a form of an electrically erasable programmable read-only memory (EEPROM) that offers several excellent features such as less noise, solid-state reliability, lower power consumption, smaller size, light weight, and higher shock resistant [1 – 5]. Flash memory acts as a slim and compact storage device. It’s main applications are such as compact flash (CF), secured digital (SD), and personal computer memory card international association (PCMCIA) cards, for storage and data transfer in most portable electronic gadgets such as mobile phones, digital cameras, personal digital assistants (PDAs), portable media players (PMPs), gobal positioning system receivers (GPS), just to name a few.

Spatial data are often stored in a database with the finest level of details. The problem with cartographic generalization of spatial data is concerned with deriving spatial data with proper level of details when the data are used for an application. The generalization process is not simply a process of selecting objects or parts of objects suitable for an application; it should maintain the semantic information carried with the spatial data, such as topological relationships among objects and the overall look and feel of the data after generalization. Most difficulties of map generalization are because the importance of objects, which determines whether an object should be included and how it should be visualized in the generalized data set, is decided not only by their size, but also the meaning of these objects to the application. The latter is application-dependent, and often subjective. With more and more computerized applications making use of shared spatial data sources, and the increasing number of applications, efficient spatial data generalization becomes a critical issue. At the same time, it becomes less and less likely to have human intervention or have pre-materialized spatial data generalization for all applications in the Internet environment. The authors investigate issues for efficient spatial data generalization, with a unique focus on spatial database design and spatial operation processing. We propose several types of extensions and novel applications of z-values, a commonly used spatial indexing mechanism, to achieve our goals.

Recently, researchers have demonstrated that learned index can improve query performance while reducing the storage overhead. It potentially offers an opportunity to address the spatial query processing challenges caused by the surge in location-based services. Although several learned indexes have been proposed to process spatial data, the main idea behind these approaches is to utilize the existing one-dimensional learned models, which requires either converting the spatial data into one-dimensional data or applying the learned model on individual dimensions separately. As a result, these approaches cannot fully leverage or take advantage of the information regarding the spatial distribution of the original spatial data. To this end, in our previous work, we proposed a spatial (multi-dimensional) interpolation function based learned model to develop a spatial learned index and designed efficient range and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN query strategies over it. However, there are some limitations in the proposed learned model, such as the prediction accuracy and index building time. In this paper, we address the limitations of our previous work and propose a new spatial learned model by employing the characteristics of the spatial interpolation functions and a novel dynamic encoding technique. Detailed experiments are conducted with real-world datasets. The results indicate that our new proposed learned model is better than our previous one in terms of building time, prediction accuracy, and storage overhead simultaneously, and the new learned spatial index is better than the existing learned spatial indexes in query execution time and index building time.

Efficient secondary memory processing of window queries on spatial data

Power, area, timing these three characteristics are very important in the design of low power VLSI systems, especially in the multimedia applications and portable devices like hearing aids, mobiles, personal digital assistants are running with battery. In the early complex designs, to achieve power, and area, we had a conventional codec design has become a major problem in the portable devices consumes more amount power and larger area. One of the mode to take this power and area problem in the portable applications is to design very optimized low power, area, a novel CODEC Design implemented using the bus Berger inverter codec specially for running with battery based portable devices. This paper suggests an altered a CODEC system of the Berger-invert codes and a new system of encoding/decoding circuitry (a codec). Physical layout execution results prove that the new optimization design reduces the codec area and power consumption respectively by up to 15% and 33% for different bus widths. Simulations and synthesis are carried out from different tools to select the vital design factors are presented. Two variations of digital coding, encoding & decoding circuit with many transistors are fabricated. The measurement results bring out the different low power and area possibilities as well as the developed a sophisticated digital Verilog description language for encoding decoding advanced design for the object applications. The useable SOC Encounter tools offer optimized physical layout design fabricated with 65nm and 45nm technology with comparsion for CODEC with limited power, area and timing which will useful for the portable devices.

Portable devices (such as personal digital assistants and laptops with wireless connectivity) are becoming ubiquitous. As their functionality and capabilities increase, their energy consumption requirements also increase. Yet, these devices have to operate on limited batteries. In order to maximize the battery lifetime, it is necessary to optimize the use of energy at various components of such a device. In this paper, we consider a single portable device operating on a limited battery that transmits information over an interference-limited wireless channel. We seek to optimize the power consumption on the communication radio in this device, by controlling both the operation mode and the transmission power. We model the general problem using dynamic programming, obtain the optimal solutions for insightful special cases and explore various design tradeoffs. Our work provides an analytical framework for stochastic modeling and optimization of energy spent for communications in battery-operated portable devices.

Flash memory chips are playing a critical role in semiconductor devices due to increased popularity of hand held electronic communication devices such as cell phones and PDAs (personal Digital Assistants). Flash memory offers two primary advantages in semiconductor devices. First, it offers flexibility of in-circuit programming capability to reduce the loss from programming errors and to significantly reduce commercialization time to market for new devices. Second, flash memory has a double density memory capability through stacked gate structures which increases the memory capability and thus saves significantly on chip real estate. However, due to stacked gate structures the requirements for manufacturing of flash memory devices are significantly different from traditional memory devices. Stacked gate structures also offer unique challenges to lithographic patterning materials such as Bottom Anti-Reflective Coating (BARC) compositions used to achieve CD control and to minimize standing wave effect in photolithography. To be applicable in flash memory manufacturing a BARC should form a conformal coating on high topography of stacked gate features as well as provide the normal anti-reflection properties for CD control. In this paper we report on a new highly conformal advanced i-line BARC for use in design and manufacture of flash memory devices. Conformal BARCs being significantly thinner in trenches than the planarizing BARCs offer the advantage of reducing BARC overetch and thus minimizing resist thickness loss.

The paper presents an innovative human-robot communication paradigm for people with disability using electromyography (EMG) signals via a personal digital assistant (PDA). The paper proposes a novel human-robot interaction architecture where the disabled people could interact with the robot using an EMG-based Morse code-inspired communication from portable devices. The user employs EMG signals to issue high-level commands to the robot that decomposes these commands into low-level primitive subtasks. Then, the robot dynamically constructs an action selection network to form the operational plan. The architecture is developed on a PDA that offers small and lightweight mobile interaction devices. Utilizing the PDA with EMG-based modified Morse code could provide flexibility, mobility, and durability for handicapped people to control the robot-aid system. The proposed paradigm has been implemented to develop a prototypical system, and the experiments also illustrate the conventionality, and the robustness of such a human-robot framework

Edge computing, as a new computing paradigm, appears to solve the record generated by a great deal of edge devices that cannot be processed by traditional cloud computing. The real-time location big data of LBS (Location-Based Services) are transferred from the cloud server to the edge nodes, which further improves the service capacity and promotes the further development of LBS. However, based on the existing partition method, dynamic location data privacy cannot be well protected. In this paper, based on the differential privacy theory, we propose RPS (Random Partition and Selection) algorithm which can protect user's location information. We achieve this by designing a dynamic partition mechanism and noise adding algorithm based on the number of users. We repartition the region in each snapshot and give a new noise probability. Experiment results show that our proposed method has the significant progress in the loss of service quality than the existing partition release, such as UG (Uniform Grid) and AG (Adaptive Grid) mechanism.