Search

The vision of the future IoT is to facilitate interoperability between the massive heterogeneous end-users' terminals and at the same time expedite smooth access to their vast and pervasive smart applications in the cloud. This huge number of things spread all over the cloud generates a significant burden on the current architecture of the IoT. Therefore, this paper aims to achieve scalability for future IoT. To reach the solution, the authors first investigated the encountered challenges when trying to attain scalability. Afterward, they define the main requirements along with their key enabling technologies to achieve scalability in the distributed IoT environment. Finally, a reference model with central management and control is designed, satisfying the defined scalability requirements to reach the desired future IoT architecture.

In dense indoor areas, high numbers of people use their smartphones and tablets to share or download pictures, videos, or data. The heterogeneous network (HetNet) solves the problems caused by the explosion of data generated by smartphones and tablets. Heterogeneous networks use a mix of Relay, Femtocell, Pico, and Macro base stations to improve spectral efficiency per unit area. Operators wish to know how to upgrade existing networks and how to design new ones. This subject has become hot in the industry. In this paper, we presented the architecture of heterogeneous networks. The parameters affecting the heterogeneous networks topology plan are discussed. Moreover, a comparison of existing solutions that consider the problems of base station layout planning is presented. Finally, a road map is given to point out to the main future directions of researches on the topological design of dense area heterogeneous mobile networks.

Densification is one of the 5G networks solutions to increase data rate and network capacity. This technology aims for constructing a large number of small cells in a specific area which may include different wireless technologies (LTE, IEEE 802.11, WiMAX, etc.). Small cells provide shorter distance between the cell access point and the mobile node which will reduce the signal attenuation and noise. This could generate a better connection, which goes along with the expectations of high data rate requests for the year 2020. The downside of the densification is that frequent handoffs will be a challenge for the network nodes. A software-defined network SDN, on the other hand, is another promising solution for 5G networks which controls the network through centralization. Using these two solutions, the handoffs in the network are expected to be frequent and hence a more delay will be added. To reduce such delay, a distributed-mobility-management technique is used in SDN-based networks, where the mobile node takes the handoff decision. This paper has focused on the issue of the distributed-handoff delay and handover failure of mobile nodes in SDN-based IEEE 802.11 network. It proposes a hybrid clustering technique using K-mean and genetic algorithm to cluster the network. The clustering aims to reduce the scanning phase of the mobile node by minimizing the number of access points that need to be scanned for the handoff. The results show lower delays and a reduced number of handoff failure compared to the un-clustered network.

Cognitive radio sensor networks (CRSNs) have been proposed to serve as a reliable, robust, and efficient communications infrastructure that can address both the existing and future energy management requirements of the smart grid. The existing and envisioned applications of CRSN-based smart grid include substation automation, overhead transmission line monitoring, home energy management, advanced metering infrastructure, wide-area situational awareness, demand response, outage management, distribution automation, asset management. To realize these applications, in this paper, honey bee mating optimization-based routing and cooperative channel assignment algorithms have been proposed. The developed framework significantly decreases the probability of packet loss and preserves high link quality among sensor nodes in harsh smart grid spectrum environments. The proposed approach performance has been evaluated in terms of packet delivery ratio, delay, and energy consumption demonstrating that it has successfully addressed the QoS requirements of most of the SG applications presented.

Nanotechnology is enabling the development of miniature devices able to perform simple tasks at the nanoscale. The interconnection of such nano-devices with traditional wireless networks and ultimately the Internet enables a new networking paradigm known as the Internet of Nano-Things (IoNT). Despite their promising applications, nano-devices have constrained power, energy, and computation capabilities along with very limited memory on board, which may only be able to hold one packet at once and, thus, requires packets to be delivered before certain hard deadlines. Toward this goal, a fully-distributed computation-light provably-correct scheduling/MAC protocol is introduced for bufferless nano-devices, which can maximize the network throughput, while achieving perpetual operation. More specifically, the proposed scheduling algorithm allows every nano-device to make optimal transmission decisions locally based on its incoming traffic rate, virtual debts, and channel sensing results. It is proven that the proposed algorithm is timely throughput optimal in the sense that it can guarantee reliable data delivery before deadlines as long as the incoming traffic rates are within the derived maximum network capacity region. This feature not only can lead to high network throughput for the IoNT, but also guarantees that the memory of each device is empty before the next packet arrives, thus addressing the fundamental challenge imposed by the extremely limited memory of nano-devices. In addition, the optimal deadline is derived, which guarantees that all the nano-devices can achieve perpetual communications by jointly considering the energy consumption of communications over the terahertz channel and energy harvesting based on piezoelectric nano-generators.

