A Reputation based Vertical Handover Decision making Framework ...

GC'12 Workshop: The 7th IEEE International Workshop on Heterogeneous, Multi-Hop, Wireless and Mobile Networks

A Reputation based Vertical Handover Decision making Framework (R-VHDF) Mehdi Loukil∗ , Mariem Zekri∗ , Takoua Ghariani∗ and Badii Jouaber∗† ∗ INSTITUT Telecom - Telecom SudParis, Evry, France † CNRS, UMR SAMOVAR Emails: {mehdi.loukil, mariem.zekri, takoua.ghariani, badii.jouaber}@it-sudparis.eu

Abstract—Mobility management over heterogeneous wireless networks is becoming a major interest area as new technologies and services continue to proliferate the wireless networking market. Context awareness can play a major role to improve mobile networking systems and to ensure seamless mobility. In this paper, we focus on Vertical Handover (VHO) decision optimization within heterogeneous wireless environments, where users may connect to different wireless access networks simultaneously (e.g. 2.5G, 3G, WiFi, WiMax). We propose a Reputation based Vertical Handover Decision making Framework (R-VHDF) where the decision is not only based on the Received Signal Strength (RSS), as in most horizontal handover processes, but also on context information about QoS and radio conditions, as well as on networks and previous user’s experiences (QoE or Reputation). The solution is based on a flexible and evolutionary distributed context management architecture that handles dynamic and static context information and allows mobile devices to be always connected to the most suitable access network by making VHO decisions based on networks’ Reputations. The proposed framework encompasses auto-programmable platforms and distributed context management components within middleware servers and mobile devices. It is validated thought a prototype implementation and performance results are presented and discussed. Index Terms—Mobility Management, Context Awareness, Quality of Experience, Reputation, Vertical Handover, Heterogeneous Wireless Networks, Agent-Based Distributed Architecture, Virtualization

I. I NTRODUCTION Next generation networks are intended to provide mobile users with an Always Best Connected (ABC) facility for integrated and personalized services. In this vision, multihomed mobile nodes, able to connect simultaneously to more than one access network and to seamlessly switch from one network interface to another, should be considered. The forthcoming multihoming enhancements include the device’s ability to simultaneously use multiple networks of different technologies (e.g. multiple WLANs, UMTS, 4G) [1]. In this context, Vertical Handover (VHO) efficient procedures are required to allow a multihomed device to dynamically redirect its connections from one network interface to another. Till now, there are many remaining difficulties and challenges regarding VHO procedures. These include contextual information availability, reasoning and decision making, addressing, mobility and transport protocols. Indeed, devices

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should make measurements and/or retrieve information about surrounding and available networks to be able to make efficient VHO decision. This seems difficult, since with no cooperative solutions, terminals should monitor, at the same time, different networks, using different radio technologies and over different radio channels. Moreover, reasoning on contextual information, that may be rich and complex, is time and resource demanding. This can not help in making fast VHO decisions. Finally, the gathered information may be incomplete and/or not up to date, leading to non optimal and possible inappropriate decisions for VHO. In this paper, we focus on the Vertical Handover optimization by considering a context aware system and a VHO decision mechanism based on network reputation to make fast and efficient VHO decisions. The remainder of this paper is organized as follows: Related work is presented in section II. Section III describes the proposed R-VHDF architecture including a virtualization overlay system for context management and a VHO decision algorithm based on reputation. Section IV provides the implemented platform and evaluation results. Finally, section V concludes this paper. II. R ELATED W ORK In various studies and projects a variety of mobility management frameworks enabling VHO decision making and multihoming support have been reported. In [2], Balasubramania et al provide a context-aware computing-based framework that evaluates context information related to user devices and their capabilities, user preferences, application requirements, user location, network coverage and network capacity. The proposed scheme adopts a VHO decision making function based on Analytical Hierarchy Process (AHP). The handover decision mechanism is applied above the transport layer to allow a mobile node to switch between heterogeneous networks having different underlying protocol stacks. The considered architecture includes a Context Repository and an Adaptability Manager, respectively responsible for context information management and gathering, and VHO and service adaptation decision making. This solution relies on the introduction of proxies at the access interface of each network involved in the VHO procedure in order to redirect communication streams

