Vertical Mobility Management Architectures in ... - Semantic Scholar

IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 14, NO. 1, FIRST QUARTER 2012

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Vertical Mobility Management Architectures in Wireless Networks: A Comprehensive Survey and Future Directions Stenio Fernandes, Member, IEEE and Ahmed Karmouch, Member, IEEE

Abstract—Mobile users and applications are putting pressure on wireless network operators to improve the seamless handover of devices and services. Strong business competition for subscribers, along with the ever increasing availability of wireless networks will give nomadic and mobile users the opportunity, and systems the power, to make better handover decisions. In this paper, we present a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We describe the design rationale for selected architectures, with an in-depth analysis of their main goals, assumptions, and requirements. We also provide directions for further work in this field by highlighting the mandatory requirements and the features of future architectures. We then present a new architecture called ContextAware Mobility Management System (CAMMS). CAMMS is a new cross-layer, context-aware and interactive approach to seamless handover of users and services. With that proposal, we identified the essential functional entities that must be part of future architectures. Index Terms—Wireless Mobile Communication, Vertical Handover, Mobility Management

I. I NTRODUCTION

A

S WIRELESS technologies become an integral part of daily life, we are witnessing the need for a system that can put access to wireless network services in a different perspective. Mobile users and applications are putting pressure on operators to improve the seamless handover of devices and services, a pressure that will continue into the future. Handovers must be executed regardless of the network access technology or administrative domain. Operators may have to integrate several technologies seamlessly in order to deliver unlimited content to users in a world where networks of 4G and beyond are about to become widespread. Strong business competition for subscribers, along with the wide-scale and ever increasing availability of local wireless networks (such as IEEE 802.11 and its related technologies) will give nomadic and mobile users the opportunity, and systems the power, to make better handover decisions.

Manuscript received 3 November 2009; revised 24 February 2010, 14 July 2010, and 29 July 2010. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). S. Fernandes is with the School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, Postal Code K1N 6N5, Canada (e-mail: [email protected]). A. Karmouch is with the School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, K1N 6N5, Canada (e-mail: [email protected]). Digital Object Identifier 10.1109/SURV.2011.082010.00099

The availability of a massive number of wireless networks is the result of low-cost deployment of local access points and the operators’ short-term strategies of covering smaller geographic areas at low cost (such as by deploying relay stations [128]). The advent of Femtocells access points, for example, will certainly improve indoor coverage and provide reliable connectivity without the need for the cost-inefficient deployment of additional base stations [119], [22]. In dense urban areas, mobile devices very commonly detect signals from other access points and operators. In the near future, some urban areas will be served by a mix of overlapping signals reaching different distances (signals, for example, from IEEE 802.11a/b/g/n (WiFi), IEEE 802.16 (WiMAX), High-Speed Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the like). Different roaming characteristics or mobility profiles require specific handover strategies. For highly mobile users, signal quality degradation is one of the main factors that may trigger handover procedures. For less mobile and nomadic users, a change in the network environment is not the only factor that initiates handover. The availability of several networks can make one more attractive than another, given additional factors such as cost. A handover process can be initiated in a number of different ways. A handover decision may be made only on the basis of link layer measurement reports (received signal strength indicator, number of packet retransmissions, packet losses, effective rate, and so on). Or the decision may take into account the cost benefit of handover to a different network with similar quality of service (QoS) levels. To this end, the IEEE 802.21 – Media Independent Handover (MIH) standard encourages “cooperative use of information available at the mobile node and within the network infrastructure” [1]. The standard envisages scenarios in which mobile nodes and the network will make handover decisions collaboratively, in an environment with multi-interface, multi-technology user equipment (UE) and network points of attachment (PoA). It is clear that mobile devices have been evolving from single network interface phones to multimedia and multitask devices with a number of connectivity capabilities (e.g., with small-size multi-band antenna) [115]. With the recent advances in software radio technology, most mobile devices today are capable of communicating via different technologies (Bluetooth, IEEE 802.11a/b/g, IEEE 802.16 and the like). This trend will continue to grow. Intel’s chips and network adapters, for example, are paving the way to the

c 2012 IEEE 1553-877X/12/$25.00

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delivery of WiMAX connectivity to portable devices such as laptops, PDAs, or the new concept of Mobile Internet Devices (MID) [48], [51], [50]. Intel’s WiMAX/WiFi Link 5350 [53] and 5150 [52] meet the IEEE 802.16e standard and 802.11a/b/g/Draft-N1 network adapters can operate in 2.5GHz (WiMAX) and 2.4GHz/5.0GHz (WiFi). A multiradio mobile device can handle communications in a variety of frequencies (for example, from licensed to unlicensed bands), thereby benefiting users and wireless network operators. Software components in such mobile nodes have the ability to turn radio interfaces on and off, collect information from physical and link layers level, manage battery consumption, etc. Therefore, multiradio mobile devices promise to improve user’s Quality of Experience (QoE) in an “always best connected” scenario [100]. Although operating collocated radios simultaneously can lead to interference and transmission scheduling issues [62], [115], [147], [61], multiradio mobile devices are crucial to successfully providing services in heterogeneous networks. At the time of writing this survey, several laptop and mobile phone companies have integrated a number of wireless technologies as built-in adapters in their products. For example, Dell [31], Fujitsu [44], Lenovo [134] and Toshiba [135] have released WiMAX-ready notebooks, based on Intel’s 5350/5150 technology. More recently, Nokia has released its mini-laptop Nokia Booklet 3G, with wireless connectivity options such as 3G/ HSPA and Wi-Fi [94]. We expect that, in the near future, all portable Internet devices will contain multi-technology wireless chips. In order to improve the mobile experience, and in conjunction with the multi-technology devices, wireless operators have started to offer flexible connectivity plans, such as the WiMAX daily subscription (e.g., XOHM pay-as-yougo plan [145]), thereby allowing the user the flexibility to connect to different networks maintained by a number of service providers. In their review of business models for the open heterogeneous mobile network (OHMN), Murata et al. [87] explained how OHMN can foster competition between mobile broadband operators. We envisage scenarios where handover decisions evolve from taking place only when there is a strong degradation of the Received Signal Strength Indicator (RSSI) due to factors such as interference, fading channels and mobility. They will also take place in a more complex environment because of factors such as the users’ context and preferences, costs, local and end-to-end QoS resource allocation. In a dense urban area, for example, the signals from several wireless networks would form a number of overlapping coverage areas for each mobile node. Figure 1 shows an example of a typical scenario, where a mobile device receives wireless signals from a number of PoAs. For instance, with a mobile device able to connect to WiMAX, HSDPA or WiFi networks, a given user will have the choice to switch between different administrative domains (e.g., from subnet#1 to subnet#2 or #3) or to switch between network access technologies within the same wireless operator (e.g., from PoA#9 to PoA#8 at the administrative domain C). Although a large amount of research on seamless vertical mobility has been published recently, most proposed solutions only partially meet the requirements for full mobility. Decision-making functions and algorithms are usually not

context-aware and not always take into account information from several sources simultaneously. They are also at times biased towards the requirements of network operators rather than serving to bridge the gap between users and operators. But it is inconceivable that future architectures will be based on a single protocol or simple techniques. The major purpose of this survey is to present a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We describe the design rationale for selected architectures, focusing on their main goals, assumptions, and requirements. The list of papers cited is almost exhaustive and certainly serves as a good starting point for further reading. Although a few of these sources could be considered survey papers ([30], [41], [54], [79], [71], [95], [7]), they all have their own focus and scope. They may, for example, deal only with mobility protocols from the point of view of the network layer. (The number of papers on this topic has soared since the publication of standard drafts from the IEEE MIH working group.) We also provide directions for further work in this field by highlighting the mandatory requirements and the features of future architectures. As a practical illustration, we then present a new architecture called Context-Aware Mobility Management System (CAMMS). CAMMS is a new cross-layer, context-aware and interactive approach to seamless handover of users and services. Using the recent IEEE 802.21 MIH standard, it is a distributed system solution with components at both core/access network and mobile node that also takes into account factors such as QoS and cost. The paper is organized as follows: in Section II, we examine the current body of research in this area. In Section III, we present the techniques and technologies that are needed in future vertical mobility management architectures. In Section IV, we introduce the new Context-Aware Mobility Management System (CAMMS) architecture, including the optimization procedures for its core system components. Section V provides concluding remarks and directions for future work. II. M OBILITY M ANAGEMENT A RCHITECTURES IN H ETEROGENEOUS W IRELESS NETWORKS An impressive number of research papers on mobility management systems for vertical handover have been published in the past few years. In this section, we provide a summary of the papers that reflect the most advanced research in this field. In [41], Siddiqui and Zeadally covered the techniques (mostly related to protocols) for mobility in heterogeneous networks. Complementing [41], Le et al. [30], [54] present a review of mobility protocols at each TCP/IP layer, analyze some associated problems, and provide a fair comparison of the different solutions at each layer. McNair and Zhu [79] challenged traditional techniques for handoff operations and presented emerging issues on the design and performance in vertical handoff procedures. They argued that more complex handoff decision metrics and policies are required in dynamic and heterogeneous environments. Sgora and Vergados [117] provided an overview of the different types and phases of the handoff procedure, with the focus on horizontal handoff execution phase. In [71] Lampropoulos et al. presented a review of

FERNANDES and KARMOUCH: VERTICAL MOBILITY MANAGEMENT ARCHITECTURES IN WIRELESS NETWORKS

Fig. 1.

