An algorithm for optimal network planning and frequency channel assignment in indoor WLANs

The increased use of wireless local area networks has led to an increased interference and a reduced performance, as a high amount of access points are often operating on the same frequency channel. This paper presents a network planning algorithm that minimizes the number of access points required for a certain throughput and optimizes the frequency allocated to each AP, leading to reduced interference. The network planning algorithm is based on a heuristic and the frequency planning algorithm on a combination of a greedy algorithm and a Vertex-Coloring-Based Approach. The algorithm provides a good performance and has a limited computation time

Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute)

Coverage prediction and optimization algorithms for indoor environments

A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types

Intelligent TDMA heuristic scheduling by taking into account physical layer interference for an industrial IoT environment

In an Internet of Things environment, where multiple mobile devices are brought together, it is not always possible to serve all these devices simultaneously. We developed an intelligent Time Division Multiple Access (TDMA) scheduler which allows to plan the individual packets of the different streams in such a way that everyone can be served by taking into account the interference on the physical layer. The scheduler is applied in a realistic industrial environment and evaluated based on the maximum link latency, the channel occupancy, and the jitter. Two strategies are compared: one where the packets are sequentially allocated, and one periodically. Our results show that the periodically allocated strategy performs the best for the maximum link latency (for a packet size below 1200 bytes) and for the jitter. The channel occupancy is similar for both strategies. Furthermore, the performance can be improved by using a higher number of channels. Compared to classic Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), the channel occupancy and the jitter are reduced up to 69.9 and 99.9%, respectively. Considering the maximum link latency, the proposed TDMA strategies perform significantly better than the worst case CSMA/CA (up to 99.8%), however, when assuming a best case CSMA/CA scenario, CSMA/CA performs better. Furthermore, we clearly show that there are cases where it is not possible to plan all streams when using CSMA/CA while this becomes feasible when applying the proposed TDMA strategies

Joint Access Point Placement and Channel Assignment for 802.11 Wireless Local Area Networks

To deploy a multi-cell IEEE 802.11 wireless local area network (WLAN), access point (AP) placement and channel assignment are two primary design issues. For a given pattern of traffic demands, we aim at maximizing not only the overall system throughput, but also the fairness in resource sharing among mobile terminals. A novel method for estimating the system throughput of a multi-cell WLAN is proposed. An important feature of this method is that cochannel overlapping is allowed. Unlike conventional approaches that decouple AP placement and channel assignment into two phases, we propose to solve the two problems jointly for better performance. Due to the high computational complexity involved in exhaustive searching, an efficient local searching algorithm, called patching algorithm, is also designed. Numerical results show that for a typical indoor environment, the patching algorithm can provide a close-to-optimal performance with much lower time complexity.published_or_final_versio

An approach for the design of infrastructure mode indoor WLAN based on ray tracing and a binary optimizer

This paper presents an approach that combines a ray tracing tool with a binary version of the particle swarm optimization method (BPSO) for the design of infrastructure mode indoor wireless local area networks (WLAN). The approach uses the power levels of a set of candidate access point (AP) locations obtained with the ray tracing tool at a mesh of potential receiver locations or test points to allow the BPSO optimizer to carry out the design of the WLAN. For this purpose, several restrictions are imposed through a fitness function that drives the search towards the selection of a reduced number of AP locations and their channel assignments, keeping at the same time low transmission power levels. During the design, different coverage priority areas can be defined and the signal to interference ratio (SIR) levels are kept as high as possible in order to comply with the Quality of Service (QoS) requirements imposed. The performance of this approach in a real scenario at the author´s premises is reported, showing its usefulness.This work was supported by the Spanish Ministry of Science and Innovation (TEC2008-02730) and the Spanish Ministry of Economy and Competitiveness (TEC2012-33321)

Cognitive Radio-Based Power Adjustment For Wi-Fi.

In this paper the idea of using Cognitive Radio (CR) techniques to optimize the access point (AP) configuration for IEEE 802.11 WLAN was exploited. In particular, we address the issue of optimizing AP transmission power and channel allocation within the network