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between networks during vertical handover. This invokes the deployment of a proxy at each access network, which requires agreements between the operators managing these heterogeneous networks. In [3] and [4], Tawil et al. provide a mobility management framework for VHO decision making. The proposed framework is based on an IEEE 802.21 architecture and relies on a Distributed Vertical Handoff Decision (DVHD) mechanism. DVHD suggests the distribution of the VHO computing process among available networks instead of centralizing all processing at the mobile node side. Therefore, DVHD implements several functions at both mobile nodes and networks sides. To accomplish the VHO decision, the DVHD requires several messages exchanges between the MN and all available networks. This requires a continuous scanning of the neighboring networks and thus keeping all the mobile’s interfaces ON, which may be very costly in terms of battery consumption. In [5], Savith et al. propose a similar framework with a Trust Distributed handover decision scheme (T-DVHD) Technique of order preference by similarity to ideal solution (TOPSIS). In [6], Loukil et al. proposed a virtualization overlay architecture to gather, manage and make reasoning on contextual information. This architecture allows automated service discovery (at network and application levels) and adaptation to users’ and systems’ contexts. Comprehensible and unified formats are used to make contextual information available and accessible and to allow systems to self-adapt (i.e. react with minimum human interventions) . Virtualization is based on the use of Agents defined as software entities, with communication and reasoning capabilities. These Agents are able to act on behalf of the physical entities they represent. The proposed architecture offers many capabilities to develop user and network value added services. However, this architecture does not provide a specific VHO decision mechanism which is essential for its evaluation in a real test bed. In [7], Zekri et al. proposed VHO decision mechanism based on a reputation system that allows fast VHO decisions with incomplete knowledge about ambient condition. This system is based on the analysis of previous connections between Mobile Nodes (MN) and available access networks. Networks’ reputation provides an indicator about the Quality of Experience (QoE) that a mobile node experienced with a given network within a given coverage area and for a given service. The reputation calculation is based on the mobile node location and its running application requirements. Thus, each network dispose of a reputation value per class of service and per location area. Reputation computation is based on QoS parameters including Bit Error Rate (ber), delay (d), jitter (J) and bandwidth (Bwd). Weights are used to indicate the relative importance of each parameter. These are calculated using the AHP. Reputation gives more importance to both recent and negative ratings and revise reputation over time to quickly react to degradations in the system to avoid flawed VHO decisions due to erroneous reputation values. The VHO deci-

sion mechanism and the reputation system are evaluated using Matlab while considering a simplified architecture relying on a single overlay node responsible for reputation aggregation and sharing. The VHO decision making is performed on the mobile node side. In the following, we provide a solution that integrates the reputation system and the VHO decision making into the architecture proposed in our previous work and we propose a way to implement the intelligence (VHO decision making) in a real system based on the proposed solution. III. P ROPOSED S OLUTION When going through the state of the art (section II), it appears that existing solutions do not completely consider the entire complexity of the VHO process. Some solutions only consider the decision process while others focus on the architectural aspects. The VHO process requires an overall solution, including decisional and information gathering/sharing mechanisms, with guarantees on delays and complexity. First, the required information and triggers for decision making should be identified and means for exchanging them, between the different actors (e.g. networks and terminals), should be defined based on open architectures, with specific protocols and rules, while alleviating network equipments and terminals from heavy processing and overhead. Secondly, decision making processes should consider multiple performance aspects including minimizing processing complexity and decision delays, QoS guarantees for users and load balancing between networks. In this paper, we propose R-VHDF as a cooperative overlay framework for the complex heterogeneous wireless networks environment (including terminals, services, network entities, network and service providers). The proposed framework targets to ensure efficient VHO decision making with minimum delays and minimum computational requirements on both user terminals and network entities. R-VHDF alleviate terminals and network entities from additional tasks through the use of a virtualization overlay system that eases context exchange and reasoning on behalf of physical entities within the system. This cooperative environment also allows to capitalize on user experience by offering fast VHO decision making based on networks’ reputation. In the following, we provide the architectural and functional details of R-VHDF. Then performance analysis are presented and discussed. R-VHDF is based on the two virtualization layers distributed architecture defined in [8]. This architecture define two abstraction layers according to the dynamicity of contextual information. In this paper, we focus on the first abstraction layer, where dynamic informations related to VHO process are handled. We define here two VHO related agents and their interactions: the User Agent and the Network Agent. The functionalities provided by these agents are (i) reputation system management and construction, (ii) VHO related