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Future Mobile Broadband Networking Scenario

handover management solutions whereas Pahlavan et al. [95] reviewed algorithms for handover in homogeneous networks scenarios. They both discussed architectural issues in specific heterogeneous WLAN-GPRS/UMTS integration. Regarding WiMAX technologies, in [109] Ray et al. addressed handover research issues in WiMAX mobility framework whereas Papapanagiotou et al. [97] focused on surveying its physical and link layer aspects. Akyildiz et al. [7] also reviewed the body of knowledge on mobility management in all-IP wireless networks with the focus on layers 2 and 3 protocols. They proposed a mobility management architecture that relies on an element called Network Interworking Agent (NIA). NIA’s main roles are authentication, billing, and mobility. It is worth emphasizing that although these studies provide a good survey of the area, they are no longer comprehensive because of progress since their publication. In fact the IEEE 802.21 MIH framework opened up new challenges and helped developing new research efforts in this field. Our focus in this survey is to follow the mobility management issues and solutions from a higher level perspective. In other words, we will not review the advantages and drawbacks of common mobility protocols or horizontal handover techniques, but rather the present work on architectures and the likely areas for future advances. A general scenario is depicted in Figure 2. Several administrative domains can be observed from the perspective of a given Wireless Network Operator (WNO). Each provides different coverage areas through overlapping wireless signals. For instance, WNO#1 could be a Long Term Evolution (LTE) operator, whereas WNO#2 could provide a tight coupling WiMAX-WiFi coverage. WNO#3 may provide two different WiMAX coverage areas, while WNO#4 and WNO#5 are WiFi

Fig. 2.

General Scenario for Vertical Mobility Management Architectures

hotspots available for connections from local businesses such as coffee shops, train stations and airports. A. Scenario and scenes The following scenes illustrate the various ways in which future mobility management architectures will need to accomplish seamless handover for nomadic or mobile users. 1) Scene 1 (WiMAX-WiFi-WiMAX-Cell): Alice is a third year university student in the Department of Architecture. While waiting for her Introduction to Calculus class, she uses the neighborhood-wide WiMAX network to check her online social networks inbox. Walking into the lecture building, she connects to the Wi-Fi network seamlessly, finishes gossiping in online social networks and points her browser to the course website. After the class, she needs to attend a video

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webinar conference promoted by a large software developer for architecture and interior design offices. As she also needs to visit a friend in the west end of the city, she participates in the lecture on the bus using the WiMAX network again. As she moves out of the WiFi and WiMAX coverage areas, her device detects two different signals from city-wide cellular operators. Based on her context and preferences, her mobile node makes a handoff to the cheaper wireless operator, while keeping the QoS of the current streaming video. 2) Scene 2 (WiMAX-WiFi): Victor is on a bus on his way home. As he finds it difficult to focus on work in the packed bus, he uses his WiMAX operator to download the latest operating system updates and virus database. Getting off the bus at a park near his home, he decides to stay there for a while in the summer weather. He starts playing a point-andshoot online game. As the QoS requirement for this kind of application is less restrictive than multimedia streaming, his mobile device requests access to one of the available WiFi signals. Handover takes place seamlessly and he enjoys the game for an hour. 3) Scene 3 (WiFi-WiMAX/Cell-WiFi): Anna is a University professor. She is in the lab accessing the local WiFi network provided by the University IT department when a colleague instant-messages her asking if she can join her for lunch. As she needs to upload a large presentation to the university’s distance-learning website, she leaves the lab heading to the cafeteria, keeping the upload session alive. On the way to the cafeteria, her mobile node detects several WiFi access points and easily performs several horizontal handoffs. In the cafeteria, after having lunch and uploading the presentation, she decides to show her colleague a live presentation from the keynote speaker at a scientific conference. This multimedia streaming means that the WiFi network in the cafeteria cannot keep up with the QoS requests from the high bandwidth demand. Her mobile node discovers three WiMAX networks and two cellular operators in the same location. After negotiating QoS requirements by sending traffic specifications, the node decides to hand over to a cellular network that offers a cheap pay-as-you-go plan with no access fee. As she walks back to the lab, her mobile device wireless connection is reestablished with the local WiFi network. B. State of the Art Several new architectures and techniques have been proposed for dealing with scenarios such as the ones described. Some provide protocols only for the application layer [25], [118], [108], [116], [92], [72], [73], [3], the transport layer [20] or the network layer [75], [63], [88], [82], [9], [68], [73], [3]. A number of mobility management systems have also been designed to support specific applications such as multimedia streaming [25], [13], [86] or to interwork within specific network architectures (such as IP Multimedia Subsystem and Ambient Networks) [24], [118], [85], [84], [112], [113], [23], [114], [102]. As their scope is generally narrow, we do not provide a full description here. Several other studies propose and evaluate complete mobility management architectures that combine different protocols and techniques. Some architectures deal only

with interworking between specific network technologies (between 3GPP/3GPP2-WLAN, WiMAX-WLAN, WiMAX3GPP/3GPP2, for example) [107], [67], [26], [85], [84], [126], [132], [142], [130], [124], [129], [42], [6]. Others rely on different theoretical backgrounds, such as game theory or an optimization theoretic approach [20], [127], [93], [46]. Several papers have a broader perspective and include the IEEE 802.21 Media Independent Handover (MIH) framework [25], [107], [142], [90], [29]. This framework allows the development of mobility management systems in heterogeneous networks [1]. See [29], [139], [107], [90] for an overview. Griffith et al. [45] look at the performance analysis of latencies in IEEE 802.21 signaling. Essentially, the IEEE 802.21 MIH consists of a framework, a set of handover-enabling functions (MIH Functions - MIHF), and a Media-Independent Handover Service Access Point (MIH SAP and MIH LINK SAP). The MIHF provides the Media-Independent Event Service (MIES), the Media-Independent Information Service (MIIS), and the Media-Independent Command Service (MICS). In general, the MIHF provides mobility management system services to MIH users through the MIH SAP and interfaces (i.e., receives events and send commands) with lower layers through mediaspecific SAPs (MIH LINK SAP). The framework’s design rationale and assumptions are consistent with current and most foreseeable future networking scenarios. It assumes, for example, that mobile nodes are multi-technology-enabled. As a logical entity, MIHF can receive and transmit information about access network status in the area surrounding the mobile node; MIHF is also able to exchange messages with other MIHF peers (that is, it can communicate remotely with a network element) and to get information at lower layers through SAP service primitives. In the following subsections, we describe studies that address specific aspects of vertical mobility. Some of them can be considered as complete mobility management architectures while others deal only with tools and techniques that support mobility. 1) Application, Transport, Network and Cross-layer designs for mobility management: This subsection briefly reviews recent studies that address mobility management issues by deploying protocols in a given TCP/IP layer. See [30], [41], [54] for a more detailed review. A performance comparison of these protocols is provided in [86]. In [20], Casetti et al. propose an autonomic selection of radio interfaces for mobile users by means of a transport layer protocol called Stream Control Transmission Protocol (SCTP). Their solution (AISLE – Autonomic Interface SeLEction) relies on the ability of the multi-interface UE to connect simultaneously to different PoA. This gives rise to battery consumption problems, since most mobile UEs still have serious power restrictions. The main focus of AISLE is to provide fairness to users in the same coverage area by spreading their connectivity between the available PoAs. In other words, they try to find an optimal distribution of users between PoAs. AISLE does not take into account the type of applications that are currently running at the UE or their QoS requirements. The word “autonomic” seems misused, since not all self-* features was considered in AISLE and therefore


