Showing papers by "Nirwan Ansari published in 2020"
TL;DR: Simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.
Abstract: In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.
212 citations
TL;DR: This work proposes a load balancing scheme in a fog network to minimize the latency of data flows in the communications and processing procedures by associating IoT devices to suitable BSs and proves the convergence and the optimality of the proposed workload balancing scheme.
Abstract: As latency is the key performance metric for IoT applications, fog nodes co-located with cellular base stations can move the computing resources close to IoT devices Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing However, the latency of data flows in IoT devices consist of both the communications latency and computing latency Owing to the spatial and temporal dynamics of IoT device distributions, some BSs and fog nodes are lightly loaded, while others, which may be overloaded, may incur congestion Thus, the traffic load allocation among base stations (BSs) and computing load allocation among fog nodes affect the communications latency and computing latency of data flows, respectively To solve this problem, we propose a workload balancing scheme in a fog network to minimize the latency of data flows in the communications and processing procedures by associating IoT devices to suitable BSs We further prove the convergence and the optimality of the proposed workload balancing scheme Through extensive simulations, we have compared the performance of the proposed load balancing scheme with other schemes and verified its advantages for fog networking
142 citations
TL;DR: This survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics, which consists of three fusion characteristics: source, algorithm, and weight spaces, and discusses their lessons, challenges, and countermeasures.
Abstract: Demands for indoor positioning based services (IPS) in commercial and military fields have spurred many positioning systems and techniques. Complex electromagnetic environments (CEEs) may, however, degenerate the accuracy and robustness of some existing single systems and techniques. To overcome this drawback, fusion-based positioning of multiple systems and/or techniques have been proposed to revamp the positioning performance in CEEs. In this paper, we survey the fusion-based indoor positioning techniques and systems from seminal works to elicit the state of the art within our proposed unified fusion-based positioning framework, which consists of three fusion characteristics: source, algorithm, and weight spaces. Different from other surveys, this survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics. Meanwhile, discussions in terms of lessons, challenges, and countermeasures are also presented. This survey is invaluable for researchers to acquire a clear concept of indoor fusion-based positioning systems and techniques and also to gain insights from this survey to further develop other advanced fusion-based positioning systems and techniques in the future.
133 citations
TL;DR: The UAV is utilized as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and a proposed approximation algorithm is proposed that is superior to three baseline algorithms to minimize the average latency of all UEs.
Abstract: Advances in wireless communications are empowering the emerging Internet-of-Things (IoT) applications and services with billions of connected devices Mobile-edge computing (MEC) has been proposed to reduce the round-trip delay of these applications as IoT devices may have limited computing resources and the resource-rich mobile cloud may be far away On the other aspect, unmanned aerial vehicles (UAVs) may potentially be employed to improve the quality of service and the channel conditions of users We thus propose to utilize the UAV as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and formulate the UAV-MEC problem with the objective to minimize the average latency of all UEs As the UAV-MEC problem is NP-hard, we decompose it into three subproblems We propose an approximation algorithm with low complexity to solve the first subproblem and then we obtain the optimal solutions of the remaining two subproblems, upon which another proposed approximation algorithm employs these solutions to finally solve the UAV-MEC problem The evaluation results demonstrate that the proposed algorithm is superior to three baseline algorithms
96 citations
TL;DR: A novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC) and a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization.
Abstract: It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.
88 citations
TL;DR: The notion of such convergence is described and an architectural framework for converged network-cloud/edge systems is presented and the state of the art of enabling technologies for network- cloud/edge convergence is surveyed by reviewing recent progress in relevant standardization and technology developments in representative research projects.
Abstract: The wide applications of virtualization and service-oriented principles in various emerging networking technologies introduce a trend of network cloudification that enables network systems to be realized based on cloud technologies and allows network services to be provisioned following the cloud service model. Network cloudification together with the critical role of networking in the latest cloud/edge computing technologies leads to the convergence of networking and cloud/edge computing, which calls for a holistic vision across the fields of networking and computing that may shape relevant technology developments. in this article, we attempt to sketch a big picture to reflect the current status of on-going research toward network-cloud/edge convergence. We first describe the notion of such convergence and present an architectural framework for converged network-cloud/edge systems. Then, we survey the state of the art of enabling technologies for network-cloud/edge convergence by reviewing recent progress in relevant standardization and technology developments in representative research projects. We also discuss challenges that must be fully addressed for realizing the convergence of networking and cloud/ edge computing and identify some opportunities for future research in this exciting interdisciplinary field.