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information gathering, updating and sharing and (iii) VHO decision making and notification. As defined in [8], Agents refer to autonomous software components representing different entities such as users, services, mobile nodes, network devices, service providers, etc. Each physical entity can delegate its reasoning activities to its corresponding Agent. Agents rely on unified interfaces to exchange data, allowing indirect cooperation between heterogeneous entities and systems. They share common context and cooperate to explore available resources and services. Agents also communicate with the upper abstraction level [8], that include an Orchestrator and a global Context Manager, in order to provide, update and/or request static context information, profiles, preferences, statistics, etc.

them to the VHO decision process in block d. - Block d: this block is responsible of VHO decision making. It is composed of different processes that make decisions on behalf of the represented entity. As depicted in Fig.2, this block goes through three main phases: (a) Vertical Handover initiation, (b) Network Selection and (c) notification for Vertical Handover execution.

Agents’ functional architecture and their interactions in the proposed framework are represented in Figure 1 and described below. A. User Agent A User Agent is defined for each mobile node. Its purpose is to alleviate the mobile node by making context information gathering, analysis and decisions at the virtualization layer instead on behalf of the mobile node. These agents exchange information with the physical entities they represent (i.e. the mobile node) as well as with other Network Agents representing the networks available at the mobile node area. Interfaces e1 and e2 are used for information exchange between User and Network agents. Information exchanges are cyclic and event based. Each T cc User Agents update their dynamic context. When an important event happens (e.g. discovery of a new network), context information exchanges can be triggered by the Mobile Node. After context gathering, Agents analyze the new situation, make decisions based on the context and then send these decisions to the represented Mobile Nodes for execution. Agents are composed of three main functional blocks as follows: - Block b : this process collects through Interface a1 and Interface a2 all required decision parameters and contextual information. For each represented entity, the gathered context information is stored as a dynamic workprofile. This allows the other processes in the architecture to easily access up-to-date contextual information and to make appropriate decisions. - Block c : this is a reasoning process that analyses the work-profile in Block b and provides the VHO decision processes in Block d with the required information. For instance, it semantically selects networks related information (e.g. current network, perceived BER, delay, available bandwidth, other available networks, reputation values) and service related parameters from the work-profile and sends

Fig. 2: Proposed Vertical Handover Mechanism a) Vertical handover initiation : the VHO may be initiated in two cases: (i) if the Received Signal Strength goes below a minimum threshold and (ii) if a mobile node perceived QoS is lower than required. b) Network Selection : during the selection process, the User Agent checks, on behalf of the Mobile Node, for available networks reputation values and selects the best reputed and not overloaded one as a target network. If this latter provides sufficient QoS, the Agent decides handover to this network. c) Notification and Vertical Handover execution : network selection notifications are sent to the mobile node through interface f for VHO execution. In addition to network selection functionalities, this block is responsible for the evaluation (or scoring) of the network to which the user is currently connected. This reflects the

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Fig. 1: Proposed Functional Architecture

Quality of Experience (QoE) of the mobile node with the current network. The network scoring is calculated according to the QoS perceived by the mobile node. It is then sent to the agent representing this network through interface e. Network scoring is made each time a Mobile Node m, connected to a network n, decide to handover to another network. The Mobile Node computes its perceived quality and concludes whether the offered quality satisfied its requirements or not. If the perceived quality is equal or better than the required quality, the mobile node rates the network positively; otherwise, it rates it negatively as follows: • Positive (r+ (m, n) = 1) if its perceived QoS is satisfying. • Negative (r− (m, n) = −1) if it isn’t. For more details on the used perceived QoS evaluation please refer to [7], [9]. B. Network Agents These agents represent networks within the virtualization overlay. They are responsible of the aggregation of network reputation values. They communicate with User Agents to get scores and to share the aggregated reputation values. Network Agents are compose of three main functional blocks. - Block g : this block forms the network’s agent work profile. It periodically receives the scores given by the users that are connected to the represented network. These scores are then forwarded, as a part of the network’s agent work profile, to Block h where these are aggregated. This process also gathers load information. In case of overload, the network’s agent generates notifications through Block i and sends them to users’ agents via Interface e2. This Block also communicates with the second abstraction level, through Interface j, to get or to update static context information related to this network. - Block h : Rating values collected in the Network Agent Block g are aggregated in this block to compute the

global network reputation. This is done in two steps through equations (1) and (2) respectively. - Step 1: rn (t) = w+



r+ (m, n) + w−



r− (m, n)

