EA-MAC protocol is proposed on the basis of SMAC protocol to remedy the shortcomings of SMAC. In the EA-MAC, node correlation analysis algorithm and traffic adaptive duty cycle mechanism are added. Meanwhile, the residual energy is introduced into the existing traffic adaptive back-off 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 back-off mechanisms, by adjusting the value of contention window and back-off 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 back-off time, which can keep the balance of the whole network energy consumption and lengthen network lifetime. Simulation results show that the EA-MAC protocol has better energy saving, throughput, shorter delay performance, and low packet loss rate than that of SMAC protocol in dynamic traffic sensor networks.

Due to the battery limitation of wireless sensor network (WSN), there is imperative requirement of energy saving and optimization in practical application of WSN. In order to optimize the energy of WSN, in this paper, a stochastic energy model for WSN is proposed. The model is based on the continuous time Markovian decision processes (CMDP) machine and spans both energy consumption and ensued transition costs. We use PRISM to analyze stochastic CMDP strategies of sensor devices as the formal framework. Simulation experiment is carried out to evaluate the model with given performance criteria. The result shows that the given model can effectively balance energy optimization and perform with high efficiency in WSN.

Nodes in original S-MAC protocol just can visit the channel in the scheduling and listening stage. The working schema may result in data latency and high conflict. To solve those above problems, we split scheduling duty into multiple micro-duties. By using different micro-dispersed contention channel, the sensor nodes reduce the collision probability of the data. Aiming at detecting the fixed duty cycle in S-MAC protocol, on the basis of the micro-duty and buffer queue length, this paper presents an adaptive duty cycle and back-off algorithm. While using different back-off algorithm with different duty cycles, sensor node Fast-Binary Exponential Backoff and Conflict-Avoid-Binary exponential Backoff algorithm separated are applied to reduce data latency further reduce the conflict probability. Combining both of the improvements, we propose a modified S-MAC protocol. Comparing the performance of S-MAC protocol and Division-Multiple Access-Media Access Control (MDA-SMAC) protocol on the NS-2 simulation platform, the results show that MDA-SMAC protocol performs better in terms of energy consumption, latency, and effective throughput than S-MAC protocol