Network Selection for Group Handover in Multi-Access Networks

A main target of the next generation wireless networks is to integrate multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. Network selection is one of the most significant challenges for load balancing to avoid network congestion and performance degradation because of the heterogeneous wireless access environment in next-generation wireless heterogenous networks. In this paper, we present an efficient network selection mechanism to guarantee mobile users selecting a most appropriate wireless network to connect from the heterogenous wireless networks using the theory of games. We prove the network selection game is a potential game, and the solution to the sum rate maximization problem constitutes a feasible pure strategy Nash equilibrium (NE) of the formulated game. Furthermore, a distributed algorithm to learn the NE of the proposed game with limited feedback is presented based on the concept of learning automata. Simulations show that this algorithm which outperforms random selection algorithm in a highly efficient manner can converge to the NE with our designed utility function and achieve near optimal or optimal sum rate performance.

In heterogeneous wireless networks, network selection algorithms provide the user with the optimum network access choice. The optimal network is evaluated according to network parameters. Considering that the network parameters are dynamic and unavailable for the user in realistic heterogeneous wireless network environments, most existing network selection algorithms cannot work effectively. Learning‐based algorithms can address the problem of uncertain network parameters, while they commonly need considerable network handoff, resulting in unbearable handoff cost. In order to tackle the uncertainty of network parameters, we formulate the network selection problem as a multi‐armed bandit problem. Moreover, two online learning‐based network selection algorithms with a special consideration on reducing network handoff cost are proposed. By updating in a block manner, both algorithms achieve optimal logarithmic‐order regret and limited network handoff cost. The simulation indicates that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

We study the degradation in network performance caused by the selfish behavior of noncooperative network users. We consider a directed network in which each edge possesses a latency function describing the common latency incurred by all traffic on the edge as a function of the edge congestion. Given a rate of traffic between each pair of nodes in the network, we aspire toward an assignment of traffic to paths in which the sum of all travel times (the total latency) is minimized; however, in many settings network users are free to route their traffic in a selfish manner, without regard to the total latency. We therefore assume that each network user routes its traffic on the minimum-latency path available to it, given the network congestion caused by the other users. In general such a selfishly motivated assignment of traffic to paths (a Nash equilibrium) will not minimize the total latency; hence, selfish behavior carries the cost of decreased network performance. We quantify this degradation in network performance via the price of anarchy, defined as the worst possible ratio between the total latency of a Nash equilibrium and of a minimum-latency routing of the traffic.In this paper, we show that the underlying network topology plays no role in the determination of the price of anarchy. Specifically, we show that under weak hypotheses on the class of allowable edge latency functions, the worst-case ratio between the total latency of a Nash equilibrium and of a minimum-latency routing for any multicommodity flow network is achieved by a single-commodity instance on a set of parallel links. In the special case where the class of allowable latency functions includes all of the constant functions, we prove that a network with only two parallel links suffices to achieve the worst-possible ratio. Informally, these results imply that the inefficiency inherent in a flow at Nash equilibrium stems from the inability of selfish users to discern which of two competing routes is superior and not from the topological complexity arising from the diverse intersections of many paths belonging to different commoditie.Our proof techniques also give powerful methods for computing the price of anarchy with respect to an arbitrary class of latency functions. We apply these methods to function classes that have been well studied in the literature (such as degree-bounded polynomials and functions of the form $\ell(x) = (u—x)—1} that are used to model edges with capacity u), thereby achieving the first tight analyses of the price of anarchy for significant classes of latency functions outside the class of linear functions.

Mobile operators are integrating WiFi into their cellular networks as a way to address the issue of mobile data deluge. Although prominently used for its high data rates and unlicensed frequency band of operation, WiFi's contention based medium access does not guarantee that a user's quality of service will necessarily be met, especially if mobile devices simply `offload' to WiFi in preference to cellular. A network selection algorithm offers a way to enhance user experience, by always selecting the best radio access network for the user. In the literature, several techniques have been applied to the network selection problem. While most proposals aim to optimise quality of service for the user, the impact on resource utilisation for the networks is often neglected. This work proposes a network selection algorithm which attempts to find a good trade-off between user quality of service and efficient resource utilisation and elaborates on its performance evaluation through a simulation based study. Findings indicate that the proposed algorithm is able to provide enhanced QoS to the user as compared to conventional algorithms in terms of achieving higher throughput, lower delay as well as a higher overall system performance.

