Location Of Access Points Research Articles

Energy efficient hybrid wired-cum-wireless sensor network design

Abstract The broad range of wireless sensor network applications has been part of the exponential growth in wireless communications and related technological developments. As a result, the concern on energy efficient network designs is increasing as the energy consumption is becoming a global environment problem. Minimizing energy dissipation and maximizing network lifetime are among main factors, aiming for optimized designs which lead to green applications. In an industrial application, various energy efficient network configurations can be designed. One such robust configuration is a hybrid wired-cum-wireless sensor network that is composed of a wireless sensor network and a wired backbone, which are inter-connected via access points. In this paper, the joint problem of configuring a hybrid wired-cum-wireless sensor network, position-constrained cluster head location, sensor nodes allocation, and position-constrained access point placement is addressed. The design considers real wireless communication limitations in the industrial applications, optimum locations of access points and cluster heads, and hybrid transmission structure of the network with the objective of minimizing the network energy consumption and configuration cost. The problem is formulated as a mixed integer non-linear programming (MINLP) model and solved with “BARON”. Numerical results show that the hybrid configuration is more energy effective than the wireless networks and leads to a longer lifetime at the expense of a more complex design.

Optimization of underground mine development system using fuzzy shortest path length algorithm

Graphic mine design elements denote physical entities such as shafts, declines, and drives. Ore deposits are often composed of independent orebodies that must be interconnected into one integrated system. In this paper, we examine a case where access points to orebodies lie in the Euclidean plane. The key question is how to interconnect these points at minimal cost. This design problem is modeled as a network and the solution technique is outlined. We supposed that the locations of access points had been previously determined. To define the ore reserves in each orebody, we used linguistic variables and their transformation to fuzzy triangular numbers. At first, we used Kruskal’s algorithm to identify the minimum spanning tree. After that, by inserting Steiner points we defined a Steiner minimal tree as the global minimum. In a network created in such a way, it is necessary to locate a point called the major mass concentration point to which excavated ore will be delivered; from there, the excavated ore will be hauled or hoisted to a surface breakout point via an optimal development system. In this paper, we use the fuzzy shortest path length procedure to select an optimal development system.

An RSSI gradient-based AP localization algorithm

Recent rapid rise of indoor location based services for smartphones has further increased the importance of precise localization of Wi-Fi Access Point (AP). However, most existing AP localization algorithms either exhibit high errors or need specialized hardware in practical scenarios. In this paper, we propose a novel RSSI gradient-based AP localization algorithm. It consists of the following three major steps: firstly, it uses the local received signal strength variations to estimate the direction (minus gradient) of AP, then employs a direction clustering method to identify and filter measurement outliers, and finally adopts triangulation method to localize AP with the selected gradient directions. Experimental results demonstrate that the average localization error of our proposed algorithm is less than 2 meters, far outperforming that of the weighted centroid approach.

AP Deployment Research Based on Physical Distance and Channel Isolation

Aiming at the problem of inefficiency of wireless local area networks (WLAN) access point (AP) deployment in urban environment, a new algorithm for AP deployment based on physical distance and channel isolation (DPDCI) is proposed. First, it detects the position information of deployed APs and then calculates the interference penalty factor combined with physical distance and channel isolation, and finally gets the optimal location and channel of the new AP through the genetic algorithm. Comparing with NOOCA algorithm and NOFA-2 algorithm, the results of numerical simulation show that the new algorithm can minimize the mutual interference between basic service sets (BSS), can ensure the maximum of throughput based on quality of service (QoS) in BSS, and can effectively improve the system performance.

Location Optimization of WLAN Access Points Based on a Neural Network Model and Evolutionary Algorithms

In this article we intend to show the use of well-known evolutionary computation techniques—Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) - in an indoor propagation problem. Although these algorithms employ different strategies and computational efforts, they also share certain similarities. Their performance is compared with a genetic algorithm (GA), which is used as reference in this case. The ability of these algorithms to optimize access point locations using data derived from the neural network model of a particular Wireless Local Area Network (WLAN) is demonstrated. Better results are obtained by the PSO algorithm compared to the ACO algorithm. Although the ACO algorithm requires further work to optimize its parameters, improve the analysis of pheromone data and reduce computation time, the ant colony-based approach is useful for solving propagation problems.

