WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

The deployment of access points (APs) imposes a great impact on the performance of wireless local area networks (WLANs). Although there have been plenty of works on the optimal AP deployment (OAD), however, the issue remains open that how to deploy APs energy-savingly in 3-D environments while considering some practical demands. Therefore, this article investigates the OAD with the objective of minimizing the total AP transmission power while guaranteeing the effective coverage by jointly optimizing the transmission power and location of each AP. Different from existing works, our article fully considers not only real 3-D environments with various obstacles but also the differentiated coverage demand and the restrictions on AP locations. To solve this NP-hard optimization problem, this article first proposes a novel hybrid swarm intelligence optimization algorithm, named PSO-Lévy-DFOA. The computational results on 16 benchmark functions and comprehensive comparisons with other algorithms demonstrate the superior global convergence performance of PSO-Lévy-DFOA. The proposed PSO-Lévy-DFOA-based OAD method is then applied to WLAN planning in a two-story building. It has been shown that the total AP transmission power can be considerably reduced compared to the current deployment while still fulfilling the effective coverage.

Recent years have witnessed the emergence of numerous new Internet services for mobile users. Supporting mobile applications through public Wi-Fi networks has received significant research attention. Nevertheless, recent empirical studies have shown that unplanned Wi-Fi networks cannot provide satisfactory quality of service (QoS) for interactive mobile applications because of intermittent network connectivity. In this paper, we exploit statistical mobility characteristics of users to deploy Wi-Fi Access Points (APs) for continuous service for mobile users. We study two AP deployment problems that aim at maximizing the continuous user coverage and minimizing the AP deployment cost, respectively. Both problems are formulated based on mobility graphs that capture the statistical mobility patterns of users. We prove that both problems are not only NP-complete but are identical to each other as well. We develop several optimal and approximation algorithms for different topologies of mobility graphs. We prove that our approximation algorithms generate the result that is at least half of the optimal solution. The effectiveness of our approaches is validated by extensive simulations using real user mobility traces.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

Light Fidelity (LiFi) is an emerging technology for future high-speed indoor wireless communications. Co-channel interference (CCI) caused by the dense deployment of LiFi access points (APs) can be effectively mitigated by using angle diversity receivers (ADRs). ADRs require signal combining where the combining weights depend on the selection of serving APs. In this paper, the AP selection (APS) strategy considering handover is studied. A novel APS scheme based on evolutionary game theory (EGT) is proposed for the LiFi network using ADRs. The performance of the proposed scheme is comprehensively analysed and compared with the APS scheme based on signal strength strategy (SSS). The result shows that, in terms of ADRs with SBC/MRC, the EGT-based APS scheme achieves more than 5% improvement in quality of service (QoS) compared with the SSS-based APS scheme. With the sub-optimum weights of maximum ratio combining (MRC) for ADRs, the EGT-MRC scheme can achieve more than 20 Mbps data rate improvement compared with LiFi systems using single photodiode (PD) receiver.

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.

Bayesian Network is a knowledge representation formalism that has been proven to be valuable in gene regulatory network reconstruction. However, it is showed that the structure learning of Bayesian networks is an NP-hard problem. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, K2 algorithm has been found to be the most successful. However, the performance of K2 algorithm is greatly affected by a prior ordering of input nodes. If using inappropriate orderings, the accuracy of the learned structures will be relatively low. This paper represents a new structure learning method to reconstruct gene networks from the observational gene expression data. The proposed method is based on Binary Particle Swarm Optimization and K2 algorithm. It firstly identifies nodes ordering from the experimental data by using Binary Particle Swarm Optimization algorithm. Then, the nodes ordering information is used as an input of K2 algorithm so as to improve the performance and efficiency of the K2 algorithm in determining the network structure. Our method is evaluated using two benchmark network datasets with known structures. Experimental studies show that the proposed method can identify networks more efficiently and accurately than comparable methods reported in the literature.

