MultiAttribute Decision Making for EnergyEfficient Public Transport Network Selection in Smart Cities
Abstract
:1. Introduction
 We designed the Public TransportAssisted DataDissemination (PTDD) System in a smart city which will be equipped with wireless sensors and data centers to handle massive data using wired, wireless, and public transport networks;
 We applied a MultiAttribute Decision making (MADM) algorithm for best network selection based upon different user requirements and different attributes;
 We applied the Capacitated Vehicle Routing Problem (CVRP) to minimize energy consumption using public transport as a data carrier. We will use buses to offload the entire set of demands of each bus stop. Our model constrains the objective by the maximum capacity of the bus;
 For the evaluation of the best network selection, different services are considered, based upon user requirements, to find the best network in the heterogeneous network. Next, a detailed comparative analysis of energy consumption is performed for traditional and public transport networks for the various demands of users.
2. Related Work
3. Public TransportAssisted DataDissemination System
3.1. MultiAttribute Decision Making
 (a)
 Alternatives: Alternatives are defined as several different options to prioritize or select. These can be called candidates, users, or networks, etc.;
 (b)
 Decision Matrix: Any MADM problem can be mathematically defined by using a decision matrix, $L(M\times N)$:$$L=\begin{array}{cccc}& \begin{array}{cccccccc}{C}_{1}& {C}_{2}& \cdots & {C}_{j}& \cdots & {C}_{N}& & \end{array}& & \\ (& \begin{array}{cccccc}{x}_{1,1}& {x}_{1,2}& \cdots & {x}_{1,j}& \cdots & {x}_{1,N}\\ {x}_{2,1}& {x}_{2,2}& \cdots & {x}_{2,j}& \cdots & {x}_{2,N}\\ \vdots & \vdots & \ddots & \vdots & \ddots & \vdots \\ {x}_{i,1}& {x}_{i,2}& \cdots & {x}_{i,j}& \cdots & {x}_{i,N}\\ \vdots & \vdots & \ddots & \vdots & \ddots & \vdots \\ {x}_{M,1}& {x}_{M,2}& \cdots & {x}_{M,j}& \cdots & {x}_{M,N}\end{array}& )& \begin{array}{c}{A}_{1}\\ {A}_{2}\\ \vdots \\ {A}_{i}\\ \vdots \\ {A}_{M}\end{array}\end{array},$$
 (c)
 Attribute Weight: Attribute weight is the value obtained by the decisionmaker as per each attribute of the network. This weight depends upon the value assigned to the attribute. This weight is calculated by the pairwise comparison matrix;
 (d)
 Normalization: The attribute used for network selection has different measurement units. Therefore, normalization is a necessary step for this calculation.
3.1.1. Initialization Step
 Service’s Requirement: The most important aspect is the user’s requirements. For different users, they have different demands and objectives. In our proposed system, we categorize users’ requirements into three categories, such as Service 1, Service 2, and Service 3. Different services have different levels of sensitivity to the same networking attribute. For example, considering bandwidth as an attribute, if its service 1, a lower bandwidth will be used. However, if it is a large data transfer, a higher bandwidth will be used. In addition to that, it is assumed that a user can select any one service at one time. Users can select the priority of services used. They can select the urgency or nonurgency of data delivery, which relates to the data type, such as delaytolerant or delaysensitive, and helps the controller to make optimal networkselection decisions;
 Data Type: Data types belong to the type of application selected by users. It can be delaytolerant or delaysensitive. Some of the services, such as video or data type, can be categorized as a realtime or nonrealtime application and can, accordingly, be delayed for some time. This is another important piece of information to consider for optimal network selection;
