With the rapid development of the internet-of-things (IoT), the application of IoT terminals (ITs) has been growing exponentially. To address this challenge, ultra-dense networks have been widely regarded as an effective solution. However, under the constraints of task latency and resource limitations, how to achieve the joint offloading and caching of energy efficiency and security remains a critical issue. To address it, we first propose two types of secure collaborative offloading modes for this network framework, i.e., secure collaborative computation offloading with caching and non-caching. Under these two modes, we then strive to minimize the overall local energy consumption (EC) of all ITs, subject to the constraints of computational resources, latency, security cost, and caching capacity. This is achieved by jointly optimizing device association, cache decision-making, channel selection, executing decision-making, power control, secure service allocation, and multi-step task offloading. To solve the formulated nonlinear fractional problem efficiently, we put forward an improved football team training algorithm (IFTTA), which integrates a diversity-guided mutation strategy into the original football team training algorithm (FTTA). Furthermore, we conduct an in-depth analysis of the convergence properties and computational complexity of the proposed algorithm. Simulation results demonstrate that the IFTTA achieves lower total local EC and task-processing delay compared to the FTTA, while satisfying system constraints, and generally outperforms existing state-of-the-art methods.

Complex building structures fundamentally limit indoor wireless performance. While the wireless industry consistently pursues optimal channel capacity, architectural constraints remain significant factors, largely neglected by conventional workflows. To bridge the gap, this paper expands the inter-disciplinary building wireless performance (BWP) framework by introducing a layout-specific evaluation of area spectral efficiency (ASE). We propose ASE gain as a novel metric to quantify the impact of building layouts on indoor wireless channel capacity. Furthermore, we derive and validate an analytical model for the ASE gain against Monte Carlo simulations. This approach significantly reduces the computation time from hours to minutes with a relative error of less than 1%. Numerical results demonstrate that the ASE gain is significantly influenced by building layouts and wireless parameters. For example, in multi-rectangular layouts, reducing the reception threshold density of 10 dBWm−2decreases the variation range by 0.2059 at 1 GHz, whereas it increases the range by 0.1990 at 28 GHz. For non-rectangular polygonal layouts, the variation range is one order of magnitude larger than in simple rectangular rooms. This work highlights the importance of integrating the ASE gain into future building design. It complements existing metrics such as power gain and interference gain to ensure that buildings provide sufficient margin for the future deployment of high-density networks.

Edge content caching of Internet of Vehicles (IoVs) is a key technology for alleviating backhaul strain and reducing access latency. To protect the privacy of vehicular users, Federated learning (FL) is employed by sharing vehicles’ local models instead of data. However, vehicles may leave the coverage range of serving node before completing the local model training. To enhance model aggregation efficiency, asynchronous Federated learning (AFL) is employed, which allows asynchronous aggregation without waiting for all vehicles to update their local models. In practice, the local models are susceptible to malicious tampering during the global aggregation process. To solve this problem, we propose a secure AFL (SAFL) framework by incorporating a Z-score-based weight detection method within AFL. Moreover, To improve caching efficiency and adapt to the highly dynamic IoV environments, we introduce an innovative proactive caching approach by combining a conditional variational autoencoder and generative adversarial network to predict popular contents, thereby improving the cache hit ratio. Additionally, based on the prediction results of popular content, we optimize intelligent decision-making using multiagent deep reinforcement learning (DRL) to reduce the content transmission delay. Extensive simulations are performed based on real-world datasets and experimental results demonstrate that the proposed SAFL and multiagent DRL hybrid technique outperforms other baseline approaches.

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.

Ultradense networks are widely regarded as a promising solution to explosively growing applications of Internet of Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-OMA (NOMA) are considered under base station (BS) clustering. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computation resources, and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control, and computation offloading are performed for minimizing the overall energy consumed by all IMDs. It is noteworthy that multistep computation offloading is concentrated to balance the network loads and fully utilize computation resources. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computation complexity, and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under strictly satisfying constraints of latency and security cost.

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.