it cannot be considered a full autonomic system [59]. AISLE does not consider QoS mechanisms in wireless networks, such as IEEE 802.16 and IEEE 802.11e. However, AISLE’s time hysteresis is a mechanism that future mobility management architectures must include. This is because it prevents a backand-forth effect between PoAs at the moment of handover. In [75], Makaya and Pierre propose a protocol called Handoff Protocol for Integrated Networks (HPIN) as part of the Integrated InterSystem Architecture (IISA). HPIN is essentially a network-level protocol that addresses access network discovery, fast handoff, and localized mobility management with a view to selecting the best available network. The researchers claim that IISA allows seamless mobility between any type of wireless networks. Bernaschi et al [13] present experimental results on the performance of a cross-layer approach to handoff VoIP applications in a 3G/WLAN scenario. Essentially, their application and network layer solution is based on a slight modification at the mobile node, a mobility manager component, in order to collect information about network parameters and handoff events. Although it resembles the IEEE MIH, their approach is deployed at the user-space level instead of with a typical MIH user or within the device’s network stack. The mobility manager is fully responsible for monitoring network-level parameters (delay, jitter, and throughput), for making MIPv6based handoff decisions and for informing applications about network connectivity status and changes. The adaptation itself at each networked application is based on the information received from the mobility manager. Using similar scenarios for streaming applications, Muntean and Ciubotaru [86] propose a handoff subsystem called Smooth Adaptive Soft Handover Algorithm (SASHA). Their approach tries to balance loads efficiently among different networks in a cross-layer manner. SASHA is actually the handover management subsystem of the Multimedia Mobility Management System (M3S). Its main feature is the use of multiple simultaneous connections in order to maximize QoS and user-perceived quality. The analysis in [86] shows that SASHA outperforms Mobile IP, Mobile DCCP and Mobile SIP. M3S’ main engine relies on a decision-making function (Quality of Multimedia Streaming Metric), which has as input parameters several variables, such as energy consumption, a QoS measurement, and user preferences. Cross-layer design in mobility management has been considered in several studies [67], [82], [73], [3], [43]. In [67], Kashihara et al. propose a cross-layer (link and transport layer) service-oriented scheme for handoffs in WLAN only, although it is possible to extend it to vertical WLAN-WiMAX scenarios. A principle component of their solution, called handover manager (HM), is a transport layer approach, implemented at the mobile device. They provide good evidence that the number of frame retransmissions can achieve better accuracy in detecting signal degradation than the Received Signal Strength Indicator (RSSI). They show that their new handover criteria can detect the reduction of signal strength and radio interference as well as define unambiguous handover threshold settings. In [9] Assouma et al. present new link layer schemes for intersystem registration and the updating of mobile node locations with the aim of reducing signalling cost and intersystem paging delay.

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The studies by Yeh et al [63] and Sim et al. [114] share the same focus of providing mobility support to WiMAX networks. Yeh et al propose a cross-layer (link and network layer) protocol called Fast Intra-Network and Cross-layer Handover (FINCH). They argue that FINCH, as a generic protocol for other IEEE 802-series standards, can also be used in heterogeneous networks, such as WiFi-WiMAX tightcoupling networks. Sim et al. also presented a cross-layer approach in a flat architecture for mobile WiMAX networks. Their solution makes use of IETF’s Proxy Mobile-IP (PMIP) and Fast Mobile-IP (FMIP). Several recent works try to include the IEEE MIH framework to improve the performance of traditional network layer mobility protocols. For instance, in [88], Mussabbir and Yao propose a mechanism to optimize the handover procedure of FMIPv6. Specifically, their mechanism enhances both radio access discovery and candidate AR discovery of FMIPv6 by using IEEE 802.21 MIIS. Similarly, Magagula and Chan [73] propose a joint IEEE MIH and PMIPv6 approach. Some recent research has focused on minimizing the drawbacks of traditional mobility protocols without using IEEE 802.21 MIH. Kong et al. [68], for example, provide qualitative and quantitative analyses of Mobile IPv6 (MIPv6) and Proxy Mobile IPv6 (PMIPv6). They provide a good qualitative analysis of most existing network layer mobility protocols. Through analytical modeling, their main conclusion suggests PMIPv6 as an excellent candidate for future architectures. SIP-based mobility has been a topic in network research for some time [25], [108], [116], [92], [72], [118], [65], [70], [144], [141], [39], [148]. Although SIP has been used in many mobility architectures for heterogeneous wireless networks, it is well-known that it introduces intolerable delays. As an application layer solution to mobility, performance of transport protocols is the main cause of substantial delays in SIP-based handover techniques. Recently, Yang and Chen [118] propose an integrated mobility and QoS provisioning support within the scope of heterogeneous IMS. The mobility approach is based on multicast routing with QoS support coming from three resource reservation models: conventional reservation (CR), predictive reservation (PR) and temporary reservation (TR). Based on the concept of SIP multicast [144], they modeled UE mobility as a transition in the multicast group membership. In a similar approach, Lai et al. [70] try to minimize the Duplicate Address Detection (DAD) delay by proposing the use of multicast addressing to minimize losses during handover. The scenario of their work is in the context of FMIPv6 framework. In [65], Baik et al. focused on improving performance of PMIPv6 inter-domain handover. With the help of AAA (Authentication, Authorization, and Accounting) servers within PMIPv6 framework, they proposed a mechanism to switch between multicast groups by using AAA cache servers. In general, mobility management architectures (MMAs) have been recently adopting a cross-layer approach. In other words, gathering an assortment of information from several sources turned out to be a common technique. Table I presents some selected MMA with their correspondent supporting protocols and main features. In a number of cases, MMAs use the received information from link layer to application layer

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TABLE I M AIN F EATURES OF S ELECTED M OBILITY M ANAGEMENT A RCHITECTURES Name

Support Protocol

Main Goals and Features

AISLE [20]

SCTP

Provide fairness (bandwidth) among users in the coverage area; Simultaneous use of network interfaces; Use of time hysteresis mechanism to avoid ping-pong effect

IISA [75]

HPIN

Handoff anticipation; Introduces special nodes: Interworking Decision Engine (IDE), Access Edge Node (AEN), Border Edge Node (BEN); General mobility architecture for homogeneous and heterogeneous networks

MM [13]

MIPv6

Cross-layer approach for mobility management; Focus on VoIP applications

SASHA & M3S [86]

N/A

Maximization of the userperceived quality; Decision making function is cross-layer based

HM [67]

N/A

Selection of optimal network based on the number of frame retransmission; Application in horizontal handovers, but can be extended to the vertical ones

FINCH [63]

MIP

Cross-layer protocol (L2 and L3); Focus on IEEE 802 networks integration; Performance analysis in mobile WiMAX scenarios

in order to feed specific handover decision making engines. It is clear that advanced cross-layer handover functions can be derived from these research studies. 2) Mobility Management for Interworking between Specific Networking Technologies: Pontes et al. [107] give an overview of handover management in integrated WLANWiMAX scenarios. They include a brief discussion on how the IEEE MIH framework could better integrate these networks in heterogeneous environments. Similarly, Wu et al. [142] work on an architecture for integrating WLAN and WiMAX, and for achieving QoS support, through IEEE MIH services. They focus on applying multiple attributes decisionmaking (MADM) theory. Niyato and Hossain [93] establish the validity of game theory in obtaining an optimal pricing model for sharing bandwidth between WiMAX and WiFi PoAs. Starting with demands from the WiFi PoAs, their model tries to maximize profit on the WiMAX network. An analytical solution for the Stackelberg equilibrium was provided and a genetic algorithm was used to obtain the solution when only partial information is available. In a hands-on approach, Lim et al [140] implement and evaluate the experimental integration of WLAN and WiMAX networks through the IEEE MIH framework. Their implementation contains the IEEE MIH framework’s MIES, MICS, and MIIS services. They show that performance can be enhanced by a new system component

TABLE II I NTERWORKING BETWEEN S PECIFIC N ETWORKING T ECHNOLOGIES Scenario

Main Challenges

WLAN & WiMAX [93], [107], [142], [140]

Provide network discovery services; Handover engine must make decisions based on a tradeoff between coverage area, available data rates, cost and mobility patterns

WiMAX & Cellular [42], [85], [129], [84], [6], [124], [126], [130], [132]

Deployment of IEEE MIH’s Information Services (MIS). This should facilitate integration of networks with large coverage areas.

