Proximity Of Users Research Articles

Developing a Novel Contact Tracing Mobile Application for Coronavirus Disease 2019 (COVID-19) for Improving Testing Capacity and Controlling the Spread of COVID-19 in Nigeria

In this study, a novel contact tracing model that leverages smartphone technology to enhance efficiency, reduce costs, and extend the duration of contact tracing efforts is developed. This model utilizes smartphones as identification systems, collecting data on the proximity of other smartphone users through integrated Bluetooth and GPS technology. The study examines the frequency, duration, and proximity of interactions between smartphone devices in a clinical setting, highlighting potential implications for infectious disease transmission to pilot the mobile application developed. Contact data from six pairs of devices were analyzed, focusing on metrics such as total contacts, total contact time, average contact time, average distance, and the percentage of contacts occurring within 1.5 meters. The results showed varying levels of interaction across device pairs, with Devices 1 & 3 showing the highest number of contacts (175), and Devices 3 & 4 displaying the longest average contact time (20,133,193.01 seconds). Correlation analysis revealed weak and statistically insignificant relationships between total contacts and average distance (r = 0.13, p = 0.81), contact time and the percentage of close contacts (r = -0.15, p = 0.78). These findings suggest that while there are observable trends in contact patterns, the statistical insignificance highlights the need for further investigation to establish stronger associations that could inform infection control practices in healthcare settings.

Adaptation in Edge Computing: A Review on Design Principles and Research Challenges

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings.

HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users’ choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users’ check-ins, temporal properties of users’ decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users’ check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users’ decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization.

Power-efficient full-duplex near user with power allocation and antenna selection in NOMA-based systems

This paper proposes a power scaling (PS) technique aimed at mitigating the outage floor problem commonly encountered in multi-user full-duplex (FD) non-orthogonal multiple access (NOMA) systems. Two antenna modes denominated as adaptive antenna mode (AAM) and fixed antenna mode (FAM) are utilized in this work for L FD near users in close proximity. In addition, a combined method of selecting both antenna mode and near user integrated with PS is employed to improve the overall network performance. Moreover, a power allocation between the chosen near user and far user is considered. In the low-to-moderate power regions, by AAM and FAM, we achieve twice of full diversity gain and full diversity gain, respectively. The research presents mathematical expressions for deriving the average capacity and outage probability and supports the theoretical findings with simulation-based evidence. The results of this work show that PS method not only contributes to a greater spatial diversity but also leads to superior performance compared to traditional FD NOMA systems. Moreover, our proposed method overcomes the outage floor and capacity ceiling. Furthermore, the work can be developed to 6G massive MIMO technology in multi-user FD cooperative NOMA systems.

Real time implementation of downlink orthogonal frequency division multiplexing based non-orthogonal multiple access transceiver using NI USRP platform

The growing fame and utilization of wireless multimedia approaches have led to the advancement of the wireless system. The fifth generation (5G) of wireless communication was developed to serve users with enhanced proficiency, low latency, reliable communication, and lesser battery exhausts. The non-orthogonal multiple access (NOMA) scheme is a proficient multiple access scheme to fulfil the requirement of a 5G mobile system. NOMA enables a remarkable enhancement in the systems throughput and ability to connect devices. NOMA distinguishes each user by allocating a distinct power level, superimposing all the user’s signals utilizing superposition coding while transmitting and at the reception, each user’s signals are decoded by employing successive interference cancellation (SIC). This study builds a real time downlink orthogonal frequency division multiplexing based NOMA (OFDM-NOMA) transceiver system using the NI USRP 2944R and 2901 platforms. The performance evaluation of the proposed OFDM-NOMA system is carried out in terms of signal to noise ratio by adjusting transmitting gain and the distance of users from the base station system. Experimental results show that the signal-to-noise ratio (SNR) of each user relies on the power allocation factor and proximity of users from the base station, and SIC output is compared with constellation variations.

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

SummaryMatrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.

