The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive 5G network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multi-band cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multi-band approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multi-band approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality of service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for cross-layer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

During the past decade, the Internet of Things (IoT) has revolutionized the ubiquitous computing with multitude of applications built around various types of sensors. A vast amount of activity is seen in IoT based product-lines and this activity is expected to grow in years to come with projections as high as billions of devices with on average 6-7 devices per person by year 2020. With most of the issues at device and protocol levels solved during the past decade, there is now a growing trend in integration of sensors and sensor based systems with cyber physical systems and device-to-device (D2D) communications. 5 ^th generation wireless systems (5G) are on the horizon and IoT is taking the center stage as devices are expected to form a major portion of this 5G network paradigm. IoT technologies such as machine to machine communication complemented with intelligent data analytics are expected to drastically change landscape of various industries. The emergence of cloud computing and its extension to fog paradigm with proliferation of intelligent `smart' devices is expected to lead further innovation in IoT. These developments excite us and form a motivation to survey existing work, design new techniques, and identify new applications of IoT. Researchers, scientists, and engineers face emerging challenges in designing IoT based systems that can efficiently be integrated with the 5G wireless communications.

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for cognitive radio sensor networks (CRSN). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient RF sources. We then propose an energy management scheme that consists of 1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and 2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intra-cluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.