called Connection Manager (CM), an entity that performs network discovery (i.e., searches WLAN PoAs) and manages mobility. A number of architectures for integrating WiMAX with other networks have been proposed recently. Examples are EVDO [42] and UMTS/3G [85], [129], [84]. But a number of other works address interworking with WLAN and cellular networks [6], [124], [126]. In a general approach, Tansu and Salamah [132] propose an efficient vertical handoff system (EVHS) for interworking between microcellular networks (those with limited coverage area, low cost and high data rates such as WLAN and Bluetooth) and macrocellular networks (those with wide coverage area, higher cost and mobility patterns such as WiMAX and LTE). They present several algorithms dealing with mobility issues in different situations. Their decision-making algorithms take into account a number of input parameters, such as RSSI, the user’s speed and preferences and network conditions. Similarly, Taha et al. [130] categorize general handoff by coining the terms user motivated vertical handoff (UMVH) and operator motivated vertical handoff (OMVH). They propose OMVH as a suitable strategy that allows operators to meet network requirements. OMVH takes charge of the procedures necessary for vertical handoff, including identifying potential users based on their profile and attributes, application requirements, terminal capabilities, and the like. They also argue that this approach may be beneficial to end users. As we will discuss later, we advocate that a handover decision might require cooperative negotiations between users and wireless network operators. Favoring either user or network requirements leads to a biased solution that is less than optimal for both sides. Finally, Ahmed et al. [6] propose a decision model in which user context information feeds a simple AHP handover decision engine (as in [142]). They deploy and test their system in a GPRS-WLAN scenario. Table II presents some selected MMA that address handover mechanisms between specific wireless networks. As the attractiveness of new wireless technologies rises, integration with the existing ones is a must. In the last decade, previous research on vertical handovers focused on seamless integration between cellular and WiFi technologies. However, in the last few years, deployment of new wireless network technologies (e.g., WiMAX) and the evolution of cellular networks (3G and beyond) pushed research on vertical mobility management into a new perspective. Most research studies highlighted in this section still focused on solving issues between two specific technologies. In order to address a


variety of possible network combinations, several algorithms must be deployed, which could be impractical. 3) Mobility Management within Specific Network Architectures: With the advent of architectural frameworks for next generation networks, such as IP Multimedia Subsystem (IMS) [40] and Ambient Networks [91], mobility management for users, networks and services, has been an active area of research [23], [24], [26], [118], [84], [85], [102], [112], [113]. In [23] Cherian et al. tackled issues of WiMAX integration with IMS. They argue that WiMAX networks have specific requirements that need additional features beyond the current standard and propose the functionality required for proper IMS-WiMAX interworking. Munasinghe and Jamalipour [84] propose an IMS internetworking model that allows seamless session mobility between WLAN and a Universal Mobile Telecommunications System (UMTS) network. They also propose a SIP-IMS architecture to provide full interworking between WLAN and 3G cellular networks [85]. From the ambient networks (AN) perspective, mobility management across distinct networks has been studied in [102], [112], [113]. While not an exhaustive list, these studies are representative of the body of knowledge in this area. In [24], Chiron et al. propose a network-assisted system using a dedicated IP Multimedia Subsystem (IMS) application server. Although their architecture has a tight coupled interworking with IMS infrastructure, its requirements, functions and logical architecture share similarities with most related works. DAIDALOS [32] is an EU Framework Programme 6 Integrated Project with a highly complex architecture that resembles most approaches for general requirements and goals. The DAIDALOS framework has some essential features that other architectures do not deal with, such as mandatory support for security, service discovery and composition, identity management, and multicast [5], [60], [99]. DAIDALOS handover decisions are based on signal quality, an enhanced version of MIPv6, and QoS performance measures. With available performance information from PoAs, the handover decisionmaker tries to optimize the traffic load among PoAs in a given administrative domain, which also guarantees local and end-to-end QoS support through the use of QoS brokers. DAIDALOS does not consider factors such as cost, user context, and performance metrics such as the number of packet retransmissions, instead of signal quality level [67]. DAIDALOS’ scope is restricted to the availability of several networks in a single administrative domain. Vidales et al. [138] used concepts of finite state automata, autonomic computing and policy-based management in the design of a policy-based mobility management system called PROTON. PROTON is made up of three layers: context management, policy management and enforcement. In their architecture, they represent policies with Finite State Transducers (FST). After formal procedures such as policy translation, conflict resolution and policy evaluation, PROTON delivers the handoff decision through the executors component in the enforcement layer. Its main focus, then, is on policies, with some components to be further integrated in a more general architecture.

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4) IEEE 802.21 MIH-based Mobility Architectures: In addition to the studies described in subsection B.II.2), a number of recent architectures have IEEE 802.21 MIH as their basic support for mobility management [25], [90], [107], [127], [139], [142], [73], [3], [140], [38]. Again, [29] provides a good overview of the IEEE 802.21 MIH framework. It should be mentioned that improvements to the IEEE MIH have recently been discussed outside the IEEE working group. For instance, Neves et al. [90] proposed an improvement to IEEE 802.21 in order to add the capacity to manipulate QoS resources in the target network during the handover preparation phase. The work of Wang et al. [139] shares some goals and assumptions with our vision. Their work is complementary and useful to other architectures. They propose enhancements to IEEE 802.21 MIH in order to incorporate additional information from higher layers, such as application and user context. They then focus on how to deal with partial information and evaluate its impact on the handover decision, a problem normally beyond the scope of the core issues in mobility management systems. Some of their assumptions are very relevant for future systems. For instance, they propose a mechanism to collect information from the user and network side to feed a handover decision-maker. The main drawback in their system is that the context information is considered as a static value. Clearly, the user’s changing location and the various applications running at different times of the day make the context dynamic and require it to be evaluated on the fly. Another issue is that, after a decision is made, no mechanism prevents back-and-forth connections to the previous network. Finally, requests for different QoS classes in each network type were ignored. Very recently, a new handover decision technique using IEEE 802.21 MIH services was proposed by Wu et al. aiming at integrating WLAN and WiMAX networks with QoS provision [142]. Their decision engine uses the Analytical Hierarchical Process (AHP). Although their basic assumptions, components and scenarios are consistent with future wireless network environments, their research was not able to provide evidence that it could work well in scenarios beyond WLANWiMAX interworking. Nor did they did address pricing as an important input parameter for handover decisions that take into account the user’s context, policies and preferences. In [127], Lee at al. propose a vertical handover algorithm that tries to balance the overall load among all PoAs and maximize the collective battery lifetime of mobile nodes. As is common recently, it uses the IEEE 802.21 Media-Independent Handover (MIH) framework to provide decision inputs. From the perspective of optimization theory, their algorithm aims to optimize a combined cost function involving the battery lifetime of the MNs and load balancing over the PoAs, essentially an application of goal and mixed integer programming. They argue that the proposed method can give the operator the flexibility of maximizing the overall MN’s battery lifetime and of distributing traffic load fairly over several PoAs. Although their performance analysis is outstanding, the applicability of their proposal is confined to one administrative domain. We argue that, in current and future scenarios, different network operators will clearly provide coverage areas for mobile nodes. A solution for global maximization of battery lifetime and

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fairness between PoAs may therefore be infeasible due to the strong business competition among operators. We feel that it is important for future architecture to be independent of administrative concerns. 5) Broadcasting Wireless Technologies in Heterogeneous Environments: Digital Video Broadcasting (DVB) technologies are well known for providing high link level bitrates and wide coverage [35]. In general, they have the potential to be used as broadband wireless IP connectivity. DVB technologies (i.e., DVB-Satellite, DVB-Terrestrial, DVB-Handheld and the like) are the key enabling technologies for high definition mobile television. In addition, DVB-IPDC (IP Datacast) aims at providing convergence of broadcast networks and mobile telecommunications networks [36]. Indeed DVB technologies are an alternative approach for traditional wireless technologies. Research and operational issues for integration of DVB systems into mobility management architectures in heterogeneous networks have recently received some attention from the research community. One of the challenges in this field is that both multicast and broadcast services (e.g. in DVB broadcast/multicast services) do not have reverse communication channel for bidirectional transmission [143], [64], [74], [122], [123], [49]. In other words, DVB technologies provide high capacity data channel but limited to only unidirectional links. With the advent of the IEEE 802.21 MIH, Buburuzan et al. [19] described the mandatory enhancements on such framework in order to allow mobility management systems to deal with unidirectional DVB interfaces. Arguing that traditional mobility management and QoS provisioning mechanisms cannot be directly applied to broadcasting technologies, they discussed issues on how to enhance mobility frameworks developed for bidirectional communications to support this new requirement. They then presented a mobile terminal architecture, within the context of the IST Daidalos project, for supporting vertical mobility and seamless integrating broadcasting technologies in multi-interfaces UEs. In [120] Sinkar et al proposed an integrated system resilient to packet loss, which could be extended to several networks such as WiMax, WiFi, or DVB. In [69] Kouis et al studied, developed and validated such approach, by evaluating DVB-T in a loose-coupling heterogeneous wireless network architecture. Such evaluation includes a deployment of an experimental network platform that includes a DVB-T coverage area, a traditional IEEE 802.11b WLAN, and a GPRS cellular network. They provide evidences that DVB-T can improve the capacity and coverage levels of the overall composite network while preserving QoS. In [146] Yang et al also proposed a heterogeneous wireless network architecture that consider as a possibility the integration of DVB technologies (DVB-H in this case) for vertical mobility. They focused on the network architecture challenges and its service-driven adaptation and management in order to maximize the overall network flexibility and robustness. Finally, in [89] Negru et al. proposed IP mobility support for users performing handovers within a heterogeneous broadcast environment. They presented the architecture designed to inter-operate DVB technologies with Internet protocols, within the framework of the IST-funded European project ATHENA. They also show that transporting IP over DVB can be done

