Research On Energy Optimization And Data Communication In Wireless Networks

The appearance and development of wireless sensor networks and WiFi networksprovide significant convenience to human daily life, especially providing a series ofhuman-centric applications, such as assisted living, fall detection and emergencyresponse. In these applications, the energy consumption of sensor nodes and the datatransmission are the bottlenecks of the development of wireless networks. Therefore, inthis study, we investigate the issues of energy optimization of sensor nodes andunderlying behavior of packet deliveries in wireless networks as well as Hash algorithmfor rateless code technology in wireless communication.The main contributions of this study can be summarized as:①We present an optimal packet size solution that optimizes the communicationenergy consumption in the heterogeneous wireless networks. We first introduce theBSN-WiFi network system in detail, then analyze the the communication energyconsumption of BSN and WiFi networks, respectively, and build a communicationenergy consumption optimization model with the constraints of throughput and timedelay. Furthermore, we convert this model into a Geometric Programming (GP)problem, which is then numerically solved by a software cvx. Next, we tabulate theoptimal solutions as an optimal packet size lookup table, which is then installed on theaggregator, so that the aggregator can automatically select optimal packet size from thetable according to current Packet Delivery Rate (PDR). Finally, we set up a BSN-WiFinetwork platform to evaluate the energy consumption model. The results show that theoptimal solution can save energy up to70%comparing to solutions using fixed packetsizes.②We present a data rate adaptation solution to optimize the communicationenergy consumption in BSN-WiFi networks. We first abstract the BSN-WiFi networksystem as four consecutive phases, analyze the communication energy consumption,throughput and time delay, and SNR-PDR mappings in both BSN and WiFi networks,respectively, and build an energy consumption optimization model of BSN-WiFinetworks with constraints of SNR-PDR mappings, throughput and time delay. With theinput of SNR, we solve this model by cvx and tabulate the optimal solutions as anoptimal data rate lookup table for online selection. Finally, we collect about20-minutedata from a specified BSN-WiFi network system for performance evaluation. The results show that the data rate adaptation solution can achieve energy savings up to86%comparing with the solutions that use fixed data rates.③We propose a novel discrete-time Markov model to simulate the burstinessbehavior of wireless links, which will give researchers insights into underlying behaviorof packet delivery. More specifically, we first present a discrete-time Markov modelwith the input of β value and the output of a sequence trace of burstiness traffic. Thenwe design an algorithm to simulate the Markov model, where the state transitionrepresents the packet receptions or losses. Finally, we evaluate the model with100-runsimulations of1,000,000packet transmissions for each input of β, and the resultsdemonstrate that our proposed model is able to accurately simulate the burstinessbehavior, with standard deviations between simulated β and β of less than0.0187.④We present two key algorithms, which can be applied to rateless codes inwireless networks: lookup table of functions based Hash algorithm and cellular neuralnetwork based Hash algorithm. First, we provide a novel Hash algorithm based on adynamic lookup table of functions: we convert the blocked message into thecorresponding ASCII code values, check the equivalent index in the lookup table offunctions, and find the corresponding composite functions. For each message block, thefour buffers are reassigned by the corresponding composite function and then thelookup table of functions is dynamically updated. After all the message blocks areprocessed, the final128-bit Hash value is obtained by cascading the last reassigned fourbuffers. Then, we present a Hash algorithm based on a four-dimensional cellular neuralnetwork. We divide the arbitrary length of message with padding into512-bit blocks,then partition each of them into128-bit sub-block, and finally separate each of theminto four32-bit values, where the four values are mixed into four new values generatedby the chaotic cat map. The obtained four new values are performed by the bit-wiseexclusive OR operation with four initial values or previously generated four values, andthen, they are used as the inputs of the cellular neural network. By iterating the cellularneural network, another four values as the middle Hash value are generated. Thegenerated values of all blocks are inputted into the compression function to produce thefinal128-bit Hash value.