Ayoub Alsarhan

Cluster-Based Spectrum Management Using Cognitive Radios in Wireless Mesh Network

Wireless mesh networks (WMNs) have emerged recently to extend internet access and other networking services. WMNs routers provide network access to the clients and other networking functions such as routing, and packet forwarding. Bandwidth scarcity is the main challenge that limits the performance of WMNs. Although considerable research has been conducted on spectrum allocation, spectrum management is still considered an important open problem. This problem can be solved using cognitive radio technology that allows radios to intelligently locate free frequencies and use them efficiently. In this work, we propose a new spectrum management scheme that supports local and global management for a wireless network. Our scheme is based on clusters where the coordinator for each cluster manages spectrum information by keeping the required information at cluster level and for the whole network. Our scheme provides robust operation against any cluster head failure, as well as clients mobility.

A dynamic replication strategy based on categorization for Data Grid

Data Replication is copying the data from a certain location to another location. Replication is used in Data Grid to have two or more copies of the same data at different locations. In this paper, a Category-based dynamic replication strategy (CDRS) is proposed. The strategy takes into account that the replicas exist on a node belong to different categories. Each of these categories is given a value that determines its importance for the node. When the nodes storage is full, the node starts to store only the replicas that belong to the category with the highest value. The results of the simulations show that the new proposed strategy achieved better performance than Plain Caching and Fast Spread strategies in terms of total transit time and total bandwidth consumption.

Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic.

Spectrum sharing in multi-service cognitive network using reinforcement learning

In this paper the issue of spectrum sharing in multi-service cognitive wireless network is addressed. The problem is formulated as a revenue maximization problem and a framework is presented that is capable of adequately solving a class of problem where resources are shared in radio environment. Primary users (PUs) exchange channels dynamically and based on the availability of idle channels at neighbors. Secondary users (SUs) of different classes form a mesh network and rent a spectrum from primary users. For such cognitive wireless mesh networks, the main challenge facing a PU is to satisfy the following conflicting objectives: maximizing its total revenue, maintaining its quality of service (QoS) (that degrades due to renting its spectrum to SUs) and reducing secondary user delay times. In this work machine learning paradigm is presented as a means for extracting the optimal control policy for spectrum sharing. To obtain different requirements, the objective function is defined to maximize the total revenue gained by primary users. Value iteration algorithm is applied to find an optimal control policy that maximizes the difference between reward and cost (revenue). Performance evaluation of the proposed spectrum sharing approach shows that the scheme is able to find an efficient trade-off between PUs revenue and SUs delay.

Efficient 3D placement of a UAV using particle swarm optimization

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.

Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

We develop a novel auction-based scheme for trading free processors in cloud computing environment. Our scheme allows clients to fairly compete for the offered processors. Second-price auction is used where each client bids for the processors. The client that makes the higher bid wins the auction and starts using the processors for executing its jobs. The winner pays the second highest bid. Maximising profit is the key objective for the cloud provider (CP). The optimisation problem of renting processors on the cloud market is a complex and challenging task. The proposed mechanism takes into account the market demand and the economic concerns for the CP and clients. Within this context, we show the existence of a Nash equilibrium which leads to a unique allocation under certain processors distribution. Numerical analysis stresses the ability of our approach to maximise the clients utility under varying cloud environment conditions.

A survey of the satisfiability-problems solving algorithms

The propositional satisfiability SAT problem is one of the most fundamental problems in computer science. SAT solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. Empirical research has been very fruitful for the development of efficient methods for SAT problems, such as classical Davis-Putnam method, greedy SAT GSAT method and neural network SAT method. This paper gives a survey about the methods used for solving the SAT problems with an emphasis on surveying the local search algorithms.

Resource trading in cloud environments for profit maximisation using an auction model

Cloud computing is a promising computing paradigm for trading computing resources over internet. We consider the approach where the cloud provider (CP) rents free processors for its clients. Maximising reward is the key objective for the CP. The optimisation problem of renting processors on the cloud is a complex and challenging task. In addition to consider the requirements of the CP and clients, the commercialisation of clouds requires policies that take into account the economic concerns for the CP and clients. Within this context, we propose an optimal auction based on the concept of virtual valuation for renting processors. The concept of virtual valuation is used in our auction mechanism. Numerical analysis stresses the ability of our approach to maximise the CPs reward under varying cloud environment conditions.

Dynamic Auction for Revenue Maximization in Spectrum Market

AbstractThe paradigm of spectrum trading has spontaneously prompted a wide interest in spectrum market where licensed users (primary users, PUs) aim at efficiently share spectrum with unlicensed users (secondary users, SUs). SUs pay PUs for radio resource usage. Spectrum pricing plays a pivotal role towards the success of spectrum trading. Most of the existing pricing schemes neglect service guarantees. Furthermore, they use static pricing scheme where the price cannot respond quickly to the changes in the spectrum market. To overcome these problems, we design dynamic auction where spectrum is periodically auctioned off to meet SUs demands over time. Our scheme determines the size of spectrum to be auctioned for each session. Performance evaluation of the proposed scheme shows the ability of our scheme to maximize the reported revenue for the PU under different spectrum market conditions.

An Index-Based Approach for Wireless Sensor Networks

Sensor nodes have significant power constraints (battery life). Thus, power-aware approaches must be employed to prolong the network lifetime. However, most of the literature considers only routing-based approaches to prolong it. In this paper, we propose an index-based approach that provides a new way for reducing the energy consumption. The idea behind this new proposed approach is having an index for each possible value for a sensed reading. The index length will have much less length than the reading if the possible values for the sensed reading are limited. In this case, sending the corresponding index for a reading instead of the reading itself will result in decreasing the size of the submitted packet and therefore reducing the consumed energy. The experimental results show that our approach reduces both the total energy consumption and total elapsed time in the case the number of the possible different values for each sensed reading is up to 32,768