Wireless Network Selection Research Articles

Liberal method of access point selection in wireless mesh networks

IEEE 802.11-based wireless mesh networks (WMNs) are emerging as the promising technology to provide wireless broadband internet access. In order to access the internet via a pre-deployed WMN, a user has to associate with one of the access points (APs) present in the network. Received signal strength (RSS)-based association method defined by the IEEE 802.11 standard does not consider the channel conditions and the AP load into association process. This paper proposes a novel method of AP selection which considers the channel conditions, AP load and wireless mesh backhaul conditions. As lower-rate user association drops down the performance of higher-rate users already associated with the same AP, our method tries to minimise this negative impact. The proposed AP selection method is scalable and also independent of routing protocol and routing metric used in WMN. Through the simulations, we show that our method performs better than RSS-based AP selection method in terms of improving system throughput in WMNs.

Dynamic Network Selection in Heterogeneous Wireless Networks: A user-centric scheme for improved delivery

The increasing tendency toward extreme network densification has motivated network operators to leverage spectrum across multiple radio access networks to significantly enhance spectral efficiency, quality of service, and network capacity. Therefore, there is a substantial need to develop innovative network selection mechanisms that consider energy efficiency while meeting application quality requirements. In this context, in accordance with the new trends foreseen for fifthgeneration (5G) systems, we propose a user-centric scheme for efficient network selection. Our solution accounts for network characteristics and application requirements, as well as for different user objectives, by assigning them different weights and dynamically updating them. Numerical results show the efficiency of the proposed solution and its ability to grasp the conflicting nature of users' objectives while achieving an excellent balance between them. Results also show that our solution leads to a significantly increased operating time of user devices.

Energy-aware relay selection in cooperative wireless networks: An assignment game approach

In the future connected societies, everyone and everything will be inter-connected, under the umbrella of the Internet of Things, where tens to hundreds of devices will be serving every single citizen. However, connecting this massive number of energy-constrained devices pose a serious challenge on the wireless networking paradigm; energy efficiency still represents a major challenge within the design of the future generation of wireless networking (5G). In this paper, we address reducing energy consumption of energy-constrained wireless devices, through energy-aware cooperative relaying in future heterogeneous networks (HetNets). Using game theory concepts, we formulate the problem as an assignment game. In the first stage of the game, the optimal relay selection is formulated as a linear programming problem, whose solution assigns suitable relays to their perspective sources. In the final stage, the core solution is derived, which guarantees fair distribution of the payoff among players, to keep them satisfied and discourage them from quitting the coalition. Our solution also proposes a credit based system to reward cooperative players; hence possibly excluding selfish users from cooperative coalitions. The proposed relay selection algorithm is evaluated using extensive NS2 simulations. The simulation results show significant energy savings using cooperative relaying, reaching up to 22%, with extensive increase in battery lifetime.

MDP-Based Network Selection Scheme by Genetic Algorithm and Simulated Annealing for Vertical-Handover in Heterogeneous Wireless Networks

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user's preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA---SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA---SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the `Ping-Pong' effect.

Mobility and Network Selection in Heterogeneous Wireless Networks: User Approach and Implementation

The Intelligent Transport Systems (ITS) wireless infrastructure needs to support various safety and non-safety services for both autonomous and non-autonomous vehicles.The existing wireless infrastructures can already be used for communicating with different mobile entities at various monetary costs.A packet scheduler, included in a shim layer between the network layer and the medium access (MAC) layer, which is able to schedule packets between uncoordinated Radio Access Technologies (RATs) without modification of the wireless standards, has been devised and its performance evaluated.In this paper, we focus on the influence of mobility type in heterogeneous wireless networks.Three cases are considered based on the mobility in the city: walking, cycling, and driving. Realistic simulations are performed by generating mobility traces of Oxford from Google Maps and overlaying the real locations of existing WiFi Access Points. Results demonstrate that the shim layer approach can accommodate different user profiles and can be a useful abstraction to support Intelligent Transport Systems where there is no coordination between different wireless operators.

