Internet of Things (IoT) networks are key to the realization of modern industries and societies. A key application of IoT is in smart-grid communications. Smart-grid networks are resource constrained in terms of computing power and energy capacity. Similarly, the wireless links between devices are typically associated with high packet-loss rates, low throughput, and instability. To provide a sustainable communication mechanism, an IoT network stack is proposed for these devices. However, each network stack layer has its own constraints. For example, to facilitate the operation of these low-power and lossy network (LLN) devices, the international engineering task force (IETF) standardized a network-layer protocol called a routing protocol for low-power and lossy networks (RPLs). RPL often creates an inefficient network in densely deployed and varying traffic load conditions. Future dense IoT-based networks are expected to automatically optimize the reliability and efficiency of communication by inferring the diverse features of both the environments and actions of the devices. Machine learning (ML) provides a promising framework for such a dense network environment. In this study, we examine the underlying perspective of ML for such systems. We utilize the multiarmed bandit (MAB)-based expected energy count (BEEX) technique, which provides nodes the ability to effectively optimize their operation. Using the proposed mechanism, nodes can intelligently adapt their network-layer behavior. The performance of the proposed (BEEX) algorithm is evaluated through a Contiki 3.0 Cooja simulation. The proposed method improves the energy consumption and packet delivery ratio and produces a lower control overhead than other state-of-the-art mechanisms.

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

Information-centric networking (ICN) is a promising paradigm shift that aims to tackle the traditional Internet architectural problems and to fulfill the future Internet requirements. The traditional Internet architecture is a host-oriented architecture (i.e., TCP/Internet protocol (IP) approach) due to which the Internet of Things (IoT) have been facing issues related to data dissemination across the distant locations. Therefore, a quick comprehension to enhance the communication for improving the content transmission services is of upmost importance. To deal with the challenges of traditional IP networks, the ICN paradigm was proposed which is different from traditional IP networking in terms of: 1) naming; 2) routing and forwarding; and 3) caching. One of the most common and important features of ICN architectures is in-network caching, which can significantly reduce content retrieval latency and improve data availability. Furthermore, in an ICN-based IoT environment, content caching at intermediate network nodes reduces the path stretch between end users and caches the content to meet future demands. This article compares and thoroughly investigates ICN-based caching strategies in terms of content retrieval latency, cache hit ratio, stretch, and link load, with a focus on IoT-based environments. Following a thorough simulation study, we discovered that ICN in-network caching is one of the most beneficial features for enhancing IoT-based networks.

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.

To sustain dense user equipment (UE) deployments, one of the difficult issues is to provide a proficient way to various channel access in the cellular communication networks, such as long-term evolution-Advanced (LTE-A) networks. In LTE-A, random access (RA) is the necessary process to set up the wireless connection between a UE and an evolved node B (eNB). The performance of the RA straightforwardly influences the performance of the whole system. Currently, a discrete uniform distribution (DUD) is used to avoid the potential collision in the network, while multiple UEs try to access the channel resources. However, in a DUD, every UE has an equal chance to choose similar contention preamble near to the expected value of the DUD, which may cause an increase in a collision among the UEs. In this work, we propose a method based on the existing alternatives, such as continuous exponential distribution (CED). CED distributes the random values between two bounds in a poison point process, in which random variables occur continuously and independently with a constant average rate. In this way, our proposed method can distribute the UEs in a parametric set of the probability distribution. Our proposed mechanism is named as CED RA (CED-RA), which replaces the DUD mechanism in RA with CED to show enhanced reliability and lower latency.

To enable the internet of things (IoT) devices with increased coverage and optimized power consumption, the 3rdgeneration partnership (3GPP) standardizes the idea of narrowband IoT (NB-IoT) technology in the fifth generation (5G) of cellular communication. Re-transmission of control and data packets due to a poor link between user equipment (UE) and the base station (BS), is considered as one of the key feature in NB-IoT to ensure the data delivery of delay-sensitive applications, e.g. ambulance services and body sensor networks (BSN). This phenomenon degrades the energy efficiency of the already resource constrained systems. One key solution for NB-IoT UE is to exploit the device-to-device (D2D) communication using two hops instead of transmitting on a direct uplink, due to which the system performance increases. In an attempt to transmit the NB-IoT UE uplink data packet, splendid researchers have focused towards developing a D2D communication based strategy, which typically optimizes the expected packet delivery ratio (EDR) and end-to-end delay (EED) through an opportunistic method. However, such methodology imposes an additional delay due to the unavailability of active relaying nodes and increases the overall energy consumption of the system. This necessitates us to design an intelligent deterministic D2D (2D2D) relay selection strategy for delay sensitive NB-IoT UEs. The EDR and EED have been improved using deterministic programming based algorithm. Simulations with various parameters are carried out, and results are presented. Simulation results show that the deterministic algorithm gives better performance with a 10% increase in EDR and overcomes the additional delay.

This paper proposes an adaptively scaled back-off (ASB) mechanism to mitigate the performance degradations in the Binary Exponential Back-off (BEB) of IEEE 802.11 CSMA/CA in the highly dense environment such as IEEE 802.11ax high efficiency WLAN (HEW). The proposed ASB mechanism selects the optimal CW size to achieve maximized network performance adaptively based on the measured conditional collision probability (pc) and the estimated number of contending stations. The ASB protocol can provide higher efficiency than the legacy Binary Exponential Back-off (BEB) that simply adjust the back-off contention window (CW) size by blind exponential increase at repeated collision avoidance and resetting to the minimum value (CWmin) at successful transmission. The performance analysis of the proposed ASB scheme with ns-3 network simulation shows that the proposed ASB scheme can achieve 21.14% higher throughput and take 32.45% less average interval between successful transmissions than the BEB mechanism in highly dense WLANs with saturated traffic environment.1