On data minimization and anonymity in pervasive mobile-to-mobile recommender systems

Data minimization is a legal principle that mandates limiting the collection of personal data to a necessary minimum. In this context, we address ourselves to pervasive mobile-to-mobile recommender systems in which users establish ad hoc wireless connections between their mobile computing devices in physical proximity to exchange ratings that represent personal data on which they calculate recommendations. The specific problem is: How can users minimize the collection of ratings over all users while only being able to communicate with a subset of other users in physical proximity? A main difficulty is the mobility of users, which prevents, for instance, the creation and use of an overlay network to coordinate data collection. Users, therefore, have to decide whether to exchange ratings and how many when an ad hoc wireless connection is established. We model the randomness of these connections and apply an algorithm based on distributed gradient descent to solve the distributed data minimization problem at hand. We show that the algorithm robustly produces the least amount of connections and also the least amount of collected ratings compared to an array of baselines. We find that this simultaneously reduces the chances of an attacker relating users to ratings. In this sense, the algorithm also preserves the anonymity of users, yet only of those users who do not establish an ad hoc wireless connection with each other. Users who do establish a connection with each other are trivially not anonymous toward each other. We find that users can further minimize data collection and preserve their anonymity if they aggregate multiple ratings on the same item into a single rating and change their identifiers between connections.

Average-Case Analysis of Greedy Matching for Large-Scale D2D Resource Sharing

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log n)$</tex-math></inline-formula> parallel complexity (in user number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> ) for large-scale D2D sharing. Practical cases indicate that the greedy matching's average performance is far better than the worst-case approximation ratio 50% as compared to the optimum. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings and this paper is the first to present such analysis for multiple representative classes of graphs. For 1D linear networks, we prove that our greedy algorithm performs better than 86.5% of the optimum. For 2D grids, though dynamic programming cannot be directly applied, we still prove this average performance ratio to be above 76%. For the more challenging Erdos-Rényi random graphs, we equivalently reduce to the asymptotic analysis of random trees and successfully prove a ratio up to 79%. Finally, we conduct experiments using real data to simulate realistic D2D networks, and show that our analytical performance measure approximates well practical cases.

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

Investigating the impact of greenery elements in office environments on cognitive performance, visual attention and distraction: An eye-tracking pilot-study in virtual reality

The human-nature connection is one of the main aspects determining supportive and comfortable office environments. In this context, the application of eye-tracking-equipped Virtual Reality (VR) devices to support an evaluation on the effect of greenery elements indoors on individuals’ efficiency and engagement is limited. A new approach to investigate visual attention, distraction, cognitive load and performance in this field is carried out via a pilot-study comparing three virtual office layouts (Indoor Green, Outdoor Green and Non-Biophilic). 63 participants completed cognitive tasks and surveys while measuring gaze behaviour. Sense of presence, immersivity and cybersickness results supported the ecological validity of VR. Visual attention was positively influenced by the proximity of users to the greenery element, while visual distraction from tasks was negatively influenced by the dimension of the greenery. In the presence of greenery elements, lower cognitive loads and more efficient information searching, resulting in improved performance, were also highlighted.

Online joint localization without user interactions

Wi-Fi fingerprinting has been a popular indoor localization due to the widespread layout of indoor WLAN. However, the signal fluctuations in the complex environments make it difficult to maintain high accuracy localization for the received signal strength (RSS) fingerprinting. Various positioning solutions have emerged to address this challenge, either working in stand-alone mode or in collaborative mode. In the former case, the user only utilizes his own RSS observation to request location service, while the latter usually requires information transfer between users. Considering the spatial correlation of wireless signal distribution, we propose an online joint localization scheme (JointLoc) that does not require direct interaction between users. The fact that the signals observed by users in physical proximity characterize the surroundings is used by JointLoc to identify neighboring users for joint localization. Besides this, JointLoc further integrates a novel subset-based localization scheme, thus the influence of anomalous RSS signals is eliminated before making the final location decision. We have fully evaluated the performance of JointLoc in two RSS datasets collected in real environments. Compared with conventional algorithms and the latest ones, results show that JointLoc is robust against signal fluctuations, and achieves good localization accuracy.

An empirical examination of push-pull-mooring factors influencing restaurant customers’ intention to use mobile payment: a comparison of mobile payment service types

ABSTRACTWith recent advances in mobile technologies, mobile payment systems that offer advantages to service providers have been widely used in the service industry. In this paper, the authors analyze the impact of mobile system characteristics on customers’ behaviour across different types of mobile payment systems (i.e. mobile proximity payment, mobile peer-to-peer payment, mobile in-app payment) in the restaurant field. Based on the push-pull-mooring factor model, the authors evaluate the proposed model using survey data collected from restaurant customers regarding their experiences with mobile payments. The results indicate that the mooring factor (i.e. subjective norm) and the pull factor (i.e. perceived ease of use, perceived usefulness) are predictors of the attitude to use a mobile payment system. However, the push factor (i.e. perceived risk) does not influence customers’ behaviour. Moreover, the relationship between the mobile payment system experience and its outcome varies across different types of systems. The perceived ease of use influences only proximity users, not in-app payment and peer-to-peer users. Findings also highlight the role of perceived usefulness in the mobile payment context across different demographic characteristics. Thus, the findings of this study provide guidelines for future mobile payment-related studies and business strategies.

