On the effect of combining cooperative communication with sleep mode

Cooperation is crucial in (next-generation) wireless networks as it can greatly attribute to ensuring connectivity, reliability, performance, ... Relaying looks promising in a wide variety of network types (cellular, ad-hoc on-demand), each using a certain protocol. Energy efficiency constitutes another key aspect of such networks, as battery power is often limited, and is typically achieved by sleep mode operation. As the range of applications is very broad, rather than modelling one of the protocols in detail, we construct a high-level model capturing the two essential characteristics of cooperation and energy efficiency: relaying and sleep mode, and study their interaction. The used analytical approach allows for accurate performance evaluation and enables us to unveil less trivial trade-offs and to formulate rules-of-thumb applicable across all potential scenarios

SEABASS: Symmetric-keychain Encryption and Authentication for Building Automation Systems

There is an increasing security risk in Building Automation Systems (BAS) in that its communication is unprotected, resulting in the adversary having the capability to inject spurious commands to the actuators to alter the behaviour of BAS. The communication between the Human-Machine-Interface (HMI) and the controller (PLC) is vulnerable as there is no secret key being used to protect the authenticity, confidentiality and integrity of the sensor data and commands. We propose SEABASS, a lightweight key management scheme to distribute and manage session keys between HMI and PLCs, providing a secure communication channel between any two communicating devices in BAS through a symmetric-key based hash-chain encryption and authentication of message exchange. Our scheme facilitates automatic renewal of session keys periodically based on the use of a reversed hash-chain. A prototype was implemented using the BACnet/IP communication protocol and the preliminary results show that the symmetric keychain approach is lightweight and incurs low latency

Framework for Content Distribution over Wireless LANs

Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive technology for Intent access. Due to the low-cost of chipsets and support for high data rates, Wi-Fi has become a universal solution for ever-increasing application space which includes, video streaming, content delivery, emergency communication, vehicular communication and Internet-of-Things (IoT). Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11 standard has been amended several times over the last two decades, to incorporate the requirement of future applications. The 802.11 based Wi-Fi networks are infrastructure networks in which devices communicate through an access point. However, in 2010, Wi-Fi Alliance has released a specification to standardize direct communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi Direct after 9 years of its release is still used for very basic services (connectivity, file transfer etc.), despite the potential to support a wide range of applications. The reason behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit its performance in dense networks. These include the issues related to topology design, such as non-optimal group formation, Group Owner selection problem, clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense networks where the topology changes frequently which directly affects the network performance. The dynamic nature of such networks challenges the operators to design and make optimum planifications. In this dissertation, we propose solutions to the aforementioned problems. We contributed to the existing Wi-Fi Direct technology by enhancing the group formation process. The proposed group formation scheme is backwards-compatible and incorporates role selection based on the device's capabilities to improve network performance. Optimum clustering scheme using mixed integer programming is proposed to design efficient topologies in fixed dense networks, which improves network throughput and reduces packet loss ratio. A novel architecture using Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive networks using machine-learning algorithms to predict the network changes ahead of time and self-configuring the network

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig