Cooperative Secure Transmission by Exploiting Social Ties in Random Networks

Social awareness and social ties are becoming increasingly popular with emerging mobile and handheld devices. Social trust degree describing the strength of the social ties has drawn lots of research interests in many fields in wireless communications, such as resource sharing, cooperative communication and so on. In this paper, we propose a hybrid cooperative beamforming and jamming scheme to secure communication based on the social trust degree under a stochastic geometry framework. The friendly nodes are categorized into relays and jammers according to their locations and social trust degrees with the source node. We aim to analyze the involved connection outage probability (COP) and secrecy outage probability (SOP) of the performance in the networks. To achieve this target, we propose a double Gamma ratio (DGR) approach through Gamma approximation. Based on this, the COP and SOP are tractably obtained in closed-form. We further consider the SOP in the presence of Poisson Point Process (PPP) distributed eavesdroppers and derive an upper bound. The simulation results verify our theoretical findings, and validate that the social trust degree has dramatic influences on the security performance in the networks.Comment: 30 pages, 11 figures, to be published in IEEE Transactions on Communication

SAI: safety application identifier algorithm at MAC layer for vehicular safety message dissemination over LTE VANET networks

Vehicular safety applications have much significance in preventing road accidents and fatalities. Among others, cellular networks have been under investigation for the procurement of these applications subject to stringent requirements for latency, transmission parameters, and successful delivery of messages. Earlier contributions have studied utilization of Long-Term Evolution (LTE) under single cell, Friis radio, or simplified higher layer. In this paper, we study the utilization of LTE under multicell and multipath fading environment and introduce the use of adaptive awareness range. Then, we propose an algorithm that uses the concept of quality of service (QoS) class identifiers (QCIs) along with dynamic adaptive awareness range. Furthermore, we investigate the impact of background traffic on the proposed algorithm. Finally, we utilize medium access control (MAC) layer elements in order to fulfill vehicular application requirements through extensive system-level simulations. The results show that, by using an awareness range of up to 250 m, the LTE system is capable of fulfilling the safety application requirements for up to 10 beacons/s with 150 vehicles in an area of 2 × 2 km2. The urban vehicular radio environment has a significant impact and decreases the probability for end-to-end delay to be ≤100 ms from 93%–97% to 76%–78% compared to the Friis radio environment. The proposed algorithm reduces the amount of vehicular application traffic from 21 Mbps to 13 Mbps, while improving the probability of end-to-end delay being ≤100 ms by 20%. Lastly, use of MAC layer control elements brings the processing of messages towards the edge of network increasing capacity of the system by about 50%

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