An adaptive traffic MAC protocol based on correlation of nodes
 Donghong Xu^{1, 2} and
 Ke Wang^{1}Email author
https://doi.org/10.1186/s136380150488x
© Xu and Wang. 2015
Received: 21 September 2015
Accepted: 27 November 2015
Published: 9 December 2015
Abstract
EAMAC protocol is proposed on the basis of SMAC protocol to remedy the shortcomings of SMAC. In the EAMAC, node correlation analysis algorithm and traffic adaptive duty cycle mechanism are added. Meanwhile, the residual energy is introduced into the existing traffic adaptive backoff mechanism to measure the saving efficiency. In the node correlation algorithm, all network nodes are divided into several areas by computing node correlation according to the collected information. The clustering mechanism is applied for choosing representative node in each area for transmitting data. This method can effectively reduce redundant nodes transmitting duplicate data. In traffic adaptive duty cycle mechanism, the duty cycle is regulated dynamically to decrease idle listening by comparing the threshold set with the flow value obtained from the predict flow model. In backoff mechanisms, by adjusting the value of contention window and backoff time, data collisions can be effectively avoided when network traffic is heavy. In addition, nodes with more remaining energy have priority to access the channel and have shorter backoff time, which can keep the balance of the whole network energy consumption and lengthen network lifetime. Simulation results show that the EAMAC protocol has better energy saving, throughput, shorter delay performance, and low packet loss rate than that of SMAC protocol in dynamic traffic sensor networks.
Keywords
1 Introduction

(1) Nodes are not differentially divided. In SMAC protocol, the nodes in the network use the same mechanism to work, but in reality, some information collected by nodes in the network is not reliable or redundant. In the context of reliable or redundant information, it may result in unnecessary waste of energy when letting all nodes involved in the channel contention. Therefore, it is better to selectively accept the information from partial nodes under certain conditions of network characteristics.