 Network Alternatives: In our proposed work, we are demonstrating the offloading of data from traditional networks to road networks with delaytolerant conditions. Therefore, to choose among a list of networks, we will be considering WLAN, UMTS, and Vehicular Networks. The controller will choose the best optimal network among these networks based upon user requirements and data type. Three of these networks have different properties. The vehicular network is used for all delaytolerant applications, such as emails, data backup, video download, and photos, which significantly contribute to energy efficiency without a negative effect on user satisfaction. We assume that all vehicles are equipped with OnBoard Units (OBU) to carry data. If we compare the other two networks, WLAN networks are managed for higher bandwidths and lower delay applications, although UMTS networks are the most energyefficient with lower bandwidth requirements and large delays.
3.1.2. PreMADM
 Utility function—theorybased network:Utility functions measure the level of satisfaction for each user as per different attributes of each network alternative. We design utility functions to map decision factors to the respective utility metrics in order to evaluate the decision factors of network selection. We consider user requirements as per their profile, delaytolerant indicator (DTI), both network properties, and QoS requirements. There are generally three types of utility functions that network selection uses: (1) sigmoid; (2) monotonically increasing; (3) linearly decreasing. These functions are further categorized as beneficial or nonbeneficial criteria. The sigmoid utility function is used with given minimum and maximum requirements. Bandwidth and energy efficiency are beneficial criteria and can be represented as a sigmoid function. The utility theory states that utility functions must satisfy twice differentiability, monotonicity, and concavity–convexity [37]. Therefore, we design different utility functions for different objectives. The value of the utility function lies between 0 and 1. For the most satisfied user, it is 1, and for the least satisfied user, it counts as 0.
 Utility function for Energy Efficiency EE: In this utility function, EE, as discussed, is a beneficial criterion, and the energyefficient utility function will be modeled as a sigmoid curve. The sigmoidal utility function is defined below:$$u\left(e\right)=\frac{1}{1+x{e}^{c({e}_{avg}e)}};e>0,$$
 Utility function for Network Bandwidth: Network Bandwidth is an important attribute for network selection. For three of these networks, the network bandwidth has a different value. When the network bandwidth is lower than the required bandwidth, as per different service requirements, then there is a compromise in QoS, and there will be a loss of packets. We are using the following utility function to define bandwidth requirements for different applications:$$u\left(b\right)=\left(\right)open="\{"\; close>\begin{array}{cc}0,\hfill & ;b{b}_{min}\hfill \\ \frac{{\left(\frac{b}{{b}_{med}}\right)}^{x4}}{1+{\left(\frac{b}{{b}_{med}}\right)}^{x4}}\hfill & ;b\le {b}_{min}\le {b}_{med}\hfill \\ 1\frac{{\left(\frac{{b}_{max}b}{{b}_{max}{b}_{med}}\right)}^{x4}}{1+{\left(\frac{{b}_{max}b}{{b}_{max}{b}_{med}}\right)}^{x4}}\hfill & ;{b}_{med}\le b\le {b}_{max}\hfill \\ 1\hfill & ;b{b}_{max},\hfill \end{array}$$
 Utility function for Delay Tolerance: Generally, incremental latency values are acceptable in a DelayTolerant Networks (DTN). While designing the utility function for network delay tolerance, a larger network delay value will result in a lower utility value. It is a decreasing criterion to measure network delay. Delay varies in both networks as per the data volume. u(d) is defined as a utility function for the delay, as below:$$\begin{array}{c}\hfill u\left(d\right)=1{u}^{\prime}\left(d\right)\end{array}$$$$\begin{array}{c}\hfill {u}^{\prime}\left(d\right)=\left(\right)open="\{"\; close>\begin{array}{cc}\frac{{\left(\frac{d}{{d}_{med}}\right)}^{x3}}{1+{\left(\frac{d}{{d}_{med}}\right)}^{x3}}\hfill & ;d\le {d}_{min}\le {d}_{med}\hfill \\ 1\frac{{\left(\frac{{d}_{max}d}{{d}_{max}{d}_{med}}\right)}^{x3}}{1+{\left(\frac{{d}_{max}d}{{d}_{max}{d}_{med}}\right)}^{x3}}\hfill & ;{d}_{med}\le d\le {d}_{max}\hfill \\ 1\hfill & ;d{d}_{max},\hfill \end{array}\end{array}$$
