Amr El Mougy

A context and application-aware framework for resource management in dynamic collaborative wireless M2M networks

Achieving end-to-end goals of Wireless Machine to Machine Networks (M2M) is a highly challenging task. These goals include guaranteeing connectivity and maximizing throughput while satisfying application layer Quality of Service (QoS) requirements. Moreover, the problem becomes more complicated in the context of emerging dynamic allocation of application layer software components. This paper presents a collaborative framework for communications in M2M wireless networks. This framework is composed of a context-aware and application-aware software platform and a reasoning machine for network management. The software platform uses multi-level adaptation mechanisms to support dynamic collaboration activities. The platform is also capable of installing the required software components on the appropriate nodes. On the other hand, the reasoning machine for network management is designed using the tool known as Weighted Cognitive Map (WCM). The inference properties of WCMs allow the system to self-organize while considering multiple objectives and constraints. Methods for achieving different objectives using WCMs are illustrated, as well as how system processes can operate coherently to achieve end-to-end goals. Computer simulations show that the system achieves excellent performance results in metrics of call dropping and blocking probabilities and achieving the required Quality of Service (QoS) parameters of the applications.

Cognitive Approaches to Routing in Wireless Sensor Networks

Energy efficiency and network lifetime are key factors in characterizing wireless sensor networks due to the limited energy of nodes. In this paper we present two approaches to routing in wireless sensor networks that utilize the ideas of node cooperation and information exchange to achieve cognition across multiple network layers. In the first proposal, nodes exchange information about their statistical channel parameters to achieve awareness of the coverage area and use this information in path choice and transmit power adaptation. In the second proposal, nodes share information about energy states and utilize this information in achieving load balancing across nodes in the network. Nodes also cooperate with each other to reduce unnecessary transmissions. We evaluate our proposals through computer simulations and the results show that the energy efficiency of our proposals significantly outperform existing techniques, thus achieving the greater goal of extending network lifetime.

Classification of technological privacy techniques for LTE-based public safety networks

Public Protection and Disaster Relief (PPDR) organizations emphasize the need for dedicated and broadband Public Safety Networks (PSNs) with the capability of providing a high level of security for critical communications. Considering the preceding fact, Long Term Evolution (LTE) has been chosen as the leading candidate technology for PSNs. However, a study of privacy challenges and requirements in LTE-based PSNs has not yet emerged. This paper aims to highlight those challenges and further discusses possible scenarios in which privacy might be violated in this particular environment. Then, a classification of technological privacy techniques is proposed in order to protect and enhance privacy in LTE-based PSNs. The given classification is a useful means for comparison and assessment of applicable privacy preserving methods. Moreover, our classification highlights further requirements and open problems for which available privacy techniques are not sufficient.

Achieving end-to-end goals of WSN using Weighted Cognitive Maps

In this paper, a novel cognitive engine for Wireless Sensor Networks (WSN) is proposed in order to achieve its end-to-end goals. This engine is designed using the tool known as Weighted Cognitive Maps (WCM). WCMs have the advantage of being able to consider multiple conflicting objectives and constraints with low complexity. Their inference properties also allow them to resolve complex network interactions using simple mathematical operations. Methods for designing the WCM system are illustrated. The performance of the proposed system is evaluated using computer simulations. Simulation results show that the WCM system outperforms its existing counterparts in metrics of network lifetime, throughput, and PLR.

A cognitive WSN framework for highway safety based on weighted cognitive maps and Q-learning

Wireless technology can provide new efficient techniques for improving highway safety. Wireless Sensor Networks (WSN) have been identified as a key enabling technology for monitoring road conditions and providing early warning messages to drivers about any dangers that may be present. In order to ensure that the end-to-end goals of such a WSN are achieved, this paper proposes a cognitive framework based on the mathematical tools known as Weighted Cognitive Maps (WCM) and Q-learning. WCM is used to design a reasoning machine that can consider multiple conflicting constraints with low complexity. On the other hand, the Q-learning algorithm is used to design a learning protocol that can build a knowledge base which enables the WCM system to make more informed decisions. Thus, a reward system is developed that directly addresses the end-to-end goals of the system. The performance of the cognitive framework is evaluated using extensive computer simulations and compared to state of the art systems. Simulation results show significant performance improvements with the proposed cognitive framework.