Relay selection for secrecy connectivity in random wireless networks

AbstractIn the paper, we study the problem of secure connectivity for colluding eavesdroppers using relay selection in random wireless networks, where the relay nodes and eavesdroppers are all randomly distributed according to two independent Poisson point process. The decode‐and‐forward and randomize‐and‐forward two relay strategies are considered, and a new metric is defined for best relay selection and random relay selection. We derive closed‐form expressions for the secrecy outage probability for the two relay strategies. In particular, the effect of power allocation ratio and the maximum ratio combing at the destination node on the secrecy outage probability is demonstrated for the decode‐and‐forward relay strategy. Numerical results illustrate the secrecy performance gains with collaborative transmit diversity. © 2016 The Authors. Wireless Communications and Mobile Computing Published by John Wiley & Sons Ltd.

Network selection in heterogeneous wireless networks using multi-criteria decision-making algorithms: a review

It is expected that next-generation wireless networks will provide a plethora of mobile wireless services to users and ubiquitous network coverage at all times. Meeting these expected goals requires that new and existing networks be seamlessly integrated together to form Heterogeneous Wireless Networks (HWNs). Thus, seamless and efficient handover mechanisms are pertinent for optimal network performance in HWNs; so that the mobile user can switch from one access network to another, in search of the best connection for the demanded services. The HWNs' performance can be reduced, if efficient network selection is not achieved. In HWNs, network-selection decisions can be evaluated by using multi-criteria, or a single criterion. However, network selection and decision-making in HWNs often involves taking into account a large number of complex and conflicting network-decision factors, or criteria. Thus multi-criteria decision-making techniques are more efficient than single-criterion techniques. Multi-Criteria Decision-Making (MCDM) techniques comprise of a developed branch of operational research for assisting in the resolution of complex decision-making problems. MCDM is an important tool that has been used to model and analyze handover-decisions and network-selection problems in HWNs. This paper reviews and classifies the most significant MCDM algorithms that have been used to address the network decision-making problems in HWNs in terms of algorithmic approach, the type of calls, the cardinality of decision criteria employed, handover-control points and the types of network utilities. Comprehensive step-wise mathematical implementations of the reviewed MCDM schemes are presented, while pointing out their strengths and limitations. This paper review fills a research gap in the investigation on network-selection criteria's interdependence and interactions, and their effects on criteria's weight of importance. It then provides an insight into the importance of network-criteria weighting and the current research trend in the application of MCDM algorithms to network-selection problems in HWNs.

Self-servicing energy efficient routing strategy for smart forest

Monitoring the health and population of all forms of wildlife is a challenge for experts. Nature can be unforgiving with incidents of earthquakes, forest fires, tsunamis, and floods appearing without warning. The effects of industrialization take a toll on wildlife as well. As a result, many species of animals have reportedly gone extinct. The practice of installing wireless sensor networks in forests or in remote environments is not new. In this paper, we present a novel approach in deploying static and mobile sensors in forests. The sensors are distributed at random locations throughout a forest region to cover the maximum area possible. Data collected by these sensors is uploaded to cloud for further analysis. At the core of our model, we propose an energy-efficient algorithm by means of which individual sensors are able to transmit data over shortest path. In simulated experiments, results reveal that our method yields a stable packet delivery ratio. It is also observed to be more energy efficient compared to models that use cluster head selection in wireless sensor networks. Using this model, a region in a forest or wildlife habitat can be monitored for an extended period without compromising network throughput.

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Abstract Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.

A game theoretic learning solution for distributed relay selection on throughput optimization

In this paper, we study the problem of distributed relay selection in wireless networks using a game theoretic approach. Specifically, we consider a system model where one relay node can be shared ...

