Vertical handoff

[3] Dongyeon Lee, Youngnam Han and Jinyup Hwang “Qos-Based Vertical Handoff Decision Algorithm In Heterogeneous Systems”, IEEE. 2006. [4] Hsin-Pei Chen and Wen-Pin Hsut,”Adapted Queuing Priority Algorithm for Soft Handoff in Mobile Communication Networks”, IEEE, 2006. [5] Wonjun Lee, “Movement-Aware Vertical Handoff of WLAN and Mobile WiMAX for Seamless Ubiquitous Access”, IEEE Transactions on Consumer Electronics, Vol. 53, No. 4, November 2007.

Several works is going on using cross layer approach. Each work has its own strength and weakness. In cross layer approach[1], congestion control can allocate data rates without requiring precise prior knowledge of the capacity region because network jointly optimizes both the data rates of the users and the resource allocation at under lying layer . In this paper, handoff call dropping and call blocking, number handoffs where not addressed. Paper [13] explains that cross- layer solutions are mainly proposed for handoff management. It aims to achieve layer 3 handoff with help of layer 2. Layer 2 reports the signal strength and movement detection information in advance, the system can make better preparation for the network layer handoff so that the packet loss is eliminated and the handoff latency is reduced. But this paper did not consider the application type to choose the best network. Some other works for vertical handoff employing some form of cross-layer approach [3] [4] [13] [14] which uses a policy based information and others like presence and absence of beacon and data packets to offer advanced mobility support. User defined policies are used to decide the most suitable network to satisfy the user‟s expectations. In [13], location information is used to perform transition analysis in deciding handoffs. None of these approaches define a complete cross-layer platform for mobility management. If the mobility management is not properly done then in turn handoff management is also an unsuccessful one. Normally the heterogeneous network has physical layer, data link layer, network layer, transport layer and application layer. In which the Link Layer is responsible for handoff initiation by using Media Independent Handover protocol(MIH) , Network Layer provides the IP address of the mobile device which uses Mobile IP protocol which is used to consider the destination based vertical handoff , and Transport Layer is responsible for handoff execution while the Application Layer is responsible for selecting the network based on application. So that, when we do vertical handoff it is necessary to consider all these layer‟s information in order to select the best QoS network. Most of the existing work is not considering the cross layer information. Some papers take this method into account, but the author is not considering the Call Blocking and Call dropping as a parameter for handoff decision making. So this paper presents a framework which considers both cross layer based and call admission control based network selection method.

In this paper propose a vertical handoff algorithm based on differential pre-decision and DFT-based trend detection method in heterogeneous power networks. We employed the Fourier Transform based approach to detect the signal trend. We use the forward differential prediction method to carry out the decision by predicting the next time received signal strength of the network. The main contribution of this paper is significantly reducing the impact of the ping-pong effect.

and the type of call (new or HO) in the same geographical area. In [8], proposed HO algorithm using threshold and hysteresis, as well as a HO algorithm based on distance and RSS measurements. The paper [9] proposes the signal strength model of MN and presents a new vertical handoff decision algorithm. The algorithm can adapt to the change of MN velocity and improve the handoff efficiency significantly. In [3], discusses an efficient network selection mechanism for next generation networks to guarantee mobile users being always best connected. Networks discover analysis was proposed in [10]. Then the MN decides whether to initiate handoff or not. We adopt a WLAN first based VHO algorithm decision algorithm that uses inputs as RSS values, signal thresholds and available radio resources. In [11] presents the effect of velocity on handoff delay and velocity has no significance on handoff delay experienced by user if the handoff is initiated based radio signal measurement. A time adaptive vertical handoff decision scheme for overlapping wireless networks is proposed in [12]. This scheme discovers all the available networks and then selects the most suitable network based on user preferences and service requirements.

