In this paper, we investigated the available energy-efficient MAC protocols for wireless sensor networks and provide a fair comparison based on certain metrics. This paper presents the FTA-MAC (Fast Traffic Adaptive MAC) protocol, a novel energy-efficient MAC protocol based on asynchronous duty cycling for Wireless. Energy efficient MAC protocols for Wireless Sensor Networks 3 chapter in section 5 with a summary and a discussion of possible future research directions.

• Energy-efficient Tdma Mac Protocol For Wireless Sensor Networks Applications
• Most Energy Efficient Tv

Abstract We introduce the Selective-Awakening MAC (SA-MAC) protocol which is a synchronized duty-cycled protocol with pipelined scheduling for Linear Sensor Networks (LSNs). In the proposed protocol, nodes selectively awake depending on node density and traffic load conditions and on the state of the buffers of the receiving nodes. In order to characterize the performance of the proposed protocol, we present a Discrete-Time Markov Chain-based analysis that is validated through extensive discrete-event simulations. Our results show that SA-MAC significantly outperforms previous proposals in terms of energy consumption, throughput, and packet loss probability. This is particularly true under high node density and high traffic load conditions, which are expected to be common scenarios in the context of IoT applications. We also present an analysis by grade (i.e., the number of hops to the sink, which is located at one end of the LSN) that reveals that LSNs exhibit heterogeneous performance depending on the nodes’ grade. Such results can be used as a design guideline for future LSN implementations.

Game Theory provides a mathematical tool for the analysis of interactions between the agents with conflicting interests, hence it is well suitable tool to model some problems in communication systems, especially, to wireless sensor networks (WSNs) where the prime goal is to minimize energy consumption than high throughput and low delay. In this paper, we use the concept of incomplete cooperative game theory to model an energy efficient MAC protocol for WSNs. This allows us to introduce improved backoff algorithm for energy efficient MAC protocol in WSNs. Finally, our research results show that the improved back off algorithm can improve the overall performance as well as achieve all the goals simultaneously for MAC protocol in WSNs. Communication in wireless sensor networks is divided into several layers. Medium Access Control (MAC) is one of those layers, which enables the successful operation of the network.

MAC protocol tries to avoid collisions by not allowing two interfering nodes to transmit at the same time. The main design goal of a typical MAC protocols is to provide high throughput and QoS. On the other hand, wireless sensor MAC protocol gives higher priority to minimize energy consumption than QoS requirements.

Energy gets wasted in traditional MAC layer protocols due to idle listening, collision, protocol overhead, and overhearing ,. There are some MAC protocols that have been especially developed for wireless sensor networks. Typical examples include S-MAC, T-MAC, and H-MAC –. To maximize the battery lifetime, sensor networks MAC protocols implement the variation of active/sleep mechanism. S-MAC and T-MAC protocols trades networks QoS for energy savings, while H-MAC protocol reduces the comparable amount of energy consumption along with maintaining good network QoS. However, their backoff algorithm is similar to that of the IEEE 802.11 Distributed Coordinated Function (DCF), which is based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) Mechanism. The energy consumption using CSMA/CA is high when nodes are in backoff procedure and in idle mode.

Moreover, a node that successfully transmits resets it Contention Window (CW) to a small, fixed minimum value of CW. Therefore, the node has to rediscover the correct CW, wasting channel capacity, and increase the access delay as well. So, during the CSMA/CA mechanism, backoff window size and the number of active nodes are the major factors to have impact on the network performance and over all energy efficiency of MAC protocol. Hence, it is necessary to estimate the number of nodes in network to optimize the CSMA/CA operation. Furthermore, optimizing CSMA/CA operation is more challenging task for self-organizing and distributed networks as there are no central nodes to assign channel access in sensor nodes. Download free machacha for mac.