through Data Piping (i.e., IP fragments are directly packed in the payload of transport stream packets), Program Elementary Stream (PES) Encapsulation Data Streaming, following the ISO/IEC 13818-1 (MPEG-2 system) standard, Multi-Protocol Encapsulation (MPE) (i.e., encapsulation of a single IP packet into a single MPEG-2 section regardless its packet size), and the recent encapsulation method called Ultra Lightweight Encapsulation (ULE). III. D ESIGN C HALLENGES FOR F UTURE V ERTICAL M OBILITY M ANAGEMENT A RCHITECTURES In this section, we identify a number of issues and discussed how related research could help in the design of future vertical mobility management architectures. We address the following issues: 1) QoS Classes in Wireless Networks 2) Resource Allocation for supporting service differentiation (QoS classes) in wireless networks 3) QoS Mapping Support 4) Pricing 5) QoS budget and the optimization problem 6) Additional features: Mapping preferences to policies; defining traffic specifications; making handover prediction; and Quality of Experience in vertical mobility architectures A. QoS Classes in Wireless Networks In future architectures, one main assumption is that every wireless technology involved in vertical handover scenarios should offer QoS classes that meet the requirements of several application classes. In order to keep traffic parameters within acceptable limits when switching to a different technology, UEs must therefore submit a QoS class resource reservation request to PoAs, which in turn must follow the resource allocation and AC procedures. Offering QoS classes in IEEE 802.11 has recently become a reality. Most IEEE 802.11 access points have chipsets that support the IEEE 802.11e Enhanced Distributed Channel Access (EDCA). See [33] for a broad survey of admission control procedures and for details of QoS support in WLANs through the IEEE 802.11e standard. Lee et al. [58] provide a practical solution to configuring QoS levels for different WLAN QoS classes (access categories in 802.11e terminology) through proper EDCA parameters, along with a performance analysis of QoS provisioning of voice traffic. In IEEE 802.16 networks, QoS mechanisms were incorporated into five different classes as a design goal. These are Unsolicited Grant Service (UGS), Extended Real Time Polling Service (ertPS), Real Time Polling Service (rtPS), Non Real Time Polling Service (nrtPS) and Best Effort Service (BE). It must be recalled that, in these networks, mobile nodes are not allowed to transmit data unless they submit information on their bandwidth request and registration. This fits into future architectures well, because mobile nodes will have to request allocation to a specific QoS class based on the QoS requirements for the current applications. Although the IEEE 802.16 standard does not specify resource provisioning or admission control mechanisms, several packet scheduling


algorithms have been proposed [21]. Therefore, QoS granting mechanisms need to rely on the latest standard recommendation in order to allocate resources per subscriber station (Grant per Subscriber Station – GPSS). On the other hand, resource allocation per flow session is a task for the mobile node’s internal scheduler. The algorithm called Modified Largest Weighted Delay First (M-LWDF) proposed by Andrews et al. [8] is the most appropriate for next generation architectures, since it can provide QoS guarantees with minimum throughput and bounded delays. Siomina and Wanstedt [121] address the problem of QoS provisioning in Long Term Evolution (LTE) networks and point out some further references for QoS in cellular networks. From the system level, our assumption is that each PoA has a pre-defined number of QoS classes, according to the specific technology, UMTS, WLAN, WiMAX, etc. B. Resource Allocation for supporting service differentiation (QoS classes) in wireless networks The allocation of resources or capacity for QoS services is a crucial component in any architecture since it defines the optimization problem to be solved by an architecture’s component (the CAMMS’ OPTIM component in our proposal). In other words, PoAs, or a core network component, must run an optimization algorithm that will try to fit each UE’s traffic request into a specific QoS class, maximizing revenue without degrading services of the UEs currently attached. Fundamentally, in order to keep tight QoS ratios between different service classes, the need is for a resource allocation policy through packet scheduling rules and the regulation of incoming traffic through admission control. In [80], Menache and Shimkin address the problem of service class allocation capacity using single and multi-hop scenarios. They argue that proportional QoS can be offered with absolute bounds on the QoS measures (delay, losses, throughput and the like). The time scale for providing proportional QoS can range from seconds to hours, depending on the manager objectives and the pre-defined Service Level Agreements (SLA). This capacity allocation phase would work on longer time scales than the procedure proposed in [80]. The main reason is that there is no clear need to redefine the proportionality of resources per QoS class prior to solving the AC to UEs requesting access to a given PoA. Although the capacity allocation phase introduces additional delay into the decision-making process, the delay can be considered negligible since it will be spread over a longer term. In summary, longer time scales are proposed for fixed capacity allocation, while performance measures are averaged over less time. Menache and Shimkin [80] also show that the information required to solve the capacity allocation problem for a singlehop scenario (as in our case) is the total flow at each QoS service class. In most cases, for initial configuration at each PoA, it can be assumed that this per-QoS-class resource allocation problem can be solved by using historic records of flow requests or previous capacity assignment. This would provide a dynamic way of finding the optimal capacity allocation. In addition, any static (albeit intuitive) configuration can be

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applied, since a proper resource allocation technique, such as the one proposed in [80], can converge asymptotically to the unique and best response capacity allocation. Although in the future there will be different ways of buying mobile services on demand, in our vision, the capacity allocation solution can be simplified, at least at system level. This can be done by assuming that every mobile node requesting services from PoAs is a dynamic SLA user buying differentiated services on demand and paying a price per unit of traffic over each QoS class. Defining cost functions are beyond the scope of this paper and, for our present purposes, we assume that capacity allocation prices are static for a given time period (that is, a dynamic SLA user buys access in a QoS class to use within 24 hours). Like Menache and Shimkin, we also assume that the performance measures in each QoS class are not affected by the events in other QoS classes, such as delay, losses and traffic congestion. This is attainable if one relies on well-known packet schedule disciplines or on more elaborate techniques, such as the Post-Admission Control (PAC) proposed by Spenst et al. [125]. Obviously, the AC (admission control) component in any architecture must monitor and regulate the acceptance or denial of services. A comprehensive architecture must take into account both static (long-term contract) and dynamic SLA users. It should also reserve a certain amount of resources for each class of users. C. QoS Mapping Support Since some UEs may try to keep their current QoS service level when handing over between different technologies, future mobility management architectures may need to provide both horizontal and vertical QoS mapping mechanisms. Horizontal QoS mapping is somewhat related to a simple admission control procedure in the target network. It also involves signalling between the source and destination, since a PoA will decide if it can meet QoS requirements without worrying about protocol adaptation. Vertical QoS mapping is because of different technologies at the wireless hotspots and the backhaul connectivity. Vertical mapping occurs when there is vertical interaction between network layers in cascade [76]. Based on the concept of the Technology-Independent Service Access Point (TI-SAP), Marchese and Mongelli [76] describe a vertical QoS mapping solution as cascades of queue models. Vertical handoff also occurs when a user using one technology decides to hand over to a different network while keeping the current QoS support. An example is if a UE moves from IEEE 802.11 to IEEE 802.16). Protocol adaptation at link layer is also an issue, since different networks have different protocol architectures that require adaptation for proper interworking [93]. In addition, QoS support can be deployed in different granularity levels (per-flow, per-application class, traffic aggregate and the like). This can trigger debates similar to the IntServ vs. DiffServ discussion on performance-complexity balance. Using two specific technologies only, Passas et al. [101] propose a solution in both tight and loose UMTS-WLAN interworking scenarios. See [142] for a comparison of parameter mapping among IEEE 802.11e, IEEE 802.16, and 3GPP. Future architectures will have to deal with QoS requirements at different granularities. Each QoS requirement will affect the construction variables of the optimization model.

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scenario, service providers’ pricing policies are expected to be defined as flat, linear, or somewhere in between. A service satisfaction function (SSF) is used to trigger handover negotiations between the users and PoAs. An SFF is a function that determines the probability that a given user will be satisfied with the current service. Our view is that utility and pricing functions can be slightly different from their model, since the achieved data rate may not be the main variable in determining utility and pricing values. For instance, they may be evaluated using QoS class as the discrete independent variable. The utility and pricing function would then be as shown in Figure 3. Fig. 3.