As a matter of fact, network conditions have an important influence on vertical handoff decisions. In Ref.[9], the authors propose a number of VHO algorithms for two new network models considering integration of WLAN and 3G networks. In [10], a vertical handoff algorithm based on Q-learning is proposed. Q-learning can provide the decider with self-adaptive ability for handling the terminal handoff requests with different motion types and channel conditions. In Ref.[11], a new adaptive vertical handoff algorithm based on compensating time is devised to analyze the necessity of access to WLAN. They could reduce the ping-pong effect. The previous researches, however, could be considered insufficiently the influences of fading effects on RSS. In this paper, a vertical handoff algorithm using Savitzky-Golay filtering will be proposed for different fading effects. The handoff between WLAN and LTE in wireless access technology is a typical representation. Meanwhile, the paper introduces the basic principles and the decision processes. The algorithm is able to reduce effectively the fading effect and the unnecessary handoff. It is also applicable to vertical handoff among other wireless access technologies.

THE earlier study on Vertical Handoff (VHO) basically considered number of parameters for various wireless systems like GSM, LTE systems. According to user the need of wireless technology is to exchange data, voice communication and video communication with mobility feature. A Heterogeneous Wireless Network (HWN) is a network that consists of multiple wireless networks with different capabilities and technologies. So in HWNs multiple wireless networks coexist with their signal coverage overlapping. To provide mobility in case of HWNs Vertical Handoff (VHO) is used. Handoff (HO) is a process for transferring a call from one Base Station (BS) to another without interruption. There are two types of HO; Vertical handoff is used in heterogeneous wireless networks while Horizontal handoff is used in homogeneous wireless networks. So here the parameters are analyzed for VHO process in HWN formed by GSM, UMTS, WLAN and LTE. It is assumed that UE can support all types of wireless networks present in HWN. RSS measurement report is reported by UE through a Broadcast Control Channel (BCCH). Then the RSS is calculated using empirical models. In case of other parameters, the utility values are calculated by using utility functions. In HWN, utility is the degree of satisfaction with the network service provided to the user. For most satisfaction of user, utility value is 1 and for least satisfaction utility value is 0.

Earlier, vertical handoff was based on received signal strength (RSS) criterion. In this the vertical handoff decisions are made by comparing the RSS between the reset threshold and hysteresis val- ues. Now a days, users demand all kind of services with mobility for which, the QoS have important role for providing all services (voice, video streaming, web browsing, telemetry services) with mobility. The QoS directly depends upon the signal to interference and noise ratio (SINR). So the performance of the system based on RSS is far from the desired.[1, 2] The VHOS in KHWN follows a hierarchal process which is based on pairwise comparison of QoS among each tier of the networks. We propose the Saaty’s analytic and hierarchical process (AHP) in this paper for VHOS in KHWN. This process reveals the parameters as well as the various alterna- tives to be considered in the VHOS determination. It is followed by number of pairwise comparisons for determining factor weights and factor evaluations. [2, 3, 4]

Goyal et al. Proposed a dynamic decision model for VHO across heterogeneous wireless networks (ADDMVHO). This model makes the right VHO decisions by determining the “best” network at “best” time among available networks based on dynamic factors such as RSS and velocity of mobile station as well as static factors. A handoff Management Center (HMC) monitors the various inputs collected from the network interfaces and their base stations (BS) analyze this information and make handoff decisions. The dynamic algorithm has different phases. The Priority Phase is used to remove all the unwanted and ineligible networks from the prospective candidate networks. The Normal Phase is used to accommodate user-specific preferences regarding the usage of network interfaces. Finally, the Decision Phase is used to select the “Best” network and executing the handoff to the selected network .decision by this method is not so efficient and effective cause of less parameter so we need to consider more parameters [11]. Tokekar et al proposed handoff technique which depend on static and dynamic parameters. the static signal strength sometimes is not sufficient to trigger the handoff and therefore along with signal strength other information like network load, type of application, speed of mobile node (MN) may be consider. Vertical handoff process can be accomplished in three phases namely- Network discovery, Handoff decision and Handoff execution. Network discovery is a procedure to find appropriate network in such a way that the target network satisfies user preferences with required QoS. After successful discovery of appropriate network, next decisive factor which affects seamless vertical handoff is when to make a handoff Decision. Too early handoff results unnecessary handoff Whereas an outcome of late handoff is reduced QoS Finally, in this case actually handoff execution should be at right time means trigger of handoff decision will be done by considering all the parameters properly so Qos is not affected also unnecessary hand off should be avoided [12].