55 citations
TL;DR: Simulation results show that GAP can save 57.1 and 57.6 percent of on-grid power consumption as compared to the two other Avatar placement strategies, i.e., Static Avatar Placement and Follow me AvataR, respectively.
Abstract: In the Green Cloudlet Network (GCN) architecture, each User Equipment (UE) is associated with an Avatar (a private virtual machine for executing its UE's offloaded tasks) in a cloudlet located at the network edge. In order to reduce the operational expenditure for maintaining the distributed cloudlets, each cloudlet is powered by green energy and uses on-grid power as a backup. Owing to the spatial dynamics of energy demands and green energy generations, the energy gap (i.e., energy demand minus green energy generation) among different cloudlets in the network is unbalanced, i.e., some cloudlets’ energy demands can be fully provisioned by their green energy generations but others need to utilize on-grid power to meet their energy demands. The unbalanced energy gap increases the on-grid power consumption of the cloudlets. In this paper, we propose the Green-energy aware Avatar Placement (GAP) strategy to minimize the total on-grid power consumption of the cloudlets by migrating Avatars among the cloudlets according to the cloudlets’ residual green energy, while guaranteeing the service level agreement (the End-to-End (E2E) delay requirement between a UE and its Avatar). Simulation results show that GAP can save 57.1 and 57.6 percent of on-grid power consumption as compared to the two other Avatar placement strategies, i.e., Static Avatar Placement and Follow me AvataR, respectively.
52 citations
TL;DR: A new scheme is designed for the blockchain-based FNC (BFNC) to recover ACL automatically and a heuristic algorithm is proposed to reduce the time to acquire hash values of blocks by computing cooperatively with all available devices.
Abstract: Fog computing is an emerging paradigm in provisioning computing and storage resources for the Internet-of-Things (IoT) devices. In a fog computing system, all devices can offload their data or computationally intensive tasks to nearby fog nodes, instead of to the distant cloud. As compared with cloud computing, fog computing can significantly reduce the transmission delay between IoT devices and computing servers. However, the current fog system is rather susceptible to malicious attacks. To increase the security level, we propose to partition the fog system into fog node clusters (FNCs), with fog nodes (FNs) in one cluster sharing the same access control list (ACL) which is protected by a blockchain. Generating blockchains requires tremendous computing power and can rapidly drain the computing capacities of FNs. In this article, we first customize the blockchain for FNC to reduce the required computing power consumption and storage spaces. Second, a new scheme is designed for the blockchain-based FNC (BFNC) to recover ACL automatically. In addition, we propose a heuristic algorithm to reduce the time to acquire hash values of blocks by computing cooperatively with all available devices. The simulation results have demonstrated that using the cooperative computing strategy can reduce the time of computing a block hash than noncooperative strategies.
47 citations
TL;DR: The results demonstrate that the proposed task assignment method offers reduced latency compared to state-of-the-art task assignment approaches and hence improves the quality of service offered to mobile devices.
40 citations
01 Jun 2020
TL;DR: An online reinforcement learning algorithm is designed to address the task allocation in fog-aided mobile IoT networks and achieves the optimal performance asymptotically, the computational complexity and theoretical bound are derived and analyzed.
Abstract: Fog-aided mobile IoT is proposed to speed up service response by deploying fog nodes at network edges. We investigate the task allocation in fog-aided mobile IoT networks, where mobile users generate computing tasks at different locations and offload them to fog nodes, i.e., to intelligently distribute tasks to different fog nodes in order to adapt to the varying wireless channel conditions and different fog resources. The objective is to minimize the average task completion time constrained by the mobile device’s battery capacity and each task’s completion deadline. In practice, future tasks are usually unknown in advance owing to the unpredictable environments and hence an online algorithm is required to make decisions on the fly. Moreover, the local task information may be incomplete and hence historical statistics should be utilized to estimate the most appropriate fog node for the current task. Therefore, we design an online reinforcement learning algorithm to address the two challenges. We also derive and analyze the computational complexity and theoretical bound. Simulation results show that our online algorithm achieves the optimal performance asymptotically, illustrate the performances of our online reinforcement learning algorithm as compared with existing works, and validate the theoretical bound analysis.