Nodes’ clustering in wireless networks is one of the solutions that used to improve network performance. This paper discusses the clustering in wireless networks. Then it presents a novel clustering algorithm named Pragmatic Genetic Algorithm (PGA). It combines two of the well known artificial intelligence techniques: K-means and Genetic algorithm. The proposed algorithm aims at minimizing the execution time of the clustering, especially in time-sensitive wireless networks applications. The performance of PGA has been compared with the classical clustering algorithms, namely, K-means and KGA. The experiments have been conducted using synthetic and real data from public repositories. PGA obtained excellent results in execution and stable accuracy even when the number of nodes was increased.

Nowadays, the popularity of smart phones creates huge capacity requirements for networks during mass events where thousands of people coexist in specific areas. At such events, large numbers of people use their smartphones to share pictures and download information. This behavior creates traffic profiles that differ from those typically observed in legacy -less populated- networks where lower uplink traffic volumes are generated. Thus, novel network planning and radio resource management mechanisms have to be considered for such dense network conditions, one of which is macrocell offloading. In this context, reducing the cell size has always been the best way to increase the network capacity of LTE. Femtocells are used to enable offloading data-traffic from macrocell network to increase the capacity. In this paper, to achieve efficient user offloading from macro to femtocells, we propose an offloading algorithm based on a perceived rate threshold in combination with uplink power control for hybrid femtocells also considering resource block partitioning. The proposed algorithm enhances the network capacity so that more mobile users can satisfy their minimum Quality of Service (QoS) requirements, thus the overall performance of dense cellular networks increases. The proposed offloading mechanism is assessed in terms of the achieved total throughput and outage probability figures. Simulation results demonstrate the potential increase of the number of supported users per macrocell in joint macro and hybrid femtocell deployments.

Wireless Sensor Networks (WSNs) are one of the most promising solutions for smart grid applications due to advantages, such as their low-cost, different functionalities, and successful adoption to smart grid environments. However, providing quality of service (QoS) requirements of smart grid applications with WSNs is difficult because of the power constraints of sensor nodes and harsh smart grid channel conditions, such as RF interference, noise, multi-path fading and node contentions. To address these communication challenges, in this paper link-quality-aware routing algorithm (LQ-CMST) as well as the priority and channel-aware multi-channel (PCA-MC) scheduling algorithm have been proposed for smart grid applications. Furthermore, the effect of different modulation and encoding schemes on the performance of the proposed algorithms has been evaluated under harsh smart grid channel conditions. Comparative performance evaluations through extensive simulations show that the proposed algorithms significantly reduce communication delay and the choice of encoding and modulation schemes is critical to meet the requirements of envisioned smart grid applications.

The majority of contemporary 5G networks employ HetNets which improve data rate and the number of serving users over previous cell architecture. HetNets are comprised of different small-cell types such as femtocells and microcells. The tradeoff of these smaller cells is that they do not meet QoS requirements for flexibility and low latency because the increased frequency of handoffs increases network delay. This paper compares the state- of-the-art mobility management approaches that use SDN and (or) NFV concepts in 5G cellular networks. These approaches are evaluated in terms of scalability, signaling cost, probability of handoff failures, and system load balance.

The number of mobile hosts that will be used in 2020 will be exceeding 50 billion hosts [5]. This increase in the amount of mobile devices is intersected with the evolution of the Internet of things (IoT) technology. The demand to create intelligent environments such as smart cities and smart homes is increased. The evolution in this area results on the concept of IoT where devices of everyday are connected [5]. These interactions between a large numbers of heterogeneous devices increase a substantial demand on providing high connectivity, extremely high data rates, low latency, designing special applications to serve the IoT and many other communication requirements [8]. Thus, the promising 5G cellular networks can be considered as the key enabler for IoT technology. Moreover, the 5G wireless networks can be used to achieve the high communication requirements of the IoT. This survey is conducted to investigate the role of 5G cellular networks in the evolution of IoT. Also, this survey aims to compare existing wireless networks to emphasize the role of 5G as enabler driver for IoT. This survey highlights both requirements and enabling technologies of the IoT. Furthermore, it discusses some of the challenges that face the evolution of IoT.

