User Location Information Research Articles

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain.

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

Location Search Application for Refuelling Stations on Android Based Mobile Devices

The infrastructure provided by PT Pertamina to meet fuel needs is the gas station (SPBU). Gas station infrastructure is an important infrastructure in every city. Information about the location of the gas station is necessary for motorists, especially during emergencies, namely the availability of fuel in vehicles that are running low when the user is on his way. The purpose of this study is to create a mobile device that provides gas station location information to make it easy for users to get a gas station that is close to the user’s position. The object of this research is a gas station in the city of Bandung, Indonesia. The method used to find the nearest location is Location-Based Services (LBS), a cellular service that uses user location information consisting of X and Y coordinates generated by GPS, most of which use positioning techniques. Functional analysis using Unified Modeling Language (UML). Based on testing that has been done using alpha testing, the application made already provides information about the location of the nearest gas station. Based on the results of discussions with application users, this application can help users find the closest gas station.

SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications.

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the “on” and “off” state of global positioning system sensor in smartphones. To address this problem, this paper proposes “SenseCrypt”, a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.

Machine Learning for Beam Alignment in Millimeter Wave Massive MIMO

This letter investigates beam alignment for multi-user millimeter wave (mmWave) massive multi-input multi-output system. Unlike the existing works using machine learning (ML), an alignment method with partial beams using ML (AMPBML) is proposed without any prior knowledge such as user location information. The neural network (NN) for the AMPBML is trained offline using simulated environments according to the mmWave channel model and is then deployed online to predict the beam distribution vector using partial beams. Afterwards, the beams for all users are all aligned simultaneously based on the indices of the dominant entries of the obtained beam distribution vector. Simulation results demonstrate that the AMPBML outperforms the existing methods, including the adaptive compressed sensing, hierarchical search, and multi-path decomposition and recovery, in terms of the total training time slots and the spectral efficiency.

Location Based Services for Sharing Using Trusted Server

The main aim of location-sharing is to provide current location information to their designated users. Nowadays, Location Based Service (LBS) has become one of the popular services which are provided by social networks. As LBS activity makes use of the user's identity and current location information, an appropriate path has to be utilized to protect the location privacy. However, as per our knowledge, there is no access to protecting the location sharing with the complete privacy of the location. To consider this issue, we put forward a new cryptographic primitive functional pseudonym for location sharing that make sure privacy of the data. Also, the proposed approach notably reduces the computational overhead of users by delegating part of the computation for location sharing to a server, therefore it is endurable. The primitive can be widely used in many MOSNs to authorize LBS with enhanced privacy and sustainability. As a result, it will contribute to proliferate LBS by eliminating user's privacy concerns.

Privacy Enhanced Location Sharing for Mobile Online Social Networks

As a primitive function of location-based services (LBSs), the location sharing aims to provide a user's current location information to other designated users. In recent years, LBSs have become one of the most popular services provided by mobile online social networks (mOSNs). As LBSs actively exploit the users’ identity and current location information, appropriate approaches have to be utilized to protect the location privacy of the users. Several recent reports have discussed the significance of friendship privacy protection with the goal of hiding the friendship relation of users from unintended entities. However, to the best of our knowledge, there hasn't been an approach for protecting the location sharing with complete privacy of location and friendship connections. To address this issue, we propose a new cryptographic primitive, functional pseudonym, for location sharing in mOSNs that ensures both of them. Unlike many of the existing solutions, our approach does not require a fully trusted server and does not assume pre-established secrets among friends, and therefore is highly practical. Also, the proposed approach significantly reduces computational overhead of users by delegating part of the computations for location sharing to a server, therefore it is highly sustainable. Our primitive can be widely used in many mOSNs to enable LBSs with improved privacy and sustainability. Consequently, it will contribute to proliferate LBSs by eliminating users privacy concerns.

Massive MIMO User Location Based Pilot Allocation in Massive MIMO Systems

Aiming at the problem of massive MIMO pilot contamination, a user location information based pilot allocation scheme is proposed. The proposed scheme sorts the users and assigns pilots in accordance to the polar angle of the user's location in the polar coordinate system with the base station of the cell as the pole. The scheme combines the characteristics of the directional large line, and by controlling the reuse distance of the pilots in a relatively long range, the goals of reducing pilot contamination is improving system reachability and speed. Simulation results show that the proposed scheme not only effectively reduce pilot contamination among users, but also improves the performance gap between different users with the improved system fairness.

Energy Efficient Mobile Adhoc Network using Raspberry PI

This paper based on generation of power using wearable technologies from sustainable resources to establish Mobile Adhoc Network communication between multiple nodes using Raspberry pi-3 microprocessor. It mainly focuses on aiding in the search and rescue operations of explorers or mountaineers in case of avalanches or in case of some unfortunate disasters. An advanced routing mechanism called Better Approach to Mobile Adhoc Networking is used to establish communication between the explorers to transfer user location information to the base station.

A Location Privacy Attack Based on the Location Sharing Mechanism with Erroneous Distance in Geosocial Networks.

