This page has only limited features, please log in for full access.

Unclaimed
Aisha Fatima
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 18 June 2020 in Energies
Reads 0
Downloads 0

The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario,community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.

ACS Style

Rasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies 2020, 13, 3164 .

AMA Style

Rasool Bukhsh, Muhammad Umar Javed, Aisha Fatima, Nadeem Javaid, Muhammad Shafiq, Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies. 2020; 13 (12):3164.

Chicago/Turabian Style

Rasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †." Energies 13, no. 12: 3164.

Journal article
Published: 29 November 2019 in Applied Sciences
Reads 0
Downloads 0

An increase in the world’s population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user’s load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user’s total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability ( L P S P m a x ) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four L P S P m a x values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.

ACS Style

Asif Khan; Turki Ali Alghamdi; Zahoor Ali Khan; Aisha Fatima; Samia Abid; Adia Khalid; Nadeem Javaid. Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System. Applied Sciences 2019, 9, 5197 .

AMA Style

Asif Khan, Turki Ali Alghamdi, Zahoor Ali Khan, Aisha Fatima, Samia Abid, Adia Khalid, Nadeem Javaid. Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System. Applied Sciences. 2019; 9 (23):5197.

Chicago/Turabian Style

Asif Khan; Turki Ali Alghamdi; Zahoor Ali Khan; Aisha Fatima; Samia Abid; Adia Khalid; Nadeem Javaid. 2019. "Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System." Applied Sciences 9, no. 23: 5197.

Journal article
Published: 28 October 2019 in IEEE Access
Reads 0
Downloads 0

Smart Grid (SG) plays vital role in modern electricity grid. The data is increasing with the drastic increase in number of users. An efficient technology is required to handle this dramatic growth of data. Cloud computing is then used to store the data and to provide numerous services to the consumers. There are various cloud Data Centers (DC), which deal with the requests coming from consumers. However, there is a chance of delay due to the large geographical area between cloud and consumer. So, a concept of fog computing is presented to minimize the delay and to maximize the efficiency. However, the issue of load balancing is raising; as the number of consumers and services provided by fog grow. So, an enhanced mechanism is required to balance the load of fog. In this paper, a three-layered architecture comprising of cloud, fog and consumer layers is proposed. A meta-heuristic algorithm: Improved Particle Swarm Optimization with Levy Walk (IPSOLW) is proposed to balance the load of fog. Consumers send request to the fog servers, which then provide services. Further, cloud is deployed to save the records of all consumers and to provide the services to the consumers, if fog layer is failed. The proposed algorithm is then compared with existing algorithms: genetic algorithm, particle swarm optimization, binary PSO, cuckoo with levy walk and BAT. Further, service broker policies are used for efficient selection of DC. The service broker policies used in this paper are: closest data center, optimize response time, reconfigure dynamically with load and new advance service broker policy. Moreover, response time and processing time are minimized. The IPSOLW has outperformed to its counterpart algorithms with almost 4.89% better results.

ACS Style

Zahoor Ali Khan; Ayesha Anjum Butt; Turki Ali Alghamdi; Aisha Fatima; Mariam Akbar; Muhammad Ramzan; Nadeem Javaid. Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms. IEEE Access 2019, 7, 157254 -157267.

AMA Style

Zahoor Ali Khan, Ayesha Anjum Butt, Turki Ali Alghamdi, Aisha Fatima, Mariam Akbar, Muhammad Ramzan, Nadeem Javaid. Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms. IEEE Access. 2019; 7 (99):157254-157267.

Chicago/Turabian Style

Zahoor Ali Khan; Ayesha Anjum Butt; Turki Ali Alghamdi; Aisha Fatima; Mariam Akbar; Muhammad Ramzan; Nadeem Javaid. 2019. "Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms." IEEE Access 7, no. 99: 157254-157267.