(2) Nodes cannot suit to the requirement of the dynamic changes of network traffic due to time for sleeping and listening being fixed for nodes through periodic dormant and listening method in SMAC. Nodes cannot make the appropriate adjustments when large changes of network traffic occur. For example, when the network load suddenly increases, the listener time will become short and will result in multiple retransmissions of data; meanwhile, it will increase the chances of conflict and delay. When network traffic is low, energy maybe wasted due to the idle listening.
The paper [6–8] proposed a new schema to balance the lifetime of the deployed sensor network by assigning weights to links based on the remaining power level of individual nodes. Meanwhile, they developed a centralized heuristic to reduce its computational complexity. The methods and conclusion of the papers are taken as a basis for this paper. Han et al. [9] surveyed data communication protocols in DSWSNS and the survey can be served as motivations and inspiration for our research. Xiao et al. [10] studied the underwater sensor networks according to the tight performance bounds of multihop fair access for MAC protocols. The papers [11–13] proposed a novel compressive data collection scheme. The measurement methods give our basis for measuring our methods. In the research of channel access, He et al. [14] proposed a semirandom backoff method [15] that enables resource reservation in contentionbased wireless LANs and the method can be readily applied to both 802.11.DCF and 802.11e EDCA networks with minimum modification.
For solving these two above discussed drawbacks, the paper proposes node correlation based on flow adaptive Efficient Adaptive Medium Access Control (EAMAC) protocol to classify nodes by computing node correlation before data transmission. The algorithm for correlation computing between nodes can divide similar nodes to a certain area and choose the representative node for data transmission in the region. Representative node can cyclically be elected upon some parameters such as node residual energy and distance to sink node. During data transfer process, according to the prediction of flow changes, the system selfadaptively changes the duty cycle and contention window of the node. Conflict probability is significantly controlled by altering the backoff range and constraining the backoff window size, effectively reducing energy consumption.
The paper is organized as follows. In Section 2, we pose the problem and give the calculating processing. Meanwhile, ARMA model for node flow forecasting is presented. In Section 3, we describe the EAMAC protocol. In Section 4, the data and result of the experiment including average energy consumption, endtoend delay, and effective throughput are given out. At last, we conclude with a summary and some directions for future work.
2 Problem descriptions
2.1 Problem statement
2.2 Calculation of correlation of nodes
In the above two formulas, H(x), H(y) separately represent the entropy of node i, j. p(x _{ i }) is the probability of events detected by the node i. p(y _{ j }) is the probability of events detected by the node j, and qis the sum of all events which are being collected.
When 0<K≤ε, the similarity of nodes is low. When ε<K≤1, then the similarity of nodes becomes high. By calculating their joint entropy and their own information entropy, correlation coefficient between nodes can be obtained. The nodes satisfying ε<K≤1 are divided into a group. All the nodes in the similarity coefficient are automatically divided into several smaller areas, called the relevant area (correlated area, CA). For each node within relevant area, cyclical formula 4 and 5 are used as merits of priority measure. The selected node within the relevant area is set as a representative node (RN) and cycle length is T. The value of T must be larger than and at least twice the value of the EAMAC protocol listener ∖sleep cycle to prevent the disruption of original data transmission caused by the switch of RN. Collected data will only be transmitted to sink node by RN.
2.3 ARMA model for node flow forecasting
Flow in the sensor network is usually dynamically changed with the practical applications and exhibits nonequilibrium with the position change of the nodes. Nodes nearby the base station bear heavy traffic. In some eventdriven applications, accident data stream is collected, while in the periodic data acquisition applications, network traffic is relatively stable. Therefore, the selection of the sensor network flow model must be consistent with its application scenarios. Literature [18–20] proposed an ARMA model for flow predictions, which applies to periodic data acquisition.
2.3.1 ARMA model for periodic data applications
Select the ARMA (2p, 2p1) model to analyze the flow of sensor network, where the positive integer pis the order number. In order to avoid too much calculation, let p=1. Specific modeling process for ARMA (2, 1) is as follows: Assuming the sliding time window is n, the data traffic sequences \(X_{0}^{\prime },X_{1}^{\prime },\cdots,X_{i}^{\prime },X_{n}^{\prime }\) during each time slot do not appear smoothly and steady, so firstly taking the logarithmic to smooth it and getting the smooth sequences X _{0},X _{1},⋯,X _{ i },X _{ n } and then creating ARMAmodel with stationary series to predict the value of n + 1 flow rate. Hence, we can know that a predicted value can be obtained for each forward sliding of the time window.
2.3.2 Parameter selection
When using ARMA traffic forecasting model to predict the flow of nodes, firstly, a traffic acquisition cycle is necessary to be set. For example, if data are collected in 1 s, the forecasting result is the next second flow. In most realtime applications, the acquisition cycle should be set to a smaller value to observe the flow changes timely. The ARMA model needs to calculate parameter values based on its historical data. During the periodic collection of data, the same data streams are generated as long as the transmission rate and the sleep/listening cycles of nodes are the same, so part of the data sequence is selected from [21–23]. In the paper, the parameters of the ARMA model are estimated as \(\hat {\phi }_{1}=0.86579, \hat {\phi }_{2}=0.07356,\hat {\theta }_{1}=0.68954\), and \(\hat {\sigma }_{a}^{2}=0.00186.\) The advantage of ARMA model is to provide accurate prediction of the actual flow of the node, and it is widely used for general periodic data collection. Traffic adaptive mechanism proposed in this paper applies flow f _{node} prediction ARMA model to adjust the duty cycle and the contention window.
3 Description of EAMAC protocol
3.1 Node classification strategy
EMSG message frame structure
Frame type  Node ID  Frame size  Node residual  Distance between 

energy  sink nodes 
3.2 Selfadaptive algorithm of duty cycle
(2) When the flow predictive value L<f _{node}<H, network traffic is moderate, the duty cycle will be restored to its original size.
3.3 Selfadaptive backoff algorithm

Selection of C W _{min} and C W _{max}
For the selection of C W _{min} and C W _{max}, node traffic information f _{node} needs to be combined to adjust the contention window. When f _{node}<L, half the value of the C W _{min} and C W _{max} is given, thus reducing the waiting, the backoff time, and the delay time. When f _{node}>H, it increases the value of C W _{min} and C W _{max}, thus reducing the backoff time for channel listening and collisions.