Power allocation, relay selection and energy cooperation strategies in energy harvesting cooperative wireless networks

AbstractIn this paper, optimal power allocation and relay selection strategies in energy harvesting cooperative wireless networks are studied. In particular, signal‐to‐noise ratio (SNR)‐maximizing based power allocation and relay selection without and with energy cooperation—via wireless energy transfer—are considered. Moreover, total relay power minimization subject to target end‐to‐end SNR is investigated. The different optimal strategies are formulated as optimization problems, which are non‐convex. Thus, intelligent transformations are applied to transform non‐convex problems into convex ones, and polynomial‐time solution procedures are proposed. Simulation results illustrate that power allocation strategies achieve higher end‐to‐end SNR than relay selection ones. Finally, energy cooperation is shown to be effective in improving end‐to‐end SNR, while total relay power minimization balances end‐to‐end SNR, transmit power consumption, and harvested energy. Copyright © 2016 John Wiley & Sons, Ltd.

Energy‐efficient partner selection in cooperative wireless networks: a matching‐theoretic approach

SummaryIn this paper, the problem of stable energy‐efficient partner selection in cooperative wireless networks is studied. Each node aims to be paired with another node so as to minimize the total energy consumption required to meet a target end‐to‐end signal‐to‐noise ratio requirement and thus maintain quality of service. Specifically, each node ranks every other node in the network according to their energy saving achievable through cooperation. Two polynomial time complexity algorithms based on the stable roommates matching problem are proposed through which nodes are paired according to their preference lists. The first algorithm, denoted Irving's stable matching, may not always have a stable solution. Therefore, the second algorithm—which is a modified version of Irving's algorithm and denoted maximum stable matching—is proposed to find the maximum number of stable disjoint pairs. Simulation results are provided to validate the efficiency of the proposed algorithms in comparison with centralized energy‐efficient partner selection as well as other matching algorithms, yielding a trade‐off between stability and total energy consumption, but comparable symbol error rate performance and network sum rate. Copyright © 2016 John Wiley & Sons, Ltd.

AORS: adaptive mobile data offloading based on attractor selection in heterogeneous wireless networks

The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and is in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an attractor-aware offloading ratio selection (AORS) algorithm, which can adaptive select an optimum offloading ratio based on attractor selection for the current networks environment. In the proposed algorithm, the throughput of AP and the cellular load corresponding to the coverage area of the AP, are mapped into the cell activity, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor for each users, the optimal offloading ratio $$\phi $$?, to adapt to the dynamic network environment. Hence, according to the offloading ratio $$\phi $$?, the part of the cellular traffic will be transmitted via WiFi networks. Through simulation, we show that the proposed AORS algorithm outperforms the existing ones with 42 % higher heterogeneous network throughput in a dense traffic environment.

A location aware history-based approach for network selection in heterogeneous wireless networks

Efficient decision making in vertical handoff and network selection algorithms improves users' quality of service and helps users meet service requirements, anywhere and at any time. Hence, in this paper, a user-centric network selection algorithm is proposed, utilizing the estimated reputation of the available candidate networks based on user location and combined experienced users' utility. User utility is dened based on 1) quality of service, 2) monetary cost, and 3) energy consumption metrics. In the proposed history aware-based user location algorithm, the past experience of users for available networks is considered to estimate the future utility that a user can obtain from a candidate network. The reputation factor for networks is used based on knowledge of users from each other while receiving service. Simulation results indicate that the average obtained utility by users is improved and handoff criteria, i.e. handoff number and failed and unnecessary handoffs, decrease. It can be seen that users choose networks with good past operations and this can encourage operators to provide good quality services for increasing their revenue.

Compensatory Analysis and Optimization for MADM for Heterogeneous Wireless Network Selection

In the next-generation heterogeneous wireless networks, a mobile terminal with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a network selection scheme, which is for users that select an optimal network with best comprehensive performance between different types of networks. However, the optimal network may be not the most reasonable one due to compensation of MADM (Multiple Attribute Decision Making), and the network is called pseudo-optimal network. This paper conducts a performance evaluation of a number of widely used MADM-based methods for network selection that aim to keep the mobile users always best connected anywhere and anytime, where subjective weight and objective weight are all considered. The performance analysis shows that the selection scheme based on MEW (weighted multiplicative method) and combination weight can better avoid accessing pseudo-optimal network for balancing network load and reducing ping-pong effect in comparison with three other MADM solutions.