 Utility function for the Delivery Probability: Delivery probability is to be defined as the volume of data to be sent using any of the networks. We defined the utility function of delivery probability as $u\left(dp\right)$, where $dp\u03f5[0,1]$, in case of successful delivery, is 1, and otherwise, for packet loss, it will be considered as 0. Otherwise, it lies between 0 and 1. $dp$ is the delivery probability obtained and $d{p}_{max}$ is the maximum delivery probability that is acceptable to the user, and is shown in Figure 8.$$\begin{array}{c}\hfill u\left(dp\right)=\left(\right)open="\{"\; close>\begin{array}{cc}\frac{dp}{d{p}_{max}}\hfill & ;0\le dp\le d{p}_{max}\hfill \\ 1\hfill & ;dpd{p}_{max}\hfill \end{array}\end{array}$$
3.1.3. MADM
 Analytical Hierarchical ProcessThe analytical Hierarchical process (AHP) method is a multicriteria decisionmaking process for network selection. It was developed at the Wharton School of Business by Thomas Saaty in the 1970s [38]. AHP works on the function of priority and rank to evaluate subjective weights to achieve the specified goals. We have used this process to select a bestfeatured network from the given alternatives for the given service class based on the following criteria—Energy Consumption, Bandwidth, Delay, and Delivery Probability. We have also used this process for choosing a priority of network types for each data type. Network weighing is an important factor to characterize the network performance and user’s preferences. We use the hierarchy analysis method to allocate the appropriate weight to each selection metric.We further categorize traditional networks into WLAN and UMTS networks for impartial scheming with different attributes, as shown in Figure 9. The logical flowchart of the AHP algorithm considers the hierarchical structure with the main goal, multiple criteria, and network alternatives to select. We have defined utility functions for all the attributes for a network assessment. A user’s preference will be based on multiple criteria for network selection. We assume that WLAN users have wireless access to their system, but with a fixed location—or we can say a local network—and that they use all their devices to avail the services and disseminate data to nearby RSUs for further transmission. However, they have good speed and bandwidth values. On the other hand, UMTS is a mobile cellular device and can roam around with their data plans, but with limited bandwidths and larger delays as per the delivery probability and data network’s range.
 Subdivide a problem into further subproblems by defining an objective function, criteria, and possible alternatives. Here, the objective is our goal of achieving optimal network selection. The multiple criteria are the factors affecting the preference for selection.
 Develop the hierarchy model of all objectives along with their elements to obtain the priorities of criteria through pairwise comparison matrices.
 Construct a pairwise comparison matrix for each criterion of hierarchical structure in such a way that all associated criteria are compared with each other as per the intensity of importance [39], with respect to the scale. We believe that a pairwise comparison between alternatives helps for qualitative judgment. This qualitative pairwise comparison follows the importance scale, as shown in Table 1.$$P=\begin{array}{cccc}& \begin{array}{cccccccc}{C}_{1}& {C}_{2}& \cdots & {C}_{j}& \cdots & {C}_{N}& & \end{array}& & \\ (& \begin{array}{cccccc}1& {x}_{1,2}& \cdots & {x}_{1,j}& \cdots & {x}_{1,N}\\ {x}_{2,1}& 1& \cdots & {x}_{2,j}& \cdots & {x}_{2,N}\\ \vdots & \vdots & 1& \vdots & \ddots & \vdots \\ {x}_{i,1}& {x}_{i,2}& \cdots & 1& \cdots & {x}_{i,N}\\ \vdots & \vdots & \ddots & \vdots & 1& \vdots \\ {x}_{M,1}& {x}_{M,2}& \cdots & {x}_{M,j}& \cdots & 1\end{array}& )& \begin{array}{c}{C}_{1}\\ {C}_{2}\\ \vdots \\ {C}_{i}\\ \vdots \\ {C}_{N}\end{array}\end{array}$$
