Energy efficiency perspectives of femtocells in internet of things : recent advances and challenges

Energy efficiency is a growing concern in every aspect of the technology. Apart from maintaining profitability, energy efficiency means a decrease in the overall environmental effects, which is a serious concern in today's world. Using a femtocell in Internet of Things (IoT) can boost energy efficiency. To illustrate, femtocells can be used in smart homes, which is a subpart of the smart grid, as a communication mechanism in order to manage energy efficiency. Moreover, femtocells can be used in many IoT applications in order to provide communication. However, it is important to evaluate the energy efficiency of femtocells. This paper investigates recent advances and challenges in the energy efficiency of the femtocell in IoT. First, we introduce the idea of femtocells in the context of IoT and their role in IoT applications. Next, we describe prominent performance metrics in order to understand how the energy efficiency is evaluated. Then, we elucidate how energy can be modeled in terms of femtocell and provide some models from the literature. Since femtocells are used in heterogeneous networks to manage energy efficiency, we also express some energy efficiency schemes for deployment. The factors that affect the energy usage of a femtocell base station are discussed and then the power consumption of user equipment under femtocell coverage is mentioned. Finally, we highlight prominent open research issues and challenges. © 2013 IEEE

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses

An authentic-based privacy preservation protocol for smart e-healthcare systems in iot

© 2013 IEEE. Emerging technologies rapidly change the essential qualities of modern societies in terms of smart environments. To utilize the surrounding environment data, tiny sensing devices and smart gateways are highly involved. It has been used to collect and analyze the real-time data remotely in all Industrial Internet of Things (IIoT). Since the IIoT environment gathers and transmits the data over insecure public networks, a promising solution known as authentication and key agreement (AKA) is preferred to prevent illegal access. In the medical industry, the Internet of Medical Things (IoM) has become an expert application system. It is used to gather and analyze the physiological parameters of patients. To practically examine the medical sensor-nodes, which are imbedded in the patient\u27s body. It would in turn sense the patient medical information using smart portable devices. Since the patient information is so sensitive to reveal other than a medical professional, the security protection and privacy of medical data are becoming a challenging issue of the IoM. Thus, an anonymity-based user authentication protocol is preferred to resolve the privacy preservation issues in the IoM. In this paper, a Secure and Anonymous Biometric Based User Authentication Scheme (SAB-UAS) is proposed to ensure secure communication in healthcare applications. This paper also proves that an adversary cannot impersonate as a legitimate user to illegally access or revoke the smart handheld card. A formal analysis based on the random-oracle model and resource analysis is provided to show security and resource efficiencies in medical application systems. In addition, the proposed scheme takes a part of the performance analysis to show that it has high-security features to build smart healthcare application systems in the IoM. To this end, experimental analysis has been conducted for the analysis of network parameters using NS3 simulator. The collected results have shown superiority in terms of the packet delivery ratio, end-to-end delay, throughput rates, and routing overhead for the proposed SAB-UAS in comparison to other existing protocols

Towards Augmenting Federated Wireless Sensor Networks

AbstractEnvironmental Monitoring (EM) has witnessed significant improvements in recent years due to the great utility of Wireless Sensor Networks (WSNs). Nevertheless, due to harsh operational conditions in such applications, WSNs often suffer large scale damage in which nodes fail concurrently and the network gets partitioned into disjoint sectors. Thus, reestablishing connectivity between the sectors, via their remaining functional nodes, is of utmost importance in EM; especially in forestry. In this regard, considerable work has been proposed in the literature tackling this problem by deploying Relay Nodes (RNs) aimed at re-establishing connectivity. Although finding the minimum relay count and positions is NP-Hard, efficient heuristic approaches have been anticipated. However, the majority of these approaches ignore the surrounding environment characteristics and the infinite 3-Dimensional (3-D) search space which significantly degrades network performance in practice. Therefore, we propose a 3-D grid-based deployment for relay nodes in which the relays are efficiently placed on grid vertices. We present a novel approach, named FADI, based on a minimum spanning tree construction to re-connect the disjointed WSN sectors. The performance of the proposed approach is validated and assessed through extensive simulations, and comparisons with two main stream approaches are presented. Our protocol outperforms the related work in terms of the average relay node count and distribution, the scalability of the federated WSNs in large scale applications, and the robustness of the topologies formed

AI Techniques for COVID-19

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses

Combination of GIS and SHM in prognosis and diagnosis of bridges in earthquake-prone locations

