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Internet of Things enabled smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management. Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms, such as harmony search algorithm, enhanced differential evolution, and harmony search differential evolution, is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours. We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range, which is bearable for consumer with respect to electricity bill. Feasible regions are computed to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak-to-average ratio, and waiting time minimization.

Named data networking (NDN) is one of the future networking architectures that communicates content using names, instead of the node addresses. It uses a very simple pull-based communication mechanism to retrieve content by sending an Interest message, and the node that has the required content or producer node replies with the data message. In wireless networks, the interest is flooded in the network to find the data provider node. The directional diffusion method is used to pull further content from the provider node. Due to broadcast nature and without node addresses, interest flooding causes network congestion and wastes network resources, especially bandwidth and battery power. These resources have prime importance in the case of wireless sensor networks (WSNs) because all WSN nodes operate on battery and have limited bandwidth. In this paper, we propose an interest broadcast suppression scheme that considers interest holding time using the distance between forwarder and receiver of the interest, energy, angle, and distance from the beeline between consumer and the spatial region, to avoid broadcasting of unnecessary copies of Interest. The simulation results show that the proposed scheme mitigates the interest broadcast and conserves battery power of the wireless nodes compared with the state-of-the-art scheme in the domain.

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.

As valuable as the Internet of Things (IoT) has been to explore and monitor the land surface of the earth, the Internet of Underwater Things (IoUT) has been crucial to the investigation of the underwater environment. Due to the unique characteristics of the IoUT, such as long propagation delays, high bit error rates, and limited bandwidth, acoustic sensors that use sound waves are preferred for deployment. Consequently, it is difficult to apply the medium access control (MAC) protocol designed for the IoT sensors that use radio frequencies (RFs) in the IoUT. Particularly for densely populated sensor networks, the challenging and dynamic underwater environment leads to the loss of control and data packets, resulting in energy waste and unreliable connections. In addition to a sensor being constrained by energy, replacing its battery is infeasible. Therefore, packet loss and energy conservation should be handled at the MAC layer in order to improve the throughput and longevity of the network. In this paper, we propose a REceiver-Initiated MAC based on Energy-efficient Duty cycling called REMEDY-MAC. Using autonomous underwater vehicles (AUV) for data collection, REMEDY allows a group of nodes to conserve energy by announcing the duty cycling schedule until an AUV arrives at a certain location. Once an AUV reaches a location, it receives data according to a pre-established schedule managed by the active sender node and moves to the next set of nodes after completion of its tasks. The simulation results indicate that the proposed REMEDY protocol significantly outperforms other MAC schemes with respect to network parameters such as energy efficiency.

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.

Internet of things (IoT) applications aim to provide access to widespread interconnected networks of smart devices, services, and information. This can be achieved by integrating IoT and cloud computing (CC). By using cloud computing service composition (SC), multiple services from various providers can be combined to meet users' requirements. However, SC is known for its complexity and is classified as an NP-hard problem; such problems are usually approached using heuristics, such as bioinspired algorithms. This paper aims at developing a bio-inspired algorithm that mimics the behavior of cuckoo birds (which examine the nests of other birds to find eggs similar to their own) to find a composite service that fulfills a user's request in a multi-cloud environment (MCE). Previous work on cuckoo-inspired algorithms has generally utilized metaheuristics, which try to fit a “good”solution to a general optimization problem. In contrast, we propose a problem-dependent heuristic that considers the SC problem and its particularities in MCE. The proposed algorithm, MultiCuckoo, was thoroughly evaluated based on a well-controlled experimental framework that benchmarks the performance of the new algorithm to other outstanding SC algorithms, including the all clouds combination algorithm, base cloud combination algorithm, and combinatorial optimization algorithm for multiple cloud service Composition. The results show that our algorithm is more efficient in terms of decreasing the number of examined services, the composed clouds, and the running time in comparison to the benchmark algorithms.

Nowadays research is heading towards the integration of cloud computing and Internet of Things thus creating a Cloud of Things (CoT). This combination generates a new paradigm for pervasive and ubiquitous computing. However, reliable CoT-based services, particularly, highly delay-sensitive services, such as, healthcare, require energy-efficient CoT architectures. Considerable efforts have been proposed to improve the efficiency of CoT architectures. This paper analyses CoT architectures and platforms, as well as the implementation of CoT in the context of smart healthcare. Subsequently, the paper explains some related issues of CoT, including the lack of standardization. Moreover, it focuses on energy efficiency with an in depth analysis of the most relevant proposals available in the literature. An evaluation of all the energy efficiency solutions investigated in this paper shows there is still a need to improve energy efficiency, especially regarding QoS and performance.

Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN's forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.

Cloud computing is a paradigm that offers storage, computation, and software services on demand in the Cloud and far from end users. These services and computations are then extended to the Edge of the network with the Edge computing paradigm. This paradigm offers computation, data, and application services in close proximity of end users. Future Internet architectures will result in a fast information response time, and low latency will be a main feature of evolving 5th generation (5G) radio networks. To ensure the widespread adoption of 5G applications, low latency, security, mobility, and scalable content distribution support is required. Information-centric networking (ICN) is a newly proposed future Internet paradigm in which communication is based on content names irrespective of their locations. At the same time, ICN promises efficient content delivery, mobility support, scalability, and security for content. The Edge computing and ICN provide an opportunity to reduce latency, support mobility, security, and scalability. In this paper, discussions on Edge computing with ICN are provided. In detail, the Edge computing proposals, use cases, differences among Edge computing proposals and drivers for Edge computing are investigated. The Edge computing standardization, research, and industry/vendors collaboration overview are studied. Applications of ICN integration in Edge computing and their advantages and limitations are highlighted. We conclude our paper by describing potential directions for future research in the field of ICN over Edge computing.