38 citations
TL;DR: A dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications.
Abstract: In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra’s shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.
TL;DR: This paper designs algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context and implements them based on a SPDZ protocol, a computation framework of MPC.
TL;DR: This work designs an online algorithm to provide strategies for task allocation and flying control when the drone visits each location without knowing the future, and forms this joint optimization problem as a mixed integer non-linear programming (MINLP) problem.
Abstract: Fog-aided Internet of Drones (IoD) networks employ fog nodes to provide computing resources for the delay-sensitive tasks offloaded from drones. In IoD networks, drones are launched to complete a journey in which several locations of interest are visited. At each location, a drone collects the ground information, generates computing tasks and offloads them to the fog nodes for processing. In our work, we consider both the task allocation (which distributes tasks to different fog nodes) and the flying control (which adjusts the drone's flying speed) to minimize the drone‘s journey completion time constrained by the drone's battery capacity and task completion deadlines. We formulate this joint optimization problem as a mixed integer non-linear programming (MINLP) problem. In consideration of the practical scenario that the future task information is difficult to obtain, we design an online algorithm to provide strategies for task allocation and flying control when the drone visits each location without knowing the future. The performances of our proposed online algorithm are demonstrated via extensive simulations.
TL;DR: The 3D deployment and resource allocation of a DBS in a given hotspot area is studied with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power.
Abstract: Deploying a Drone Base Station (DBS) over a hotspot area is a promising solution to improve the user Quality of Service (QoS) by helping the Macro Base Station (MBS) transmit traffic to the users. Essentially, the DBS, which works as a relay node between the users and the MBS, can increase the users’ data rates by virtue of more likely short-distance Line of Sight (LoS) communication links. Furthermore, deploying DBS is more cost-effective and flexible as compared to deploying small cells. The DBS can employ Free Space Optical (FSO) links for backhauling between the DBS and the MBS. In this paper, we study the 3D deployment and resource allocation of a DBS in a given hotspot area with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power. To solve the problem, we first decompose the primal problem into two subproblems, i.e., the 3D DBS placement problem and the resource allocation problem. Second, we propose a cyclic iterative algorithm to solve the two sub-problems separately and use the output of one as the input of the other. The performance of the algorithm is demonstrated via extensive simulations.
TL;DR: This article proposes SmartLoc, a smart wireless indoor localization framework to enhance indoor localization, and proposes a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously.
Abstract: Recently, machine learning (ML) has been widely adopted for fingerprint-based indoor localization because of its potency in delineating relationships between received signal strength (RSS) information and labels accurately. Existing ML-based indoor localization systems are less robust because they only adopt the output with the highest probability. This affects the final location estimate, hence compromising accuracy due to the severity of RSS fluctuations. Since different ML algorithms (MLAs) yield different performances, it is therefore intuitive to fuse predictions from multiple MLAs to improve the positioning performance in the presence of signal fluctuation. In this article, we propose SmartLoc, a smart wireless indoor localization framework to enhance indoor localization. In the offline phase, multiple MLAs are trained by utilizing an offline database. We further apply probability alignment to guarantee the predicted probabilities of each MLA at the same confidence level. In the online phase, given a testing RSS sample of a user at an unknown location, we extract the labels with probabilities greater than a certain threshold from each MLA to construct the space of candidate labels (SCL). The size of SCL can be adaptively determined by using our proposed dynamic size determination algorithm. Based on the SCL, we propose a probabilistic model to intelligently estimate the user's location by evaluating the label credibility simultaneously. A high label credibility indicates that the frequently occurred label is more likely to be true. Experimental results in a real changing environment verify the superiority of SmartLoc, outperforming the best among comparative methods by 10.8% in 75th percentile accuracy.
