Abdul Samad Ismail

Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks

In many wireless sensor network applications, such as warning systems or healthcare services, it is necessary to update the captured data with location information. A promising solution for statically deployed sensors is to benefit from mobile beacon-assisted localization. The main challenge is to design and develop an optimum path planning mechanism for a mobile beacon to decrease the required time for determining location, increase the accuracy of the estimated position, and increase the coverage. In this paper, we propose a novel superior path planning mechanism called Z-curve. Our proposed trajectory can successfully localize all deployed sensors with high precision and the shortest required time for localization. We also introduce critical metrics, including the ineffective position rate for further evaluation of mobile beacon trajectories. In addition, we consider an accurate and reliable channel model, which helps to provide more realistic evaluation. Z-curve is compared with five existing path planning schemes based on three different localization techniques such as weighted centroid localization and trilateration with time priority and accuracy priority. Furthermore, the performance of the Z-curve is evaluated at the presence of obstacles and Z-curve obstacle-handling trajectory is proposed to mitigate the obstacle problem on localization. Simulation results show the advantages of our proposed path planning scheme over the existing schemes.

Intelligent Web proxy caching approaches based on machine learning techniques

In this paper, machine learning techniques are used to enhance the performances of conventional Web proxy caching policies such as Least-Recently-Used (LRU), Greedy-Dual-Size (GDS) and Greedy-Dual-Size-Frequency (GDSF). A support vector machine (SVM) and a decision tree (C4.5) are intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches known as SVM-LRU, SVM-GDSF and C4.5-GDS. The proposed intelligent approaches are evaluated by trace-driven simulation and compared with the most relevant Web proxy caching polices. Experimental results have revealed that the proposed SVM-LRU, SVM-GDSF and C4.5-GDS significantly improve the performances of LRU, GDSF and GDS respectively.

A Reverse Localization Scheme for Underwater Acoustic Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) provide new opportunities to observe and predict the behavior of aquatic environments. In some applications like target tracking or disaster prevention, sensed data is meaningless without location information. In this paper, we propose a novel 3D centralized, localization scheme for mobile underwater wireless sensor network, named Reverse Localization Scheme or RLS in short. RLS is an event-driven localization method triggered by detector sensors for launching localization process. RLS is suitable for surveillance applications that require very fast reactions to events and could report the location of the occurrence. In this method, mobile sensor nodes report the event toward the surface anchors as soon as they detect it. They do not require waiting to receive location information from anchors. Simulation results confirm that the proposed scheme improves the energy efficiency and reduces significantly localization response time with a proper level of accuracy in terms of mobility model of water currents. Major contributions of this method lie on reducing the numbers of message exchange for localization, saving the energy and decreasing the average localization response time.

A learning automata-based algorithm for solving coverage problem in directional sensor networks

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors’ energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor’s appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

Solving target coverage problem using cover sets in wireless sensor networks based on learning automata

Recent years have witnessed a significant increase in employing wireless sensor networks (WSNs) for a variety of applications. Monitoring a set of discrete targets and, at the same time, extending the network lifetime is a critical issue in WSNs. One method to solve this problem is designing an efficient scheduling algorithm that is able to organize sensor nodes into several cover sets in such a way that each cover set could monitor all the targets. This study presents three learning automata-based scheduling algorithms to solve the problem. Moreover, several pruning rules are devised to avoid the selection of redundant sensors and manage critical sensors for extending the network lifetime. To evaluate the performance of proposed algorithms, we conducted several experiments, and the obtained results indicated that Algorithm 3 was more successful in terms of extending the network lifetime.

Intelligent Naïve Bayes-based approaches for Web proxy caching

Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular Web objects that are likely to be revisited in the near future are stored on the proxy server, which plays the key roles between users and Web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining the ideal Web objects that will be re-visited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, a Naive Bayes (NB) classifier is used to enhance the performance of conventional Web proxy caching approaches such as Least-Recently-Used (LRU) and Greedy-Dual-Size (GDS). NB is intelligently incorporated with conventional Web proxy caching techniques to form intelligent and effective caching approaches known as NB-GDS, NB-LRU and NB-DA. Experimental results have revealed that the proposed NB-GDS, NB-LRU and NB-DA significantly improve the performances of the existing Web proxy caching approaches across several proxy datasets.

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.

Learning automata-based algorithms for finding cover sets in wireless sensor networks

Wireless sensor networks (WSNs) have been widely used in different applications. One of the most significant issues in WSNs is developing an efficient algorithm to monitor all the targets and, at the same time, extend the network lifetime. As sensors are often densely deployed, employing scheduling algorithms can be considered a promising approach that is able ultimately to result in extending total network lifetime. In this paper, we propose three learning automata-based scheduling algorithms for solving target coverage problem in WSNs. The proposed algorithms employ learning automata (LA) to determine the sensors that should be activated at each stage for monitoring all the targets. Additionally, we design a pruning rule and manage critical targets in order to maximize network lifetime. In order to evaluate the performance of the proposed algorithms, extensive simulation experiments were carried out, which demonstrated the effectiveness of the proposed algorithms in terms of extending the network lifetime. Simulation results also revealed that by a proper choice of the learning rate, a proper trade-off could be achieved between the network lifetime and running time.

Efficient localization via Middle-node cooperation in wireless sensor networks

Localization is one of the key issues in the wireless sensors network. Regarding the mobility of the nodes in some of the applications, it is necessary to have a localization algorithm that can support the mobility of nodes. Most of the approaches that have been presented so far have required instruments for the measurement of the distance and the angle or they have needed many beacon nodes for localization. In this paper a demand-based algorithm has been presented which uses these two techniques: the first one is using the localized nodes to localize the unknown nodes and the second one is utilizing the information from localization message by the middle nodes which are located in the return route of the message. Using these two techniques, the suggested method that called ELoc(Efficient Localization) has been able to present a higher speed and range of success, by reducing the sent messages and consequently reducing the energy consumption quite significantly. Furthermore, this method with a high ability of scalability and low complexity can be very efficient in wireless sensor networks. By using Omnet++ simulator software, the ELoc has been compared to Dv-hop and ECLS methods and it has been evaluated. The results of simulation have confirmed the above-mentioned propositions.

SmartSim: Graphical Sensor Network Simulation Based on TinyOS and Tossim

Obtaining hundreds of sensor nodes to experiment new algorithms on Wireless Sensor Network (WSN) in real testbed is costly and time consuming. Simulation environment can reduce these costs though it may not produce exactly same results as real testbeds. There is many kind of simulations available for WSN such as Cooja and Tossim. None of these simulation environments provides an all-in-one solution to help researchers design topologies, running the network step by step and giving extensive reports of generation environment. In this paper a new simulation environment, SmartSim, has been developed to provide an useful all-in-one solution. It has new, unique features such as detailed graphical presentation of topology, ability to proceed through network events either forward or backward and a comprehensive power usage report generation. SmartSim eases the algorithm implementation in TinyOS based WSN, provides troubleshooting tools and linear and non-linear energy usage of the network.