Mobilewirelesssensornetworks(MWSNs),thecontinuousmovementofsensornodes,maycause completedisconnectionofthenetworkoratbestapartofit.Thedesignofsuchnetworksshould guaranteethatallsensornodesatalltimeshaveapathtothesinknode(s).Prolongingthelifetimesof the MWSNsisacrucialdesignissuebutshouldnotbeattheexpenseofotheressentialfunctionssuch as connectivity.Inthispaper,weproposeanenergyefficientdistributedframeworkforconnectivity maintenanceofMWSNs.Intheproposedframework,sensornodesoftheMWSNsscheduleandcontrol their radiofrequency(RF)modulesbasedondynamiccoordinatedreconstructionmechanism,some senornodesmaylosetheirconnectivitywiththenetworkduetomobility,depletionofenergyand/or incurredfaults.TwoprotocolsaredevelopedtopatchupthedisconnectionoftheMWSNs.Thefirst protocolreestablishesthenetworkconnectionconsumingfarlessenergythanstate-of-the-art alternatives.However,itiscapableofreestablishingtheconnectionwhennomorethan20%ofthe MWSN backbonenodesloseconnectionconcurrently.Thesecondprotocolextendsthislimittooperate when upto35%oftheMWSNbackbonenodesloseconnectionsimultaneously.

Mobilewirelesssensornetworks(MWSNs),thecontinuousmovementofsensornodes,maycause completedisconnectionofthenetworkoratbestapartofit.Thedesignofsuchnetworksshould guaranteethatallsensornodesatalltimeshaveapathtothesinknode(s).Prolongingthelifetimesof the MWSNsisacrucialdesignissuebutshouldnotbeattheexpenseofotheressentialfunctionssuch as connectivity.Inthispaper,weproposeanenergyefficientdistributedframeworkforconnectivity maintenanceofMWSNs.Intheproposedframework,sensornodesoftheMWSNsscheduleandcontrol their radiofrequency(RF)modulesbasedondynamiccoordinatedreconstructionmechanism,some senornodesmaylosetheirconnectivitywiththenetworkduetomobility,depletionofenergyand/or incurredfaults.TwoprotocolsaredevelopedtopatchupthedisconnectionoftheMWSNs.Thefirst protocolreestablishesthenetworkconnectionconsumingfarlessenergythanstate-of-the-art alternatives.However,itiscapableofreestablishingtheconnectionwhennomorethan20%ofthe MWSN backbonenodesloseconnectionconcurrently.Thesecondprotocolextendsthislimittooperate when upto35%oftheMWSNbackbonenodesloseconnectionsimultaneously.

A hybrid positioning system is merely one in which multiple systems are used for positioning purposes. This virtually always, though not necessarily, includes Global Positioning System (GPS) as it is the only global positioning network currently. Combination of mobile network and GPS positioning techniques provide a higher accuracy of mobile loca-tion than positions based on a standalone GPS or mobile network based positions. High accuracy of mobile position is mainly essential for emergency, military and many other location based services such as productivity enhancement, entertainment, position-based advertising, navigation, asset management and geographic information access. Assisted GPS, also known as A-GPS or AGPS, enhances the performance of the standard GPS in devices connected to the cellu-lar network. This paper introduces a new hybrid technique for mobile location determination utilizing Universal Mobile Telecommunication System (UMTS) network, Mobile Station (MS) and GPS positioning characteristics. Different po-sitioning techniques are chosen according to positioning parameters. The minimum required number of UMTS base stations, location measurement units and GPS satellites are calculated in this paper. The required number of GPS satel-lites is reduced from four satellites to three ones while using three dimension positioning and from three satellites to two ones at two dimension positioning. Moreover, MS receiver main functions including both network and GPS received paths to achieve output assisted data are discussed. In this paper many drawbacks such as indoor positioning, receiver high power consumption, delay in first time to fix position, low position accuracy as well as large number of required satellites and base stations are improved.