 Perform the normalization of a given matrix P, which is now denoted as ${P}_{Norm}$:$${P}_{Norm}=\begin{array}{cccc}& \begin{array}{cccccccc}{C}_{1}& {C}_{2}& \cdots & {C}_{j}& \cdots & {C}_{N}& & \end{array}& & \\ (& \begin{array}{cccccc}1& {z}_{1,2}& \cdots & {z}_{1,j}& \cdots & {z}_{1,N}\\ {z}_{2,1}& 1& \cdots & {z}_{2,j}& \cdots & {z}_{2,N}\\ \vdots & \vdots & 1& \vdots & \ddots & \vdots \\ {z}_{i,1}& {z}_{i,2}& \cdots & 1& \cdots & {z}_{i,N}\\ \vdots & \vdots & \ddots & \vdots & 1& \vdots \\ {z}_{M,1}& {z}_{M,2}& \cdots & {z}_{M,j}& \cdots & 1\end{array}& )& \begin{array}{c}{C}_{1}\\ {C}_{2}\\ \vdots \\ {C}_{i}\\ \vdots \\ {C}_{N}\end{array}\end{array}$$$$\mathrm{where},{z}_{i,j}=\frac{{x}_{i,j}}{{\sum}_{i=1}^{N}{x}_{i,j}}.$$
 The contributions of each normalized metric are multiplied by the assigned importance weight wj, and can be calculated for the ith criteria, as below:$${P}_{w}=\frac{{\sum}_{i=1}^{N}{Z}_{i,j}}{N}\mathrm{with}\sum _{i=1}^{N}{P}_{w}=1,$$
 Calculate the consistency index, where ${\lambda}_{m}ax$ is the largest eigenvalue of ${P}_{Norm}$, and it is determined from the eigenvalue computation of ${P}_{Norm}$:$$CI=\frac{{\lambda}_{max}N}{N1}.$$
 In the last step, evaluate the consistency of the comparison using the Consistency Ratio (CR), defined as:$$CR=\frac{CI}{RI},$$In such a way, AHP helps with network selection among different networks based upon different attributes. After the selection of the public transport network, the next section will elaborate further about allocating data onto buses as per their staytime at each bus stop.
3.2. Capacitated Vehicle Routing Problem (CVRP)
 Problem DefinitionTo offload data onto buses, there is n number of demands being fulfilled by a DC, and a nearby stop is a depot to start the bus journey and return to the same bus stop after finishing its route. B is the set of buses, CB is the capacity of the bus, D is the deadline for the message delivery, which also considers the number of trips being taken by a bus. Each DC has different demands di for different locations. We define our problem in a graph $G(V,E)$, where $V=0,1,2\dots n$ is a set of all nodes of the graph and E is the set of edges $(i,j)\dots (I,j)\u03f5N$. Arc $(i,j)$ represents the path from node i to node j. The energy cost $\left({E}_{i,j}\right)$ is calculated for each bus to carry data from the source until the destination. The minimum number of buses required to fulfill all the demands is $\frac{{\sum}_{i=1}^{n}{d}_{i}}{{C}_{B}}$. The controller will assign demands onto each bus as per the destination location. A CVRP can be formulated as follows:Objective: To minimize$$\sum _{b\in B}\sum _{i=1}^{n}\sum _{j=1}^{n}{E}_{i,j}{X}_{i,j,b},$$Subjected to:$$\sum _{i=1,i\ne j}^{n}\sum _{b\u03f5B}{X}_{b,i,j}=1\phantom{\rule{2.em}{0ex}}\forall j=1,.....n$$$$\sum _{j=1}^{n}{X}_{b,0,j}=1\phantom{\rule{2.em}{0ex}}\forall \phantom{\rule{4pt}{0ex}}b\u03f5({B}_{1},{B}_{2},\dots ..{B}_{n})$$$$\sum _{i=1,i\ne j}^{n}{X}_{b,i,j}=\sum _{i=1}^{n}{X}_{b,i,j}\phantom{\rule{2.em}{0ex}}\forall j=1,.....n,\phantom{\rule{1.em}{0ex}}b\u03f5({B}_{1},{B}_{2},\dots ..{B}_{n})$$$$\sum _{i=1}^{n}\sum _{j=1,i\ne j}^{n}{d}_{j}{X}_{b,i,j}\le {C}_{B}\phantom{\rule{2.em}{0ex}}\forall b\u03f5({B}_{1},{B}_{2},\dots ..{B}_{n})$$$$\sum _{b={B}_{1}}^{{B}_{n}}\sum _{i\u03f5T}\sum _{j\u03f5T,i\ne j}{X}_{b,i,j}\le \leftT\right1\phantom{\rule{2.em}{0ex}}\forall T\subseteq (1,.....n)$$$${X}_{b,i,j}\u03f5\left(0,1\right)\phantom{\rule{2.em}{0ex}}\forall b\u03f5({B}_{1},{B}_{2},\dots ..{B}_{n});i,j=\left(1,.....n\right)$$
4. Numerical Analysis and Results
4.1. Case Study I
4.1.1. Service 1
4.1.2. Service 2
4.1.3. Service 3
4.2. Network Selection for Different Services
Algorithm 1 Optimal Network Selection  
Input  : Different services as per user’s profile: energy efficient ${e}_{u}$, delivery probability $d{p}_{u}$, delay demand ${d}_{u}$, available bandwidth ${b}_{i}$ of both networks, available network list ${I}_{an}$. 
Output  : Decision factor weight and rank of selected newtork, energy efficient weight ${w}^{e}$, bandwidth weight ${w}^{b}$, delivery probability weight ${w}^{dp}$, delay weight ${w}^{d}$. 

