Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.
Providing widespread and consistent availability to mobile terminals while moving among heterogeneous wireless environments is probably the greatest challenge, analysts have faced for numerous years. Therefore, in the heterogeneous network environment, the primary focus of this challenge is to make an appropriate and clever choice for handover. In an active session, it is necessary for the Mobile Node (MN) to accomplish the horizontal handover (HHO) or VHO when it moves within a heterogeneous wireless network (Hetnets), to maintain its connection. Altogether, an intellectual Handover Decision Systems (HDS) is needed to choose the finest network for the handover. One of the important factors in the decision making is the mobility speed of the mobile terminal, which need to be considered in the decision making technique to reduce the false handovers that generally contribute to the number of unnecessary handovers.
The main emphasis of next generation mobile networks is on flawlessly connecting the prevailing wireless technologies like Wi-Fi, 2G, 3G, 4G, Wi-Max, and WLAN into an all-IP based heterogeneous network. Perhaps a vital challenge for next generation wireless networks is to bring together these various wireless network technologies into one all IP-based. These different network technologies ought to be synchronized in an enhanced manner with the main aim being to provide the services requested by the user along with the user QoS requirements. One of the most perplexing problems among the networks is VHO in a heterogeneous network environment. Heterogeneous wireless networks are described as the framework encompassing various radio based communication networks such as Wireless Fidelity (Wi-Fi), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (Wi-MAX), Global System for Mobile Communications (GSM), and Wireless Local Area Network (WLAN) [
Handover is a technique in mobile communications wherein a mobile device is in a linked information session starting from one cell site and moving onto the next [
False handover [
The rest of the paper is organized as follows. Section 2 reviews the different state-of-the-art multi attribute decision making and fuzzy based algorithms used in VHO processes for the selection of the appropriate network to perform handover. Section 3 presents the implementation architecture of the mobility management mechanisms and the proposed methodologies based on multi-criteria and fuzzy based decision making algorithms. Section 4 describes the experimental setup and discusses the results obtained from the proposed algorithms over the existing approaches. Section 5 provides the conclusion of the research and future work.
The paper by Bhuvaneswari et al. [
Various papers that explain MADM algorithms are discussed by Santhi et al. [
The proposal by Sharma et al. [
The fuzzy IF–THEN rules that exist in the rule base are: It consists of the defined membership functions of the fuzzy sets. The inference operations take place in the decision making part of the rules. A fuzzification interface of the fuzzy rule is used to propagate the fragile inputs into the degrees of matches with linguistic values. Then, a defuzzification interface is used to transform the fuzzy results into crispy output.
Heterogeneous wireless networks can provide widespread and seamless connectivity for any types of service user. Heterogeneous wireless networks are well-defined as the structure consisting of numerous radio accessing technologies such as Wireless Fidelity (Wi-Fi), Universal Mobile Telecommunications System (UMTS), Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WI-MAX). Each wireless technology has its own distinct characteristics in terms of bandwidth, coverage, data rate, network occupancy and RSS. Throughout the movement of the MN attached to the home network, the MN should have the best connectivity with the finest network. The main goal is to connect with the best network for the mobile node moving across networks but the selection of network may vary depending on the QoS requirement of each application running on the mobile node. The optimal decision making algorithm is mandatory in order to meet the user service requirements based on the QoS criteria with the selected best network. This proposed system is about enhanced VHO decision making by using a Multi-criteria Fuzzy-Based algorithm to select the best network. Enhanced Multi-Criteria algorithms and Fuzzy Logic based algorithms are used effectively to select the optimal network and also to reduce the probability of false handover. Additionally, a packet buffer is used to reduce the packet loss in the mobility management mechanism. An efficient mobility management mechanism is also provided in the networking environment and this also has network controls for concerned mobile nodes which are in mobility. To assess the proposal on the Multi-criteria and Fuzzy based Decision making algorithm, the assessment is based on network selection performance, for which the Network simulator tools are used to develop the environment and experiment the system.