The core issue of network selection is to select the optimal network from available network access point (NAP) of heterogeneous wireless networks (HWN). Many previous works evaluate the networks in an ideal environment, i.e., they generally assume that the network state information (NSI) is known and static. However, due to the varying traffic load and radio channel, the NSI could be dynamic and even unavailable for the user in realistic HWN environment, thus most existing network selection algorithms cannot work effectively. Learning-based algorithms can address the problem of uncertain and dynamic NSI, while they commonly need sufficient samples on each option, resulting in unbearable handoff cost. Therefore, this chapter formulates the network selection problem as a multi-armed bandit (MAB) problem and designs two RL-based network selection algorithms with a special consideration on reducing network handoff cost. We prove that the proposed algorithms can achieve optimal order, e.g., logarithmic order regret with limited network handoff cost. Simulation results indicate that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously.

In a heterogeneous wireless environment, one of the important aspects of seamless communication for ubiquitous computing is the dynamic selection of the best access network. The problem of access network selection has been addressed through various decision methods based on available network information. Available link bandwidth is one of the important information parameters, which can be used as criterion for network selection. In this paper, we consider available bandwidth as a dynamic parameter to select the network in heterogeneous environment. First, we propose a bootstrap approximation based technique to estimate available bandwidth and then utilize it for the selection of the best suitable network in the heterogeneous environment consisting of 2G and 3G standards based wireless networks. The proposed algorithm is implemented in temporal and spatial domains to check its robustness. Estimation time with varying size of files is used as the performance metric. Through numerical results, it is shown that the proposed algorithm gives improved performance as compared to the existing algorithm.

Network selection technique will be one of the deciding factors in determining the success of 4G network. This is because even though the 4G network technologies such as IEEE 802.16m and LTE-A is capable of providing 4G performance, the benefits that these technologies extol will not trickle down to the mobile nodes (MN) involved if the network selection method is inefficient. High speed MNs will be a feature of 4G networks as these networks are capable of supporting MNs that move at a speed of up to 250km/hr. Therefore, a network selection mechanism that takes into account MN's mobility scenario is absolutely necessary. Network communication environment that is heterogeneous, whereby MNs with multiple interfaces can connect to is also becoming the norm. This means network selection method that can provide personalized Always Best Connected (ABC) is vital in maximizing the potential of heterogeneous candidate networks (CN) in fulfilling user's needs. This paper discusses how this can be attained using a network selection methodology that encompasses dynamic weights and detailed user requirement data collection.

In a beyond 3G context, many wireless technologies can cooperate or interoperate, within dynamic and reconfigurable networks. In this changing radio environment, the challenge for the cognitive terminal is to be able to connect to the relevant network, according to several criteria. E2R has developed concepts and solutions for a Cognitive Pilot Channel (CPC) to bring enough information to the terminals for network selection when terminals are proceeding either initial connection or handoff. This paper focuses on the network selection strategy according to the information which could be brought by CPC and also some information of user preference stored in terminals previously. In the latter part, a network selection algorithm is presented, which can both improve the system performance and satisfy users well.

Future mobile networks will provide support for real-time control applications. The tight real-time and reliability constraints of these applications introduce novel challenges for mobility management. Legacy individual handover schemes do not sufficiently address these issues, as they do not consider physical interactions between mobile nodes. A novel group handover scheme is proposed which allows for the simultaneous handover of a group of nodes. Both the individual and the group handover are modeled as discrete-time Markov chains. Based on these models expressions for the stochastic handover delay are derived. The results are numerically evaluated in a vehicle platooning scenario. The group handover is shown to significantly reduce the handover delay in comparison to the individual handover. Furthermore, the group handover is shown to scale well when the number of vehicles increases. These improvements are shown to come at the cost of an increased messaging overhead.

