MAXIMIZING LIFETIME IN WIRELESS SENSOR NETWORKFOR STRUCTURAL HEALTH MONITORING WITH AND WITHOUT ENERGY HARVESTING
ABSTRACT
This paper presents an optimization framework tomaximize lifetime of wireless sensor networks for structuralhealth monitoring with and without energy harvesting. Wedevelop a mathematical model and formulate the problem as alarge-scale mixed integer non-linear programming problem. Wealso propose a solution based on the Branch-and-Bound algorithmaugmented with reducing the search space. The proposedstrategy builds up the optimal route from each source to thesink node by providing the best set of hops in each route andthe optimal power allocation of each sensor node. To reducethe computational complexity, we propose a heuristic routingalgorithms. In this heuristic algorithm, the power levels areselected from the optimal predefined values and the problem isformulated by an Integer Non-Linear Programming and Branchand-Boundreduced space algorithm is used to solve the problem.Moreover, we propose two sub-optimal algorithms to reduce thecomputation complexity. In the first algorithm, after selectingthe optimal transmission power levels from a predefined values,a genetic algorithm is used to solve the Integer Non-LinearProblem. In the second sub-optimal algorithm, we solve theproblem by decoupling the optimal power allocation scheme fromoptimal route selection. Therefore, the problem is formulatedby Integer Non-Linear Programming, which is solved usingBranch-and-Bound space reduced method with reduced binaryvariables (i.e., reduced complexity) and after the optimum routeselection, the optimal power is allocated for each node. Thenumerical results reveal that the presented algorithm can prolongthe network lifetime significantly compared with the existingschemes. Moreover, we mathematically formulate the adaptiveenergy harvesting period to increase the network lifetime with thepossibility to approach infinity. Finally, the minimum harvestingperiod to have infinite lifetime is obtained.
EXISTING SYSTEM:
Traditionally, a sensor node is mainly powered by a nonrechargeablebattery, which has a limited energy storage capacity.As a result, a WSN can only function for a limitedamount of time. A lot of research efforts have been dedicatedto prolong the lifetime of a WSN by improving its energyefficiency. Joint energy efficient routing and node placementalgorithm, namely JR-SPEM, presented in, reduces energyconsumption in structural health monitong WSN to prolongthe network lifetime. Moreover, the MWCDCT algorithm, proposedin, investigated the sleep-mode scheduling problemin order to maximize the network lifetime by only turning onthe specific subset of sensor nodes for monitoring the targetspots and for exploiting the transmission of the sensed dataover multiple hops toward the base station. Alternatively, theidea of energy harvesting was proposed to address the problemof limited lifetime in a WSN by enabling the wireless sensornodes to replenish energy from ambient sources. There are anumber of studies on energy harvesting, recharging and theirimplications in WSN. The authors infocuson energy harvesting from renewable as well as traditionalenergy resources in sustainable WSNs. In this paper theavailable sources for different applications of WSNs, techniquesused for scavenging, storage methods and deploymentarchitecture are discussed. In the MC-OMLU algorithm [6],the rechargeable batteries are augmented with the solar energyharvesting panel and the authors proposed maximum lifetimeutility function which seek a balance between maximum totalremaining energy and maximum minimum remaining energyin order to maximize network lifetime.
PROPOSED SYSTEM:
In this paper, assumingthe latest definition for the network lifetime, we propose aframework to maximize network lifetime with and withoutenergy harvesting. Lifetime maximization in WSNs is a wellstudied topic; however, to the best of our knowledge, there isno analytical model which can accurately formulate optimumrouting to maximize lifetime of energy harvesting WSN forstructural health monitoring.The rechargeable battery of an energy harvesting sensornode can be modeled as an energy buffer, where the harvestedenergy can be stored according to a given battery chargingcharacteristics. Unlike a traditional wireless sensor network(WSN) powered by non rechargeable batteries, the energymanagement policy of an energy harvesting WSN needs totake into account the energy replenishment process. Therefore,due to the random and uncertainty of the energy supplyin energy harvesting systems, the design and considerationsin the energy harvesting WSNs are different from a nonrechargeablebattery powered WSNs in many ways and theenergy management strategy for an energy harvesting WSNneeds to take into account the energy replenishment process.As a result, the existing protocols to prolong the networklifetime in WSNs are no more valid for the energy harvestingWSNs.
CONCLUSION
In this paper, we presented the optimal solution to maximizethe lifetime of wireless sensor network for structural healthmonitoring system by joint use of optimal power and route selectionwith and without energy harvesting. This optimizationproblem is inherently complex due to its mixed-integer nature,non-linearity, and a large solution space. We developed anefficient solution procedure based on the Branch-and-Boundtechnique augmented with a space reduction algorithm tospeed up the computation. Then, we proposed the heuristicrouting algorithm to reduce the computational complexityby decoupling transmission power allocation in the routingalgorithm from the optimal route selection. Results reveal thatthe heuristic routing algorithm performs similar to the optimalrouting using Branch-and-Bound space reduced algorithm. Wealso proposed two sub-optimal routing to reduce the computationalcomplexity. In the first algorithm the fixed transmissionpower is used in the routing selection and then transmissionpower is allocated. In the second sub optimal algorithm theGenetic Algorithm is used to solve the optimization ratherthan the Branch-and-Bound algorithm. The optimal solutionand heuristic solution outperform the sub-optimal routing solutions.The performance of the proposed routing algorithms iscompared with existing algorithms and the results demonstratethe significant gains that can be achieved by incorporatingenergy harvesting and power allocation in route selectionfor maximizing the lifetime of wireless sensor networks.Moreover, we presented the adaptive energy harvesting periodand the infinite lifetime achieved using the minimum energyharvesting period. There are several directions for future work,including development of a dynamic routing algorithm thatestablish rerouting automatically as soon as the critical nodedepletes to a predefined remaining energy.
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