A Fuzzy Logic-Based System for Indoor Localization Using WiFi in Ambient Intelligent Environments

Ambient intelligence is a new information paradigm, where people are empowered through a digital environment that is “aware” of their presence and context and is sensitive, adaptive, and responsive to their needs. Hence, one of the important requirements for ambient intelligent environments (AIEs) is the ability to localize the whereabouts of the user in the AIE to address her/his needs. In order to protect user privacy, the use of cameras is not desirable in AIEs, and hence, there is a need to rely on nonintrusive sensors. There are various localization means that are available for outdoor spaces such as those which rely on satellite signals triangulation. However, these outdoor localization means cannot be used in indoor environments. The majority of nonintrusive and noncamera-based indoor localization systems require the installation of extra hardware such as ultrasound emitters/antennas, radio-frequency identification (RFID) antennas, etc. In this paper, we propose a novel indoor localization system that is based on WiFi signals which are free to receive, and they are available in abundance in the majority of domestic spaces. However, free WiFi signals are noisy and uncertain, and their strengths and availability are continuously changing. Hence, we present a fuzzy logic-based system which employs free available WiFi signals to localize a given user in AIEs. The proposed system receives WiFi signals from a large number of existing WiFi access points (up to 170 access points), where no prior knowledge of the access points locations and the environment is required. The system employs an incremental lifelong learning approach to adjust its behavior to the varying and changing WiFi signals to provide a zero-cost localization system which can provide high accuracy in real-world living spaces. We have compared our system in both simulated and real environments with other relevant techniques in the literature, and we have found that our system outperforms the other systems in the offline learning process, whereas our system was the only system which is capable of performing online learning and adaptation. The proposed system was tested in real-world spaces from a living lab intelligent apartment (iSpace) to a town center apartment to a block of offices. In all these experiments, our system has been highly accurate in detecting the user in the given AIEs, and the system was able to adapt its behavior to changes in the AIE or the WiFi signals. We envisage that the proposed system will play an important role in AIEs, especially for privacy concerned situations like elderly care scenarios.

RSSI-based Algorithm for Indoor Localization

Wireless node localization is one of the key technologies for wireless sensor networks. Outdoor localization can use GPS, AGPS (Assisted Global Positioning System) [6], but in buildings like supermarkets and underground parking, the accuracy of GPS and even AGPS will be greatly reduced. Since Indoor localization requests higher accuracy, using GPS or AGPS for indoor localization is not feasible in the current view. RSSI-based trilateral localization algorithm, due to its low cost, no additional hardware support, and easy-understanding, it becomes the mainstream localization algorithm in wireless sensor networks. With the development of wireless sensor networks and smart devices, the number of WIFI access point in these buildings is increasing, as long as a mobile smart device can detect three or three more known WIFI hotspots’ positions, it would be relatively easy to realize self-localization (Usually WIFI access points locations are fixed). The key problem is that the RSSI value is relatively vulnerable to the influence of the physical environment, causing large calculation error in RSSI-based localization algorithm. The paper proposes an improved RSSI-based algorithm, the experimental results show that compared with original RSSI-based localization algorithms the algorithm improves the localization accuracy and reduces the deviation.

Isolated Upper Pole Access in Percutaneous Nephrolithotomy: A Large-Scale Analysis from the CROES Percutaneous Nephrolithotomy Global Study

We analyzed the indications for and outcomes of percutaneous nephrolithotomy using upper pole access. Between 2007 and 2009 prospective data were collected by the Clinical Research Office of the Endourological Society (CROES) from consecutive patients at 96 centers globally. Data on 4,494 patients were included in this analysis. Patients were divided into upper and lower pole access groups based on the location of percutaneous renal access. Preoperative characteristics and outcomes were compared between the 2 groups by univariate and multivariate tests. The upper pole access group had more staghorn stones (21.7% vs 15.5%, p <0.001) and a greater stone burden (mean ± SD 476 ± 390.5 vs 442 ± 344.9 mm(2), p = 0.091). Mean operative time was 92.4 ± 46.1 and 75.1 ± 41.3 minutes in the upper and lower pole groups, respectively (p <0.001). The stone-free rate was lower in the upper pole access group (77.1% vs 81.6%, p = 0.030). The overall complication rate was higher in the upper pole group with a higher incidence of hydrothorax (5.8% vs 1.5%) but a lower incidence of pelvic perforation (1.8% vs 3.2%). Mean hospital stay was longer in the upper pole group (p = 0.048). Success and complication rates were similar in upper pole access subgroups, defined as definitive (staghorn and isolated upper calyceal stones) and elective (pelvic, middle calyceal and lower pole stones) indications. Isolated upper pole access is indicated in a select group of patients with complex stones. Upper calyceal and staghorn stones are more commonly managed by upper pole access, which is associated with a higher complication rate and longer hospital stay as well as a lower stone-free rate due to procedure complexity.