TL;DR: This paper formulation of the problem of jointly optimizing the 3D DBS placement and user association to maximize the overall SE in the context of drone assisted mobile networks and the performance of STAR is demonstrated via extensive simulations.
Abstract: In drone assisted mobile networks, a drone mounted base station (DBS) is deployed over a hotspot area to help user equipments (UEs) download their traffic from the macro base station (MBS), thus improving the throughput and spectrum efficiency (SE) of the UEs. Finding the optimal 3D position of the DBS to maximize the overall SE of the UEs in the hotspot area is challenging because the 3D DBS placement and user association problems are coupled together. In this paper, we formulate the problem of jointly optimizing the 3D DBS placement and user association to maximize the overall SE in the context of drone assisted mobile networks. The spectrum efficiency aware DBS placement and user association (STAR) algorithm is designed to decompose the original problem into two subproblems, i.e., user association and DBS placement, and to iteratively solve the two subproblems until the overall SE of the hotspot area cannot be improved further. The performance of STAR is demonstrated via extensive simulations.
TL;DR: Numerical results demonstrate that using an optical beam to charge and communicate with a DBS can gain 25% extra hovering time of the DBS and achieve high network throughput.
TL;DR: The base stations in the HPS incorporate four wireless protocols, namely, WiFi, Bluetooth, ZigBee, and utral-wideband, which can localize persons/ objects with different positioning devices simultaneously and show better target detection.
Abstract: In this article, we propose a hybrid positioning system (HPS) for various seamless localization applications. The base stations in the HPS incorporate four wireless protocols, namely, WiFi, Bluetooth, ZigBee, and utral-wideband (UWB); as a result, the HPS shows better target detection because it can localize persons/ objects with different positioning devices (e.g., smartphone, UWB tag, Bluetooth tag) simultaneously. Moreover, it also exhibits good environmental adaptability because it can be deployed in different environments. We first present the system architecture of our HPS and then delve into the main hardware constituents. We detail the functions of each protocol for efficient positioning. The main motivation behind the design of our HPS is to realize a robust and precise indoor and outdoor seamless positioning system that is universally deployable from small to large-scale environments. Our HPS can effectively compensate for the shortcomings of existing standalone positioning systems and provide sufficient space for information fusion to yield more accurate positioning. Experimental results in a typical office environment have demonstrated the superiority of our proposed HPS in comparison with different fusion strategies. Two case studies are included to validate the powerful functions for providing personalized location- based services.
TL;DR: In this article, the authors discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack.
Abstract: There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Due to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.
21 May 2020
TL;DR: A new optimization method is designed that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator and allows black- box optimizer to query NPs instead of the real system.
Abstract: Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices and servers. The challenge of designing FedVision is to optimally use the computing and networking resources for video analytics. Since there is no closed-form expression of the system performance, black-box optimization is employed to optimize the system performance. However, using black-box optimization directly incurs excessive system queries that lead to very poor system performance. To solve this problem, we design a new optimization method that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator. This method allows black-box optimizer to query NPs instead of the real system. We validate the performance of FedVision and the new optimization method using both numerical results and experiments with a testbed.
TL;DR: In this article, the problem of jointly optimizing the DBS placement as well as the access link bandwidth allocation is formulated to maximize the hovering time of a DBS and guarantee the data rate requirements of the MUs.
Abstract: In a disaster struck area (DSA), macro base stations (MBSs) are usually damaged, and thus the wireless network becomes dysfunctional. To efficiently recover the communications, drone mounted base stations (DBSs) are deployed to relay data between the mobile users (MUs) in a DSA and working MBSs in the proximity of the DSA. However, a DBS may be deployed far away from a working MBS, thus limiting the backhaul link capacity between the DBS and the MBS. Also, the hovering time of current drones is limited, and thus caps the usage of DBSs. In order to increase the backhaul link capacity and prolong the hovering time of a DBS, we propose to apply free space optics to enable an MBS to simultaneously transmit data streams and energy to a DBS with high efficiency. The problem of jointly optimizing the DBS placement as well as the access link bandwidth allocation is formulated to maximize the hovering time of a DBS and guarantee the data rate requirements of the MUs. The join t band w idth allocat i on DB S placemen t (TWIST) algorithm is proposed to solve the problem. The performance of TWIST is demonstrated through simulations.