Journal article
Published: 18 October 2019 in Applied Sciences
Reads 0
Downloads 0

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

ACS Style

Sana Mujeeb; Turki Ali Alghamdi; Sameeh Ullah; Aisha Fatima; Nadeem Javaid; Tanzila Saba. Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics. Applied Sciences 2019, 9, 4417 .

AMA Style

Sana Mujeeb, Turki Ali Alghamdi, Sameeh Ullah, Aisha Fatima, Nadeem Javaid, Tanzila Saba. Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics. Applied Sciences. 2019; 9 (20):4417.

Chicago/Turabian Style

Sana Mujeeb; Turki Ali Alghamdi; Sameeh Ullah; Aisha Fatima; Nadeem Javaid; Tanzila Saba. 2019. "Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics." Applied Sciences 9, no. 20: 4417.

Journal article
Published: 16 February 2019 in Electronics
Reads 0
Downloads 0

Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

ACS Style

Aisha Fatima; Nadeem Javaid; Ayesha Anjum Butt; Tanzeela Sultana; Waqar Hussain; Muhammad Bilal; Muhammad Aqeel Ur Rehman Hashmi; Mariam Akbar; Manzoor Ilahi. An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers. Electronics 2019, 8, 218 .

AMA Style

Aisha Fatima, Nadeem Javaid, Ayesha Anjum Butt, Tanzeela Sultana, Waqar Hussain, Muhammad Bilal, Muhammad Aqeel Ur Rehman Hashmi, Mariam Akbar, Manzoor Ilahi. An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers. Electronics. 2019; 8 (2):218.

Chicago/Turabian Style

Aisha Fatima; Nadeem Javaid; Ayesha Anjum Butt; Tanzeela Sultana; Waqar Hussain; Muhammad Bilal; Muhammad Aqeel Ur Rehman Hashmi; Mariam Akbar; Manzoor Ilahi. 2019. "An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers." Electronics 8, no. 2: 218.

Journal article
Published: 04 December 2018 in Electronics
Reads 0
Downloads 0

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

ACS Style

Aisha Fatima; Nadeem Javaid; Tanzeela Sultana; Waqar Hussain; Muhammad Bilal; Shaista Shabbir; Yousra Asim; Mariam Akbar; Manzoor Ilahi. Virtual Machine Placement via Bin Packing in Cloud Data Centers. Electronics 2018, 7, 389 .

AMA Style

Aisha Fatima, Nadeem Javaid, Tanzeela Sultana, Waqar Hussain, Muhammad Bilal, Shaista Shabbir, Yousra Asim, Mariam Akbar, Manzoor Ilahi. Virtual Machine Placement via Bin Packing in Cloud Data Centers. Electronics. 2018; 7 (12):389.

Chicago/Turabian Style

Aisha Fatima; Nadeem Javaid; Tanzeela Sultana; Waqar Hussain; Muhammad Bilal; Shaista Shabbir; Yousra Asim; Mariam Akbar; Manzoor Ilahi. 2018. "Virtual Machine Placement via Bin Packing in Cloud Data Centers." Electronics 7, no. 12: 389.

Conference paper
Published: 19 October 2018 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
Reads 0
Downloads 0

Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies (ICT) traditional electricity grids, meters, buildings and appliances became Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs) and Smart Appliances (SAs). The SBs consists of a large number of SAs. These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes response delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers a new load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), Response Time (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the Round Robin (RR) and Throttled algorithms, due to its limitations in network delays and RT.

ACS Style

Tooba Nazar; Nadeem Javaid; Moomina Waheed; Aisha Fatima; Hamida Bano; Nouman Ahmed. Modified Shortest Job First for Load Balancing in Cloud-Fog Computing. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2018, 63 -76.

AMA Style

Tooba Nazar, Nadeem Javaid, Moomina Waheed, Aisha Fatima, Hamida Bano, Nouman Ahmed. Modified Shortest Job First for Load Balancing in Cloud-Fog Computing. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2018; ():63-76.

Chicago/Turabian Style

Tooba Nazar; Nadeem Javaid; Moomina Waheed; Aisha Fatima; Hamida Bano; Nouman Ahmed. 2018. "Modified Shortest Job First for Load Balancing in Cloud-Fog Computing." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 63-76.