In sensor networks, each node has a direct influence on its neighboring nodes while accessing the channel. So, these interactions between nodes and aforementioned observations lead us to use the concepts of game theory that could improve the energy efficiency as well as the delay performance of MAC protocol. More on this will be discussed in section two of this paper. Recently lots of researchers have started using game theory as a tool to analyze the wireless networks. Their game theoretic approaches were proposed to the wide area of wireless communication right from the security issues to power control, and so forth–. To model WSNs problems into full information game theoretic problems is an extremely difficult task due to distributed nature of WSNs. In addition, full information sharing also results into additional energy and bandwidth consumption.

So, we use the concept of incomplete cooperative game theory to solve the aforementioned challenges. In this paper, we present the basic idea of adjusting nodes' equilibrium strategy based on estimation of network conditions without full information. More details on this will be discussed in later part of this paper. To the best of our knowledge, there is very little work on the incomplete cooperative game theory in wireless networks.

In , , authors used the concept of incomplete cooperative game theory in wireless networks for first time and proposed the G-MAC protocol for the same. However, their proposed scheme is not suitable for all traffic conditions, especially, nonsaturation traffic condition which is most likely in sensor networks. In authors presented a virtual CSMA/CA mechanism to handle the nonsaturation traffic condition which is too heavy and complex for the sensor networks.

We also work on similar baseline and present our suboptimal solution for an energy efficient MAC protocol in wireless sensor networks. In short, the main contributions of this paper are as follows. Game Theory is a collection of mathematical tools to study the interactive decision problems between the rational players (In rest of the paper, we keep using terms 'node' and 'player' interchangeably) (Here, it is sensor nodes). Furthermore, it also helps to predict the possible outcome of the interactive decision problem.

The most possible outcome for any decision process is 'Nash Equilibrium.' A Nash equilibrium is an outcome of a game where no node (player) has any extra benefit for just changing its strategy one-sidedly ,. From last few years, game theory has gained a notable amount of popularity in solving communication and networking issues. These issues include congestion control, routing, power control, and other issues in wired and wireless communications systems, to name a few. A game is set of three fundamental components: A set of players, a set of actions, and a set of preferences.

Players or nodes are the decision takers in the game. The actions (strategies) are the different choices available to nodes. In a wireless system, action may include the available options like coding scheme, power control, transmitting, listening, and so forth, factors that are under the control of the node. When each player selects its own strategy, the resulting strategy profile decides the outcome of the game. Finally, a utility function (preferences) decides the all possible outcomes for each player.

Table shows typical components of a wireless networking game. Games can be classified formally at many level of detail, here we ingeneral tried to classify the games for better understanding. As shown in Figure, strategic games are broadly classified as cooperative and noncooperative games. In noncooperative games the player cannot make commitments to coordinate their strategies. A noncooperative game investigates answer for selecting an optimum strategy to player to face his/her opponent who also has a strategy of his/her own. Conversely, a co-operative game is a game where groups of player may enforce to work together to maximize their returns (payoffs). Hence, a co-operative game is a competition between coalitions of players, rather then between individual players.

Furthermore, according to the players' moves, simultaneously or one by one, games can be further divided into two categories: static and dynamic games. In static game, players move their strategy simultaneously without any knowledge of what other players are going to play.

In the dynamic game, players move their strategy in predetermined order and they also know what other players have played before them. So, according to the knowledge of players on all aspect of game, the noncooperative/co-operative game further classified into two categories: complete and incomplete information games. In the complete information game, each player has all the knowledge about others' characteristics, strategy spaces, payoff functions, and so forth, but all these information are not necessarily available in incomplete information game. Figure 1 Classification of games. Incomplete Cooperative Game As we mentioned earlier, energy efficiency of MAC protocol in WSN is very sensitive to number of nodes competing for the access channel.

It will be very difficult for a MAC protocol to accurately estimate the different parameters like collision probability, transmission probability, and so forth, by detecting channel. Because dynamics of WSN keep on changing due to various reasons like mobility of nodes, joining of some new nodes, and dying out of some exhausted nodes. Also, estimating about the other neighboring nodes information is too complex, as every node takes a distributed approach to estimate the current state of networks. For all these reasons, an incomplete cooperative game could be a perfect candidate to optimize the performance of MAC protocol in sensor networks. In this paper, we considered a MAC protocol with active/sleep duty cycle (we can easily relate the 'Considered MAC Protocol' with available MAC protocols and standards for wireless sensor networks, as most of the popular MAC protocols are based on the active/sleep cycle mechanism) to minimize the energy consumption of a node. In this MAC protocol, time is divided into super-frames, and every super frame into two basic parts: active part and sleep part, as shown in Figure. During the active part, a node tries to contend the channel if there is any data in buffer and turn down its radio during the sleeping part to save energy.