Utility Function for pricing QoS classes

D. Pricing Different pricing schemes for each PoA will certainly influence resource allocation and QoS support, and therefore the revenue of the service providers. The problem can be addressed by several approaches (bid-auction-based, optimization theory, game theory, etc.); reference [93] provides a good overview. In future architectures, we feel that price will be a factor in the user’s decision-making. But, more importantly, we see no linear relationship between QoS requirements and price, since additional factors such as the users’ context and policies define utility functions for them. For instance, a user may decide to hand over to a more expensive network if it can meet the QoS requirements of the current applications that the user consider essential (such as an important video conference while roaming). Scene 3 (WiFi-WiMAX/Cell-WiFi) provides a good example. In [93], Nyiato and Hossain propose a pricing model for resource sharing in an integrated IEEE 802.16/IEEE 802.11 network. In their scenarios, the IEEE 802.16 BS charges the IEEE 802.11 APs for using its licensed spectrum. They also consider IEEE 802.16 subscriber stations (SS), which may have fixed resource (bandwidth) demands. Arguing that IEEE 802.11 APs have dynamic demands, depending on the user’s QoS requirements, the authors suggest that the WiMAX service provider (BS) may charge the WiFi networks with adjustable pricing, and then formulate the pricing problem using game theory. One of the main findings in [93] is that the WiMAX BS should charge the same price to its clients (the WiFi routers) even when their bandwidth demands (the number of mobile nodes or load) are different. Conversely, since pricing negotiation between services providers at different levels (access and backbone networks) may not be the main focus of some future architectures, we do not consider pricing problems as an issue at the core network. In this paper, the pricing negotiation between AP owners and service providers is reflected as a simple utility function that charges users according to their QoS requirements. In [10], Baida, Merlin and Zorzi address the problem of defining pricing strategies in IEEE 802.11 networks, by evaluating QoS performance and revenue as a way to identify a suitable pricing policy for a given PoA. In their modeling, they consider the trade-off between the requirement for a satisfactory QoS and the reaction to the pricing. In this

E. QoS budget and the optimization problem As we mentioned in Section III.A, each type of wireless network has its own set of QoS classes defined so that the network can give priorities when transmitting packets within its boundaries. Most standard network architectures can provide four different QoS classes: conversational, streaming, interactive and background [96], [33], [121], [21]. The amount of available capacity in each QoS class (in order to accept a new user session without degrading existing users) can be determined with the simple algorithm proposed by Panken and Hoekstra [96]. The algorithm grants each mobile node, if accepted, a QoS budget that corresponds with the current QoS requirements (the application profile). Although this algorithm is not needed in a system-level simulation, it is important in determining a given amount of QoS units (QU) per PoA and per QoS class within a PoA. In other words, knowing its QoS budget, a PoA manager must statically configure and distribute QUs among its different priority QoS classes. For instance, in an IEEE 802.11e PoA, 100 QUs can be divided equally, with 25 QUs for each of four WLAN QoS classes. QoS parameters can also be negotiated by a multi-attribute description of requirements. This is done by the application of non-linear utility and quality functions that represent the user’s and provider’s perceptions of QoS levels, as proposed by Comuzzi et al [27]. F. Additional features: Mapping preferences to policies; defining traffic specifications; making handover prediction; and Quality of Experience in vertical mobility architectures Although, in some mobility management systems, user preferences and policies are treated as static decisions and configurations, future architectures should support the use of utility functions to map context to preferences and policies. Ideas along these lines are discussed by Curescu and NadjimTehrani [28], who argue that utility function can be used to make resource allocation decisions with optimized resource usage. We feel that the best approach to QoS negotiations is to formalize traffic profiles. For instance, UEs may send their QoS requirements by means of Traffic Specifications (TSPECS) [137], [14] similar to the Flows Specification [98] in the Intserv and DiffServ architectures. Once the handoff decision has been made, it is executed by a component that follow the handover procedures. This can be done in several ways. One promising technique is described


in the BT-Intel’s architecture that uses SIP and the Mixed Network (MxN) system [25]. Performance measurements of this SIP-MxN architecture show that handover delay can be kept below 300ms with no packet losses in a loose WiMAXWLAN interworking. With pre-authentication procedures [37], the delay can be reduced to around 100ms. These results may be used to evaluate the effect of the overall delay in the whole architecture. Future architectures may also keep historic data on handover delays so that a prediction algorithm [78] can be applied. The concept of QoS and its related metrics have been widely used in computer networks. There are a number of objective metrics (e.g., packet losses, peak signal-to-noise ratio (PSNR), and the like) that can be used as quality measures of networked applications data flow. Such quality metrics are referred to as unimodal quality metrics and are available for a plethora of multimedia applications. However, there is a strong need for metrics that reliably report user’s perceived quality in multimodal content. In [110] Reiter argues that objective and subjective factors regarding quality put pressure for shifting paradigms in assessing user perceived experience. He argues that moving from QoS to Quality of Experience (QoE) will represent a major challenge on research. He also addresses some factors that could be included in model for subjective assessments. QoE is defined by ITU-T as “the overall acceptability of an application or service, as perceived subjectively by the end-user”. It includes “the complete endto-end system effects”, and “may be influenced by user expectations and context” [55]. Arguing that experience is subjective and context-dependent, Jain [56] raises some issues regarding factors that can make QoE metrics realistic. In a practical approach, Uemura et al. [136] provided a real implementation of measurements of QoS (i.e., network level metrics, such as loss rate, delay, jitter and the like) and QoE (i.e., metrics regarding user’s perception, such as the E-Model R factor, Mean Opinion Score – Listening and Conversational Quality, and the like). They showed that network conditions and user’s perceptual quality measurements are highly feasible to achieve in real-time. Their experimental results evaluated speech quality in cell phones and showed that the proposed can have high accuracy regardless of the codec type. Utilization of QoE in wireless environments has been recently addressed [105], [12], [11], [77], [133]. In [105] Piamrat et al. conduct a performance evaluation of video streaming application over WiFi, using a QoE metric called Pseudo Subjective Quality Assessment (PSQA). Bernardo et al. [12] assessed VoIP sessions quality in WiMAX environments. They also applied QoE concepts in QoS-aware mobility management mechanisms [11]. In a scenario of video over IP, Tasaka et al. [133] try to derive estimations of QoE from application-level QoS using traditional regression analysis. In addition, using QoE as either input or tool for assessing handover decision mechanisms have been recently subject of research [104], [81]. In a network-assisted mobility management approach, Piamrat et al. [104] uses the PSQA tool to provide users relevant information on QoE in order to perform horizontal handover in IEEE 802.11 environments. Using similar techniques, they also used PSQA in wireless networks to support an admission control mechanism based

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on QoE [103]. Finally, in [81] Mitra et al. proposed a mapping between QoS and QoE and then tried to predict the QoE in target networks. Based on these results, a selection of the most adequate network is performed. IV. A SYSTEM ARCHITECTURE FOR CONTEXT-AWARE SEAMLESS MOBILITY MANAGEMENT As a secondary goal, this survey introduces a new flexible mobility management architecture. Some of the System Requirements Specification (SRS) are described below to a level of detail that will allow the architecture to be designed. All assumptions and requirements were derived from the works presented in Sections II and III. Throughout this section, the reader must interpret the key words as MUST (NOT), SHOULD (NOT) and MAY as stated in RFC 2119 [16]. We use the format SysReq-#: description/assumptions. A. System Requirements Specification SysReq-1: The cross-layer handover decision criterion SHOULD gather reliable information from at least two layers (from link to application TCP/IP layer). SysReq-2: Network discovery SHOULD be based on the IEEE 802.21 MIH. There is a strong tendency in the wireless and mobile industry to adopt IEEE 802.21 for the deployment of handover services over heterogeneous networks. SysReq-3:Post-Handover Self-Management. UE and network component SHOULD check whether the new PoA has consistent QoS levels. It also SHOULD avoid a “back-andforth” effect in the selection of PoAs. SysReq-4: It SHOULD have an adaptive execution of the handover according to application profiles (such as real-time applications and elastic traffic applications). Some applications (such as streaming sessions) demand network performance parameters to be strictly within proper thresholds in order to execute handover functions seamlessly. Other classes of applications are more relaxed, such as web browsing and instant messaging. SysReq-5: A mobile node SHOULD NOT use several radio interfaces simultaneously. When a UE has more than one radio interface active simultaneously, it consumes scarce resources, such as processing power and battery. SysReq-6: It SHOULD rely on any pre-authentication method to minimize handover delays. Authentication, Authorization and Accounting (AAA) services are known to contribute significantly to the overall handover delay. The use of multi-domain security associations may avoid excessive delays in the AAA process. Pre-authentication SHOULD be included in the handover decision phase. SysReq-7: Handover decision phase SHOULD take into account the availability of several networks. It is expected that IEEE 802.11 networks will continue to proliferate as well as LTE, WiMax and the like. Therefore, CAMMS is expected to make optimal decisions when multiple network signals are covering the current location of the UE. SysReq-8: Initiation of handover MUST be based (a) on the detection, knowledge or inference of changes in wireless link conditions that are affecting applications and (b) on the

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Fig. 4.


