Modeling and performance study of AP placement framework for indoor location-awareness and network performance

With the fast-growing demand for mobile services, where to place the access points (APs) for providing uniformly and appropriately distributed signals in a wireless local area network (WLAN) becomes an important issue in the wireless network planning. Basically, AP placement will affect the coverage and strength of signals in a WLAN. The number and locations of APs in a WLAN are often decided on the basis of the trial-and-error method. Based on this method, the network planner first selects suitable locations to place APs through observation, and then keeps changing the locations to improve the signal strength based on the received signal. Such process is complicated, laborious and time-consuming. To overcome this problem, we investigate the back-propagation neural network (BPNN) algorithm to improve over the traditional trial-and-error method. Without increasing the number of APs, our approach only needs to adjust AP locations to overcome weak signal problems and thus increase the signal coverage for the Internet connection anywhere within the area. In our experiment, we established a WLAN on a C campus. Our experiments also indicate that placing APs according to the BPNN provided better signal coverage and met the students’ demands for connecting to the Internet from anywhere in the classroom.

This manuscript presents a novel approach based on hybrid optimization techniques for planning Wireless Local Area Networks in two stages: i) network structure design for access point (AP) placement and channel assignment and ii) channel assignment enhancement. We consider two objective functions: network load balance and signal-to-interference-plus-noise ratio; and three hard constraints: maximum AP capacities, client demand attendance, and minimum coverage levels. The proposed algorithm delivers an approximation of the efficient solution set, considering the two functions described above. The results from two scenarios were compared to the following four approaches: two multiobjective evolutionary algorithms, a well-known commercial tool, and a greedy technique. Finally, the solutions were subjected to sensitivity analysis to validate their robustness regarding user mobility and AP failures.

In wireless local area network (WLAN), signal coverage is obtained by proper placement of access points (APs). The impact of incorrect placement of APs is significant. If they are placed too far apart, they generate a coverage gap but if they are too close to each other, this leads to excessive co-channel interferences. In this paper, we describe a mathematical model we have developed to find the optimal number and location of APs. To solve the problem, we use an optimization algorithm developed at the University of Ballarat called discrete gradient algorithm. Results indicate that our model is able to solve optimal coverage problems for different numbers of users

This paper presents the use of a multi-objective genetic algorithm (MOGA) for solving an access point placement problem in a wireless LAN. The aim is to maximize signal coverage over the interested area. The problem has been formulated as a multi-objective optimization problem where the decision variables are derived from the locations of the access points in the target area. The objectives consist of the number of access points and the average SNR over the whole area. The major advantage of using MOGA is that multiple optimal placement configurations for different numbers of access points can be obtained from a single run. A set of solutions provides more alternatives to the network designer. The simulation results indicate that the MOGA is capable of generating a placement result which is superior to that produced using standard placement techniques. In addition, the result assessment has been confirmed using statistical and analyzed data profile.

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

Fingerprint-based localization systems mainly utilize the received signal strength (RSS) to estimate the location of a receiver, and the localization accuracy depends largely on the number and placement of access points (APs). In this paper, we propose a novel method for placing the APs, which aims to minimize the total number of similar fingerprints (SFs) over the entire array of training locations. Minimizing SFs will increase the diversity of RSS array and hence improve localization accuracy. To solve the problem using a simple brute force search would be highly computationally expensive and inefficient. Instead, we propose a heuristic optimization algorithm based on Simulated Annealing (SA). Numerical results are obtained for both the brute force search and the SA based approach. Finally, the proposed algorithm's outputs, i.e., the APs' locations, are used with a K-Nearest Neighbors based localization algorithm, and the resulting localization errors are analyzed.

This paper investigates the optimal access point (AP) placement (OAPP) in simultaneous broadcast system using orthogonal frequency division multiplexing (OFDM) for high data rate indoor wireless LAN. We aim to determine the best combination of multiple AP placements that minimizes average bit error rate (BER) of terminals when in put information including an indoor configuration, a number APs, and total transmission power is given a priori. Since OAPP is categorized as a hard combinatorial problem with many variables and constraints, we applied a nonlinear optimization scheme called uniform design simulated annealing (UDSA) to search for an approximate optimal solution and analyzed its performance in detail. The simulation results demonstrate that the UDSA algorithm has been successfully applied to OAPP and its effectiveness was also confirmed in comparison with very fast simulated annealing (VFSA) and local search (LS) algorithms.