Cooperative Relaying in a Three User Downlink NOMA System Using Dynamic Power Allocation

Non-orthogonal multiple access (NOMA) represents the latest addition to the array of multiple access techniques, enabling simultaneous servicing of multiple users within a singular resource block in terms of time, frequency, and code. A typical NOMA configuration comprises a base station along with proximate and distant users. The proximity users experience more favorable channel conditions in contrast to distant users, resulting in a compromised performance for the latter due to the less favorable channel conditions. When cooperative communication is integrated with NOMA, the overall system performance, including spectral efficiency and capacity, is further elevated. This study introduces a cooperative NOMA setup in the downlink, involving three users, and employs dynamic power allocation (DPA). Within this framework, User 2 acts as a relay, functioning under the decode-and-forward protocol, forwarding signals to both User 1 and User 3. This arrangement aims to bolster the performance of the user positioned farthest from the base station, who is adversely affected by weaker channel conditions. Theoretical and simulation outcomes reveal enhancements within the system’s performance.

SEFSD: an effective deployment algorithm for fog computing systems

Fog computing aims to mitigate data communication delay by deploying fog nodes to provide servers in the proximity of users and offload resource-hungry tasks that would otherwise be sent to distant cloud servers. In this paper, we propose an effective fog device deployment algorithm based on a new metaheuristic algorithm–search economics–to solve the optimization problem for the deployment of fog computing systems. The term “effective” in this paper refers to that the developed algorithm can achieve better performance in terms of metrics such as lower latency and less resource usage. Compared with conventional metaheuristic algorithms, the proposed algorithm is unique in that it first divides the solution space into a set of regions to increase search diversity of the search and then allocates different computational resources to each region according to its potential. To verify the effectiveness of the proposed algorithm, we compare it with several classical fog computing deployment algorithms. The simulation results indicate that the proposed algorithm provides lower network latency and higher quality of service than the other deployment algorithms evaluated in this study.

SmartMagnet: Proximity-Based Access Control for IoT Devices With Smartphones and Magnets

Ubiquitous smartphones can be powerful tools to access IoT devices. Proximity-based access control (PBAC) is needed such that IoT devices only allow data access by legitimate users in close proximity. Traditional smartphone-based authentication techniques do not satisfy the PBAC requirements. This paper presents SmartMagnet, a novel scheme that combines smartphones and cheap magnets to achieve PBAC for IoT devices. SmartMagnet explores a few cheap, tiny commodity magnets which we propose to attach to or embed into IoT devices, as well as the magnetometer and attitude sensor on commodity smartphones. Each legitimate user performs a self-chosen 3D password gesture near the target IoT device with the enrolled smartphone. Then the system server uses the IoT device's confidential magnet configuration parameters to reconstruct the user gesture from the magnetometer and attitude sensor data submitted by the smartphone. If the reconstructed gesture matches the stored template of the purported user, the smartphone user is deemed legitimate and allowed access to the IoT device. Extensive experiments confirm the high usability of SmartMagnet and its strong resilience to lost/stolen smartphones and also remote attacks via signal relaying.

Prospects of soft biopotential interfaces for wearable human-machine interactive devices and applications

Human interaction with machines can be made easy, comfortable, and accessible by introducing user-friendly interfaces. In the case of wearable devices, their sensors and other interfacing elements are very well within the proximity of users. Since biopotential signals can be accessed from the surface of the human skin, users can have seamless interaction with wearable human-computer interactive devices. Rigid interfaces can hinder the user experience, and therefore, the need for soft biopotential interfaces is important. Imperceptible and unobtrusive soft biopotential interfaces will drastically enhance many aspects of human-computer interaction. This paper reviews the use of soft, flexible, and stretchable biopotential interfaces in wearable human-machine interactive devices. Additionally, attention is brought to the scope of other possible applications of soft biopotential interfaces in wearable devices.

Cooperative caching strategy based mobile vehicle social‐aware in internet of vehicles

AbstractThe dramatic growth of smart in‐vehicle applications in the Internet of Vehicles and the increasing quality of experience for vehicle users have put a huge traffic load on the mobile core network. Mobile edge caching has been proposed to place content at the edge of the network to serve users in close proximity to them. However, the dynamic topology of existing edge networks and the inherent storage limitations of edge devices pose a serious challenge to vehicular edge caching. Therefore, in this article, we propose a cooperative caching strategy based on mobile prediction and social awareness that allows collaborative content decision making among edge devices. Specifically, the long short‐term memory network is used to predict vehicle trajectories, social relationships are computed using content similarity and contact rates among vehicle users to select vehicles that can serve as caching nodes, and deep reinforcement learning is employed to achieve the final caching decision. Simulation results show that the caching strategy proposed in this article achieves up to 7.7%, 24.2%, and 27.3% gains, respectively, in terms of content acquirement delay compared to the reinforcement learning algorithm, the algorithm without considering mobility, and the non‐cooperative algorithm.