Selection value of node CW
According to Eq. (9), it is clear that when the value of residual energy of the node is larger, the mean value of the normal distribution will be relatively smaller. Meanwhile, the probability of backoff time CW to select a smaller value is larger. When the remaining energy is smaller, the mean value of the normal distribution will be relatively larger. Meanwhile, the probability that CW achieves greater value is larger. From Eq. (10), it is obvious that D(CW) selection is related to C W _{min}. When the flow rate is increased, C W _{min} increases together, and the variance of the normal distribution becomes large, showing a large normal unevenness. So backoff time difference selected by the sending node is relatively large with minimal conflict potential. In this backoff method, backoff time selection is related to the normal distribution function in formulas 9 and 10, allowing the node with more residual energy access channel with priority, taking more responsibility for data transmission, thus balancing network energy distribution, and extending the network life cycle. When network traffic is large, the selection range of the backoff time is increased as well as the difference, reducing the possibility of simultaneous data sending. It is not necessary for the node to inform the neighbor nodes the remaining energy, thus avoiding the overhead of broadcast communication.
4 Simulations
Parameter setting
Packet size(B)  50  SIFS(ms)  5 

Sending power(mW)  360  DIFS(ms)  10 
Sending power(mW)  360  DIFS(ms)  10 
Receiving power(mW)  360  ILPC(mW)  340 
Sleep power consumption(uW)  50  Routing protocol  AODV 
Node initial energy(J)  100  Simulation time(s)  650 
In the singlehop scene, packet interval is sent to change the variation of network traffic. With network traffic varying, the duty cycle for listening/sleep also alters. Delay and backoff time for channel listening vary with the residual energy and network traffic. The initial operation period of the network is 0–100 s. The medium phase of network operation is 100–200 s. For the period of 200–300 s, transmission interval is reduced, and the network traffic is moderate. During 300–400 s, the transmission interval is further reduced, and the network load is high. Late network operation period: 400–650 s, the transmission interval is increased, and the network is in a low flow state. Gawk [32] is used to run the simulation script files and analyze the generated trance file. It also can obtain delay, average energy consumption of the network and packet loss rate, and corresponding file. Simulation data is extracted with 10s interval and plotted below.
4.1 Average energy consumption
4.2 Endtoend delay
4.3 Effective throughput
4.4 Packet loss rate
5 Conclusions
In this paper, an improved algorithm of EAMAC protocol is given to address the deficiencies of the SMAC protocol. Firstly, an algorithm for correlation computing between nodes is proposed which divides similar nodes to a certain area and chooses the representative node for data transmission in the considered region. Representative node is cyclically elected upon some parameters such as node residual energy and distance to sink node. In the transmission, network traffic prediction is conducted by ARMA mode and the duty cycle is dynamically changed accordingly through comparing the predicted value with the presetting threshold value. Conflict probability is significantly controlled by altering the backoff range and constraining the backoff window size, effectively reducing energy consumption. Finally, NS2 is adopted to implement for simulations of EAMAC protocol and SMAC protocol. Simulation results including the network average energy consumption, latency and effective throughput, and packet loss rate are analyzed, showing that EAMAC protocol performs superior to that of SMAC protocol.
Declarations
Acknowledgements
This work was supported by “the Fundamental Research Funds for the Central Universities” (2013QNB15) and the National Science Foundation of China under grant no. 61203304. Thanks for the contributors on this paper.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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