The traditional power grid in many countries suffers from high maintenance costs and scalability issues along with the huge expense of building new power stations, and lack of efficient system monitoring that could increase the overall performance by acting proactively in preventing potential failures. To address these problems, a next-generation electric power system, called the smart grid (SG), has been proposed as an evolutionary system for power generation, transmission, and distribution. To this end, the SGs utilize renewable energy generation, smart meters and modern sensing and communication technologies for effective power system management, and hence, succeeding in addressing many of the requirements of a modern power grid system while significantly increase its performance. Recently, wireless sensor networks (WSNs) have been recognized as a promising technology to achieve seamless, energy efficient, reliable, and low-cost remote monitoring and control in SG applications. In these systems, the required information can be provided to electric utilities by wireless sensor systems to enable them to achieve high system efficiency. The real-time information gathered from these sensors can be analyzed to diagnose problems early and serve as a basis for taking remedial action. In this paper, first WSN-based SG applications have been explored along with their technical challenges. Then, design challenges and protocol objectives have been discussed for WSN-based SG applications. After exploring applications and design challenges, communication protocols for WSN-based SG applications have been explained in detail. Here, our goal is to elaborate on the role of WSNs for smart grid applications and to provide an overview of the most recent advances in MAC and routing protocols for WSNs in this timely and exciting field.

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. The limited capabilities of individual nanomachines and the Terahertz (THz) band channel behavior lead to error-prone wireless links. In this paper, a cross-layer analysis of error-control strategies for nanonetworks in the THz band is presented. A mathematical framework is developed and used to analyze the tradeoffs between Bit Error Rate, Packet Error Rate, energy consumption and latency, for five different error-control strategies, namely, Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), two types of Error Prevention Codes (EPC) and a hybrid EPC. The cross-layer effects between the physical and the link layers as well as the impact of the nanomachine capabilities in both layers are taken into account. At the physical layer, nanomachines are considered to communicate by following a time-spread on-off keying modulation based on the transmission of femtosecond-long pulses. At the link layer, nanomachines are considered to access the channel in an uncoordinated fashion, by leveraging the possibility to interleave pulse-based transmissions from different nodes. Throughout the analysis, accurate path loss, noise and multi-user interference models, validated by means of electromagnetic simulation, are utilized. In addition, the energy consumption and latency introduced by a hardware implementation of each error control technique, as well as, the additional constraints imposed by the use of energy-harvesting mechanisms to power the nanomachines, are taken into account. The results show that, despite their simplicity, EPCs outperform traditional ARQ and FEC schemes, in terms of error correcting capabilities, which results in further energy savings and reduced latency.

Wireless networks have resource limitations; in a dense area, cellular spectrum resources are insufficient and affect the system performance. A Long Term Evolution (LTE) network aims to serve heterogeneous users with different QoS requirements. Traditional approaches need new infrastructure and degrade performance of delay sensitive applications, which may result in users with minimum rate requirements being in high blocking probability. To utilize wireless resources efficiently, users want to access the same medium to connect with the same multicast group and be overhauled at the same time. In this paper, a new technique is proposed for operator-controlled called the QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces (QTHN) to improve QoS for Constant Bit Rate (CBR) and Best Effort (BE) users. The proposed QTHN converts the whole dense wireless network into hexagonal clusters via two layer network communication. In a cluster, one node is selected as a cluster head and all other nodes act as slaves. Within a cluster, a cluster head acts as an access point known as a Hotspot (H), which is further connected to the Base-station (BS). The proposed QTHN aims to improve QoS within heterogeneous wireless network using LTE and unused White Spaces in a wireless dense area. Simulation results show that the proposed QTHN reduced the numbers of blocked users and improved network utility.

Wireless NanoSensor Networks (WNSNs) will allow novel intelligent nanomaterial-based sensors, or nanosensors, to detect new types of events at the nanoscale in a distributed fashion over extended areas. Two main characteristics are expected to guide the design of WNSNs architectures and protocols, namely, their Terahertz Band wireless communication and their nanoscale energy harvesting process. In this paper, a routing framework for WNSNs is proposed to optimize the use of the harvested energy to guarantee the perpetual operation of the WNSN while, at the same time, increasing the overall network throughput. The proposed routing framework, which is based on a previously proposed medium access control protocol for the joint throughput and lifetime optimization in WNSNs, uses a hierarchical cluster-based architecture that offloads the network operation complexity from the individual nanosensors towards the cluster heads, or nano-controllers. This framework is based on the evaluation of the probability of saving energy through a multi-hop transmission, the tuning of the transmission power of each nanosensor for throughput and hop distance optimization, and the selection of the next hop nanosensor on the basis of their available energy and current load. The performance of this framework is also numerically evaluated in terms of energy, capacity, and delay, and compared to that of the single-hop communication for the same WNSN scenario. The results show how the energy per bit consumption and the achievable throughput can be jointly maximized by exploiting the peculiarities of this networking paradigm.