On the benefits of location-based relay selection in mobile wireless networks

We consider infrastructure-based mobile networks that are assisted by a single relay transmission where both the downstream destination and relay nodes are mobile. Selecting the optimal transmission path for a destination node requires up-to-date link quality estimates of all relevant links. If the relay selection is based on link quality measurements, the number of links to update grows quadratically with the number of nodes, and measurements need to be updated frequently when nodes are mobile. In this paper, we consider a location-based relay selection scheme where link qualities are estimated from node positions; in the scenario of a node-based location system such as GPS, the location-based approach reduces signaling overhead, which in this case only grows linearly with the number of nodes. This paper studies these two relay selection approaches and investigates how they are affected with varying information update interval, node mobility, location inaccuracy, and inaccurate propagation model parameters. Our results show that location-based relay selection performs better than SNR-based relay selection at typical levels of location error when medium-scale fading can be neglected or accurately predicted.

Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks

The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offloading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment.

Slotted ALOHA Based Greedy Relay Selection in Large-scale Wireless Networks

Since the decentralized structure and the blindness of a large-scale wireless network make it difficult to collect the real-time channel state or other information from random distributed relays, a fundamental question is whether it is feasible to perform the relay selection without this knowledge. In this paper, a Slotted ALOHA based Greedy Relay Selection (SAGRS) scheme is presented. The proposed scheme allows the relays satisfying the user’s minimum transmission request to compete for selection by randomly accessing the channel through the slotted ALOHA protocol without the need for the information collection procedure. Moreover, a greedy selection mechanism is introduced with which a user can wait for an even better relay when a suitable one is successfully stored. The optimal access probability of a relay is determined through the utilization of the available relay region, a geographical region consisting of all the relays that satisfy the minimum transmission demand of the user. The average number of the selection slots and the failure probability of the scheme are analyzed in this paper. By simulations, the validation and the effectiveness of the SAGRS scheme are confirmed. With a balance between the selection slots and the instantaneous rate of the selected relay, the proposed scheme outperforms other random access selection schemes.

Investigation on MDP-based radio access technology selection in heterogeneous wireless networks

The new generation of wireless networks is characterized by heterogeneity i.e. the coexistence of two or more radio access technologies (RAT) in the same geographical area. While this coexistence of RATs offers various advantages, it also imposes many challenges for the network operator, whose aim is to maximize the generated revenue while satisfying the customers’ increasing demands. One important mechanism in heterogeneous wireless networks (HWN) is the RAT selection. It is normally triggered when a new call arrives, and provides the decision on whether the call can be admitted or not, and by which RAT it has to be served. Different approaches can be used to tackle the problem of RAT selection in HWNs. In this paper, we study Markov Decision Process (MDP)-based RAT selection in a cellular/WLAN heterogeneous network where the maximization of the revenue is considered as objective. An optimal RAT selection policy is therefore derived. The performance of the optimal scheme is evaluated in comparison with two other policies, namely Cellular-First policy and Load Balancing policy.

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

In heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize network performance and capacity, efficient common radio resource management (CRRM) mechanisms need to be defined. This paper tackles the radio access technology (RAT) selection, a key CRRM functionality, and proposes a hybrid decision framework that dynamically integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS information. Our hybrid approach involves two inter-dependent decision-making processes. The first one, on the network side, consists in deriving appropriate network information so as to guide user decisions in a way to meet operator objectives. The second one, where individual users combine their needs and preferences with the signaled network information, consists in selecting the RAT to be associated with in a way to maximize user utility. We first focus on the user side and present a satisfaction-based multi-criteria decision-making method. By avoiding inadequate decisions, our algorithm outperforms existing solutions and maximizes user utility. Further, we introduce two heuristic methods, namely the staircase and the slope tuning policies, to dynamically derive network information in a way to enhance resource utilization. The performance of each decision-making process, on the network and user sides, is evaluated separately through extensive simulations. A comparison of our hybrid approach with six different RAT selection schemes is also presented.