Critical node discovery plays a vital role in assessing the vulnerability of a computer network to malicious attacks and failures and provides a useful tool with which one can greatly improve network security and reliability. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary computer network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node׳s attributes relative to its neighbours and the Banzhaf power index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. The proposed metric is evaluated using analysis and simulations. The criticality of nodes in a network is assessed based on the degradation in network performance achieved when these nodes are removed. We use several performance metrics to evaluate network performance including the algebraic connectivity which is a spectral metric characterizing the connectivity robustness of the network. Extensive simulations in a number of network topologies indicate that the proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

One of the factors influencing the quality of a mobile user's multimedia experience is the rate at which they can receive data. To connect to the wireless network that best meets their needs a user must first detect available networks and then select the most suitable network. However, it may not always be in the best interest of the user to actually invoke a network detection and selection strategy. Simulation results show that in certain conditions it is detrimental to the end user to switch networks, even when an apparently ‘better’ network is detected. In order to help decide when it is appropriate to invoke network detection and selection algorithms, this paper introduces the Scan-Or-Not-to-Scan (SONS) framework. SONS decides based on environmental inputs, when to invoke or not a network detection and selection algorithm. The use of the SONS framework enables the user to conserve energy by shutting down unused interfaces and maximise data throughput. Reducing the number of unnecessary handovers helps maintain the mobile user multimedia quality of experience.

Against the satellite heterogeneous network optimal selection problem, in this paper we propose a network selection algorithm based on the Analytic Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The algorithm considers the parameters that may affect the network selection like quality of business services, user preferences, network load, and so on. The use of AHP is to calculate the weights of various parameters, and TOPSIS is used to obtain the final access network ranking. The simulation results verify the correctness and validity of the algorithm.

We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route traffic such that the sum of all travel times---the total latency---is minimized.In many settings, it may be expensive or impossible to regulate network traffic so as to implement an optimal assignment of routes. In the absence of regulation by some central authority, we assume that each network user routes its traffic on the minimum-latency path available to it, given the network congestion caused by the other users. In general such a "selfishly motivated" assignment of traffic to paths will not minimize the total latency; hence, this lack of regulation carries the cost of decreased network performance.In this article, we quantify the degradation in network performance due to unregulated traffic. We prove that if the latency of each edge is a linear function of its congestion, then the total latency of the routes chosen by selfish network users is at most 4/3 times the minimum possible total latency (subject to the condition that all traffic must be routed). We also consider the more general setting in which edge latency functions are assumed only to be continuous and nondecreasing in the edge congestion. Here, the total latency of the routes chosen by unregulated selfish network users may be arbitrarily larger than the minimum possible total latency; however, we prove that it is no more than the total latency incurred by optimally routingtwiceas much traffic.

Inadequate telecommunication infrastructure is one of the main reason of drawback on telecardiology implementation in developing countries. A new network selection algorithm is proposed to overcome this problem by providing adaptive heterogeneous networks. The proposed algorithm finds the most suitable wireless network according to user's health condition and service requirements. This algorithm adaptively adjusts the user bandwidth requirement by deactivating the lowest priority service level. The priority deactivation will provide sufficient bandwidth to support the higher priority service level when the best network has limited capacity. Simulation results show that the proposed algorithm improves the quality of services, cost effective and adaptive to the best networks. It outperforms conventional bandwidth based handover algorithm for telecardiology application.

Traffic offloading in Long Term Evolution (LTE) networks is a key process in cellular networks so a mobile device chooses one of the available femtocells. In the last few years, several network selection methods have been proposed based on multiple attribute decision making (MADM) techniques for traffic offloading in LTE. The Analytic Network Process (ANP) is an MADM method that has been barely studied for network selection in cellular networks; ANP configures the decision making problem as a network of attributes in order to derive priority scales of individual judgments. We propose in this paper NetANPI (Network selection ANP-based mechanism with Ideal network comparison), which is an ANP decision making mechanism for traffic offloading in LTE. We show its effectiveness by contrasting it with MADM methods proposed in the literature as AHP, ELECTRE, TOPSIS, GRA, and SAW. We also introduce a mechanism to rank the MADM algorithm using utility theory. Our results show that NetANPI outperforms the most used MADM mechanism for the interactive and conversational traffic classes.