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In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

Nonorthogonal multiple access (NOMA) is seen as an important technology for tomorrow’s Internet-of-Things (IoT) systems. In uplink power-domain NOMA (PD-NOMA), allocating the uplink transmit power of the IoT devices is important to maximize both the sum rate and the reliability of devices. However, it is challenging to optimize the uplink transmit power when the received signal power is affected by a random fading channel. Hence, in this article, the problem of uplink transmit power assignment is studied for a wireless network with PD-NOMA that serves uplink IoT services. This is posed as a problem of determining the target received signal power at the base station (BS) so that the reliability and upper bound of sum rate of the users are jointly maximized, where the received signal power at the BS is unknown to the devices due to Nakagami- $m$ fading channel. To find an optimal allocation of the lower and higher target received power values for the devices using PD-NOMA, the reliability and upper bound of sum rate are derived in terms of target received power values and power difference threshold. For a special case of Nakagami- $m$ fading channel, the theoretical analysis shows that the highest reliability and the highest upper bound of sum rate are achieved, when the target received power values are highest. For a general Nakagami- $m$ fading channel, simulation results show that there is a tradeoff between reliability and sum-rate upper bound and, thus, allocation of lower and higher target received power values is necessary to satisfy the communication requirements of IoT devices. Moreover, for a special case of Nakagami- $m$ fading channel, simulation results show that the derived optimal transmit power achieves the optimal sum-rate upper bound and reliability, and the target received power values of two devices must be highest for the maximum upper bound of sum rate and reliability. Furthermore, in simulation results, increasing the lower and higher target received power values increases both the upper bound of sum rate and reliability.

This study examines the determinants of a firm’s generative appropriation, defined as a firms’ effectiveness in capturing a larger share of cumulative inventions spawned from its prior inventions. Building on learning theory and the resource-based view, we investigate how a firm’s knowledge diversity across technological and geographic space influences generative appropriation. We also investigate how technology opportunities moderate the effect of knowledge diversity on generative appropriation. Using a longitudinal study of US manufacturing firms and using patents as a proxy for inventions, we find that technological knowledge diversity has a negative effect on generative appropriation and that technology opportunity weakens that effect. We also find that the effect of geographic diversity is contingent on the availability of technological opportunities. When technology opportunity is low, moderate geographic diversity leads to higher generative appropriation than high or low geographic diversity does. However, when technology opportunity is high, geographic diversity appears to have a monotonically negative effect on generative appropriation. These results highlight the way the cumulative and preclusive learning of prior knowledge depend on a firm’s knowledge diversity and technology opportunities.

Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation by jointly exploiting the radio and computing resources of end-user devices and cloud servers. However, the dynamic and distributed formation of local fog networks is highly challenging due to the unpredictable arrival and departure of neighboring fog nodes. Therefore, a given fog node must properly select a set of neighboring nodes and intelligently offload its computational tasks to this set of neighboring fog nodes and the cloud in order to achieve low-latency transmission and computation. In this paper, the problem of fog network formation and task distribution is jointly investigated while considering a hybrid fog-cloud architecture. The overarching goal is to minimize the maximum communication and computation latency by enabling a given fog node to form a suitable fog network and optimize the task distribution under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel online optimization framework is proposed, in which the neighboring nodes are selected by using a threshold-based online algorithm that uses a target competitive ratio, defined as the ratio between the latency of the online algorithm and the offline optimal latency. The proposed framework repeatedly updates its target competitive ratio and optimizes the distribution of the fog node’s computational tasks in order to minimize latency. The simulation results show that, for specific settings, the proposed framework can successfully select a set of neighboring nodes while reducing latency by up to 19.25% compared with a baseline approach based on the well-known online secretary framework. The results also show how, using the proposed framework, the computational tasks can be properly offloaded between the fog network and a remote cloud server in different network settings.

When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power and RIS’s reflecting configuration must be optimized so as to improve users’ data rates and reduce the BS power consumption. In this paper, the problem of energy efficiency optimization is studied in an RIS-assisted cellular network endowed with an RIS reflector powered via energy harvesting technologies. The goal of this proposed framework is to maximize the average energy efficiency by enabling a BS to determine the transmit power and RIS configuration, under uncertainty on the wireless channel and harvested energy of the RIS system. To solve this problem, a novel approach based on deep reinforcement learning is proposed, in which the BS receives the state information, consisting of the users’ channel state information feedback and the available energy reported by the RIS. Then, the BS optimizes its action composed of the BS transmit power allocation and RIS phase shift configuration using a neural network. Due to the intractability of the formulated problem under uncertainty, a case study is conducted to analyze the performance of the studied RIS-assisted downlink system by asymptotically deriving the upper bound of the energy efficiency. Simulation results show that the proposed framework improves energy efficiency up to 77.3% when the number of RIS elements increases from 9 to 25.