Conference paper
Published: 19 October 2018 in Advances on P2P, Parallel, Grid, Cloud and Internet Computing
Reads 0
Downloads 0

Energy is among the most valuable resource in the world that need to be consumed in an optimized manner. For making intelligent decisions in energy consumption Smart Grid (SG) is introduced. One of the key components of SG is communication. Cloud-Fog based environment is the most popular communication architecture nowadays. Keeping the focus on this point this article proposed an integration of Cloud-Fog based environment with Micro Grid (MG) for effective resource management. For experimentation, the word is divided into 6 regions based on the division of continents. Each region contains 6 clusters and 3 fogs connected to each of them with MG and centralized cloud. Cloud Analyst simulator is used for testing of our proposed scenario. To cater the huge load on fogs a new load balancing technique Shortest Load First (SLF) is introduced in the simulator. The load balancer technique is used to manage the requests on fogs whereas the dynamic service proximity policy is used for connection of clusters with fogs.

ACS Style

Moomina Waheed; Nadeem Javaid; Aisha Fatima; Tooba Nazar; Komal Tehreem; Kainat Ansar. Shortest Job First Load Balancing Algorithm for Efficient Resource Management in Cloud. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2018, 49 -62.

AMA Style

Moomina Waheed, Nadeem Javaid, Aisha Fatima, Tooba Nazar, Komal Tehreem, Kainat Ansar. Shortest Job First Load Balancing Algorithm for Efficient Resource Management in Cloud. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2018; ():49-62.

Chicago/Turabian Style

Moomina Waheed; Nadeem Javaid; Aisha Fatima; Tooba Nazar; Komal Tehreem; Kainat Ansar. 2018. "Shortest Job First Load Balancing Algorithm for Efficient Resource Management in Cloud." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 49-62.

Conference paper
Published: 08 June 2018 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Underwater wireless sensor networks (UWSNs) are capable of providing facilities for the wide range of aquatic applications. However, due to the adverse environment, UWSNs face huge challenges and issues i.e., limited bandwidth, node mobility, higher propagation delay, high manufacturer and deployment costs etc. In this paper, we propose two techniques: the geographic and opportunistic routing via transmission range (T-GEDAR) and the geographic and opportunistic routing via the backward transmission (B-GEDAR). Firstly, in the absence of forwarder node, we increase the transmission range to determine the forwarder node. Because of this, we can send packets to the sink; Secondly, when the forwarder node is unavailable in adjustable transmission range. Then, the B-GEDAR is used for determining the forwarder node so that the packet delivery ratio (PDR) can be increased effectively. This is because, our simulation results perform better network performance in terms of an energy efficiency, PDR, and the fraction of void nodes.

ACS Style

Ghazanfar Latif; Nadeem Javaid; Aasma Khan; Aisha Fatima; Landing Jatta; Wahab Khan. Efficient Routing in Geographic and Opportunistic Routing for Underwater WSNs. Advances in Intelligent Systems and Computing 2018, 86 -95.

AMA Style

Ghazanfar Latif, Nadeem Javaid, Aasma Khan, Aisha Fatima, Landing Jatta, Wahab Khan. Efficient Routing in Geographic and Opportunistic Routing for Underwater WSNs. Advances in Intelligent Systems and Computing. 2018; ():86-95.

Chicago/Turabian Style

Ghazanfar Latif; Nadeem Javaid; Aasma Khan; Aisha Fatima; Landing Jatta; Wahab Khan. 2018. "Efficient Routing in Geographic and Opportunistic Routing for Underwater WSNs." Advances in Intelligent Systems and Computing , no. : 86-95.