The proposal aims to provide an optimal solution for seamless connection by intelligent VHO algorithms. The architecture model to represent the handover among UMTS, WLAN, Wi-Fi, and WI-MAX in a heterogeneous environment is shown in
The Decider hub (DH) in the model, which is considered as the spine of the system is where its various connections to different networks in the scenario are maintained. When the MN is idle, a DH acts as a centralized server performing the decision making for handover in selecting the best network and buffers for the ongoing packets. The buffering mechanism is taken care of by a packet buffer which is categorized as New Packet Buffer (NPB) and Forward Packet Buffer (FPB). For monitoring the mobility of nodes, few mobility managers (MM) are present in each network depending upon the coverage area.
At initial state, the MN is attached to the Home Serving Media Access Gateway (HSMAG). HSMAG is the Media Access Gateway (MAG) of the home network by which the MN and Correspondent node (CN) interconnects. When MN enters its coverage area, it is the responsibility of MAG to authenticate it. The mobility of nodes is traced by the mobility managers and the location of the MN is passed to MAG and DH. The number of MMs might differ, and are placed in such a way that, they cover the node’s action in MM’s range. For reliable transmission to optimal networks, the DH maintains the routing table [
The Mobility Manager has the signal strength values
Usually the DH has certain algorithms to be used for selecting the best networks and it is necessary for the mobile node to be connected to the network that the DH chooses, in its idle state. The DH is equipped with a decision making algorithm with its required inputs gathered in the initiation phase. Data transfer from the correspondent node to the destination node occurs
The Decision Phase predicts the best network to attach to. Based on this the MN establishes its connection path with the new serving media access gateway (NSMAG). The DH updates its routing table information for the CN, to transmit data to the new network. The packets that are buffered using packet buffers when the MN is in idle state are transferred to the newly created connection to reduce loss of packets [
The pseudo-code of the proposed model is given below,
Set i ∈ N |
MN -> connected -> N |
MM1 -> traces Mobile Node |
MN is mobility |
If (distance == medium) && (RSS == low) |
MM1 -> HI -> decider hub and Packet Buffer |
FPB -> buffer packets |
DH -> comprises of decision making algorithms |
RSS, distance of every network to mobile node is estimated |
• Multicriteria TOPRES algorithm |
Constructs Normalized Weighted matrix |
Finds distance from ideal best and average values |
Finds relative closeness |
Ranks the networks and selects the best one |
• Multicriteria RAMOC algorithm |
Constructs Normalized Matrix |
Selects the best and average value |
Measures group utility |
Ranks networks and results the optimal one |
• Fuzzy logic-based algorithm |
Inputs are fed into Fuzzy engines |
Predicts best network based on fuzzy rules |
Major network from 3, decide the best network for handover |
MM2 -> traces MN -> DH |
DH -> forecast next network |
MN disconnects from N1 |
MN in idle state |
NPB -> buffer packets |
MN -> new connection -> N2 |
CN -> packets -> NPB and FBP -> packets buffered -> N2 |
The steps involved in the proposed system as represented in
When the mobile node (MN) is in idle state, there is no transfer of data packets between Mobile Node and Corresponding Node. Until an appropriate network is found, the packet buffer buffers the data packets and they are re-transmitted once a new network connection is established. The packet buffer stores the data packets with sequence numbers. In this mechanism, two buffers, New Packet Buffer and Forward Packet Buffer, are used for buffering packets. The stages involved in the buffering process are listed below,
As the multicriteria algorithm when combined with Fuzzy logic-based algorithms improve network performance by reducing the delay and packet loss, so TOPRES and RAMOC derived from Multicriteria algorithm and Fuzzy-based algorithms are used and described below:
Multi-criteria decision making helps in making ideal VHO decisions as there are various target network options. Likewise, multicriteria decision making delivers high throughput, reduced packet loss, flexibility and choosing the best network by considering more criteria.