- Step 2: ⎧ ⎨ rn (t) Rn (t) = ⎩ (1 − γ) · Rn (t − 1) + γ ∗ rn (t)

if t = 1 if t > 2

(1)

(2)

Where γ ∈ [0, 1] is a discounting factor and w+ and w− are weights allocated to positive and negative rates. In this paper, we give more importance to negative behaviors by setting w+ = 0.4 and w− = 0.6. This choice is motivated by the fact that negative rates usually represent an effective or sudden observed degradation on the network quality. The objective of the second step is to progressively decrease the effect of old reputation values through time to give more importance to recent behaviors. - Block i : This block saves the global reputation value it gets from Block h and the perceived QoS of the last user of this network. It forwards this information to the users’ agents requesting them through Interface e2. These may be users connected to this network at this time or users in the range of this network and looking for VHO decision making. IV. P ERFORMANCE EVALUATION To evaluate the overall proposed framework, a testbed was implemented. It is composed of two main parts. The first is a Multi-Agents sub-System (MAS). The second emulates MNs functionalities. • MAS implementation: This part is based on the use of two general purpose processors (Intel Xeon 2.8 GHz, Ram 3 GB, Cache memory 4 MB) Java Virtual Machine (JVM 1.6) is used with a 512

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MB dedicated memory within a JADE 3.4 Environment [10]. One of the main advantages of JADE is the use of the ACL (Agent Communication Language) that allows unified communications between software Agents. Communications between the Agents and their corresponding physical entities is achieved through the generation of ACL-like messages. A first PC is used for the first abstraction level (see Fig. 1) where users and networks agents are instantiated to run context gathering, network reputation management and VHO decision making processes. The other PC is used for the second abstraction level where the global context manager and users and networks profiles are managed. This is done through the use of a semantic data base and a corresponding software agent that communicates with the first abstraction level. • MN emulation: Mobile nodes are emulated using software components implemented and instantiated on distant PCs. Each component represents a MN and is responsible of sending dynamic context data to the corresponding agent at the first abstraction level, where context is analysed and VHO decisions are made. Decisions are then sent to these components to execute the VHO. Context exchange can be periodic (each tcc =3s) or on demand. Tests were run for different scenarios in order to evaluate qualitative and quantitative behaviour of the proposed framework. The objective is to check that reputation system and values are correctly built, that VHO decisions are correctly made and that the platform is scalable. The number of networks (WLAN and 3G) was varied between 2 and 12 and the number of multihomed MNs between 4 and 100. The considered mobility model is Gauss Markov [11]. The table below summarizes the simulation parameters.

Fig. 3: Average VHO delay for 12 available networks for 4 available networks. The VHO decision delay increase slightly with the increase of the number of users using the proposed overlay architecture. This may be explained by the fact that when the number of users making a VHO increases, the processing on the networks’ agents side also increases which generates a little more delay. Despite this variation, the experienced VHO delay is very acceptable. Figure 4 illustrates

TABLE I: Simulation parameters Topography

500 * 500 m

Mobile Nodes

2 - 80

Networks

2 - 12

Simulation Time

15 min

Wireless Standards Technologies

UMTS, 802.11

Mobility Model

Gauss Markov

Time for Checking Dynamic-context (tcc)

3s

Fig. 4: Average VHO delay for 20 users

The proposed architecture is tested for a video streaming application. When the current network’s RSS or QoS (or both of them) goes below a given threshold, the user’s agent detects this degradation and asks its current network for available networks’ reputations to make a VHO decision. Once a decision is made, the user’s agent sends a notification to the mobile node it represents which executes the handover. Figure 3 illustrates the average vertical handover processing decision delay when the number of users making simultaneous vertical handovers increases. We notice that, when the number of users varies between 5 and 80, the average VHO processing decision delay varies between 58 and 76 milliseconds for 12 available networks and between 15 and 29 milliseconds

the average vertical handover processing decision delay, for 20 users making a handover at the same time, when the number of available networks varies. This figure shows that the VHO processing decision delay is lower than 30 milliseconds when the number of available networks is not very important (less than 8 available networks). The delay increases to reach 58 milliseconds for 12 available networks. This variation is explained by the increase of the processing on the users’ agents side because of the increase of the exchanged messages and data with the available networks’ agents. In addition, when the number of available networks increases, a user’s agent, wishing to make a handover, sends messages to the available networks’ agents, waits for all these networks answers, checks the best reputed network’s QoS and then makes a decision which affects the VHO delay. Figure 5 provides a comparison between the VHO processing