In IEEE 802.11 wireless local area networks (WLANs), TCP suffers unfairness between uplink and downlink flows due to its asymmetric reactions towards data and ACK losses at the AP (Access Point), and the AP's inability to distinguish itself from other contending stations accessing the medium. In this paper, we propose a novel dual queue management (DQM) scheme with ECN (Explicit Congestion Notification) at the AP's downlink buffer to improve TCP performance in infrastructure WLANs. Our approach maintains two queues for TCP ACK and data respectively, with their total length controlled by a PI (Proportional Integral) controller to prevent congestion. ACK/data packets are marked/dequeued dependent on uplink/downlink time usage of the wireless channel. We also propose an opportunistic scheduling mechanism of the two queues exploiting multi-rate capability and multi-user diversity for more efficient link utilization. We evaluate the proposed approach in ns-2 and our simulation results demonstrate that this design with few states can significantly improve TCP congestion control, fairness performance and link utilization in WLANs.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Information technology in the field of transmission that is currently developing, one of which is Wi-Fi. Wi-Fi devices provide user convenience in carrying out their activities. The quality of Wi-Fi network performance can be known by the reception of the signal received by the user. If the placement of an access point (AP) is done correctly, the network will be optimized. There are several propagation models in the room that can be used as a guideline in determining the placement of the AP, one slope model is a way to measure the average level of a building and only depends on the distance of the transmitter and receiver. This research was conducted in order to overcome the problem of Wi-Fi network area coverage at the Office of Communication and Information of the City of Palembang. This study conducted an experiment to change the layout of AP's placement, measure and calculate data in priority with the one slope model. The results of measurements and calculations carried out analysis and comparison in order to determine the results of the experiments conducted. The results of this study indicate that an attempt to change the AP layout with one slope model can overcome existing problems and get better Wi-Fi coverage area performance. In the calculation with the one slope model of the 2-trial access point placement results in a decrease and an increase in signal. The signal reduction occurred in experiment 1, whereas in experiment 2 (design 2) the signal increased by 1.46dBm.

Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.

In this paper, we study the problem of enhancing the fault tolerance of IEEE 802.11 wireless local area networks in the design stage. Our goal is to place minimal number of APs (access points), such that the system can tolerate AP failures while guaranteeing QoS requirement. Given a set of clients (end-users) in a region, each client has a traffic demand for Internet access. Our concern is to find out minimal number of APs and their locations, so that both fault tolerance and QoS constraints can be satisfied. That is, when there is no AP failure, all clients' traffic demands should be met. When one AP fails, the clients it serves shall switch to other APs and a certain percentage of traffic demands of these clients affected shall still be met. We proposed a heuristic algorithm to solve this problem. Firstly we place minimal number of APs to ensure that connection requirements and per-client degrading traffic demand can be met. Secondly, we continue to add APs to meet per-client normal traffic demand. Finally, we place more APs to tolerate failure of the APs placed in the first step. Simulation are conducted to show the performance of our proposed method.

Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

Many wireless local area network (WLAN) performance estimations are done with the assumption of uniformly distributed stations (STAs). In practice, on the contrary STAs are distributed unevenly among access points (APs), causing hot-spots and under utilized APs in a wireless network. Considering a WLAN is made up of multiple APs, having some APs carrying excessive loads (i.e. hot-spots) degrades both the considered APs as well as the overall network performance. The system performance can be improved by associating incoming STAs effectively throughout the network, in a sense to balance the network load evenly between APs and relieve the hot-spot congestion. Currently employed user association method in IEEE 802.11 WLANs considers only the received signal strength of APs at STAs, and associates STAs to the closest (in signal strength sense) AP, ignoring its load and interference value.Novel user association algorithms are required for congestion relief and network performance improvement. In this work, a new distributed association algorithm taking into consideration not only the received signal strength of the APs at STAs but also AP loadings and interference is proposed. A new AP load calculation method acknowledging the interference between STAs and APs is presented. Our simulations demonstrate that the proposed algorithm can improve the overall system throughput performance more than 50% and offers a better load distribution across the network compared to conventional association algorithm.

: Wireless mesh networks (WMNs) are interconnected systems of wireless access points (APs) that provide untethered network connectivity for a group of users who require data, voice, and/or video communication. The wireless access medium of a WMN makes it particularly vulnerable to attack and exploitation. We seek a method for quickly designing WMN physical topologies (i.e., the placement of APs) that are robust to the effects of electromagnetic jamming. The conflicting interests of a network designer and attacker in respectively maximizing and minimizing network performance make this problem a natural candidate for the use of game theory. We apply the game theoretic defender-attacker-defender (DAD) methodology to the simultaneous routing, resource allocation, and coverage (SRRA+C) model of WMN performance to simulate the design, attack, and operation of a WMN. Our algorithm and associated decision-support tool can quickly prescribe jamming-robust WMN topologies that minimize the worst possible damage that an adversary can inflict. Our approach considers radio-operating characteristics, the relative importance of client coverage and network flow, and the effects of radio propagation over terrain. To our knowledge, we are the first to use an algorithm with proven global convergence to design jamming-robust WMNs, and the first to apply the DAD framework to the problem of WMN design.