Conference paper
Published: 08 June 2018 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

In this article, a resource allocation model is presented in order to optimize the resources in residential buildings. The whole world is categorized into six regions depending on its continents. The fog helps cloud computing connectivity on the edge network. It also saves data temporarily and sends to the cloud for permanent storage. Each continent has one fog which deals with three clusters having 100 buildings. Microgrids (MGs) are used for the effective electricity distribution among the consumers. The control parameters considered in this paper are: clusters, number of buildings, number of homes and load requests whereas the performance parameters are: cost, Response Time (RT) and Processing Time (PT). Particle Swarm Optimization with Simulated Annealing (PSOSA) is used for load balancing of Virtual Machines (VMs) using multiple service broker policies. Service broker policies in this paper are: new dynamic service proximity, new dynamic response time and enhanced new response time. The results of proposed service broker policies with PSOSA are compared with the existing policy: new dynamic service proximity. New dynamic response time and enhanced new dynamic response time performs better than the existing policy in terms of cost, RT and PT. However, the maximum RT and PT of proposed policies is more than the existing policy. We have used CloudAnalyst for conducting simulations for the proposed scheme.

ACS Style

Aisha Fatima; Nadeem Javaid; Momina Waheed; Tooba Nazar; Shaista Shabbir; Tanzeela Sultana. Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing. Advances in Intelligent Systems and Computing 2018, 289 -298.

AMA Style

Aisha Fatima, Nadeem Javaid, Momina Waheed, Tooba Nazar, Shaista Shabbir, Tanzeela Sultana. Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing. Advances in Intelligent Systems and Computing. 2018; ():289-298.

Chicago/Turabian Style

Aisha Fatima; Nadeem Javaid; Momina Waheed; Tooba Nazar; Shaista Shabbir; Tanzeela Sultana. 2018. "Efficient Resource Allocation Model for Residential Buildings in Smart Grid Using Fog and Cloud Computing." Advances in Intelligent Systems and Computing , no. : 289-298.

Conference paper
Published: 01 April 2018 in 2018 21st Saudi Computer Society National Computer Conference (NCC)
Reads 0
Downloads 0

Underwater Wireless Sensor Networks (UWSNs) have been considered as an emerging and promising method for exploring and monitoring deep ocean. The UWSNs face many challenges such as noise, high transmission delays, high deployment cost, movement of nodes, energy constraints, etc. In UWSNs nodes are sparsely and unevenly deployed, that may results in void hole occurrence. Secondly low propagation speed in UWSNs causes high end-to-end delay and energy consumption. In this paper, we propose two schemes: Adaptive Transmission Range in WDFAD-DBR (ATR-WDFAD-DBR) and Cluster Based WDFAD-DBR (CBWDFAD-DBR). In aforesaid scheme to reduce the probability of void hole this scheme adjusts its transmission range when it finds a void node and then continues to forward data towards the sink. In later, to minimize end-to-end delay and energy consumption network is divided into clusters. Simulation results show that our schemes outperform compared with baseline solution in terms of average Packet Delivery Ration (PDR), average energy tax, end-to-end delay and Accumulated Propagation Distance (APD).

ACS Style

Aasma Khan; Nadeem Javaid; Ghazanfar Latif; Landing Jatta; Aisha Fatima; Wahab Khan. Cluster based and Adaptive Power Controlled Routing Protocol for Underwater Wireless Sensor Networks. 2018 21st Saudi Computer Society National Computer Conference (NCC) 2018, 1 -6.

AMA Style

Aasma Khan, Nadeem Javaid, Ghazanfar Latif, Landing Jatta, Aisha Fatima, Wahab Khan. Cluster based and Adaptive Power Controlled Routing Protocol for Underwater Wireless Sensor Networks. 2018 21st Saudi Computer Society National Computer Conference (NCC). 2018; ():1-6.

Chicago/Turabian Style

Aasma Khan; Nadeem Javaid; Ghazanfar Latif; Landing Jatta; Aisha Fatima; Wahab Khan. 2018. "Cluster based and Adaptive Power Controlled Routing Protocol for Underwater Wireless Sensor Networks." 2018 21st Saudi Computer Society National Computer Conference (NCC) , no. : 1-6.