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In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

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Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams.

The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. However, the capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multiperiod variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.

The combination of multicast and directional mmWave communication paves the way for solving spectrum crunch problems, increasing spectrum efficiency, ensuring reliability, and reducing access point load. Furthermore, multi-hop relaying is considered as one of the key interest areas in future 5G+ systems to achieve enhanced system performance. Based on this approach, users located close to the base station may serve as relays towards cell-edge users in their proximity by using more robust device-to-device (D2D) links, which is essential, e.g., to reduce the power consumption for wearable devices. In this paper, we account for the limitations and capabilities of directional mmWave multicast systems by proposing a lowcomplexity heuristic solution that leverages an unsupervised machine learning algorithm for multicast group formation and by exploiting the D2D technology to deal with the blockage problem.

As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities (Social Digital Twins, SDTs) at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement, while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.

Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middlerange deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.