The results is shown in Figure 3 illustrates the decreasing of number of vertical handoff in hybrid algorithm compared with traditional algorithm. That explains the effect of selection the type and size of window. In Figure 4 and 5, the effect of adaptive signal strength threshold had been studied using the combining algorithm. Three different values of ASST were used and the results show the increase of ASST decreases the average number of VHO. In Figure 6 we see the decreasing percentage between our algorithm and the traditional algorithm in number of vertical handoff. The percentage is about (62- 37)% in linear trend line of decreasing. We show that the level of decreasing reduce when the velocity becomes high. This algorithm can be satisfying and useful in low user mobile for more attention.

In this paper ,We propose a vertical handoff model to perform vertical handoff to the best network interface at the best time moment. The proposed model make the vertical handoff decision based on the static factors (e.g. link cost, capacity ,power consumption, congestion on link) and dynamic factors(e.g. received signal strength, velocity, duration of a mobile node (MN) in a particular network). This model improve the whole system performance by reducing the unnecessary handoff and increases the user satisfaction by considering the user preferences in decision model. The result shows that the proposed vertical handoff decision model can perform vertical handoff to the “ best interface “ at the “ best moment”. This model is simple and applicable to any handoff implementation technique.

Vertical Handoff techniques deal with the scenario in which the mobile node can move between different network access technologies. This paper represents vertical handoff between Wi-Fi and Bluetooth wireless network. When mobile node travels from one network to other network or from one place to other place it require battery power. If the battery power of the mobile is utilized more effectively then it will provide better results in variety of aspects. In paper some parameters of moving mobile node is measured with minimum battery use with the help of virtualization technique. To achieve this goal and to select the best network for mobile node when moving from one network to other network it is also important to have a good utilization of space and access point. Dividing the space into some partitions and assigning access point to each partition helps mobile node in vertical handoff procedure. The combination of virtualization, space and access point is used to apply scalability in vertical handoff.

Wireless Data Network governed by radio waves deploys wireless medium for data communication. Mobility is the major challenge in integrating the wireless nodes. Handoff in mobile nodes demand uninterrupted data transmission while preserving the network integrity. Handoff process consumes lot of network resources, increases the network traffic and is also susceptible to data loss. Handoff prediction will foresee the handoff that is likely to occur in future so that the handover operations are done beforehand. This paper gives general overview of the vertical handoff prediction using Artificial Neural Networks (ANN) and Association Rule Mining (ARM). The proposed methodology uses ANNs to determine whether a handoff is necessary with the current network parameters, which is confirmed by Apriori algorithm. A detailed comparison is given between the ANN-Apriori and Support Vector Machine (SVM)-Apriori hybridization. The results indicate that the former performs better than the latter in terms of accuracy in prediction handoff.

Seamless handoff, with low latency and minimal packet loss, has become a crucial factor for mobile users who wish to receive continuous and reliable services. In traditional horizontal handoffs which happen in homogeneous networks, only information obtained from the radio-link layer such as the received signal strength (RSS) and channel availability are considered for handoff decisions. In contrast to this approach, in addition to the RSS, many parameters such as network coverage area, data rate, service cost, reliability, security, battery power, mobile terminal velocity, and network latency may be used in the vertical handoff decision process. This makes the entire vertical handoff process more complex and ambiguous since various factors should simultaneously be taken into account to make a successful handoff decision. One of the challenging issues in the multi-service NGWN is to design intelligent vertical handoff decision algorithms to determine when to perform a handoff and to provide an optimal choice of access network technology among all available access networks for users equipped with multimode mobile terminals. The design of such an intelligent vertical handoff decision algorithm is

In this paper present the different issue of vertical handoffs in heterogeneous wireless networks. The uses of wireless communications are popular and increasing its demand over worldwide. In the world every one likes to be connected seamlessly any time any where through the best network and good service. The proposed system its aim is that it provides better service to the user. In this paper consider the different parameters like available bandwidth of WLAN or WiMAX, remaining Power of node, high level security of node and cost of node. From above parameters current status of networks and detected node information is collected and based on it Vertical Handoff algorithm takes decision. Handoff decision will take place only when network cannot satisfy the QoS requirement.