This research studies on the performance of Binary Particle Swarm Optimization (BPSO) algorithm. The first part is finding the best inertia weight of BPSO from various types of inertia weight. The second part is optimizing the cross-sectional area of steel structures and topology of bracing system under vertical and lateral load. The structures studied in the research include unbraced frames and X-braced frames. Moreover, the braced frame also investigates the influence of the classification groups of elements. The elements are classified into finer groups than the original group. The design of the structure follows the AISC code. From the investigation in the first part, a constant inertia weight of 0.98 is the best. In the second part, minimum weights of unbraced frames using BPSO are the lowest weight, except three-bays, twenty-four stories frame. For braced frames with original grouping, all examples get a lower weight than the unbraced frames. For studying the influence of group, the results of the two examples are contradictory. One bay, ten stories frame with new group has a minimum weight less than the original while three-bays but twenty-four stories frame is opposite.

Here, we suggest the possibility of optical circuit design approach by employing the binary optimization of plasmonic nano rods. The proposed mechanism is based on combination of binary particle swarm optimization (BPSO) algorithm and discrete dipole approximation method. BPSO, a group of birds including a matrix with binary entries responsible for controlling nano rods in the array, shows the presence with symbol of (‘1’) and the absence with (‘0’). The current research represents a nanoscale and compact four channels plasmonic Demultiplexer as optical circuit. It includes eight coherent perfect absorption (CPA)—type filters. The operation principle is based on the absorbable formation of a conductive path in the dielectric layer of a plasmonic nano-rods waveguide. Since the CPA efficiency depends strongly on the number of plasmonic nano-rods and the nano rods location, an efficient binary optimization method based the BPSO algorithm is used to design an optimized array of the plasmonic nano-rod in order to achieve the maximum absorption coefficient in the ‘off’ state.

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

Underground coal mines are susceptible to many kinds of hazards. Mine fire is one of the most vulnerable threats that leads to loss of human lives and other resources inside the mines. Hence, continuous monitoring of underground coal mines is very important for early detection of mine fire. Nowadays, wireless sensor networks are being widely used for monitoring underground coal mines. However, the collected data from sensor nodes are inaccurate in nature, and there is a need for analyzing such data for taking preventive measure. In this article, we propose an optimized fuzzy logic-based fire monitoring system for wireless underground sensor network, which strengthens reliability of making decision in preventing mine fire. However, storing of large fuzzy rule set in the memory constraint sensor nodes and their traversal for every sensor reading are very challenging. Therefore, we use the binary particle swarm optimization (BPSO) algorithm to optimize the proposed fuzzy system that eliminates redundant rules but preserves event detection accuracy of the monitoring system. The proposed system is simulated using BPSO algorithm and Fuzzy Logic Toolbox inbuilt in the MATLAB. The simulation results demonstrate that the proposed system outperforms the existing monitoring systems for underground coal mines.

A binary-coupled dipole approximation (BCDA) is described for designing metal nanoparticles with nonperiodic structures in one, two, and three dimensions. This method can be used to simulate the variation of near- and far-field properties through the interactions of metal nanoparticles. An advantage of this method is in its combination with the binary particle swarm optimization (BPSO) algorithm to find the best array of nanoparticles from all possible arrays. The BPSO algorithm has been used to design an array of plasmonic nanospheres to achieve maximum absorption, scattering, and extinction coefficient spectra. In BPSO, a swarm consists of a matrix with binary entries controlling the presence ('1') or the absence ('0') of nanospheres in the array. This approach is useful in optical applications such as solar cells, biosensors, and plasmonic nanoantennae, and optical cloaking.