In this paper, a novel framework that uses wireless mobile miners (MMs) for computation purposes in a blockchain system is proposed. In the introduced system, the blockchain ledger is located at the communication nodes (CNs), and the MMs associated with CNs process the blockchain's proof-of-work (PoW) computation to verify the originality of the data. The MM that is the first to finish its PoW will receive a reward by sending its computing result to the CNs that are connected to other MMs. In the considered scenario, a blockchain forking event occurs if the MM having the shortest PoW delay fails to be the first to update its computing result to other MMs. To enable such mobile operations for a blockchain with minimum forking events, it is imperative to maintain low-latency wireless communications between MMs and CNs. To analyze the sensitivity of the system to latency, the probability of occurrence of a forking event is theoretically derived. The system is then designed so as to compute the forked block's PoW again to recover from a forking event. For this case, the average energy consumption of an MM is derived as a function of the system parameters such as the number of MMs and power consumed by the computing, transmission, and mobility processes of the MMs. Simulation results verify the analytical derivations and show that using a larger number of MMs can reduce the energy consumption by up to 94.5% compared to a blockchain system with a single MM.

In this paper, the problem of unmanned aerial vehicle (UAV)-assisted fog computing in Industry 4.0 smart factories is studied. In particular, a novel online framework is proposed to enable a source UAV to offload computing tasks from ground sensors within a smart factory and allocate them to neighboring fog UAVs for distributed task computing, before the source UAV arrives at its destination. The online nature of the framework allows the UAVs to optimize their task allocation and decide on which neighbors to use for fog computing, even when the tasks are revealed to the source UAV in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the problem, an online greedy algorithm is designed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio can be analytically derived as a function of the task sizes and the data rates of the UAVs. Simulation results show that the proposed online algorithm can achieve a near- optimal task allocation with an optimality gap that is no higher than 7.5% compared to the offline, optimal solution with complete knowledge of all tasks.

Enabling effective computation for emerging applications such as augmented reality or virtual reality via fog computing requires processing data with low latency. In this paper, a novel computational caching framework is proposed to minimize fog latency by storing and reusing intermediate computation results (IRs). Using this proposed paradigm, a fog node can store IRs from previous computations and can also download IRs from neighboring nodes at the expense of additional transmission latency. However, due to the unpredictable arrival of the future computational operations and the limited memory size of the fog node, it is challenging to properly maintain the set of stored IRs. Thus, under uncertainty of future computation, the goal of the proposed framework is to minimize the sum of the transmission and computational latency by selecting the IRs to be downloaded and stored. To solve the problem, an online computational caching algorithm is developed to enable the fog node to schedule, download, and manage IRs compute arriving operations. Competitive analysis is used to derive the upper bound of the competitive ratio for the online algorithm. Simulation results show that the total latency can be reduced up to 26.8% by leveraging the computational caching method when compared to the case without computational caching.

Fog computing is seen as a promising approach to perform distributed, low-latency computation for supporting Internet of Things applications. However, due to the unpredictable arrival of available neighboring fog nodes, the dynamic formation of a fog network can be challenging. In essence, a given fog node must smartly select the set of neighboring fog nodes that can provide low-latency computations. In this paper, this problem of fog network formation and task distribution is studied considering a hybrid cloud-fog architecture. The goal of the proposed framework is to minimize the maximum computational latency by enabling a given fog node to form a suitable fog network, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel approach based on the online secretary framework is proposed. To find the desired set of neighboring fog nodes, an online algorithm is developed to enable a task initiating fog node to decide on which other nodes can be used as part of its fog network, to offload computational tasks, without knowing any prior information on the future arrivals of those other nodes. Simulation results show that the proposed online algorithm can successfully select an optimal set of neighboring fog nodes while achieving a latency that is as small as the one resulting from an ideal, offline scheme that has complete knowledge of the system. The results also show how, using the proposed approach, the computational tasks can be properly distributed between the fog network and a remote cloud server., Updated Figure 4