Abstract—This paper comes up with a mechanism of optimal joint call admission control based on different service types. Considering practical wireless transmission environment, it divides the heterogeneous wireless network into several areas and also defines the peak rates that its users could get in each area. The process of call admission control is modeled into a semi-Markov process for the two typical services of streaming media service and elastic traffic which need high band width demand. In accordance with the features of different services, it also defines corresponding admission utility function. The optimal admission strategy with highest admission utility in the whole process of call admission control is obtained on the basis of guaranteeing successful session admission and vertical handoff. Simulation results show that the defined admission utility function could effectively reflect the influence of the change of network status to admission control. The suggested optimal joint call admission control excels the both schemes of non-combined call admission control and non-supporting users’ mobility in terms of average admission utility and call blocking rate, and is capable of ensuring lower blocking probability and handoff success ratio.

[4] Enrique Stevens-Navarro and Vincent W.S. Wong “Comparison between Vertical Handoff Decision Algorithms for Heterogeneous Wireless Networks”,in :IEEE 0-7803-9392-9/06 2006 Pp.947-951 [5] Xiaohuan Yan,.Ahmet S¸ ekercioˇglu,Sathyanarayan “A Survey of vertical Decision algorithms in fourth generation Heterogeneous networks”Elsevier,2010, Pp.1848-1863

In wireless communications of fourth generation the expectation to assimilate a hypothetically numerous heterogeneous wireless technologies are happened under consideration of a novel step toward worldwide smooth access. As the popularity of mobile wireless networks increases as well the challenges of merging a various number of wireless networks increases. Everyone around the world likes to have the best services through best network [11]. Therefore, the aim of NGWNs is to provide a good quality of services between various access networks [24]. To access different networks, there must be a decision algorithm to decide which is the best network for a user for a specific application. This paper presents an Overview of handoff types, handoff process, the criteria involved in VHD, parameters, Existing work on the vertical handoff with comparison tables, Analysis and Various research issues.

Received signal strength must be strong enough to maintain the on-going connection. A FIS (Fuzzy Inference System) is used to compute accurately the handoff factor to determine whether a handoff initiation is necessary. When a mobile roams in a heterogeneous networks, two vertical handoff scenes are taken into consideration i.e. handoff from UMTS to WLAN and from WLAN to UMTS. The system proposed is a fuzzy rule based handoff decision system which considers Mobile-Controlled Handoff and some assistance from the network. The mobile node continuously monitors the Received Signal Strength (RSS) of the current network and the condition of the handoff.

A number of algorithms have been designed for vertical handoff decision making. Few of them have been explained with their significance in this literature. Jyothi madaan et al.[3] have proposed a vertical handoff decision algorithm using Predicted Received signal strength and Dwell time. Battery power, network performance, and cost of service are the Parameters considered. Unnecessary handoffs are reduced by using this method, User preferences are not considered. Ms.Ashima et al. [4] have proposed an algorithm based on fuzzy logic quantitative decision algorithm with QoS fuzzy rule. Bandwidth, End-to-End Delay, Jitter, and Bit Error Rate (BER) are the parameters considered. Handoffs are reduced by using this method User preferences are not considered.

A Fuzzy MADM based numerical solution for vertical handoff decisions was first introduced by Zhang [6] (2004), where imprecise, or fuzzy data in terms of Linguistic Variables is used to specify network parameters and user preferences in the form of weights. These Linguistic Variables are first converted into crisp numbers using a fuzzy number conversion scale and then classical MADM methods like SAW and TOPSIS are applied to voice and Background traffic. The results indicate that TOPSIS is more sensitive to user-preferences and network-parameter values, and that SAW gives relative conservative ranking results.