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.

Urban 802.11 Community Networks for Mobile Users: Current Deployments and Prospectives

Few years ago some network operators launched a new service called Community Networks, where each subscriber shares its residential Internet connection with other subscribers of the same operator via his 802.11 access point. Due to the high access point density in urban areas, community networks have the potential to offer high data rate wireless Internet access for mobile users, going beyond the currently available hot-spots, which are typically deployed in a limited number of isolated locations (e.g., airports, cafes). Unlike cellular networks, the locations of community network access points is not centrally planned. We evaluate existing community networks using a participatory sensing application called Wi2Me. First, we analyze different metrics in order to characterize community networks. Second, we take advantage of this unique 802.11 network (in terms of density and coverage) to study how mobility can be supported by identifying drawbacks and proposing some possible solutions.

Outage Probability Analysis of Macro-Diversity Combining in Poisson Field of Access Points

In this letter, we analyze the outage probability of uplink macro-diversity combining when access points (APs), which can be either base stations or distributed antennas, are randomly deployed according to a Poisson point process. As the macro-diversity combining scheme, we consider both selection and maximal ratio combining. Randomized locations of APs due to the Poisson point process model make the outage probability analysis more tractable and practical than the traditional grid model does. With this Poisson point process model, we are able to derive closed-form formulas for the outage probabilities of selection and maximal ratio combining schemes as a function of the density of APs.

Secondary Access Points Placement in Cognitive Radio Networks: A Spatial Game Model With Power Discrimination

This paper studies the problem of determining the locations of secondary access points (SAPs) that belong to two competitive service providers in a certain region. Secondary users (SUs) connect to SAPs according to their preferences. A key feature of the proposed model is that it permits SAPs to set discriminatory powers for SUs, i.e., SUs in different locations will be scheduled with different transmission powers. Profit-maximizing SAPs compete with each other by setting locations and delivered power schedules to attract SUs, taking into account the impact of the revenue obtained and the power cost incurred. To study this competitive situation, an appropriate extensive form game is defined. The game can be viewed as two-staged. The selection of transmission powers by SAPs can be investigated for each pair of locations. Then, the problem of location choice can be analyzed, anticipating what transmission powers will be chosen. We make an important observation: The Nash equilibrium of the game exists, and the global revenue maximum strategy is a Nash equilibrium strategy. Finally, we explore the equilibrium for a circular region having uniform SU distribution and illustrate how to locate SAPs for such a region.

Analytical Evaluation of Fractional Frequency Reuse for Heterogeneous Cellular Networks

Interference management techniques are critical to the performance of heterogeneous cellular networks, which will have dense and overlapping coverage areas, and experience high levels of interference. Fractional frequency reuse (FFR) is an attractive interference management technique due to its low complexity and overhead, and significant coverage improvement for low-percentile (cell-edge) users. Instead of relying on system simulations based on deterministic access point locations, this paper instead proposes an analytical model for evaluating Strict FFR and Soft Frequency Reuse (SFR) deployments based on the spatial Poisson point process. Our results both capture the non-uniformity of heterogeneous deployments and produce tractable expressions which can be used for system design with Strict FFR and SFR. We observe that the use of Strict FFR bands reserved for the users of each tier with the lowest average \sinr provides the highest gains in terms of coverage and rate, while the use of SFR allows for more efficient use of shared spectrum between the tiers, while still mitigating much of the interference. Additionally, in the context of multi-tier networks with closed access in some tiers, the proposed framework shows the impact of cross-tier interference on closed access FFR, and informs the selection of key FFR parameters in open access.