CAMMS – Main components

availability of multiple wireless links. At link and physical levels, RSSI is not a good indication of signal degradation, since it is highly dependent on specific device attributes set up by vendors. It MUST use more efficient and vendor independent information such as the number of packet retransmissions. SysReq-9: Seamless mobility MUST be cross-layer based. Information MUST be gathered from several sources sequentially or simultaneously, from link to application level. The handover procedure MUST take into account context information, economic issues, power consumption, user preferences (i.e., QoE factors).and network conditions such as bandwidth, delay, losses and overall capacity (i.e., QoS factors). SysReq-10: Functional entities MUST be able to work with several macro- and micro-mobility protocols. No single universal protocol meets all requirements for mobility management in next generation networks. It MUST interface, control and coordinate or delegate tasks to several mobility protocols. SysReq-11: At the Handover Execution (Triggering) phase, UE and core network components MUST make optimal or quasi-optimal PoA selection after considering all available input parameters. SysReq-12: Admission Control (AC) mechanisms MAY be a major component of the architecture, in order to determine whether a new connection, with certain QoS requirements, can or cannot hand over to a new PoA. B. System Architecture Design The CAMMS architecture is made up of functional entities (components) responsible for context gathering, intelligent handover decision-making, accurate handover triggering, and post-handoff management. This section provides a summary of our major components and functions. Figure 5 and Figure 6 show the location of the CAMMS in the protocol stack at PoAs and UEs. Essentially, CAMMS is a MIHF user. Figure 4 provides a general view of the main functional entities (FE) in CAMMS. These are the Multi-layer Sniffer (MLS), the Arbiter (ARB), the Actuator (ACT), and the PostHandover Manager (PHM). Each functional entity has specific roles in the architecture, as follows: I. Multi-layer Sniffer (MLS): The MLS collects information from the physical and link layers, currently running

Fig. 5.

IEEE MIH and CAMMS - Mobile Node‘s protocol stack

Fig. 6.

IEEE MIH and CAMMS - PoA‘s protocol stack

application sessions, and user context and preferences such as the surrounding environment and, budget considerations. II. Arbiter (ARB): In CAMMS, the network and UE components work collaboratively. The FE called ARB is a distributed component that judges and decides which PoA is more adequate given all the information provided by the MLS and the negotiation procedure with PoAs. III. Actuator (ACT): Based on the ARB’s decision, the Actuator (ACT) follows all the procedures necessary to ensure that the handover is performed seamlessly and in a timely fashion. IV. Post-Handover Manager (PHM): After the UE has successfully changed its PoA, it is necessary to keep track of the Quality of Experience (QoE) perceived by the UE and the network. This procedure ensures that the UE receives the required QoE and avoids a back-and-forth (ping-pong) handover effect (when the UE starts another handover procedure back to the previous network). The components have the roles described in the following section. UML Component Diagrams 1) Multi-Layer Sniffer - UE


The MLS-UE has specific sub-systems responsible for: a) Detecting, inferring or gathering information about ongoing sessions. It must encompass information from transport, application, and user profile levels b) Detecting, inferring or gathering information about wireless link availability, conditions or quality. It should rely on vendor-independent information, such as number of packet retransmissions, instead of RSSI, as indicated in [37]. For instance, at the physical level, this sub-system may evaluate two factors, namely reduction of signal strength and radio interference, to infer what is causing packet retransmissions above certain threshold. It must encompass information from physical and link levels c) Detecting, inferring or gathering information about network availability or conditions. It should rely on the IEEE MIH standard (IEEE 802.21). It must encompass information from network levels d) Determining what kind of vertical handover can be executed by the UE, such as upward or downward handovers. Different types of handover may trigger different strategies within the ARB e) Providing a set of functions to interface with IEEE 802.21 MIH f) Gathering information about UE‘s speed as an input to improve accuracy in the evaluation of the prediction time. It can take advantage of the recent integrated GPS capabilities in some modern UEs to provide this information. Another FE can use this location and movement data to improve performance and minimize losses and delays. 2) Multi-Layer Sniffer - Network Side The MLS - Network Side has specific sub-systems responsible for: a) Detecting, inferring or gathering data about network availability and conditions in the backhaul connectivity. b) Providing a set of functions to interface with IEEE 802.21 MIH 3) Arbiter - UE The Arbiter – UE has specific sub-systems responsible for: a) Determining which application deserves higher priority to execute handover functions seamlessly. It must also determine the specific QoS requirements for a proper handover negotiation b) Selecting relevant information from the user context. It establishes the criterion for the optimal handover decision c) Informing the amount of buffer occupation by a real-time streaming application. For instance, an external function can derive the remaining amount of play-out time for a given multimedia streaming session d) Providing pre-authentication methods to minimize handover delays (Anticipated Handover Authentication). As AAA services have major contributions

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to the overall handover delay, the use of multidomain security associations may avoid excessive delays in the authentication and authorization process. Using simple procedures a FE can conduct pre-authentication procedures into the target PoAs e) Providing optimal PoA selection (after taking into account all parameters available, such as costs and user context and preferences). 4) Arbiter - Network Side The Arbiter - Network Side has specific sub-systems responsible for: a) Admission controlling new UEs and their respective communication sessions. As Admission Control (AC) is a major component of the architecture, a FE determines whether a given UE, with certain QoS requirements, can be accepted to a specific PoA b) Solving a given optimization problem (e.g., maximizing revenue, maximizing network resources, etc.) Based on information gathered by the MLS, a FE must first decide if it should run heuristics to find a optimal or quasi-optimal solution for the handover decision c) Forecasting available time budget before link disruption. Based on the network conditions and the time lost in previous handover triggers, it must calculate the predicted handover time. It is expected that the time to complete the entire handover process can be modeled by using any stochastic process theory, such as time series analysis. For instance, functions may support the onestep-ahead output from an exponential weighted moving average technique. It may use previous handover characteristics (delay, losses) along with actual network conditions to improve performance or adjust UE to the impending handover d) Providing pre-authentication methods to minimize handover delays (Anticipated Handover Authentication), as in the UE counterpart. 5) Actuator - UE The Actuator - UE has specific sub-systems responsible for: a) Controlling network interface selection (Network Interface Switch). In a make-before-break approach, a FE must execute the final stage (as soon as all handover procedures have finished) by turning on the interface that will be attached to the selected PoA and then turning the previously active interface off. 6) Actuator - Network Side The Actuator - UE has specific sub-systems responsible for: a) Deciding which mobility protocol is more suitable to initiate the handover process at the UE, based on the type of mobility it is actually facing b) Initiating the actual execution of the mobility protocol (session, transport, or network) that will be necessary to provide seamless handover.