07 Jun 2020
TL;DR: Drones’ wireless transmission power is optimized at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost.
Abstract: Internet of Drones (IoD) employs drones as the internet of things (IoT) devices to provision applications such as traffic surveillance and object tracking. Data collection service is a typical application where multiple drones are deployed to collect information from the ground and send them to the IoT gateway for further processing. The performance of IoD networks is constrained by drones' battery capacities, and hence we utilize both energy harvesting technologies and power control to address this limitation. Specifically, we optimize drones' wireless transmission power at each time epoch in energy harvesting aided time-varying IoD networks for the data collection service with the objective to minimize the average system energy cost. We then formulate a Markov Decision Process (MDP) model to characterize the power control process in dynamic IoD networks, which is then solved by our proposed model-free deep actor-critic reinforcement learning algorithm. The performance of our algorithm is demonstrated via extensive simulations.
01 Jan 2020
TL;DR: This paper investigates the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network while minimizing the number of deployed DBSs.
Abstract: Drone-mounted base stations ( DBS s) are promising means to provide ubiquitous connections to users and support various emerging applications in mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we investigate the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet ( BUD ) problem with the objective to maximize the total throughput of the network while minimizing the number of deployed DBSs. Since the BUD problem is NP-hard, it is then decomposed into three sub-problems: the joint UE association, power and bandwidth assignment ( Joint-UPB ) problem, the DBS placement problem (including the vertical and horizontal positions) and the problem of determining the number of DBSs to be deployed. We propose two approximation algorithms to solve the first two sub-problems and use the linear programming to solve the last sub-problem. Finally, another approximation algorithm, referred to as the AA-BUD algorithms, is proposed to solve the BUD problem with guaranteed performance based on the solutions of the three sub-problems. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and is shown to be superior to two benchmark algorithms with up to $62\%$ throughput improvement.
TL;DR: An overview of the challenges faced in resource management for solar Powered base stations is given and state-of-the-art resource management strategies for both grid-connected and off-grid solar powered base stations are presented.
Abstract: There is an increasing need to power cellular base stations (BSs) using solar energy in many parts of the globe. This is primarily because of the high cost of running these base stations on traditional power sources such as diesel due to a lack of reliable grid availability in those areas. Apart from the high cost, increasing diesel consumption also causes harm to the environment due to its increasing global carbon footprint. Using solar energy powered base stations is a highly promising solution to address these issues. One of the main areas of concern for solar powered cellular networks is to precisely manage the resources, namely, the available spectrum and energy so as to avoid power outages and to maintain an acceptable QoS for the end users. This article gives an overview of the challenges faced in resource management for solar powered base stations and presents state-of-the-art resource management strategies for both grid-connected and off-grid solar powered base stations.
TL;DR: The POD problem is shown to be NP-hard, and thus the problem is simplified by decomposing it into three sub-problems, and an approximation algorithm is proposed that is superior to two baseline algorithms.
TL;DR: The papers in this special section examine the human factors integration of cyber-physical social systems in computer applications to address challenges in human-centric technology development by using advanced technologies such as social computing, social system, and social networking.
Abstract: The papers in this special section examine the human factors integration of cyber-physical social systems in computer applications. Human factors are becoming increasingly important in computing systems. A cyber-physical social system (CPSS), which incorporates human factors, encompasses not only cyberspace and physical world, but also human knowledge, mental capacity, and sociocultural elements. Rapid developments in cloud computing, smart grid, autonomous automotive systems, medical monitoring, process control systems, distributed robotics, and mobile networks are instigating a new paradigm shift in CPSS architectures, platforms and services by bringing improvement not only to the performance but also to the user experience in terms of the energy efficiency, reliability, security, and cost efficiency with human factors. The integration of cyber-physical systems and human social behaviors also provides a good opportunity to address challenges in human-centric technology development by using advanced technologies such as social computing, social system, and social networking.