The five important normalization techniques, listed below are compared and analyzed,
Vector Normalization Linear Max-Min Normalization Linear Sum based Normalization Linear Max Normalization Gaussian Normalization
By considering various normalization techniques [
Multi-criteria decision making is represented in matrix form. The decision matrix denoted as A is an (m * n) matrix where i
To change the dimensional characteristics into non-dimensional ones, the equation is used based on its type,
For beneficial attributes,
For non-beneficial attributes,
where,
a
m denotes the various alternatives (Wi-Fi, WiMAX, UMTS and WLAN).
n denotes the criteria/attributes (Data rate, RSS, Bandwidth, Coverage, & network occupancy).
By using the equation,
where Wj denotes the weight of the
Hetnets are ranked from maximum value as Rank 1, then Rank 2 and so on. So, by this ranking TOPRES selects the best network.
Here, the decision matrix,
The equation used is based on its type,
For beneficial attributes,
For non-beneficial attributes,
where,
a
m denotes the various alternatives (Wi-Fi, WiMAX, UMTS & WLAN).
n denotes the criteria/attributes (Datarate, RSS, Bandwidth, Coverage, & network occupancy).
Based on the values in the decision matrix and whether the values falls under beneficial and non-beneficial category of the attribute the best value (
Based on the type, the attribute is categorized as a beneficial or non-beneficial attribute. Every attribute of a network is given a weight (0 <
Then, the extreme value of each network is taken and denoted as R.
where, R
The average of S
Fuzzy logic is widely used as an instrument to improve the knowledge of decision-making mechanisms in various regions. Many efforts are made to safeguard the QoS, as the interest for real-time applications is continually growing. But the fuzzy membership functions are fixed and the design structure of most of the fuzzy logic-based handover decision algorithms is monolithic. The two drawbacks of such designs are: when the count of input parameters grows, the time of execution of algorithm increases, and with various kinds of traffic or services, performance of the network selection decreases. To overcome these issues, several engines should be utilized in handover decision algorithms. Multiple fuzzy engines are designed to choose the best network for handover.
The proposed multi-criteria fuzzy based handover decision system design is shown in
Five decision parameters (RSS, BW, NO, CO, and DR) are taken.
for k = 1, 2, 3,…,18
for k = 1, 2, 3,…,9
for k = 1, 2, 3,…,9
Defuzzification is done to get a crisp value by converting the aggregated fuzzified data. The
The same technique is used in the DS fuzzy engine. Hence, from
for k = 1, 2, 3,…,27
Rule No. | Data rate | Bandwidth | Output |
---|---|---|---|
1 | Low | Low | Low |
2 | Low | Medium | Low |
3 | Low | High | Medium |
… | |||
18 | High | High | High |
Rule No. | RSS | A |
Output |
---|---|---|---|
1 | Low | Low | Low |
2 | Low | Medium | Low-Medium |
3 | Low | High | Medium |
… | |||
9 | High | High | High |
Rule No. | N |
M |
Output |
---|---|---|---|
1 | Low | Low | Low |
2 | Low | Low | Low-Medium |
3 | Low | Low | Medium |
… | |||
27 | High | High | High |
So, based on the value obtained from the DS engine the best network is selected and the handover takes place. For example, if the candidate networks data rate is high and the networks bandwidth is high then the outcome of this rule will be high thereby the A(value) will be high, which is fed as input to the NQ engine.
The Multi-criteria algorithm itself considers a greater number of metrics to select the optimal network. Three different fuzzy engines used in the proposal consider parameters like RSS, data rate, and bandwidth that directly influence the performance of the handover. Hence the parameters packet loss, throughput, and the number of handovers executed were enough for analyzing the performance of the proposed algorithm over the existing state-of-art techniques.