4.3. Case Study II
5. Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Preferences as per Importance  Definition 

1  Equal Importance 
3  Moderate importance 
5  Strong importance 
7  Very strong importance 
9  Extreme importance 
2, 4, …, 8  Intermediate values 
N  1  2  3  4  5  6  7  8  9  10 
RI  0  0  0.58  0.9  1.12  1.24  1.32  1.41  1.45  1.49 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability 

Energy Efficiency  1  7  9  3 
Bandwidth  1/3  1  7  2 
Delay Tolerance  1/9  1/7  1  1/5 
Delivery probability  1/3  1/2  5  1 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Critera Weight 

Energy Efficiency  1  7  9  3  0.530345069 
Bandwidth  1/3  1  7  2  0.164911216 
Delay Tolerance  1/9  1/7  1  1/5  0.041457905 
Delivery probability  1/7  1/2  5  1  0.280751063 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Critera Weight  Priority Vector $\left({\mathit{P}}_{\mathit{w}}\right)$ 

Energy Efficiency  1  3  9  7  0.530345069  0.5289 
Bandwidth  1/3  1  7  2  0.164911216  0.1582 
Delay Tolerance  1/9  1/7  1  1/5  0.041457905  0.0366 
Delivery probability  1/7  1/2  5  1  0.280751063  0.2763 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability 

Energy Efficiency  1  7  1  5 
Bandwidth  1/7  1  1/7  2 
Delay Tolerance  1  7  1  7 
Delivery probability  1/5  1/2  1/7  1 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Criteria Weight 

Energy Efficiency  1  7  1  5  0.42274576 
Bandwidth  1/7  1  1/7  2  0.08567345 
Delay Tolerance  1  7  1  7  0.45678945 
Delivery probability  1/5  1/2  1/7  1  0.06435676 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Critera Weight  Priority Vector $\left({\mathit{P}}_{\mathit{w}}\right)$ 

Energy Efficiency  1  7  1  5  0.42274576  0.4163 
Bandwidth  1/7  1  1/7  2  0.08567345  0.0782 
Delay Tolerance  1  7  1  7  0.45678945  0.4455 
Delivery probability  1/5  1/2  1/7  1  0.06435676  0.0599 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability 

Energy Efficiency  1  1/6  1/6  1/7 
Bandwidth  6  1  3  1 
Delay Tolerance  6  1/3  1  1/5 
Delivery probability  7  1  5  1 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Criteria Weight 

Energy Efficiency  1  1/6  1/6  1/7  0.05355183 
Bandwidth  6  1  3  1  0.36439882 
Delay Tolerance  6  1/3  1  1/5  0.15369319 
Delivery probability  7  1  5  1  0.4540202 
Attributes  Energy Efficiency  Bandwidth  Delay Tolerance  Delivery Probability  Criteria Weight  Priority Vector $\left({\mathit{P}}_{\mathit{w}}\right)$ 

Energy Efficiency  1  1/6  1/6  1/7  0.05355183  0.0459 
Bandwidth  6  1  3  1  0.36439882  0.3613 
Delay Tolerance  6  1/3  1  1/5  0.15369319  0.1499 
Delivery probability  7  1  5  1  0.45402002  0.4429 
Number of Buses per Day  Demands from Destination Stop (TB)  Distance from Depot (0) (Km)  Bus Capacity (TB) 

1  10  5.48  150 
2  10  7.76  150 
3  20  6.95  150 
4  40  5.82  150 
5  20  2.74  150 
6  40  5.02  150 
7  80  1.94  150 
8  80  3.08  150 
9  10  1.94  150 
10  20  5.36  150 
11  10  5.02  150 
12  20  3.88  150 
13  40  3.54  150 
14  40  4.68  150 
15  80  7.76  150 
16  80  6.62  150 
Bus Number  Selected Route  Total Distance Covered During the Trip 

B1  034170  12 km 
B2  058620  13 km 
B3  0131511120  12 km 
B4  091416100  13 km 
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Munjal, R.; Liu, W.; Li, X.; Gutierrez, J.; Chong, P.H.J. MultiAttribute Decision Making for EnergyEfficient Public Transport Network Selection in Smart Cities. Future Internet 2022, 14, 42. https://doi.org/10.3390/fi14020042
Munjal R, Liu W, Li X, Gutierrez J, Chong PHJ. MultiAttribute Decision Making for EnergyEfficient Public Transport Network Selection in Smart Cities. Future Internet. 2022; 14(2):42. https://doi.org/10.3390/fi14020042
Chicago/Turabian StyleMunjal, Rashmi, William Liu, Xuejun Li, Jairo Gutierrez, and Peter Han Joo Chong. 2022. "MultiAttribute Decision Making for EnergyEfficient Public Transport Network Selection in Smart Cities" Future Internet 14, no. 2: 42. https://doi.org/10.3390/fi14020042