Research On The Strategy And Algorithm For Energy Balanced Data Transmission In Wireless Sensor Networks

Information acquisition is a core function of Internet-of-Things(Io T).It plays a key part for humans understanding the physical world.Wireless Sensor Networks(WSNs),which are formed by a large number of self-organized low-cost sensor nodes,enable us to cheaply and continuously acquire information from the physical world.Whereas,due to limits of sensor nodes' hardware capability and battery capacity,WSNs generally collect data in multi-hop and endure non-uniform consumption of nodal battery,which impairs energy efficiency and network lifetime severely.It has become a major cause for keeping WSNs from large-scale applications.Many prior efforts had been made to address the energy imbalance problem in WSNs.However,they face practical issues because they(i)need special hardware support,(ii)incur tremendous computing/communication overheads,or(iii)bring about additional costs on network deployment and maintenance.To overcome the drawbacks,the Mixed Data Transmission(MDT)strategy combines transmission power control,which is provided by the radio chips of all sensor nodes,and probabilistic forwarding to realize energy balanced data collection in WSNs.It is simple,low-cost and well suited for distributed-implementation.However,theoretical studies on the MDT strategy are still missing,and most proposed schemes can only work in small-scale networks with radius no larger than the longest transmission range of sensor node,i.e.,single-hop networks.In this dissertation,we study the theoretic aspects of MDT strategy on energy balancing in large-scale multi-hop networks.The main contributions of this dissertation can be summarized as follows.1.Revealing the causes of energy imbalance problem.By performing comprehensive analysis as well as literature surveys,we reveal four major factors that are contributing to the energy imbalance problem as: many-to-one traffic pattern of WSNs,restriction of network structures,limitation of physical environments and heterogeneity of node's hardware power consumption.We develop three energy balancing paradigms out from existing strategies,and summarize them as balanced energy deploying,decentralized traffic flowing and diversified transmitting.By using a joint technique of transmission power control and probabilistic forwarding,the MDT strategy provides the effects of both decentralized traffic flowing and diversified transmitting,which helps to realize energy balancing among neighboring nodes of different distance ranges.2.Study on the energy balancing capability of MDT.To the best of our knowledge,we are the first to study energy balancing capability of MDT under multi-hop network scenarios.We propose a model for energy balancing analysis,and a novel metric to quantitatively measure MDT's energy balancing capability.Our analysis reveals that,the capability of MDT on energy balancing is limited----only when network radius is no larger than a certain threshold,will energy balance be achieved over the whole network(i.e.,network-wide energy balance).We formally derive the bounds of MDT on energy balancing and provide the mathematical expressions of such energy balance bounds.Furthermore,we find a new system factor that is expressed as transmitting energy consumption of node's power levels.signifies the power premium ratio of MDT,compared to the basic power of multi-hop transmission.It is the only parameter that would determine the energy balancing capability of MDT.3.Method for exploiting localized energy balancing for lifetime optimization.Next,we study the problem of how to optimize network lifetime via localized energy balancing in networks where network-wide energy balance cannot be achieved.To address the problem,we first propose an Energy Balance Area(EBA)model for localized energy balancing analysis,and use it to explore the relationships between energy balancing and lifetime optimization.Our results reveal that energy balancing in smaller ranges would produce better lifetime.This establishes the theoretical foundation for exploiting localized energy balancing to optimize global network lifetime.Then,we transform the lifetime optimization problem into an EBA partition problem,and design a heuristic algorithm,based on relationships between EBA size and network lifetime,to compute MDT parameters for localized energy balancing.We prove that the proposed algorithm produces close-to-optimal lifetime results.4.Distributed algorithms of MDT for tree-based WSNs.Finally,we employ MDT idea to solve the energy imbalance problem of tree-based WSNs,and propose a novel Treebased Mixed Data Transmission(TMDT)strategy.According to previous results on localized energy balancing,we formulate the TMDT lifetime maximization problem and transform it into two sub-problems----problems of parent-child and sibling load balancing in local ranges of 1-hop neighborhood.Then,to solve the two sub-problems in a distributed manner,we propose a novel load spectrum model to compute TMDT parameters for each node in the neighborhood.Moreover,we present two lightweight TMDT algorithms.Simulation results show that the proposed TMDT scheme can improve lifetime of tree by 1~5 times.As the TMDT strategy is orthogonal to tree construction,it works with any tree-based routing algorithms and,thus,can be applied in wide ranges of network scenarios.