Shahab Tayeb

A survey on IoT communication and computation frameworks: An industrial perspective

This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the worlds physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.

An optimized approach for community detection and ranking

Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network. Such knowledge can be utilized for target marketing or grouping similar, yet distinct, nodes. The ever-growing variety of social networks necessitates detection of minute and scattered communities, which are important problems across different research fields including biology, social studies, physics, etc. Existing community detection algorithms such as fast and folding or modularity based are either incapable of finding graph anomalies or too slow and impractical for large graphs. The main contributions of this work are twofold: (i) we optimize the Attractor algorithm, speeding it up by a factor depending on complexity of the graph; i.e. the more complex a social graph is, the better result the algorithm will achieve, and (ii) we propose a community ranker algorithm for the first time. The former is achieved by amalgamating loops and incorporating breadth-first search (BFS) algorithm for edge alignments and to fill in the missing cache, preserving a constant of time equal to the number of edges in the graph. For the latter, we make the first attempt to enumerate how influential each community is in a given graph, ranking them based on their normalized impact factor.

Will an Emerging Standard Take Over the Routing Realm? An Evaluative Analysis of DUAL and SPF

This paper aims to discuss a comprehensive list of demerits associated with the use of Diffusing Update Algorithm compared to its link-state counterpart, namely, Shortest Path First algorithm which is a variant of Dijkstras algorithm. Such a comparison was neglected for the past two decades due to the proprietary nature of the former protocol. This has led to the prevalence of the latter which is why many computer network professionals adamantly recommend implementing link-state protocols in campus implementations. However, this is of importance today pursuant to the release of several IETF Internet drafts in an attempt to standardize the Enhanced Interior Gateway Routing Protocol. Dynamic routing protocols rely on algorithms computing the shortest paths using weighted digraphs and tree traversals. In this paper, not only are the algorithms discussed but also an in-depth analysis of the various features of the aforementioned protocols is conducted. Abandoning the periodicity of update massages and operating in an event- driven fashion with automatic failover capability are some of the features that will be analyzed. Part of the novelty of this paper lies in the mathematical representation of decision-making processes and metric computation. One of the notable findings of this paper is an evaluative analysis of convergence times achieved in a typical university campus routing implementation. Moreover, using wide metric vectors contributes to energy-aware routing and improved performance for jitter-sensitive services.

Minimizing energy consumption of smart grid data centers using cloud computing

Cloud computing provides its services via high speed networks as a service to users. In private clouds, the consumers access resources through a broker. This prevents wasting resources by owning idle systems and thus, lowering the cost. In this paper, a method is proposed to minimize the energy consumption of smart grid computation systems. To achieve this, different properties of private clouds, such as distributed computing among an array of data centers, were utilized. The proposed method was simulated on the Cloud Analyst test bed. Simulation results demonstrate that the proposed method achieves lower energy consumption as compared to predominant methods, which results in a significant cost reduction.

Vulnerability Analysis of Iframe Attacks on Websites

Clickjacking attacks are emerging threats to websites of different sizes and shapes. They are particularly used by threat agents to get more likes and/or followers in Online Social Networks (OSNs). This paper reviews the clickjacking attacks and the classic solutions to tackle various forms of those attacks. Different approaches of Cross-Site Scripting attacks are implemented in this study to study the attack tools and methods. Various iFrame attacks have been developed to tamper with the integrity of the website interactions at the application layer. By visually demonstrating the attacks such as Cross-Site scripting (XSS) and Cross-Site Request Forgery (CSRF), users will be able to have a better understanding of such attacks in their formulation and the risks associated with them.

A Raspberry-Pi Prototype of Smart Transportation

This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.

Improving Discovery Using Meta-Heuristic Echolocation

This paper discusses a meta-heuristic echolocation mathematical model, as a possible method to discover adjacent vehicles and road-side units in a smart transportation setting with levels 3, 4, and 5 autonomous vehicles. The operation of IoAV based on monitoring several parameters as well as major obstacles for the proliferation of level 4 and 5 autonomous vehicles are also analyzed. In this paper, we make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications. Simulation results demonstrated that the proposed model incurs minimal computation and communication overheads.

A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems

This paper presents the use of the Support Vector Regression (SVR) technique to forecast the reliability of a system. Future predicted values of system reliability are highly sensitive to the choice of SVR parameters and the type of kernel SVR function. The dataset of a turbocharged diesel engine system was used as a case study. The Normalize Root Mean Square Error (NRMSE) measure was used to evaluate the SVR model in predicting the reliability of the system. Many experimental attempts were done using the optimal SVR parameters and the proper kernel function. Results showed that Order 5 of the polynomial kernel outperformed both Gaussian and linear kernel functions in predicting the future reliability values with minimal NRMSE. Experimentally, choosing the proper parameters for the SVR is a hard process, and there are no guarantees that the good parameters and the best kernel function are used. Therefore, artificial intelligence must be used. A genetic algorithm (GA) was used as an AI search optimization method to optimize both the SVR parameters and the type of the kernel function by generating a GA-SVR model. The GA successfully optimized the SVR model to ensure accurate predictions. The adaptive GASVR model was used to overcome such problems as small size of the dataset, varying lifetimes of the system components, and odd behavior of the system because of external environmental causes. Results confirmed the efficiency of the adaptive model to predict precisely the reliability of the system, even with a small dataset.

Load Balancing in WSNs using a Novel Markov Decision Process Based Routing Algorithm

The Wireless Sensor Networks (WSNs) consist of many sensor nodes which are vital to various applications in our daily lives. Load-balancing is a key challenge for WSNs. Improving load-balancing can help with controlling traffic, saving energy and eventually, resulting in a better lifetime. In this paper, a modification of the Energy Efficient Credit-Based (EECB) routing algorithm is proposed, which selects optimal routes based on priority of relay nodes using Markov Decision Process (MDP). Simulation results demonstrate that the proposed algorithm achieves better load-balancing, better lifetime, and lower energy consumption, at the expense of slightly higher packet loss and lower data delivery rate. The results are presented in comparison with the commonly used Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm.