Amin Majd

PICA: Multi-population Implementation of Parallel Imperialist Competitive Algorithms

The importance of optimization and NP-problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that are mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared-memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.

Placement of Smart Mobile Access Points in Wireless Sensor Networks and Cyber-Physical Systems Using Fog Computing

Increasingly sophisticated, complex,, energy-efficient cyber-physical systems, wireless sensor networks are emerging, facilitated by recent advances in computing, sensor technologies. Integration of cyber-physical systems, wireless sensor networks with other contemporary technologies, such as unmanned aerial vehicles, fog or edge computing, enable creation of completely new smart solutions. We present the concept of a Smart Mobile Access Point (SMAP), which is a key building block for a smart network,, propose an efficient placement approach for such SMAPs. SMAPs predict the behavior of the network, based on information collected from the network,, select the best approach to support the network at any given time. When needed, they autonomously change their positions to obtain a better configuration from the network performance perspective. Therefore, placement of SMAPs is an important issue in such a system. Initial placement of SMAPs is an NP problem,, evolutionary algorithms provide an efficient means to solve it. Specifically, we present a parallel implementation of the imperialistic competitive algorithm, an efficient evaluation or fitness function to solve the initial placement of SMAPs in the fog computing context.

SEECC: A secure and efficient elliptic curve cryptosystem for E-health applications

Security is an essential factor in wireless sensor networks especially for E-health applications. One of the common mechanisms to satisfy the security requirements is cryptography. Among the cryptographic methods, elliptic curve cryptography is well-known, as by having a small key length it provides the same security level in comparison with the other public key cryptosystems. The small key sizes make ECC very interesting for devices with limited processing power or memory such as wearable devices for E-health applications. It is vitally important that elliptic curves are protected against all kinds of attacks concerning the security of elliptic curve cryptography. Selection of a secure elliptic curve is a mathematically difficult problem. In this paper, an efficient elliptic curve selection framework, called SEECC, is proposed to select a secure and efficient curve from all the available elliptic curves. This method enhances the security and efficiency of elliptic curve cryptosystems by using a parallel genetic algorithm.

Parallel imperialist competitive algorithms

The importance of optimization and NP‐problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods; they are mostly sequential but some of them have parallel implementations as well. We propose a multi‐population method to parallelize the Imperialist Competitive Algorithm. The algorithm has been implemented with the Message Passing Interface on 2 computer platforms, and we have tested our method based on shared memory and message passing architectural models. An outstanding performance is obtained, demonstrating that the proposed method is very efficient concerning both speed and accuracy. In addition, compared with a set of existing well‐known parallel algorithms, our approach obtains more accurate results within a shorter time period.

Integrating Safety-Aware Route Optimisation and Run-Time Safety Monitoring in Controlling Swarms of Drones

Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goals. Firstly, it should ensure safety, i.e., guarantee collision avoidance. Secondly, it should prevent a premature depletion of the batteries of the drones by minimizing their travel paths. In this paper, we propose an approach that combines run-time safety monitoring and high performance evolutionary algorithm to predict dynamically emerging hazards. High performance of the route calculation algorithm allows us to ensure that the routes of drones are dynamically adjusted to avoid collisions while maintaining efficiency. The benchmarking of the proposed approach validates its efficiency and safety.

Safety-Aware Control of Swarms of Drones

In this paper, we propose a novel approach to ensuring safety while planning and controlling an operation of swarms of drones. We derive the safety constraints that should be verified both during the mission planning and at the run-time and propose an approach to safety-aware mission planning using evolutionary algorithms. High performance of the proposed algorithm allows us to use it also at run-time to predict and resolve in a safe and optimal way dynamically emerging hazards. The benchmarking of the proposed approach validate its efficiency and safety.

Towards a realtime, collision-free motion coordination and navigation system for a UAV fleet

This paper presents a realtime, collision-free motion coordination and navigation system for an Unmanned Aerial Vehicle (UAV) fleet. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-to-moving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee a risk of a collision in realtime and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire fleet. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments. The results demonstrate that the proposed system successfully predicts and avoids all three kinds of collisions in realtime. Moreover, it generates efficient UAV routes, has an excellent runtime performance, efficiently scales to large-sized problem instances involving dozens of UAVs and obstacles, and is suitable for some densely populated, cluttered flying zones.

Deriving Mode Logic for Autonomous Resilient Systems

Ensuring system resilience – dependability in presence of changes – is a complex engineering task. To achieve resilience, a system should not only autonomously cope with non-deterministically changing internal state and external operating conditions but also proactively reconfigure to maintain efficiency. To facilitate structuring and verifying such complex system behavior, in this paper, we demonstrate how to derive resilience-enhancing mode transition logic from the goals that the system should achieve. Our approach is formalised in Event-B that allows us to reason about resilience mechanisms at different architectural levels. We illustrate the proposed approach by an example – safe and efficient navigation of a swarm of drones.

Hierarchal Placement of Smart Mobile Access Points in Wireless Sensor Networks Using Fog Computing

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.

A reliable weighted feature selection for auto medical diagnosis

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.