Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability and insufficiency by the Internet of Hybrid Energy Harvesting Things (IoHEHT). In this work, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT things for Smart City concept.

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer solutions have been investigated to satisfy only scheduled URLLC traffic, there is a lack of study on enabling transmission of non-scheduled URLLC traffic, especially in coexistence with the scheduled URLLC traffic. Machine learning (ML) is an important enabler for such a co-existence scenario due to its ability to exploit spatial/temporal correlation in user behaviors and use of radio resources. Hence, in this paper, we first study the coexistence design challenges, especially the radio resource management (RRM) problem and propose a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each URLLC traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the benefits of leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99.99% reliability of both scheduled and nonscheduled traffic types is satisfied.

Wireless sensor networks (WSN) with dynamic spectrum access (DSA) capability, namely cognitive radio sensor networks (CRSN), is a promising solution for spectrum scarcity problem. Despite improvement in spectrum utilization by DSA capability, energy-efficient solutions for CRSN are required due to resource-constrained nature of CRSN inherited from WSN. Clustering is an efficient way to decrease energy consumption. Existing clustering approaches for WSN are not applicable in CRSN and existing solutions for cognitive radio networks are not suitable for sensor networks. In this paper, we propose an event-driven clustering protocol which forms temporal cluster for each event in CRSN. Upon detection of an event, we determine eligible nodes for clustering according to local position of nodes between event and sink. Cluster-heads are selected among eligible nodes according to node degree, available channels and distance to the sink in their neighborhood. They select one-hop members for maximizing the number of two-hop neighbors that are accessible by one-hop neighbors through cluster channels to increase connectivity between clusters. Clusters are between event and sink and are no longer available after the end of the event. This avoids energy consumption due to unnecessary cluster formation and maintenance overheads. Performance evaluation reveals that our solution is energy-efficient with a delay due to spontaneous cluster formation.

Extensive use of amateur drones (ADrs) poses threat to the public safety for their possible misuse. Hence, surveillance drones (SDrs) are utilized to detect and eliminate potential threats. However, limited battery, and lack of efficient communication and networking solutions degrade the quality of surveillance. To this end, we conceptualize Energy Neutral Internet of Drones (enIoD) to enable enhanced connectivity between drones by overcoming energy limitations for autonomous and continuous operation. Power provisioning with recharging stations is introduced by wireless power transfer (WPT) to energize the drones. Renewable energy harvesting (EH) is utilized to realize energy neutrality, which is minimization of deficit in harvested and consumed energy in enIoD. Communication and networking architectures and protocols for realization of multidimensional objectives are presented. Finally, possible application areas are explained with a case study to show how enIoD operates.

Cognitive Radio (CR) enables dynamic spectrum access to utilize licensed spectrum when it is idle. CR technology is applied to wireless ad hoc and sensor networks, to form Cognitive Radio Ad Hoc Networks (CRAHNs) and Cognitive Radio Sensor Networks (CRSNs), respectively. Clustering is an efficient topology management technique to regulate communication and allocate spectrum resources by CR capabilities of nodes in CRAHNs and CRSNs. In this paper, we thoroughly investigate benefits and functionalities of clustering such as topology, spectrum and energy management in these networks. We also overview motivations for and challenges of clustering in CRAHNs and CRSNs. Existing clustering schemes are reviewed and compared. We conclude by revealing key considerations and possible solutions for spectrum-aware clustering in multi-channel CRAHNs and CRSNs.