Three various algorithms are considered to choose the finest network. RAMOC, TOPRES, and the Fuzzy-based algorithm are used in the decision making algorithm. These organized algorithms are simulated using a Network simulator-2 tools. To assess the network selection performance, the proposed decision making algorithms that involves different network environments like WLAN, WIFI, WIMAX and UMTS are created. The overall activity of the nodes is monitored by the DH. The movements of the mobile nodes along with the network are monitored in the DH so that it is easy to trail the movement when the MN enters the environment. The mobile nodes and hetnets are configured as same in the simulation environment. The mobility of mobile nodes is organized by the attaching network. This makes the mobile node attached to the network unaware of the handover process and thereby providing service utilization. Information is passed among mobile nodes and corresponding nodes that are routed by the connected network and the server. The delay of packets is calculated from the time at which the communication is broken and the time at which the communication resumes when it is associated with a new destination network. The network performance is calculated based on various QoS like RSS, data rate, bandwidth, network occupancy, and the coverage.
Based on the coverage of every network, location of the mobility manager is proportional to trail movement of the mobile node. The mobility manager transfers the collected information to the new serving media access gateway (NSMAG) that in turn passes it to the DH. The pre-judgement of the interruption of MN by computing received signal strength, speed and distance of moving node and these are considered by the mobility manager in the message flow. The autonomous distribution values that are frequently generated for each input decision parameter are continuously fed into the decision-making phase which is located in the DH. The simulation runs for a number of times and the average value of the best network is selected from multiple trials obtained. Each algorithm generates the best network as a result. The network which is most often selected by the algorithms is taken into consideration and the handover process is initiated.
The proposed model uses the DH, which starts buffering the packets in the Forward Packet Buffer before receiving the HIM from the HSMAG. Upon the MN disconnection from the HSMAG, each transmitted packet is stored in the New Packet Buffer with its sequence number. So, the packet loss is significantly reduced by using two buffers in the double buffer mechanisms. As soon as the proposed algorithms forecast the candidate network for the MN to handoff, the buffered packets are sent to the Mobile Node and the ongoing packets are rerouted to the newly attached network and then to the MN
Throughput refers to the total number of data packets that are transferred successfully from the corresponding node to the mobile node in a certain time period. Generally, the transmission of data packets takes place through the DH and packet buffer with the authentication of the MAG of the network attached. The DH starts evaluating three algorithms when it is intimated that the RSS is weak, and Distance is far, and MN is going to enter an idle state. As the new network for the MN to attach to is predicted by the DH earlier, the throughput is higher, as expected by the user in mobility state. When compared with the existing algorithm [
The goodness of the proposed technique is clearly demonstrated as the outcome reduces the number of handovers for selecting the network among all these algorithms when compared with the existing systems. Average number of handovers over each traffic class shown in
The problems in the mobility management mechanism and VHO are addressed and these issues are overcome by proposing an intellectual decision making algorithm. By combining multi-criteria with fuzzy based decision algorithms, an uninterrupted handover with reduced packet loss and less handover delay is achieved. With various aspects or characteristics, the MN uses algorithms like TOPRES, RAMOC, and formulated fuzzy rules to choose the optimal network. Here, the selection of networks is made based on the result acquired from TOPRES, RAMOC and fuzzy algorithms. The network which produces the optimum result from these algorithms is chosen as the destination network for handover. In addition, in this proposed work the possibility of a mobile node’s false handover to the new serving network is calculated, which minimizes the number of false handover. In addition, a double buffering mechanism, which is the usage of two separate buffers, reduces packet loss. The proposed system achieved an enhancement of throughput of up to 10.5% and a reduction of packet loss of up to 1.5% to maintain a seamless connection. Further, the number of handovers executed is reduced by up to 50% when compared to the existing approach and it also avoids the execution of false handover by predicting the best network for handover using a decision making algorithm which combines multi-criteria with fuzzy-based decision making algorithms.
In future works, by reducing the number of input parameters, whose standards are calculated for selecting the best network, VHO algorithms will be optimized. If the numbers of input parameters are reduced further, the number of fuzzy rules used in the fuzzy engines to make the handover decision will also be reduced, thereby further reducing the delay in the VHO process.
Authors would like to thank the Department of Electrical and Computer Engineering of North South University and Taif University Researchers Supporting Project Number (TURSP-2020/36), Taif University, Saudi Arabia.