On the integration of software-defined and information-centric networking paradigms

Information-Centric Networks (ICN) is a communication paradigm that promises to transfer the Internet from a host-to-host communication network to a massive information delivery system, capable of supporting the future needs of the users. ICNs are based on three main concepts: content forwarding, in-network caching, and multicasting. To realize these three key concepts, Software-Defined Networking (SDN) has emerged as an important potential candidate, due to its ability to implement all network intelligence in a reprogrammable device known as the controller. However, the integration of these two networking paradigms presents many challenges. This paper studies these challenges and highlights key areas where more effort is needed from the research community. Particularly, the paper focuses on the architectural modifications necessary to incorporate SDN in ICN architectures, including the necessary modifications to the popular OpenFlow protocol. In addition, the paper studies several implementation challenges arising from the integration of SDN and ICN such as the need for a scalable and distributed SDN control that is highly responsive in meeting dynamic user requests, and the need for a semantic naming scheme for content that provides the users with a large degree of expressiveness in specifying their requests. The paper surveys key research efforts that address these challenges and highlights some promising solutions and open research problems therein.

Throughput optimization of a power-aware MAC for WLANs in correlated shadowing environments

In this paper we propose a call admission protocol that adapts the transmit power and data rate of interfering links in WLANs in order to maximize the aggregate and per-node throughput of the network. Power and data rate are adapted in a distributed way by discovering the statistical parameters of the channel and communicating those parameters to neighboring nodes. To ensure accurate results, we consider a realistic and comprehensive propagation model, where shadowing among links is assumed to be correlated. Simulation results show that the proposed model achieves significant gains in aggregate and per-node throughput over traditional WLANs.

Dynamic Mapping of Road Conditions Using Smartphone Sensors and Machine Learning Techniques

Road surface conditions can cause serious traffic accidents, often with tragic consequences. Thus, an efficient system for mapping road anomalies can significantly promote the safety of drivers and pedestrians. This paper proposes a novel road anomaly mapping system that is able to detect a wide variety of conditions with high accuracy. The smartphones accelerometer and GPS sensors are used for detection to minimize infrastructure costs. In addition, to ensure the system is adaptive to different road conditions, pattern recognition techniques are used to automatically calculate the detection threshold. Furthermore, to compensate for GPS inaccuracies, reinforcement learning based on a proposed reward system is used to maximize confidence in the detected anomalies. The reward system is also able to forget anomalies that have been fixed. Moreover, the system is implemented in a distributed way between the smartphone and a cloud server to minimize cellular bandwidth usage, while still retaining the accuracy advantages of a centralized cloud. Live tests have been conducted to evaluate the performance of the system and the results show it is accurate under different driving conditions.

Reliable data transfer for collecting data from BLE sensors using smartphones as relays to the cloud

Bluetooth Low Energy (BLE) has recently positioned itself as one of the key enabling technologies in the Internet of Things (IoT). In order to provide scalability, one promising way to collect data from BLE sensors is to utilize the existing population of smartphones in their range as relays to the cloud. In this case, providing reliable data transfer becomes complicated due to challenges such as the varying smartphone density, the sleep cycle of the sensors, and the varying Internet connectivity or unreliability of the smartphones. Therefore, this paper proposes a reliable data transfer protocol that builds upon some of the principles of TCP to address the aforementioned challenges. The protocol is evaluated using computer simulations and a hardware prototype is implemented. The evaluations not only show that the proposed protocol provides reliable data transfer, but also how to configure the parameters of the protocol to ensure energy-efficiency.

Enhancing the privacy of LTE-based public safety networks

The existence of Public Safety Network(PSN) is critical for saving lives in tragic incidents. Within PSN, privacy preservation becomes vital since the information being transferred may be personally identifiable (or private for short). Long Term Evolution(LTE) has been chosen to be the suitable technology for PSN because of its unique characteristics such as higher capacity, higher spectral efficiency, enhanced security and reliability and so on. In spite of those features, several issues related to privacy within LTE have been detected by the experts. However, a study of privacy challenges and requirements in PSN has not yet emerged. This study aims to highlight privacy issues of the LTE related to PSN. Thus, a comprehensive review of the relevant works proposed to improve privacy of the LTE security architecture is provided. Furthermore, the feasibility of those efforts for PSN is discussed. Finally, an efficient solution is proposed to enhance the privacy of the LTE-based PSN. The idea is to hide the real International Mobile Subscriber Identity (IMSI) within a random bit stream of certain size where only the subscriber and HSS could extract the respective IMSI. Furthermore, performance evaluation and security analysis of the proposed protocol are explored.