Opportunistic Networking for Improving the Energy Efficiency of Multi-Hop Cellular Networks

Relaying technologies can help address the capacity and energy-efficiency challenges faced by cellular networks as a result of the rapid increase in mobile data consumption. A nonnegligible portion of such consumption corresponds to delay tolerant services. This delay tolerance offers the possibility for opportunistic networking to exploit contact opportunities between mobile devices in order to reduce the impact of data traffic on the cellular capacity and energy-efficiency without sacrificing the end-user quality of service. In this context, this paper investigates the use of opportunistic forwarding in MCNMR (Multi-hop Cellular Networks with Mobile Relays) to reduce energy consumption in the case of delay tolerant services. The study proposes to exploit context information provided at a low cost by the cellular infrastructure to efficiently select the forwarding node in a two-hop MCN-MR scenario. The proposed solution results in significant energy savings compared to traditional single-hop cellular communications and other forwarding solutions reported in the literatureThis work is supported in part by the Spanish Ministry of Economy and Competitiveness and FEDER funds (TEC201126109),and the Local Government of Valencia with reference ACIF/2010/161 and BEFPI/2012/06

Store, carry and forward for energy efficiency in multi-hop cellular networks with mobile relays

Abstract The wide scale adoption of smartphones is boosting cellular data traffic with the consequent capacity constraints of cellular systems and increase in energy consumption. A significant portion of cellular data traffic can be deemed as delay tolerant. Such tolerance offers possibilities for designing novel communications and networking solutions that can accommodate the delay tolerant cellular data traffic while reducing their impact on the overall cellular capacity and energy consumption. In this context, this work studies the use of opportunistic store, carry and forward techniques in Multi-Hop Cellular Networks (MCN) to reduce energy consumption for delay tolerant traffic. The study focuses on two-hop MCN networks using mobile relays (MCN-MR), and identifies the optimum mobile relay location and the location from which the relay should start forwarding the information to the cellular base station in order to minimize the overall energy consumption. The study shows that the use of opportunistic store, carry and forward techniques in MCN-MR can significantly reduce energy consumption compared to other solutions, including traditional single-hop cellular systems or direct contact store, carry and forward solutions.This work is supported in part by the Spanish Ministry of Economy and Competitiveness and FEDER funds (TEC2011–26109)and the Local Government of Valencia with reference ACIF/2010/161 and BEFPI/2012/06

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces

Outage Performance of Two-Hop OFDM Systems with Spatially Random Decode-and-Forward Relays

In this paper, we analyze the outage performance of different multicarrier relay selection schemes for two-hop orthogonal frequency-division multiplexing (OFDM) systems in a Poisson field of relays. In particular, special emphasis is placed on decode-and-forward (DF) relay systems, equipped with bulk and per-subcarrier selection schemes, respectively. The exact expressions for outage probability are derived in integrals for general cases. In addition, asymptotic expressions for outage probability in the high signal-to-noise ratio (SNR) region in the finite circle relay distribution region are determined in closed forms for both relay selection schemes. Also, the outage probabilities for free space in the infinite relay distribution region are derived in closed forms. Meanwhile, a series of important properties related to cooperative systems in random networks are investigated, including diversity, outage probability ratio of two selection schemes and optimization of the number of subcarriers in terms of system throughput. All analysis is numerically verified by simulations. Finally, a framework for analyzing the outage performance of OFDM systems with spatially random relays is constructed, which can be easily modified to analyze other similar cases with different forwarding protocols, location distributions and/or channel conditions

Next Generation Opportunistic Networking in Beyond 5G Networks

Beyond 5G networks are expected to support massive traffic through decentralized solutions and advanced networking mechanisms. This paper aims at contributing towards this vision through the integration of device-centric wireless networks, including Device-to-Device (D2D) communications, and the Next Generation of Opportunistic networking (NGO). This integration offers multiple communication modes such as opportunistic cellular and opportunistic D2D-aided communications. Previous studies have demonstrated the potential and benefits of this integration in terms of energy efficiency, spectral efficiency and traffic offloading. We propose an integration of device-centric wireless networks and NGO that is not driven by a precise knowledge of the presence of the links. The proposed technique utilizes a novel concept of graph to model the evolution of the networking conditions and network connectivity. Uncertainties and future conditions are included in the proposed graph model through anticipatory mobile networking to estimate the transmission energy cost of the different communication modes. Based on these estimates, the devices schedule their transmissions using the most efficient communication mode. These decisions are later revisited in real-time using more precise knowledge about the network state. The conducted evaluation shows that the proposed technique significantly reduces the energy consumption (from 60% to 90% depending on the scenario) compared to traditional single-hop cellular communications and performs closely to an ideal “oracle based” system with full knowledge of present and future events. The transmission and computational overheads of the proposed technique show small impact on such energy gains.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities, AEI, and FEDER funds (TEC2017-88612-R)the Ministry of Science, Innovation and Universities (IJC2018-036862-I)the UMH (‘Ayudas a la Investigación e Innovación de la Universidad Miguel Hernández de Elche 2018’)and by the European Commission under the H2020 REPLICATE (691735), SoBigData (654024) and AUTOWARE (723909) project