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- Majd, A., et al.
(författare)
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Hierarchal Placement of Smart Mobile Access Points in Wireless Sensor Networks Using Fog Computing
- 2017
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Ingår i: Proceedings - 2017 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2017. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509060580 ; , s. 176-180
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Konferensbidrag (refereegranskat)abstract
- Recent advances in computing and sensor technologies have facilitated the emergence of increasingly sophisticated and complex cyber-physical systems and wireless sensor networks. Moreover, integration of cyber-physical systems and wireless sensor networks with other contemporary technologies, such as unmanned aerial vehicles (i.e. drones) and fog computing, enables the creation of completely new smart solutions. By building upon the concept of a Smart Mobile Access Point (SMAP), which is a key element for a smart network, we propose a novel hierarchical placement strategy for SMAPs to improve scalability of SMAP based monitoring systems. SMAPs predict communication behavior based on information collected from the network, and select the best approach to support the network at any given time. In order to improve the network performance, they can autonomously change their positions. Therefore, placement of SMAPs has an important role in such systems. Initial placement of SMAPs is an NP problem. We solve it using a parallel implementation of the genetic algorithm with an efficient evaluation phase. The adopted hierarchical placement approach is scalable, it enables construction of arbitrarily large SMAP based systems.
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- Majd, A., et al.
(författare)
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Improving motion safety and efficiency of intelligent autonomous swarm of drones
- 2020
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Ingår i: Drones. - : MDPI AG. - 2504-446X. ; 4:3, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions.
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- Majd, A., et al.
(författare)
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Optimal smart mobile access point placement for maximal coverage and minimal communication
- 2017
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Ingår i: CM International Conference Proceeding Series, Volume Part F130524. - New York, NY, USA : Association for Computing Machinery. - 9781450348430
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Konferensbidrag (refereegranskat)abstract
- A selection of the optimal placements of the access points and sensors constitutes one of the fundamental challenges in the monitoring of spatial phenomena in wireless sensor networks (WSNs). Access points should occupy the best locations in order to obtain a sufficient degree of coverage with a low communication cost. Finding an optimal placement is an NP-hard problem that is further complicated by the real-world conditions such as obstacles, radiation interference etc. In this paper, we propose a compound method to select the best near-optimal placement of SMAPs with the goal to maximize the monitoring coverage and to minimize the communication cost. Our approach combinesa parallel implementation of the Imperialist Competitive Algorithm (ICA) with a greedy method.The benchmarking of the proposed approach demonstrates its clear advantages in solving and optimizing the placement problem.
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- Majd, A., et al.
(författare)
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Optimizing scheduling for heterogeneous computing systems using combinatorial meta-heuristic solution
- 2017
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Ingår i: 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings. ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Today, based on fast development especially in Network-on-Chip (NoC)-based many-core systems, the task scheduling problem plays a critical role in high-performance computing. It is an NP-hard problem. The complexity increases further when the scheduling problem is applied to heterogeneous platforms. Exploring the whole search space in order to find the optimal solution is not time efficient, thus metaheuristics are mostly used to find a near-optimal solution in a reasonable amount of time. We propose a compound method to select the best near-optimal task schedule in the heterogeneous platform in order to minimize the execution time. For this, we combine a new parallel meta-heuristic method with a greedy scheme. We introduce a novel metaheuristic method for near-optimal scheduling that can provide performance guarantees for multiple applications implemented on a shared platform. Applications are modeled as directed acyclic task graphs (DAG) for execution on a heterogeneous NoC-based many-core platform with given communication costs. We introduce an order-based encoding especially for pipelined operation that improves (decreases) execution time by more than 46%. Moreover, we present a novel multi-population method inspired by both genetic and imperialist competitive algorithms specialized for the scheduling problem, improving the convergence policy and selection pressure. The potential of the approach is demonstrated by experiments using a Sobel filter, SUSAN filter, RASTA-PLP, and JPEG encoder as real-world case studies.
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- Sahebi, G., et al.
(författare)
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A reliable weighted feature selection for auto medical diagnosis
- 2017
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Ingår i: Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538608371 ; , s. 985-991
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Konferensbidrag (refereegranskat)abstract
- Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed. The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. Accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of66.76% using all the features.
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