A hybrid positioning system is merely one in which multiple systems are used for positioning purposes. This virtually always, though not necessarily, includes Global Positioning System (GPS) as it is the only global positioning network currently. Combination of mobile network and GPS positioning techniques provide a higher accuracy of mobile loca-tion than positions based on a standalone GPS or mobile network based positions. High accuracy of mobile position is mainly essential for emergency, military and many other location based services such as productivity enhancement, entertainment, position-based advertising, navigation, asset management and geographic information access. Assisted GPS, also known as A-GPS or AGPS, enhances the performance of the standard GPS in devices connected to the cellu-lar network. This paper introduces a new hybrid technique for mobile location determination utilizing Universal Mobile Telecommunication System (UMTS) network, Mobile Station (MS) and GPS positioning characteristics. Different po-sitioning techniques are chosen according to positioning parameters. The minimum required number of UMTS base stations, location measurement units and GPS satellites are calculated in this paper. The required number of GPS satel-lites is reduced from four satellites to three ones while using three dimension positioning and from three satellites to two ones at two dimension positioning. Moreover, MS receiver main functions including both network and GPS received paths to achieve output assisted data are discussed. In this paper many drawbacks such as indoor positioning, receiver high power consumption, delay in first time to fix position, low position accuracy as well as large number of required satellites and base stations are improved.

This paper presents a new adaptive mutation approach for fastening the convergence of immune algorithms (IAs). This method is adopted to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the IA. In this method, the mutation rate (pm) is adaptively varied depending on the fitness values of the so-lutions. Solutions of high fitness are protected, while solutions with sub-average fitness are totally disrupted. A solution to the problem of deciding the optimal value of pm is obtained. Experiments are carried out to compare the proposed approach to traditional one on a set of optimization problems. These are namely: 1) an exponential multi-variable func-tion; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simula-tion results show that the proposed method efficiently improves IA’s performance and prevents it from getting stuck at a local optimum.

This paper presents a new adaptive mutation approach for fastening the convergence of immune algorithms (IAs). This method is adopted to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the IA. In this method, the mutation rate (pm) is adaptively varied depending on the fitness values of the so-lutions. Solutions of high fitness are protected, while solutions with sub-average fitness are totally disrupted. A solution to the problem of deciding the optimal value of pm is obtained. Experiments are carried out to compare the proposed approach to traditional one on a set of optimization problems. These are namely: 1) an exponential multi-variable func-tion; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simula-tion results show that the proposed method efficiently improves IA’s performance and prevents it from getting stuck at a local optimum.

This paper presents a new adaptive mutation approach for fastening the convergence of immune algorithms (IAs). This method is adopted to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the IA. In this method, the mutation rate (pm) is adaptively varied depending on the fitness values of the so-lutions. Solutions of high fitness are protected, while solutions with sub-average fitness are totally disrupted. A solution to the problem of deciding the optimal value of pm is obtained. Experiments are carried out to compare the proposed approach to traditional one on a set of optimization problems. These are namely: 1) an exponential multi-variable func-tion; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simula-tion results show that the proposed method efficiently improves IA’s performance and prevents it from getting stuck at a local optimum.

In this paper, a generalized formulation for intelligent energy management of amicrogrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.

The necessity to solve global warming problems by reducing CO2 emission in the electricity generation field had led to increasing interest in microgrids (MGs), particularly those containing the renewable sources such as solar and wind generation. Wind speed fluctuations cause high variations in the output power of a wind turbine which cause fluctuations in frequency and voltage of the MG during islanding mode and originate stability problems. In this paper, three techniques are proposed for solving and reducing the consequences of this problem. In the first technique, we develop a new fuzzy logic pitch angle controller. In the second technique, we design an energy-storage ultracapacitor which directly smoothes the output power of the wind turbine and enhances the performance of the MG during the islanding mode. In the third technique, storage batteries are used to support the MG in the islanding mode.

This paper presents an ECG compression algorithm based on the optimal selection of wavelet filters and threshold levels in different subbands that achieve maximum data volume reduction while guaranteeing reconstruction quality. The proposed algorithm starts by segmenting the ECG signal into frames; where each frame is decomposed into m subbands through optimized wavelet filters. The resulting wavelet coefficients are thresholded and those having absolute values below specified threshold levels in all subbands are deleted and the remaining coefficients are appropriately encoded with a modified version of the run-length coding scheme. The threshold levels to use, before encoding, are adjusted in an optimum manner, until predefined compression ratio and signal quality are achieved. Extensive experimental tests were made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The compression ratio (CR), the root-mean-square difference (PRD) and the zero-mean percent rootmean- square difference (PRD1) measures are used for measuring the algorithm performance (high CR with excellent reconstruction quality). From the obtained results, it can be deduced that the performance of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets. However, the computational complexity of the proposed technique is the price paid for the improvement in the compression performance measures.