Wireless LAN access point placement using a multi-objective genetic algorithm

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 paper presents an AP (Access Point) placement framework considering indoor location-awareness and AP-based WLAN (Wireless Local Area Networks) performance enhancement. We first develop four objective functions that yield objective goals significant to the optimal AP placement in terms of location-awareness and network performance factors. Then, we develop three meta-heuristic algorithms based on simulated annealing, tabu search, and genetic algorithm. These algorithms generate a near-optimal solution for a given objective function. The performance of the AP placement framework presented in this paper is measured under the environments simulating indoor spaces, and numerical results obtained by experimental evaluation of proposed objective functions and algorithms confirm that the proposed AP placement framework can achieve a near-optimal solution to a given objective function.

Most practical problems are formulated as multiobjective optimization problems (MOP) so as to meet the diversified demands of a decision maker. Usually, there is a trade-off relation among objective functions, and thus there does not necessarily exist the solution that optimizes all objective functions simultaneously in MOP. Therefore, Pareto optimal solution is used as a definition of solution to MOP. Recently, evolutionary algorithms have been developed remarkably in order to obtain approximate solutions to optimization problems. Particularly, multiobjective genetic algorithms (MOGA) have been developed for generating Pareto optimal solutions. However, there are two problems in MOGA: how to assign the fitness to individuals, and how to keep the diversification of individuals. Many existing MOGAs have made an effort in order to overcome these problems, and so does this paper. First, this paper suggests a fitness function in MOGA using generalized data envelopment analysis (GDEA) which was suggested for evaluating the relative efficiency of individuals under several items of assessment management science. It is shown that the GDEA method can approximate Pareto optimal solutions more effectively and faster than the ranking method which is mostly used in MOGA, and generate well-distributed Pareto optimal solutions. Furthermore, this paper suggests the aspiration-level based GDEA method to generate the most interesting part (not the whole Pareto optimal solutions) to an aspiration level of decision maker for choosing a final solution from many Pareto optimal solutions. Finally, this paper illustrates the effectiveness of the methods using GDEA through several numerical examples.

In order to obtain the optimal parameters of flight control laws for a certain helicopterpsilas coordinated turning motion, a novel method based on multi-objective genetic algorithm (MOGA) is proposed. In this method, design of flight control laws for coordinated turning motion is viewed as a multi-objective optimization problem (MOP) where obtaining optimal performances in each design flight envelop are treated as sub-objectives and that in all design flight envelops are looked upon as overall objectives, parameters of coordinated turning control laws, specified performance criteria are respectively viewed as decision variables and constraints. Then MOGA is used to solve the MOP intention to find out the optimal parameters of control laws for coordinated turning motion in all chosen flight envelops. The simulation results show that the parameters own better ability of restraining sideslip, better adaptability and robustness, which further prove that the method is effective.

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.

Cellular Manufacturing (CM) is an important application of Group Technology (GT) in which families of parts are produced in manufacturing cells or a group of various machines, which are physically close together and can entirely process a part family. The manufacturing system established based on such an idea is called Cellular Manufacturing System (CMS). A major problem associated with many CMSs is the existence of Exceptional Elements (EEs), i.e. bottleneck machines and exceptional parts. These are machines/parts that cannot be exclusively assigned to a machine cell/part family. In this paper a new model is presented for dealing with the EEs in the form of a Multi-objective Optimization Problem (MOP). This model aims to minimize: (1) intercellular parts movements, (2) total cost needed for machine duplication and part subcontracting, (3) the system's under-utilization, and (4) deviations among the cells' utilization. Attaining an ideal solution, which is optimal to all of the objectives is prohibited, as they conflict with each other. Hence, a Multi-Objective Genetic Algorithm (MOGA) is developed to provide the decision-maker with a set of non-dominated or Pareto-optimal solutions. Comparisons between the developed MOGA and three other MOGAs show its viability in three performance aspects, namely: quality, diversity and CPU time.

