Journal of Computer Networks and Communications

Volume 2019, Article ID 3217369, 16 pages

https://doi.org/10.1155/2019/3217369

## Energy-Efficient Coalition Games with Incentives in Machine-to-Machine Communications

Department of Electrical and Electronic Engineering, Tshwane University of Technology, Pretoria, South Africa

Correspondence should be addressed to Raymond W. Juma; moc.liamg@asekewjr

Received 14 February 2019; Revised 13 May 2019; Accepted 23 May 2019; Published 16 June 2019

Guest Editor: Huan Zhou

Copyright © 2019 Raymond W. Juma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The need to achieve energy efficiency in machine-to-machine (M2M) communications has been a driver of the use of coalition game-based cooperative communication schemes. The proposed schemes have shown good energy-efficient performance results in the recent past. However, sustaining cooperation amongst coalition games of M2M devices from different network-operating authorities requires appropriate incentives. A review of the literature demonstrates that a limited number of contributions have considered the use of coalition games with incentives in M2M communications. In this paper, an energy-efficient coalition game with incentives in M2M communications is proposed. This work considers a Sierpinski triangle technique to partition M2M devices into multiple networks of hierarchical zones. Based on the constructed zones, a contract-modelled incentive is invoked to stimulate multihop transmissions between devices up to the BS/sink. The results obtained demonstrate that the proposed approach is on average 10% more energy efficient than the closely related existing algorithm, the coalition game theoretical clustering (CGTC).

#### 1. Introduction

The introduction of M2M communications has catapulted the automation of various tasks in real time. The applications of M2M have been embraced today in the sectors of transport, military, healthcare, and smart cities. However, despite the growing interest of M2M technologies, one of the greatest challenges faced by M2M devices is the limited battery lifetime of operations. There is thus a need to prolong the operation of M2M devices while ensuring a stable quality of service/quality of experience (QoS/QoE). This can be achieved through cooperation among the devices in the network. Zhou et al. [1] utilise the data offloading technique through vehicular ad hoc networks (VANETs). Huang et al. [2] describe the use of credit-based clustering (CBC) scheme to encourage sharing among devices in the same social network. The application of cooperative schemes in energy-efficient management has been proposed by Raymond et al. [3] and Olwal et al. [4]. Cooperative schemes that invoke game theory are examined for energy efficiency in WSNs [5]. Over years, the application of game theory has had a considerable impact on a sizeable number of disciplines that include engineering, economics, political science, philosophy, and even psychology [6].

In recent years, game theory has been applied in the analysis of communication networks. In an effort to minimise energy consumption in a network, an efficient-energy consumption protocol (EECP) utilises fixed clusters and applies the random weight technique in selection of cluster heads (CHs) [7]. The concept of game theory is applied during multihop data transmission to the sink. A cooperative game algorithm for routing purposes which considers rewarding cooperative devices and punishing noncooperative devices is proposed for energy management in WSNs [8].

A coalition game takes into consideration the benefits of all the players in the network; players adopt strategies that strengthen the utility of another player. It may be considered an appropriate approach when considering its application in the implementation of fair and well-organised cooperative strategies in communication networks [9]. Through games of coalitions, it has been argued by Bacci et al. [10] that huge computation overheads that are associated with larger networks may be avoided. AlSkaif et al. [11] argue that coalitional games are characterised by group formation that demonstrates better performance in energy efficiency when compared to noncooperative games A further benefit of coalition game theory (CGT) is demonstrated through the reduction of power consumption in a WSN that is achieved as a result of the formation of coalition structures [12]. Under these structures, players choose strategies to maximise their own utility as a group. Such group-based strategies enable individuals to consume less energy and operate for a longer period than when each player could have strategized independently. Earlier studies had explained that a selfish node or device in a communication network caused network performance degradation [13, 14]. Due to the distance effect, the devices that were located farther from each other would consume more energy before reaching the processing device/sink.