Overlaid cellular technology has been considered as a promising candidate to enhance the capacity and extend the coverage of cellular networks, particularly indoors. The deployment of small cells (e.g. femtocells and/or picocells) in an overlaid setup is expected to reduce the operational power and to function satisfactorily with the existing cellular architecture. Among the possible deployments of small-cell access points is to manage many of them to serve specific spatial locations, while reusing the available spectrum universally. This contribution considers the aforementioned scenario with the objective to serve as many active users as possible when the available downlink spectrum is overloaded. The case study is motivated by the importance of realizing universal resource sharing in overlaid networks, while reducing the load of distributing available resources, satisfying downlink multi-channel assignment, controlling the aggregate level of interference, and maintaining desired design/operation requirements. These objectives need to be achieved in distributed manner in each spatial space with as low processing load as possible when the feedback links are capacity-limited, multiple small-cell access points can be shared, and data exchange between access points can not be coordinated. This contribution is summarized as follows. An adaptive downlink multi-channel assignment scheme when multiple co-channel and shared small-cell access points are allocated to serve active users is proposed. It is assumed that the deployed access points employ isotropic antenna arrays of arbitrary sizes, operate using the open-access strategy, and transmit on shared physical channels simultaneously. Moreover, each active user can be served by a single transmit channel per each access point at a time, and can sense the concurrent interference level associated with each transmit antenna channel non-coherently. The proposed scheme aims to identify a suitable subset of transmit channels in operating access points such that certain limits on the aggregate interference or number of serving access points are satisfied, while reducing the load of processing. The applicability of the results for some scenarios, including the identification of interference-free channels in operating access points is explained. Numerical and simulations results are shown to clarify achieved gains with the use of the proposed scheme under various operating conditions.

Network densification through the deployment of WiFi access points (APs) is a promising solution towards achieving high connectivity rates required for emerging applications. A critical first step is to discover an AP before an active association between the client and the AP can be established. Legacy AP discovery procedures initiated by the client result in high latency in the order of a few 100 ms and waste spectrum, especially when clients need to frequently switch between multiple APs. We propose CSIscan that exploits the broadcast nature of WiFi channels by embedding discovery related information within an AP’s ongoing regular transmissions. The AP does this by intelligently distorting the transmitted OFDM frame by inducing perturbations in the preamble, and these injected ‘bits’ of information are detected via changes in the perceived channel state information (CSI). A deep learning framework allocates the optimal level of distortion on a per-subcarrier basis that keeps the resulting packet error rate to less than 1%. Existing clients perceive no changes in their ongoing communication, while potential new clients quickly obtain discovery information at the same time. We experimentally demonstrate that CSIscan reduces the overall WiFi latency from 150 ms to 10 ms and improves spectrum utilization with ~72% reduction in the probe traffic. We show that CSIscan delivers up to 40 discovery information bits in the outgoing WiFi packet in an indoor environment.

The recent ubiquitous deployment of Wi-Fi access points (APs) has offered vehicles to use the high-speed and low-cost Internet service via the roadside APs. However, the high mobility of vehicles and the limited coverage of APs render some challenges. First, it results in frequent handoffs, thus leading to long delay and low service availability. Second, the available AP sets and their channel quality change dynamically, making the AP selection problem even harder. Therefore, there is a strong need to develop an efficient association control mechanism that provides efficient vehicular Wi-Fi access. In this paper, we present a theoretical framework to formulate the optimal association problem through non-linear integer programming, whose objective function is to maximize the throughput or to minimize the handoff overhead. We show that this problem holds the totally unimodular (TU) property and is thus solvable in polynomial time. Then, we study the optimality of association control by comparing existing online algorithms through real trace-based simulations. The results show that there exists a large performance gap between the performance of existing online algorithms and the optimal one. We also observe that the association control algorithm can be further improved if it has access to future knowledge. Particularly, the offline optimal with future AP information improves the performance of the local optimal by up to 10%.

A new approach has been developed to provide an overview about signal behavior in indoor environments using Cost-231 Multiwall Model (Cost-231 MWM) and Adaptive Data Rate (ADR) method. This approach used as a reference for access point (AP) placement for campus building. The Cost-231 MWM plays a role in estimating the measured power received by user (usually called as Received Signal Strength Indicator/RSSI) by considering the existence of obstacles around the transmitter (AP). We used Institut Asia Malang environments as the case study and gave some recommendations for AP placement: ten optimal placements for the first, third and fourth floor, also seven optimal placements for the second floor. These recommendations were based on the RSSI for good and excellent level signal (-50 dBm until -10dBm). This research also uses the Adaptive Data Rate (ADR) mechanism approach to reduce the amount of packet loss (kbps) resulting from obstacles that cause attenuation (-dB). With the Adaptive Data Rate mechanism, it means increasing the number of access points, the signal attenuation (-dB) occurs from the obstacles (Walls) that are penetrated by the Radio Frequency device and causes attenuation (-dB), the more Access points on the Multi-Wall, will allow communication and data transmitting stability.