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decision delay obtained by Savitha et al in [5] and our results. We notice that the VHO processing decision delay decreases significantly when using R-VHDF which insures seamless VHO.

exchanges and sharing,...). In addition, this knowledge is successfully used to make VHO decisions and to trigger VHO execution with acceptable delays. The proposed solution can be extended for advanced additional features for the next generation networks and services. Indeed, the context manager can be extended and the reasoning engine can be duplicated to offer facilities related to other aspects like service composition and adaptation, energy management and any other optimization feature that requires global knowledge over complex and extended systems. The proposed architecture is now being extended to study energy aware and green routing solutions. R EFERENCES

Fig. 5: Comparison between our proposal and T-DVHD

V. C ONCLUSION Achieving ubiquitous access with seamless mobility and services over heterogeneous wireless networks require cooperative solutions with context awareness and adapted decision making solutions. Architectural and implementations aspects are of prime importance. Indeed, contextual information management and decision making within such complex systems are resource demanding and can not be centralized or left for mobile nodes. In this paper, we propose a distributed mobility management framework based on an overlay control level composed of two virtualization layers. Virtualization allows to offer additional resources at the infrastructure that help in reasoning and decision making on behalf of the represented physical entities, such as network equipments and nodes. The proposed virtualization and decision making solution is implemented and performance results are obtained through real measurements. Results show that the proposed solution effectively allows to achieve global knowledge about the ambient context (surrounding networks, QoS, reputation

[1] G. Cunningham, P. Perry, and L. Murphy, “Soft, vertical handover of streamed video,” in 3G Mobile Communication Technologies, 2004. 3G 2004. Fifth IEE International Conference on, 2004, pp. 432 – 436. [2] S. Balasubramaniam, “Vertical handover supporting pervasive computing in future wireless networks,” Computer Communications, vol. 27, no. 8, pp. 708–719, May 2004. [Online]. Available: http://dx.doi.org/10.1016/j.comcom.2003.10.010 [3] R. Tawil, G. Pujolle, and O. Salazar, “A vertical handoff decision scheme in heterogeneous wireless systems,” in Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, may 2008, pp. 2626 –2630. [4] R. Tawil, “A distributed vertical handoff decision for the fourth generation wireless networks,” Pierre et Marie Curie University, 2009. [5] K. Savitha and C. Chandrasekar, “Network selection using topsis in vertical handover decision schemes for heterogeneous wireless networks,” CoRR, vol. abs/1106.2402, 2011. [Online]. Available: [6] M. Loukil, B. Jouaber, and D. Zeghlache, “A two-layered virtualization overlay system using software avatars,” in Computers and Communications (ISCC), 2010 IEEE Symposium on, june 2010, pp. 1086 –1090. [7] M. Zekri, B. Jouaber, and D. Zeghlache, “On the use of network QoS reputation for vertical handover decision making,” in IEEE Globecom 2010 Workshop on Advances in Communications and Networks (ACN 2010), Miami, Florida, USA, 12 2010, pp. 2006–2011. [8] M. Loukil, T. Ghariani, B. Jouaber, and D. Zeghlache, “A semantic database framework for context management in heterogeneous wireless networks,” in Wireless and Mobile Computing, Networking and Communications (WiMob), 2010 IEEE 6th International Conference on, oct. 2010, pp. 35 –39. [9] M. Zekri, J. Pokhrel, B. Jouaber, and D. Zeghlache, “Reputation for vertical handover decision making,” in 17th Asia-Pacific Conference on Communications (APCC 2011), Kota Kinabalu, Sabah, Malaysia, Oct. 2011. [10] F. Bellifemine, A. Poggi, and G. Rimassa, “JADE - a FIPAcompliant agent framework,” in Proceedings of the Practical Applications of Intelligent Agents, 1999. [Online]. Available: http://jmvidal.cse.sc.edu/library/jade.pdf [11] F. Bai and A. Helmy, “Chapter 1 a survey of mobility models in wireless adhoc networks,” University of Southern California, U.S.A.

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