The need to achieve energy efficiency in M2M communications has been attained through the use of coalition game-based cooperative communication schemes [5, 7, 15]. However, sustaining cooperation amongst coalition games of M2M devices from different network-operating authorities or regions is a great challenge. This is due to the selfish behaviour of some of the devices or operating authorities that prefer to conserve their energy instead of consuming it to assist other devices to reach the sink/BS. To mitigate this challenge, the study proposes a coalition game theoretical clustering with incentive (CGTCI) algorithm. The novel approach proposed considers a Sierpinski triangle technique to partition distributed M2M devices into multiple networks of hierarchical zones. Based on the constructed zones, coalition structures supervised by the devices elected as cluster heads are developed in the zones located far from the BS. Devices closer to the BS are not organised into coalition structures. A contract model-based incentive is invoked to stimulate multihop transmissions up to the BS/sink. The main contributions of this work can be summarised as follows:(i)A novel algorithm, coalition game theoretical clustering with incentive (CGTCI), is proposed to minimise energy consumption in M2M communications.(ii)The proposed algorithm CGTCI is designed which starts with the partitioning of M2M devices into multiple networks of hierarchical zones, formation of coalition structures in the created hierarchical multiple zones, and invoking of a contract-modelled incentive to stimulate multihop transmissions up to the BS/sink.(iii)It is demonstrated through simulations that the proposed algorithm, CGTCI, improves energy efficiency among the M2M communications when compared with the closely related traditional approaches: CGTC [15], CG-DC (an improvement of low-energy adaptive clustering hierarchy (LEACH)) [16], Raymond et al. [3], and noncoalition game (NCG) algorithms.

The remainder of this paper is organised as follows: Section 2 presents related works on coalition games. Section 3 introduces the fundamentals of the proposed algorithm. In Section 4, the system model is described. Section 5 describes the proposed solution. In Section 6, the performance and simulation results are evaluated. Section 7 finally highlights conclusions and future work.

#### 2. Related Works

The discussions of coalition games have been extensively presented in the existing literature. This section leverages such contributions made previously to review the most related and credible studies regarding energy efficiency in communication networks. The hybrid game theory and a distributed cluster technique are applied in WSNs to control energy consumption by Yang et al. [17]. Each node has a payoff that is designed based on various parameters that include node degree and distance to the base station. Based on this approach, each node computes its equilibrium probability by applying the parameters of the game. The application of equilibrium probability by the node enables it to decide its suitability of being a cluster head (CH). The node attains a good trade-off between minimizing energy dissipation and providing the required services effectively.

Yue et al. [18] propose a coalition game model which has been derived by integrating both the Markov process and theoretical approach for energy-efficient WSNs. The performance tests of the proposed model show that the lifetime and effective reachability for low-density WSNs are increased. However, the model was not evaluated for a dense network to ascertain its effectiveness of energy efficiency.

Wu et al. [19] examine data transfer strategies that are specified in relation to the proportion of the data sent by a node and that of the data forwarded by a node for energy-efficient WSNs. The formation of coalitions is based on a Markov process. The concept of determining the absorption coefficient to measure the coalitional profiles is introduced. Nash equilibrium (NE) is used to determine the formed coalitions’ approximate data transfer strategies. However, finding the exact NE in this proposal is a nondeterministic polynomial (NP) time complex problem. It is advised that a low computationally complex alternative strategy be considered.

Jing and Aida [20] present a cooperative game theoretical model for clustering algorithms in which nodes balance the energy consumption and maximise their network lifetime. The selfish behaviour of nodes in noncooperative games expedites network partition and results in an unfair residual energy distribution within the network. The work by Jing and Aida [20] considers an algorithm in which there is a trade-off in the individual cost and the network-wide cost of sensor nodes in forming a coalition. In this respect, a candidate cluster head (CCH) cooperates with a node considered to be close and almost equivalent in terms of energy (node with redundant energy), and the whose transmission covers a long distance. The Shapley value that has anonymity, dummy property, and additivity property is introduced to assign a single cost allocation to the cost-sharing game. The Shapley value through random ordering of nodes provides a relatively anonymous solution, where agents’ change of names does not alter their cost shares. However, the initial candidate selection in this approach is partly random and does not guarantee the choice of the most suitable candidate as a CH. In this approach, the direct communication between the selected device and the BS contradicts the scalability benefits achieved with the coalition game clustering.