Geographical social networks (GSN) is an emerging research area. For example, Foursquare, Yelp, and WeChat are all well-known service providers in this field. These applications are also known as location-based services (LBS). Previous studies have suggested that these location-based services may expose user location information. In order to ensure the privacy of the user’s location data, the service provider may provide corresponding protection mechanisms for its applications, including spatial cloaking, fuzzy location information, etc., so that the user’s real location cannot be easily cracked. It has been shown that if the positioning data provided by the user is not accurate enough, it is still difficult for an attacker to obtain the user’s true location. Taking this factor into consideration, our attack method is divided into two stages for the entire attack process: (1) Search stage: cover the area where the targeted user is located with unit discs, and then calculate the minimum dominating set. Use the triangle positioning method to find the minimum precision disc. (2) Inference phase: Considering the existence of errors, an Error-Adjusted Space Partition Attack Algorithm (EASPAA) was proposed during the inference phase. Improved the need for accurate distance information to be able to derive the user’s true location. In this study, we focus on the Location Sharing Mechanism with Maximal Coverage Limit to implement the whole attack. Experimental results show that the proposed method still can accurately infer the user’s real location even when there is an error in the user’s location information.

Cross-Attention Mechanism Recommendation Algorithm Based on LSTM

With the development of economy, tourism is favored by more and more people, and location-based point-of-interest recommendation (POI) begins to emerge. This paper proposes the lstm-based recommendation algorithm for cross-attention mechanism (L-Attention). Embedding method was used to accurately learn the location sequence and location information of users, and feature information of location data was extracted through LSTM network. At the same time, the cross-attention mechanism is introduced to conduct dynamic modeling of personalized time check-in sequence, which can integrate the user's behavioral preference, location sequence information and the influence of sequential location on next-poi, so as to improve the accuracy of recommendation. This article uses Foursquare as an experimental data set. The results show that compared with the traditional recommendation algorithm which only considers the popularity of POI and the deep learning recommendation algorithm which only considers the timing, the accuracy of the proposed algorithm is greatly improved.

Service Recommendation Middleware Based on Location Privacy Protection in VANET

With the help of location-based services (LBS), it makes driving more convience for drivers. However, because the untrusted LBS server may leak the user's location information, the user's privacy is threatened. Moreover, the existing methods of location privacy protection do not take into account the impact of context on privacy protection demand. In addition, heterogeneous data sensed by vehicles also increases the complexity of application development. In order to solve the above problems, we propose a context-based location privacy protection middleware architecture, named PP-OSGi. The middleware simplifies application development by shielding the heterogeneity of vehicle sensed data and upper-layer applications. Furthermore, in order to protect the real location information of service request vehicles under different vehicle densities in a certain area, we propose a dynamically adjustable k-anonymous (DAK) algorithm and a location privacy protection (DLP) algorithm based on a dummy location, which are all encapsulated in PP-OSGi. The DAK and DLP algorithms dynamically determine the location privacy protection strength in different contexts based on the user's location privacy preference model, select an anonymous group of neighboring vehicles to construct a anonymous area, and obtain a dummy location of the service request vehicle. The experimental results show that, under the premise of protecting the location privacy of vehicles, the success rate of service requests is improved and the communication cost between vehicles is reduced.

Joint Pilot Allocation and Pilot Sequence Optimization in Massive MIMO Systems

Pilot contamination (PC) is recognized as a bottleneck for the achievable throughput of multi-cell massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a joint pilot allocation and pilot sequences optimization (JPA-PSO) scheme to mitigate the effects of PC and maximize the system spectral efficiency (SE). Specifically, we first construct a range of pilot sequence in line with the coherent interval. Then, we establish an allocation basis by defining a similarity to distinguish PC among users. An angle of arrival (AOA) positioning method is designed to obtain the user's location information. Under the location information, we can monitor and estimate the distance between users in different cells to denote the potential interference of transmit power among users. Then, we execute pilot allocation strategy in line with the similarity and distance between users in different cells, that is, assign orthogonal pilots to the users with greater similarity and shorter distance. Finally, we provide a pilot sequence optimization algorithm to maximize the system SE. Simulation results verify that combining pilot allocation and pilot sequence optimization can significantly improve the system SE.

An approach to the mobile social services recommendation algorithm based on association rules mining

With the continuous development of social network, more and more research areas have emerged. Among them, service recommendation has always been a research hotspot. Yet achieving accurate personalised recommendation is challenging because of the large amount of data generated in social networks as the data available for each user is highly sparse. In this paper, it focuses on the association rules in the mobile social network recommendation algorithms. By introducing the mobile users' location information to the collaborative filtering recommendation process, the association rules between the items are minded. Then the association rules are filtered and split, which are integrated into the similarity matrix to combine user location information with them. Also, an association rule mining model is proposed, which considers time factor to further improved the recommendation accuracy. Experimental results show that the performance of our algorithm is better than the baseline.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing (MEC) has become the most possible network architecture to realize the vision of interconnection of all things. By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations (sBSs), the execution latency and device power consumption can be reduced on resource-constrained mobile devices. However, computation delay of Mobile Edge Network (MEN) tasks are neglected while the unloading decision-making is studied in depth. In this paper, we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user. We obtain the next possible location through the user's past location information, and receive the next access server according to the grid matrix. Furthermore, the next time task sequence is calculated on the base of the historical time task sequence, and the server is chosen to preload the task. In the experiments, the results demonstrate a high accuracy of our proposed model.