Figure 2 Active/sleep mechanism. In incomplete cooperative game, the considered MAC protocol can be modeled as stochastic game, which starts when there is a data packet in the node's transmission buffer and ends when the data packet is transmitted successfully or discarded. This game consists of many time slots and each time slot represents a game slot.

As every node can try to transmit an unsuccessful data packet for some predetermined limit (maximum retry limit), the game is finitely repeated rather than an infinitely repeated one. In each time slot, when the node is in active part, the node just not only tries to contend for the medium but also estimates the current game state based on history. After estimating the game state, the node adjust its own equilibrium condition by adjusting its available parameters under the given strategies (here it is contention parameters like transmitting probability, collision probability, etc.). Then all the nodes act simultaneously with their best evaluated strategies.

In this game, we considered mainly three strategies available to nodes: transmitting, listening, and sleeping. And contention window size as the parameter to adjust its equilibrium strategy. In this stochastic game, our main goal is to find an optimal equilibrium to maximize the network performance with minimum energy consumption. In general, with control theory we could achieve the best performance for an individual node rather than a whole network, and for this reason our game theoretic approach to the problem is justified. (3) Here, we define and as the transmission probability of the player 1 and player 2, respectively.

Similarly, and represents the sleeping probability of player 1 and player 2 while is the conditional collision probability of player 2. As shown in Table, there are three strategies for both the players. First, player 1 transmits a packet with a probability, whose payoff is. Second strategy of player 1 is listening with a probability, whose payoff is Third strategy of player 1 is sleeping with a probability, whose payoff is. Finally, when both the players transmits simultaneously, their payoff are, and, respectively.

Similarly, we can also calculate the probabilities of different strategies for player 2. From the strategy table and (3) we can see that every node has to play its strategies with some probabilities as here the optimum equilibrium is in mixed strategy form. In mixed strategy equilibrium, it is not possible to reach an optimum solution with one strategy so players have to mix two or more strategies probabilistically. In this paper, players have three strategies: transmitting, listening, and sleeping and probabilities for selecting these strategies represent as and, respectively.

Energy-efficient Tdma Mac Protocol For Wireless Sensor Networks Applications

Their relationship is given. (4) In addition, we can observe from the above equations that players can achieve their optimal response by helping each other to achieve their optimal utility. So the nodes have to play a cooperative game under the given constrained of energy.

Here, the players can obtain the mixed strategy-based optimum response by adjusting their transmission probabilities to the variable game states. The value of the transmitting probability can be adjusted by tuning contention parameters, such as the minimum contention window ), the maximum contention window ( ), retry limit ( ), the maximum backoff stage ( ), arbitrary interface spaces (AIFS), and so forth. For simplicity, we choose contention window (i.e., properly estimating the number of competing nodes) as tuning parameter for adjusting transmission probability of a node. Estimation of Competing Nodes. In the proposed game, every node estimates the game state by anticipating the number of competing nodes from various parameters, especially, from transmitting probability. Many researchers have presented several performance and analysis models to calculate. However, majority of the work has neglected the contention counter freezing effect and considered only saturated traffic condition which is mostly suitable for WLAN and adhoc networks than sensor networks.

Most Energy Efficient Tv

Arguably, nonsaturation traffic condition is most likely traffic pattern in WSNs and need to be considered for a WSN MAC protocol designing as well. From and other previous analysis results, we can show that the number ( N) of competing nodes is the function of frame collision probability ( ) of a competing node. Also, the probability is constant and independent at each transmission attempt. A node transmits a data packet with the probability in a randomly chosen slot can be expressed as function of as in.