Afsar [15] shows how a coalitional game theoretical clustering (CGTC) algorithm can be used to control energy consumption in WSNs through the adoption of multihop communications. The coalitions are demarcated into two major groups based on the location (i.e., either far or in the vicinity) of sensor nodes in relation to the BS. A set of nodes with the highest residual energy in the far region are referred to as coalition head nominees (CHNs) which initiate cooperative games within their surroundings. The CHNs along with two other nodes then shape final coalitions. The vicinity region on the contrary considers that small coalitions are formed to tackle the energy-consuming data-relaying task. In the CGTC approach, local parameters such as residual energy, number of neighbours, and proximity to the BS are important in forming the coalitions since the main objective is energy efficiency (EE). The Shapley value is applied to distribute the average of marginal contributions to coalitions generated. However, the random candidate election could easily result in the acceleration to a dead state in cases where a candidate of low residual energy is picked as a cluster head.

Miao and Xu [21] discuss a power control solution that is based on the trade-off between energy efficiency and end-to-end delay that is applied as a technique to improve energy consumption in WSNs. A cooperative coalitional game is proposed to obtain a power control solution that achieves a fair distribution of the total cost amongst sources. It was observed that the minimization of delay is achieved by minimizing the remaining energy level. Each source node seeks to minimise its utility function of the discounted sum of transmission power increased cost and the source-to-sink delay cost. The Shapley value is used as a solution of the cooperative allocation game for fairness dissemination of the gains achieved as evaluated by Yeung and Petrosjan [22].

Tan et al. [23] develop a bidirectional cooperative clustering model that applies a cost-sharing game for EE. The algorithm examines the cooperation of cluster members and cluster heads (CHs) for reducing energy consumption in the network. The authors highlight that an algorithm based on game theory can be applied in the CH selection for the purpose of energy efficiency [24]. The algorithm is based on the subgame perfect Nash equilibrium (SPNE) approach which is used to find the Nash equilibrium (NE) in every subgame of the real game. The selection of the CHs relies on the SPNE technique.

Romero et al. [25] examine a game theory-based strategy to reduce energy consumption in cognitive WSNs. The strategy was initially meant to provide an answer to the problem of spectrum inefficiency. The proposed technique was shown to improve energy consumption through its ability to switch communication channels. The approach makes use of a decision strategy of changing the transmission channel depending on the behaviour of the rest of the network nodes through the application of a game theoretic technique.

As presented above, most of the related works are contextualised to traditional WSN-based M2M communications. In modern times, neither the devices in a network that constitutes M2M communications are homogeneously WSNs nor do the composing nodes share the same network operator. This makes the approach for the management of cooperative communications in M2M networks to be different from how WSNs are managed. The current sequel proposes a new solution that is a modification of the coalition game theoretical clustering (CGTC) proposed by Afsar [15]. The proposed approach considers the application of coalition game theoretical clustering that invokes an incentive scheme (CGTCI). The proposed approach aims at stimulating cooperation amongst the M2M devices that are not necessarily from the same operator. The incentive paves the way for transmissions at short distances in the hierarchical partitioned network which has an overall effect of reduced energy consumption in the network.

#### 3. Fundamentals of the Proposed Algorithm

This section presents the basics of the coalition game and radio model as fundamentals of the proposed algorithm.

##### 3.1. Basics of Coalition Game

A coalitional game can be an ordered pair, in which is a finite set of players referred to as the grand coalition and the characteristic function is described as . In the coalition formation game, coalition structures which are partitions of the grand coalition are constructed and defined as . In our future discussions, the following properties of coalition games will be considered.

###### 3.1.1. Individual Rationality

A player will be a member of a coalition, only when the gains achieved by being in the coalition are more than those when acting selfishly or being a free rider. This can be expressed as , where is the utility factor of a player in a coalition and is the utility factor of a free rider.

###### 3.1.2. Group Rationality

The sum of the payoffs of a coalition should be at least the value of the coalition. This is represented as and . Here, represents all the coalitions, is the grand coalition, is the value of the coalition, and is the payoff to the coalition.

##### 3.2. Radio Model

The computation of the energy consumed during data transmission from sources to the sink considers the radio model that is illustrated in Figure 1 [26]. The energy for data transmission is proportional to distance and the amount of data bits ().