We explore the use of clock skew of a wireless local area network access point (AP) as its fingerprint to detect unauthorized APs quickly and accurately. The main goal behind using clock skews is to overcome one of the major limitations of existing solutions - the inability to effectively detect Medium Access Control (MAC) address spoofing. We calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization Function (TSF) time stamps sent out in the beacon/probe response frames. We use two different methods for this purpose - one based on linear programming and the other based on least-square fit. We supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. We collect TSF time stamp data from several APs in three different residential settings. Using our measurement data as well as data obtained from a large conference setting, we find that clock skews remain consistent over time for the same AP but vary significantly across APs. Furthermore, we improve the resolution of received time stamp of the frames and show that with this enhancement, our methodology can find clock skews very quickly, using 50-100 packets in most of the cases. We also discuss and quantify the impact of various external factors including temperature variation, virtualization, clock source selection, and NTP synchronization on clock skews. Our results indicate that the use of clock skews appears to be an efficient and robust method for detecting fake APs in wireless local area networks.

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

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

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.

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.

The first giga-bit class wireless local area network (wireless LAN) below 6 GHz frequency (the IEEE 802.11ac standard) is attracting attention due to its capability of handling much more of the rapidly growing data traffic. The increased density of wireless LAN access points (APs), however, has induced inter-cell interference that severely degrades system performance. This problem seems to be getting worse with the increase of 802.11n- and 802.11ac-based APs, which establish basic service sets (BSSs) with wider channel bandwidths. In order to mitigate this problem, we propose an efficient radio resource allocation scheme called RATOP that can be applied to a managed wireless LAN system with a central coordinator. On the basis of channel monitoring results, capability, and data traffic information obtained from APs, the central coordinator computes the quasioptimal frequency channel and channel bandwidth of each AP in such a way that the given utility function is maximized. Numerous simulations with UDP traffic flows on the coexistence scenarios of 802.11a/n/ac show that the RATOP works well and reduces the number of overlapping BSSs by 65%-89%. As a result, the RATOP improves the minimum throughput and fairness among throughput of APs even when there exist foreign APs of other network domains that cannot be controlled by the coordinator.

Queueing systems to study the energy consumption of a campus WLAN

Over the past few years, wireless local area networks (WLANs) have gained an increased attention and a large number of WLANs are being deployed in universities, companies, airports etc. Majority of the IEEE 802.11 based WLANs employ distributed coordination function (DCF) in wireless access points (AP) to arbitrate the wireless channel among Wireless Stations (STAs). However, DCF poses serious unfairness problem between uplink and downlink flows. To overcome this unfairness problem, we propose a simple enhancement to the IEEE 802.11 DCF which provides priority to the AP and thus enables it to acquire a larger share of the channel when required. We have demonstrated the unfairness problem through systematic measurements in an experimental test bed of WLAN using the legacy 802.11 DCF. We also developed analytical models to calculate the throughput of AP and the STAs and verify these results through thorough simulations in ns-2. We observe that our simulation results find in good agreement with our analytical models. Results show that our proposed enhancement achieves a fair distribution of bandwidth and improves the throughput (by nearly 300%) for the downlink flows as compared to the DCF, without severely affecting the performance of uplink flows

In wireless local area networks (WLANs) made up of Extended Service Sets, access point (AP) selection is a key issue to improve the network performance and balance the traffic load. WLANs operating in the shared Industrial, Scientific and Medical band can benefit from the use of cognitive radio (CR) techniques to enable dynamic access to spectrum holes that are free from interference. In this paper, we propose an optimal Green AP Selection (GAPS) scheme, in which AP selection is optimized to maximize the system throughput while minimizing the energy consumption, for multi-rate WLANs enhanced by CRs. Different from most existing AP selection schemes, GAPS takes into account of the state transition tendency of APs and the influence of Automatic Rate Fallback mechanism in the Distributed Coordination Function. The AP selection problem is formulated as a restless bandit problem and solved by the primal-dual index heuristic algorithm based on first order relaxation to yield the GAPS scheme with the "indexability" property and hence a low complexity. GAPS is further divided into offline computation, which accounts for the bulk of the computations, and online selection, with a low complexity to facilitate implementation. Extensive simulation results illustrate the significant performance improvements of GAPS compared with existing AP selection schemes in different scenarios.