In September 2015, the United Nations General Assembly accepted the 2030 Development Agenda, which has included 92 paragraphs, and the Paragraph 91 defined 17 sustainable development goals (SDGs) and 169 associated targets. The goal of this paper is to discover the correlations among SDGs and information and communications technologies (ICTs). This paper discusses the roles and opportunities that ICTs play in pursuing the SDGs. We identify a number of research gaps to those three pillars, social, economic, and environmental perspectives, of sustainable development. After extensive literature reviews on the SDG-related research initiatives and activities, we find that the majority of contributions to SDGs recognized by the IEEE and ACM research communities have mainly focused on the technical aspects while there are lack of the holistic social good perspectives. Therefore, there are essential and urgent needs to raise the awareness and call for attentions on how to innovate and energize ICTs in order to best assist all nations to achieve the SDGs by 2030.

Small Cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones. However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites. To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection. In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network. We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components. In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances. We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

In studies of survivable networks, it is important to be able to differentiate network topologies by means of a robust numerical measure that indicates the levels of immunity of these topologies to failures of their nodes and links. Ideally, such a measure should be sensitive to the existence of nodes or links which are more important than others, for example, if their failures cause the network's disintegration. In this paper, we suggest using an algebraic connectivity metric, adopted from spectral graph theory, namely the 2^nd smallest eigenvalue of the Laplacian matrix of the network topology, instead of the average nodal degree that is usually used to characterize network connectivity in studies of the spare capacity allocation problem. Extensive simulation studies confirm that this metric is a more informative and more accurate parameter than the average nodal degree for characterizing network topologies in survivability studies.

Current trends in telecommunication networks foresee the adoption of the fifth generation (5G) of wireless networks in the near future. However, a large number of people are living without coverage and connectivity. To face this issue, we consider the possibility of deploying 5G networks in rural and low-income zones. After detailing the current state-of-the-art, we consider the main challenges that need to be faced. Moreover, we define the main pillars to follow in order to deploy 5G networks in such zones, as well as a proposal of a future network architecture.

Immediately after a disaster, the normal telecommunication infrastructure, including wired and wireless networks, is often seriously compromised and cannot guarantee regular coverage and reliable communications services. These temporarily-missing communications capabilities are crucial to rescuers and affected citizens as the responders need to effectively coordinate and communicate to minimize the loss of lives and property. A cyber-physical system (CPS) is composed of integrated communication, computation and physical objects, and cyber-physical vehicle systems (CPVSs) are an emerging field due to the rapid advancements on real-time computing, mobile communications and autonomous control in intelligent transport systems. In this paper, we propose a cyber-physical buses-and-drones mobiLe edge infrastructure (AidLife) for disaster emergency communications, which aims at a rapidly deployable resilient system capable of supporting flexible communications to serve large-scale disaster situations by utilizing the existing public transport system. In particular we envision a proposal where public buses can be recruited to temporarily host portable base station (BS) and computation units as well as power resources so as to form a buses-based mobile edge infrastructure, and also accommodate drones to extend their coverage to hard-to-reach areas. Our preliminary results show that the AidLife system can guarantee a good coverage to users, even when a large number of normal BSs that are damaged.

In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped with computing and storage capabilities to communicate with surrounding infrastructure. In this paper, we propose a subcategory of IoV as the Internet of buses (IoB), where public buses enable a service as a data carrier in a smart city by introducing a neural network-based sustainable data dissemination system (NESUDA), where opportunistic sensing comprises delay-tolerant data collection, processing and disseminating from one place to another place around the city. The objective was to use public transport to carry data from one place to another and to reduce the traffic from traditional networks and energy consumption. An advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for data allocation. We used the Auckland transport (AT) buses data set from the transport agency to validate our model for the level of accuracy in predicted bus arrival time and scheduled arrival time to disseminate data using bus services. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. The offloading capacity of our proposed data dissemination system showed that it could be utilized to effectively complement traditional data networks. Moreover, the maximum offloading capacity at each parent stop could reach up to 360 GB with a huge saving of energy consumption.