Construction of System to Support Intelligent Coffee Shop using IoT

Recently, the development of smart devices linked to the network and the diffusion of the Internet have increased the user convenience. This system maximizes the efficiency by combining the uncomfortable points in the existing cafe with the internet technology. In particular, smartphone orders can be ordered including kiosk orders, and beacon-based users can be automatically recognized. And we have developed a system that enables users to create customized orders by grasping user location information using geofences. Finally, this paper also provides weather, temperature, time and user-based recommendation services based on Big Data. In particular, it supports intelligent decision making system through big data analysis. Therefore, Real-time reservations and orders using smart phones are supported from outside. In this paper, a Smart Café system is constructed with smart devices and Internet devices being shared and cooperatively controlled, thereby reducing labor costs and enhancing user convenience.

Energy-Efficient Base Station Control Framework for 5G Cellular Networks Based on Markov Decision Process

We study the problem of base station (BS) dynamic switching for energy efficient design of fifth-generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this paper, we introduce a novel approach to design an energy-efficient BS control algorithm. We design an MDP-based algorithm to control the on/off switching of BSs in real time; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees.

Robust MUI Suppression for MIMO Visible Light Communication System With Location-Aided Chaotically Rotating Orthogonal Scheme

In practical multiple-input multiple-output (MIMO) visible light communication (VLC) systems, the imperfect channel state information brings the issue that the precoding scheme may not well suppress multi-user interferences (MUIs). In this letter, with the objective of suppressing MUIs more effectively, we utilize the location information of users obtained from positioning systems, and present a real domain location-based chaotically rotating orthogonal scheme. In our design, we first develop a range-variable chaotic mapping method and embed users’ location information into the chaotic sequences. Then, we exploit the chaotic rotation to construct orthogonal matrices with the aim to orthogonalize transmitted signals. At the receiver, with the aid of the known location information and the chaotically rotating orthogonalization structure, the information can be retrieved from received signals, hence achieving robust MUI suppressions. Simulations are performed and the results demonstrate that our proposed scheme can effectively combat the MUI and improve the reliability performances for VLC systems.

Inferring Micro-Activities Using Wearable Sensing for ADL Recognition of Home-Care Patients.

In this study, we propose a novel, context-based, location-aware algorithm for identifying low-level micro-activities that can be used to derive complex activities of daily living (ADL) performed by home-care patients. This identification is achieved by gathering the location information of the target user by using a wearable beacon embedded with a magnetometer and inertial sensors. The shortcomings of beacon-signal stability and mismatch issues in magnetic-field sequences are overcome by adopting a hybrid, three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments, where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. In addition to the recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. The experimental results demonstrate that it is possible to achieve centimeter-level accuracy for the recognition of micro-activities and to achieve the classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that the collection of accurate low-level information for ADL recognition is possible using integration of inertial sensors, magnetic field and Bluetooth low energy (BLE) technologies from the wearable beacon without relying on other infrastructural sensors.

Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization

Unmanned aerial vehicle-based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional terrestrial BSs are available and the aim is deploying multiple aerial BSs to cover a maximum number of users within a certain target area. To this end, we first propose a naive successive deployment method, which converts the non-convex constraints in the involved optimization into a combination of linear constraints through geometrical relaxation. Then, we investigate a deployment method based on $K$ -means clustering. The method divides the target area into $K$ convex subareas, where within each subarea, a mixed integer non-linear problem is solved. An iterative power efficient technique is further proposed to improve coverage probability with reduced power. Finally, we propose a robust technique for compensating the loss of coverage probability in the existence of inaccurate user location information. Our simulation results show that the proposed techniques achieve an up to 30% higher coverage probability when users are not distributed uniformly. In addition, the proposed simultaneous deployment techniques, especially the one using iterative algorithm, improve power-efficiency by up to 15% compared with the benchmark circle packing theory.

Walls Have No Ears: A Non-Intrusive WiFi-Based User Identification System for Mobile Devices

With the development and popularization of WiFi, surfing on the Internet with mobile devices has become an indispensable part of people’s daily life. However, as an infrastructure, WiFi access points (APs) are easily connected by some undesired users nearby. In this paper, we propose NiFi, a non-intrusive WiFi user-identification system based on WiFi signals that enable AP to automatically identify legitimate users in indoor environments, such as home, office, and hotel. The core idea is that legitimate and undesired users may have different physical constraints, e.g., moving area, walking path, and so on, leading to different signal sequences. NiFi analyzes and exploits the characteristics of signal sequences generated by mobile devices. NiFi proposes a practical and effective method to extract useful features and measures similarity for signal sequences while not relying on precise user location information. We implement NiFi on Commercial Off-The-Shelf APs, and the implementation does not require any modification to user devices. The experiment results demonstrate that NiFi is able to achieve an average identification accuracy at 90.83% with true positive rate at 98.89%.