The co-existence of small cell base stations (SBSs) with conventional macrocell base station is a promising approach to boost the capacity and coverage of cellular networks. However, densifying the network with a viral deployment of SBSs can significantly increase energy consumption. To reduce the reliance on unsustainable energy sources, one can adopt self-powered SBSs that rely solely on energy harvesting. Due to the uncertainty of energy arrival and the finite capacity of energy storage systems, self-powered SBSs must smartly optimize their ON and OFF schedule. In this paper, the problem of ON/OFF scheduling of self-powered SBSs is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs consisting of energy consumption and transmission delay of a network. For the original problem, we show that an algorithm can solve the problem in the illustrative case. Then, to reduce the complexity of the original problem, an approximation is proposed. To solve the approximated problem, a novel approach based on the ski rental framework, a powerful online optimization tool, is proposed. Using this approach, each SBS can effectively decide on its ON/OFF schedule autonomously, without any prior information on future energy arrivals. By using competitive analysis, a deterministic online algorithm and a randomized online algorithm (ROA) are developed. The ROA is then shown to achieve the optimal competitive ratio in the approximation problem. Simulation results show that, compared with a baseline approach, the ROA can yield performance gains reaching up to 15.6% in terms of reduced total energy consumption of SBSs and up to 20.6% in terms of per-SBS network delay reduction. The results also shed light on the fundamental aspects that impact the ON time of SBSs while demonstrating that the proposed ROA can reduce up to 69.9% the total cost compared with a baseline approach.

Full-duplex device-to-device (D2D) communications over cellular networks is a promising solution for maximizing wireless spectral efficiency. However, in practice, due to the unpredictable arrival of D2D users, the base station (BS) must smartly allocate suitable channels to arriving D2D pairs. In this paper, the problem of dynamic channel allocation is studied for full-duplex D2D networks. In particular, the goal of the proposed approach is to maximize the system sum-rate under complete uncertainty on the arrival process of D2D users. To solve this problem, a novel approach based on an online weighted bipartite matching is proposed. To find the desired solution of the channel allocation problem, a greedy online algorithm is developed to enable the BS to decide on the channel assignment for each D2D pair, without knowing any prior information on future D2D arrivals. For an illustrative case study, upper and lower bounds on the competitive ratio that compares the performance of the proposed online algorithm to that of an offline algorithm are derived. Simulation results show that the proposed online algorithm can achieve a near- optimal sum-rate with an optimality gap that is no higher than 8.3% compared to the offline, optimal solution that has complete knowledge of the system.

급증하는 데이터 트래픽을 셀룰러 망에서 처리하기 위한 대안으로 스몰셀이 관심을 받고 있다 . 나아가, 스몰셀의 배치밀도는 점차 높아지고 있어 Ultra Dense Network (UDN) 환경이 도래할 것으로 예상된다 . 따라서 본 논문에서는 UDN환경에서 소형기지국의 온/오프를 제어하기 위한 방법을 연구한다 . 본 논문에서는 소형 Access Point(AP), user equipment (UE), Small Cell Controller (SCC)로 구성된 프레임워크를 제안하고 이를 기반으로 켜져 있는 기지국 수는 최소화 하며 , 네트워크 쓰루풋을 유지시키기 위한 소형기지국 온 /오프 방법으로 UE와 같은 사용자 디바이스가 SCC로 피드백하는 채널정보를 활용하는 DANCE 알고리즘을 제안한다. DANCE 알고리즘은 피드백이 1-bit인 경우와 N-bit인 경우, 그리고 활성화시키는 소형AP를 선택하는 기준에 따른 AP-first, UE-first, Proximity ON 등으로 세분화할 수 있다 . 본 연구에서는 시뮬레이션을 통해 UE의 수와 네트워크의 쓰루풋, 에너지 효율의 관계를 분석한 후, DANCE 알고리즘을 통해 에너지 효율을 높일 수 있음을 보인다 .Key Words : Green communication, Base station operation, Small cell, Ultra dense network, Energy efficientABSTRACT

As higher performance is demanded in 5G networks, energy consumption in wireless networks increases along with the advances of various technologies, so enhancing energy efficiency also becomes an important goal to implement 5G wireless networks. In this paper, we study the energy efficiency maximization problem focused on finding a suitable set of turned-on small cell access points (APs). Finding the suitable on/off states of APs is challenging since the APs can be deployed by users while centralized network planning is not always possible. Therefore, when APs in small cells are randomly deployed and thus redundant in many cases, a mechanism of dynamic AP turning-on/off is required. We propose a device-assisted framework that exploits feedback messages from the user equipment (UE). To solve the problem, we apply an optimization method using belief propagation (BP) on a factor graph. Then, we propose a family of online algorithms inspired by BP, called DANCE, that requires low computational complexity. We perform numerical simulations, and the extensive simulations confirm that BP enhances energy efficiency significantly. Furthermore, simple, but practical DANCE exhibits close performance to BP and also better performance than other popular existing methods. Specifically, in a small-sized network, BP enhances energy efficiency 129%. Furthermore, in ultra-dense networks, DANCE algorithms successfully achieve orders of magnitude higher energy efficiency than that of the baseline. Published version