Low energy indoor network: deployment optimisation

This article considers what the minimum energy indoor access point deployment is in order to achieve a certain downlink quality-of-service. The article investigates two conventional multiple-access technologies, namely: LTE-femtocells and 802.11n Wi-Fi. This is done in a dynamic multi-user and multi-cell interference network. Our baseline results are reinforced by novel theoretical expressions. Furthermore, the work underlines the importance of considering optimisation when accounting for the capacity saturation of realistic modulation and coding schemes. The results in this article show that optimising the location of access points both within a building and within the individual rooms is critical to minimise the energy consumption.

An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy

In recent years, the indoor positioning technologies have been recognized as core technologies for realizing smart space, a ubiquitous society, context awareness, and various location-based services. There are several approaches for positioning with radio signals, but the received signal strength (RSS)-based technology is considered a promising scheme because of its simplicity and practicality in implementation. In this paper, the positioning performance of the RSS value-based scheme is analyzed with respect to the location of access points (APs) and the number of APs in an indoor environment. An adaptive AP selection scheme and a base AP changing scheme are then proposed to enhance the positioning accuracy. In order to estimate the RSS characteristics, RSS values are measured as the distance between the AP and the receiver increases. The positioning performance is evaluated with differing AP numbers, which form a triangle or a quadrilateral. The performance of the proposed schemes is evaluated via experiments using wireless local area network APs. Results show that the performance of proposed schemes is enhanced compared to that of conventional scheme.

Tracking Mobile Users in Wireless Networks via Semi-Supervised Colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.

Multi-layer hybrid wired-cum-wireless sensor network design

Sensor networks with sensing, data processing and communicating capabilities have a broad spectrum of applications. Based on application requirements, various network configurations can be designed. One such robust configuration is a hybrid wired-cum-wireless sensor network that is composed of a wireless sensor network and a wired backbone which are inter-connected via access points. In this paper, the joint problem of configuring a hybrid wired-cum-wireless sensor network, position-constrained cluster head and access point placement is proposed. The design considers real wireless communication limitations, optimum locations of access points and cluster heads, and hybrid transmission structure of the network with the objective of minimising the network configuration cost. The problem is formulated as a mixed integer linear programming and solved using 'CPLEX'. Numerical results show that hybrid configuration is more cost effective than the wireless networks and leads to a longer lifetime at the expense of a more complex design.

스마트 폰의 GPS 정보를 이용한 무선랜 접속점 위치 측정 방법

In this article, we propose a new method for precise WLAN (Wireless Area Network) AP (Access Point) localization using GPS information measured in the smart phone. The idea is that the possible area of WLAN AP location, called AP_Area, is first determined by measuring GPS information and the received signal strength in the smart phones. As the number of measurements from the smart phones increases, the AP_Area are successively narrowed down to the actual AP location. We have performed the simulation to evaluate the proposed algorithm. The simulation results show that the proposed algorithm can detect the Wi-Fi AP localization within 5 m (probability over than 90%).

Wireless LAN Access Point Placement Based on User Mobility

In a wide area campus, a university provides Wireless Local Area Network (WLAN) for users to connect to the Internet. Most users take advantage of this WLAN benefit by using their laptops. However, the number of smart phone users is growing fast. Since a smart phone is able to get an Internet connection using WLAN, users can use their smart phones without having to pay for a cellular operator. Users tend to use their smart phones more, due to their higher mobility compared to a laptop. This capability enables new services in the market, such as Fixed-Mobile Convergence (FMC), which integrates a fixed network (traditional telephony, WLAN) and a mobile network (cellular) to provide seamless voice communications anytime, anywhere. These new applications require a WLAN connection availability nearly everywhere. However, due to limited budgets, a university can only install APs in places with a high connection demand. We propose a novel WLAN AP placement technique that takes user mobility into consideration. This new approach is more complete than previous approaches, which mainly focus on coverage area and throughput data. Our technique has been implemented in our university. The results show the suitability of the WLAN access point locations in our university campus based on user mobility and activities.

Localizing WiFi Access Points Using Signal Strength

When estimating the location of WiFi access points (AP) in an anonymous environment, the required parameters of the radio propagation model are not readily obtainable. This letter investigates the use of received signal strength (RSS) for range-based AP localization when no information on the radio propagation model is provided. To achieve this goal, we linearly approximate the exponential relationship between RSS and distance, and apply a multilateration technique. Our simulation results validate that estimation of an AP location is possible given four or more RSS measurements at different locations.