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7) Post-Handover Manager - UE The Post-Handover Manager - UE has specific subsystems responsible for: a) Analyzing quality of service / experience (QoS/QoE) in the current network. At the application layer the actual QoS/QoE at the UE may starts the handover decision process again b) Avoiding triggering new handover procedures immediately after finishing one. Besides checking whether the new PoA has consistent QoS/QoE levels, this function avoids “back-and-forth” effect in the selection of PoAs Interactive Procedures in CAMMS Now we present an overview of how the main CAMMS components communicate with each other. 1) Main UML Interaction Diagram Figure 7 shows the sequence of messages in our system. Interactive vertical handoff decision-making starts with the UE’s MLS component collecting relevant information from several protocol layers. From the application layer, it determines the QoS parameters that each currently running application will require. At lower levels, MLS collects physical and link layer parameters in order to infer the actual QoS status by analyzing the number of packet retransmissions, and thereby clearly detecting the reduction of signal strength and radio interference [67]. From the network layer and via IEEE 802.21 MIH, UE collects information about the available PoAs and sends them its QoS requirements based on its context and preferences. These may determine, for example, that, if a user is watching a movie trailer while entering a meeting room, it must not keep the streaming session alive and must block all instant messaging applications. Instead of sending a requirement to keep the streaming application running, it will simply submit a request as a best effort QoS application, just to have access to its email server and to basic web browsing. After receiving offers from PoAs, each UE now has sufficient information to submit all parameters to the ARB. The ARB validates the data and makes a decision that takes into account an assortment of parameters, including link layer information (e.g., is there a “Link Going Down – LGD” message?), pricing issues (e.g., is there any access fee? What is the cost per unit of time?), available QoS class, and so on. After deciding which PoA is the best in the current context, the ARB sends the information to the ACT, which initiates the procedures necessary to make the actual handoff to the target PoA. We emphasize that, when each PoA makes offers to the UE, it temporarily reserves resources in order to guarantee that the particular UE is included in the targeted QoS class. This reservation must be held until the next interactive round. In addition, each UE may start to authenticate each candidate PoA before the final ARB decision (through the AHA component). In [34], in the context of scalable real-time QoS architectures for wireless networks with admission control and reservation schemes, we propose

techniques that avoid excessive per-user signalling for wireless link reservation. We also evaluate their impact on the probability of handoff blocking. The techniques are supported with performance data. Based on the information from the ARB, the ACT selects the suitable set of handoff protocols and then performs the transfer with minimal losses. The handover decision is communicated to the PHM, which keeps track of the recent changes of PoAs and takes measures to avoid a back-and-forth effect. The loop feature in the UML diagram is shown in Figure 7, which also shows that the PHM component can explicitly not acknowledge an ARB handover decision. For instance, given that the current QoS status for the UE is acceptable, PHM may enforce a time hysteresis (as in [20]). On the other hand, the alternative path in the UML diagram describes a situation in which the PHM accepts the new handover decision. 2) ARB - Phase I (Decision about QoS requests) CAMM’s main approach to handover decision-making consists of sequence of messages in the Arbiter (ARB). During this phase, the ARB decides what exact QoS requirements it must send to PoAs, based on the information collected from the MLS. Simultaneously, it starts the anticipated authentication to reduce handoff latency. In support of this step, Dutta et al. [37] presented experimental results showing that pre-authentication procedures reduce interdomain handover delays regardless of the chosen mobility protocol. 3) ARB - Phase II (PoA’s resource allocation procedure) The sequence of messages in the ARB component at PoAs is briefly as follows. After receiving QoS requests from each UE in the neighboring coverage areas, the ARB’s OPTIM component uses an optimization strategy in order to reserve adequate resources for each available QoS class. The formal optimization formulation is out of scope of this paper. Specifically, OPTIM has revenue maximization as its main goal. Other objectives could be easily replaced, such as maximizing network utilization. The maximization problem is therefore based on the QoS requests from UEs, the amount of available resources per QoS class at PoAs, and the revenue per each QoS class. The outcome of OPTIM is the list of UEs to which a given PoA must submit its connection offer, consisting of a QoS class and pricing info. Although each PoA reserves resources in advance in order to guarantee that it has enough resources in a particular QoS class when a UE finally decide to register, PoAs release those pre-reserved resources if UEs do not authenticate and register themselves in a short period of time. Additional information available to ARB’s components helps to make a more precise decision. For instance, PHM may keep historical records of previous handoff delays and, along with UE’s current speed, the ARB decides if there is enough time available either to proceed with the optimization procedure or to run a quasi-optimal heuristics algorithm. For a loose coupling vertical handover scenario, we feel that, even for a less powerful WLAN access point, the CPU power


Fig. 7.

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CAMMS - UML Sequence Diagram

is sufficient to run optimization algorithms within a reasonable time and in a fair range on the number of UEs [20]. In a tight coupling environment, the optimization procedure can be run in a server with more processing power. After receiving offers from PoAs that have decided that accepting a given UE will help to maximize their

revenue, UEs then make a final decision that converts their context and preferences into policies. V. C ONCLUSION In this survey, we presented a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We

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described the design rationale for selected architectures and suggested directions for further work in this field. These included the main requirements and features of a new architecture. With that proposal, we identified the essential functional entities that must be part of future architectures. Some issues still need to be tested and resolved. A single paper is not enough to present all the features, address all the problems, and properly analyze performance. Some elements were left for the future. One such element is the prediction algorithm to precisely estimate the required handover time in a situation of degrading RSSI. The predictive handover mechanism proposed by Yoo et al. [149] could be easily deployed in our architecture, since that mechanism also relies on IEEE 802.21 MIH [1]. We also intend to use simple but effective time series analysis techniques to predict the required handover time. Selecting the appropriate mobility protocol that will conduct the actual signals for macro- or micro-mobility is another topic for future work. We feel that the Reconfigurable Architecture and Mobility Platform (RAMP) [66] is a good candidate, since it has the flexibility to accommodate, control and coordinate several mobility protocols. It is frequently necessary to authenticate mobile nodes in order to have access to PoAs. However, the process of authentication (or re-authentication in the case of inter-domain handover) can lead to unacceptable delays, especially for streaming and interactive applications such as video conferencing and VoIP. Techniques and protocols to deal with handover keying were explored by Zheng and Sarikaya [150] and can be part of the authentication component of our architecture. R EFERENCES [1] ”IEEE Standard for Local and metropolitan area networks- Part 21: Media Independent Handover,” IEEE Std 802.21-2008 , vol., no., pp.c1301, Jan. 21 2009 [2] 3GPP TS 23.107, Quality of Service (QoS) concept and architecture. [3] Abdelatif, M.A.; Kalebaila, G.K.; Chan, H.A., ”A Cross-Layer Mobility Management Framework based on IEEE802.21,” Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on , vol., no., pp.1-6, 3-7 Sept. 2007. [4] Aguiar, R., et al, “Updated Daidalos II Global Architecture”, Deliverable DII-122, FP6-2004-IST-4, Nov 2007. [5] Aguiar, R.L.; Sargento, S.; Banchs, A.; Bernardo, C.J.; Calderon, M.; Soto, I.; Liebsch, M.; Melia, T.; Pacyna, P., ”Scalable QoS-aware mobility for future mobile operators”, IEEE Commun. Mag., vol.44, no.6, pp.95-102, June 2006. [6] Ahmed, T.; Kyamakya, K.; Ludwig, M., ”Architecture of a ContextAware Vertical Handover Decision Model and Its Performance Analysis for GPRS - WiFi Handover,” Computers and Communications, 2006. ISCC ’06. Proceedings. 11th IEEE Symposium on , vol., no., pp. 795801, 26-29 June 2006. [7] Akyildiz, I.F.; Jiang Xie; Mohanty, S.; , ”A survey of mobility management in next-generation all-IP-based wireless systems,” Wireless Communications, IEEE , vol.11, no.4, pp. 16- 28, Aug. 2004 [8] Andrews, M.; Kumaran, K.; Ramanan, K.; Stolyar, A.; Whiting, P.; Vijayakumar, R., ”Providing quality of service over a shared wireless link,” IEEE Commun. Mag., vol.39, no.2, pp.150-154, Feb 2001. [9] Assouma, A.D.; Beaubrun, R.; Pierre, S., ”Mobility management in heterogeneous wireless networks,” Selected Areas in Communications, IEEE Journal on , vol.24, no.3, pp. 638-648, March 2006. [10] Badia, L.; Merlin, S.; Zorzi, M., ”Resource Management in IEEE 802.11 Multiple Access Networks with Price-based Service Provisioning”, IEEE Trans. Wireless Commun., vol.7, no.11, pp.4331-4340, November 2008.

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Stenio Fernandes ([email protected]) became a member of IEEE in 1997. He received a B.S. and a M.S. degree in Electronic Engineering from the Federal University of Paraiba (UFPB, now UFCG), Campina Grande, PB, Brazil. He also received a Ph.D. in Computer Science from the Federal University of Pernambuco (UFPE), Recife, PE, Brazil. He was a visiting scholar at University of Ottawa during his PhD studies. He currently is a visiting researcher at the School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, Canada. He is also an associate professor at the Federal Institute of Education, Science and Technology (IF-AL) in Maceio, AL, Brazil and is currently on leave from that position. His current research interests include systems performance evaluation, Internet traffic measurement, modeling and analysis; Internet congestion control; multimedia streaming in the Internet; and architectures for mobility management in wireless networks.

Ahmed Karmouch ([email protected]) is a Professor of Electrical and Computer Engineering and Computer Science at the School of Information Technology and Engineering, University of Ottawa, Canada. He is involved in several projects with industry and government laboratories in Canada and Europe. His current research interests are in mobile computing, autonomic networking, context aware communications, and ambient networks. He has served on several program committees, organized several conferences and workshops, edited several books and served as Guest Editor for IEEE Communications Magazine, Computer Communications.