The futuristic Internet paradigm, named data networking (NDN), was recently introduced to solve the severe issues of current Internet architecture, such as complex usage, poor resource utilization, inefficient mapping, scalability, location dependence, and so on. Communication in NDN is based on content names decoupling from their locations. NDN also provides strong built-in functionalities, like multi-path routing, security primitives, flow balance mechanisms, and in-networking caching. Similarly, NDN-based mobile ad hoc networks are highly dynamic in nature whereby the participating nodes have experienced highly challengeable environments and constraints, such as channel fluctuations, intermittent connectivity, and low battery power. In this environment, if a node has limited residual energy, after sending a few packets, it will die soon. Furthermore, all of its pending request entries are also destroyed, which further exacerbates the communication process. To cope with this problem, we have proposed a novel protocol called the on-demand energy-based forwarding strategy (OEFS) that takes the residual energies of the nodes into account during the entire communication process. For the performance evaluations, we have used NDNSIM, which is specially designed for NDN-based networks. The simulation results show that the our OEFS outperform the existing state-of-the-art protocol in terms of content download time, interest retransmissions, the total number of Interest propagation, and data redundancy in the network. We also find the effect of OEFS on the energy threshold and show that OEFS enables mobile nodes to consume less amount of energy.

Wireless communications and networking have been continually evolving to improve and be a part of our lifestyle. This uninterrupted development is due to many research projects and practices that are being carried out to improve the quality of services and applications supported by networking technologies. Initially, the plethora of computer networks research was designed to allow users to share the thoughts and facts using textual data through addressing devices. Meanwhile, we have witnessed that users play the role of both producers and consumers at the same time. These new emerging requirements gave birth to Cloud Computing (CC), Data Centric Networking (DCN), and other advancements in IEEE standards. Moreover, researchers also intended to redesign the networking architectures and protocols with the focus on content rather than the host. The resulting new architectures are Information Centric Networks (ICN) with various extensions like Content Centric Networks (CCN), Named Data Networks (NDN), Data-Oriented Network Architecture, etc.

Internet Engineering Task Force (IETF) has proposed an extension based on Mobile IPv6 (MIPv6), named as network mobility basic support protocol (NEMO-BSP), to support NEMO in IPv6 networks. However, NEMO-BSP inherits all the drawbacks of MIPv6, such as inefficient routing path, high handover latency, and packet encapsulation overhead. To address these drawbacks of NEMO-BSP, this paper proposes an NEMO supporting scheme based on a novel Locator/ID Separation (LIDS) architecture, namely LIDS-NEMO. In LIDS-NEMO, Multiple Virtual Mapping (MVM) scheme is proposed to differentiate the intra-NEMO and inter-NEMO mobility. Besides, packets are transmitted through the most optimized route in LIDS-NEMO. The simulation results show that LIDS-NEMO reduces the signaling cost significantly when compared with NEMO-BSP and it will be a promising scheme to provide NEMO support in the LIDS context.

Smart cities have been envisioned during the last decade. Moreover, various projects have been initiated to bring this concept into reality. Smart city is basically an emergence of the existing and new Information and Communications Technologies (ICT) to make our living standard more safe and digitized. Wireless communications, such as sensors, actuators, intelligent transportation systems, and smart grids, have played a vital role in the dissemination of information under the given circumstances. Similarly, it is hard to declare any city as a smart city without taking benefits from wireless sensor networks (WSNs). However, with new requirements and delay sensitive applications, the existing WSN requires significant alterations at several layers. In this paper, a new time division multiple access (TDMA)-based medium access control (MAC) protocol, called bitmap-assisted efficient and scalable TDMA-based MAC (BEST-MAC), is proposed for adaptive traffic in hierarchical WSNs that can be deployed in the smart cities. BEST-MAC is specifically designed to improve the quality control of such smart cities applications, where diverse traffic is required and loss or delay in data traffic is unacceptable. The main contributions of BEST-MAC include: 1) it uses small size time slots; 2) the number of those time slots is more than the number of member nodes; 3) knapsack algorithm is used to schedule time slots; and 4) short node address (1 B) is proposed to identify the member nodes. First two contributions of BEST-MAC handle adaptive traffic loads of all members in an efficient manner. The knapsack algorithm not only reduces the job completion time of a node but also minimizes the average packet delay with better link utilization. The short node address reduces the control overhead that improves the energy efficiency. The simulation results verify that the proposed BEST-MAC transmits more data with less delay and energy consumption compared with the existing MAC protocols.

Energy consumption is one of the constraints in wireless sensor networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues, viz., energy consumption, network lifetime, network scalability, and packet overhead. The key issue in WSN is that these networks suffer from the packet overhead, which is the root cause of more energy consumption and degrade the QoS in sensor networks. In WSN, there are several routing protocols, which are used to enhance the performance of the network. Out of those protocols, dynamic source routing (DSR) protocol is more suitable in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the efficiency decreases as the data packets need to wait at the initial point, where the packet has been sent and this increases the waiting time and end-to-end delay of the packets, which leads to increase in energy consumption. Our problem is to identify the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets conserved. In order to resolve these issues, we propose directional transmission-based energy aware routing protocol named PDORP. The proposed protocol PDORP has the characteristics of both power efficient gathering sensor information system and DSR routing protocols. In addition, hybridization of genetic algorithm and bacterial foraging optimization is applied to proposed routing protocol to identify energy efficient optimal paths. The performance analysis, comparison through a hybridization approach of the proposed routing protocol, gives better result comprising less bit error rate, less delay, less energy consumption, and better throughput, which leads to better QoS and prolong the lifetime of the network. Moreover, the computation model is adopted to evaluate and compare the performance of the both routing protocols using soft computing techniques.