Bridge infrastructures are essential nodes in the transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is vital to identify, retrofit, reconstruct, or, if necessary, demolish the structural systems based on optimal decision-making processes. This research proposes the combined use of advanced tools used in the management and monitoring of bridges such as Geographical Information Systems (GIS) and Structural Health Monitoring (SHM) in a synergistic manner that can enable observation of bridges to construct an earthquake damage model. Post-earthquake disaster data can enhance and update this model to mitigate further damages both to the structure and transportation network in the future. Implications of new technologies such as drones and mobile devices in this scheme constitute the next step toward the future of the Cyber-Physical SHM systems. The proposed intelligent and sustainable cloud-based framework of SHM-GIS in this paper lays the core behind more robust impending systems. The synergistic behavior of the offered framework reduces the overall cost in large scale implementation and increases the accuracy of the results leading to a decision-making platform easing the management of bridges

Enhanced Data Delivery Framework for Dynamic Information-Centric Networks (ICNs)

Abstract-In this paper, we present an Enhanced 2-Phase Data Delivery (E2-PDD) framework for Information-Centric Networks (ICNs), focusing on efficient content access and distribution as opposed to mere communication between data consumers and publishers. We employ an approach of growing eminence, where requests are initiated by consumers seeking particular services that are data-dependent. High-level Controllers (HCs) receive the consumers' requests and issue queries to a multitude of data publishers. The publishers in our topology include a wide variety of ubiquitous nodes that could be either stationary or mobile, operating under different protocols. In order to consider fundamental challenges in ICNs such as node mobility and data disruption, our E2-PDD framework employs Low-level Controllers (LCs) that act as moderators between the HCs and the data publishers, executing data queries for a top tier and replying back with a set of candidate rendezvous points obtained from a bottom tier. The HCs maximize selection based on the nearest rendezvous. Extensive simulation results have been used to evaluate our E2-PDD framework in terms of key performance metrics in ICNs viz., average in-network delay, and publisher load, given different mobility pause time durations and data consumers' densities

Energy-efficient blockchain implementation for Cognitive Wireless Communication Networks (CWCNs)

Abstract Considering the computation resources available with sensor devices and the value and validity of Cognitive Wireless Communication Network (CWCN), traditional blockchain is not feasible for CWCN. Further, considering the security and privacy for CWCN that can directly impact human life (as in the case of ambient assisted living applications), blockchain provides a good solution for such applications, however, with some simplicity in the computation of Proof of Work (PoW). Therefore, the fourth objective solution comes up with a simplified energy-efficient blockchain implementation for CWCN that consumes less energy in computation time. The energy-hungry blockchain has been implemented on resource-constrained CWCN for ambient assisted living applications specialized for elderly care. The process includes a collection of physical environmental parameters on a single board computer-based CWCN. The implementation includes possible simplification in the most energy-consuming process, i.e., the mining process, which makes it energy efficient in computation time as energy consumption is a computation time factor

Seamless key agreement framework for mobile-sink in IoT based cloud-centric secured public safety sensor networks

Recently, the Internet of Things (IoT) has emerged as a significant advancement for Internet and mobile networks with various public safety network applications. An important use of IoT-based solutions is its application in post-disaster management, where the traditional telecommunication systems may be either completely or partially damaged. Since enabling technologies have restricted authentication privileges for mobile users, in this paper, a strategy of mobile-sink is introduced for the extension of user authentication over cloud-based environments. A seamless secure authentication and key agreement (S-SAKA) approach using bilinear pairing and elliptic-curve cryptosystems is presented. It is shown that the proposed S-SAKA approach satisfies the security properties, and as well as being resilient to nodecapture attacks, it also resists significant numbers of other well-known potential attacks related with data confidentiality, mutual authentication, session-key agreement, user anonymity, password guessing, and key impersonation. Moreover, the proposed approach can provide a seamless connectivity through authentication over wireless sensor networks to alleviate the computation and communication cost constraints in the system. In addition, using Burrows–Abadi–Needham logic, it is demonstrated that the proposed S-SAKA framework offers proper mutual authentication and session key agreement between the mobile-sink and the base statio

Enhancing the Access Privacy of IDaaS System Using SAML Protocol in Fog Computing

Fog environment adoption rate is increasing day by day in the industry. Unauthorized accessing of data occurs due to the preservation of Identity and information of the users either at the endpoints or at the middleware. This paper proposes a methodology to protect and preserve the Identity during data transmission of the users. It uses fog computing for storage against security issues in the cloud and database environment. Cloud and database architectures failed to protect the data and Identity of users but the Fog computing based Identity management as a service (IDaaS) system can handle it with Security Assertion Mark-up Language (SAML) protocol and Pentatope based Elliptic Curve Crypto cipher. A detailed comparative study of the proposed and existing techniques is investigated by considering multi-authentication dialogue, security services, service providers, Identity, and access management