Exploring Renewable Energy Communities integration through a hydrogen Power-to-Power system in Italy

Increasing renewable power generation is a way to decarbonize power system. Hydrogen can be used as energy vector in a Power-to-Power (PtP) system to better manage the energy flow from renewables to users. Renewable Energy Community (so called REC) is proposed by RED II European directive to involve final users in energy management and to create conditions for local development of renewables. This work introduces a PtP hydrogen-based system, involving RECs to increase electric autarchy (self-sufficiency) of final users who have installed renewables in their electric market area (proximity of users). A double REC system is proposed in which the first REC generates renewable energy through a hybrid photovoltaic and wind system, and excess energy is stored as hydrogen generated through electrolysis; the second REC is powered by the hydrogen produced by the first one by means of fuel cell. Both RECs are connected to the power grid from which they can draw energy as well as put energy into it. Despite low round trip efficiency of a Power-to-Power hydrogen supply chain, the system increases users’ autarchy substantially (from 68.6% to 83.7%). Current costs of investigated apparatuses despite the public subsidies to REC makes the proposed system not sustainable from an economic point of view.

Joint power allocation and channel assignment for device‐to‐device communication using the Hungarian model and enhanced hybrid Red Fox‐Harris Hawks Optimization

SummaryMultihop device‐to‐device (D2D) communication is a promising advancement since it allows wireless users in close proximity to communicate directly with one another without using base stations (BSs). With two nearby users, this technology intends to keep up with the accelerating development of mobile devices and the rising demands of local traffic loads. High‐data‐rate short‐range transmission is made possible by this paradigm, which also lowers network backhaul expenses while improving end‐user experience, spectral efficiency (SE) and network coverage. In order to maximize the cell's overall sum rates, this article examines the problem of selecting the optimal M‐D2D linkages and cellular users (CUs) to form spectrum‐sharing partners while taking quality of service (QoS) and energy efficiency (EE) needs into consideration. This study examines the use of a channel assignment system and a power allocation technique to manage interference in D2D communication scenarios involving many hops (more than one hop). The suggested channel assignment approach is based on the Hungarian method, but the power allocation system is based on the time‐efficient enhanced Harris hawks optimization (HHO) and red fox (RF) algorithms. A genetic algorithm (GA)‐based methodology and various baseline approaches are used to compare the effectiveness of the suggested system. Because it combines exploitation and exploration using a memory‐based local search methodology and a perturbation mechanism, the suggested approach outperforms the GA in simulations. The results clearly demonstrate that the suggested method increases EE by up to 13% by producing the appropriate channel and power assignment for CUs and M‐D2D users.

Mobility-Aware Computation Offloading in Edge Computing Using Machine Learning

Cloudlets are resource-rich computing infrastructures of edge computing that are located at physical proximity of users to provide one-hop, high-bandwidth wireless access to additional computational resources. They enable computation offloading for user applications, which compensates for the resource limitation of user devices by providing ultra-low latency processing for their applications. Although the computation capability of user devices is dramatically augmented by offloading, spatio-temporal uncertainties due to user mobility and changes in application specifications bring the most challenging obstacles in deciding where to offload to provide minimum latency. In this paper, we focus on these challenges by designing efficient offloading approaches that take into account these uncertainties and dynamics in order to minimize the turnaround time of the applications, which is constituted by offloading latency, migration delay, and execution time. We first formulate this NP-hard problem as an integer programming model to obtain optimal offloading decisions. We tackle its intractability by designing two novel offloading approaches, called S-OAMC and G-OAMC, that fully assign applications to cloudlets by considering their expected future locations and specifications predicted by Matrix Completion, a machine learning method. S-OAMC is a sampling-based approximation dynamic programming approach that enhances scalability and obtains near-optimal solutions. G-OAMC is a fast greedy-based approach for finding low-turnaround time offloading decisions. We conduct extensive experiments to assess the performance of our proposed approaches. The results show that S-OAMC and G-OAMC lead to near-optimal turnaround time in a reasonable time, and they both obtain low migration rates.