No abstract

No abstract

Information-Centric Networking (ICN) approaches have been considered as an alternative approach to TCP/IP. Contrary to the traditional IP, the ICN treats content as a firstclass citizen of the entire network, where names are given through different naming schemes to contents and are used during the retrieval. Among ICN approaches, Content Centric Networking (CCN) is one of the key protocols being explored for Internet of Things (IoT), names the contents using hierarchical naming. Moreover, CCN follows pull-based strategy and exhibits the communication loop problem because of its broadcasting mode. However, IoT requires both pull and push modes of communication with scalable and secured content names in terms of integrity. In this paper, we propose a hybrid naming scheme that names contents using hierarchical and flat components to support both push and pull communication and to provide both scalability and security, respectively. We consider an IoT based Smart Campus (SC) scenario and introduce two transmission modes namely (1) unicast mode and (2) broadcast mode to address loop problem associated with CCN. Simulation results demonstrate that proposed scheme significantly improves the rate of interest transmissions, number of covered hops, name aggregation, and reliability along with addressing the loop problem.

Femtocells represent a novel configuration for existing cellular communication, contributing towards the improvement of coverage and throughput. The dense deployment of these femtocells causes significant femto-macro and femto-femto interference, consequently deteriorating the throughput of femtocells. In this study, we compare two heuristic approaches, i.e., particle swarm optimization (PSO) and genetic algorithm (GA), for joint power assignment and resource allocation, within the context of the femtocell environment. The supposition made in this joint optimization is that the discrete power levels are available for the assignment. Furthermore, we have employed two variants of each PSO and GA: inertia weight and constriction factor model for PSO, and twopoint and uniform crossover for GA. The two proposed algorithms are in a decentralized manner, with no involvement of any centralized entity. The comparison is carried out between the two proposed algorithms for the aforementioned joint optimization problem. The contrast includes the performance metrics: including average objective function, min-max throughput of the femtocells, average throughput of the femto users, outage rate and time complexity. The results demonstrate that the decentralized PSO constriction factor outperforms the others in terms of the aforementioned performance metrics.

The cognitive radio (CR) is evolved as the potential technology to solve the problem of spectrum scarcity and to meet the stringent requirements of upcoming wireless services. CR has two distinct features, the spectrum sensing and the parameter adaptation. The former feature helps the CR to find the vacant spectrum slots/channels in the radio band while the latter mechanism allows it to adjust the operating parameters (e.g. frequency band, modulation and power, etc.) accordingly. The primary user (PU) activity has serious effects on the overall performance of the cognitive radio network (CRN).The CR should vacate the channel if it detects the arrival of the primary user (PU) in order to avoid the interference. The channel eviction/switching phenomenon severely degrade the quality of service (QoS) of the CR user and it is perhaps the key challenge for the CRN. In this paper, we propose the dynamic channel selection and parameter adaptation (CSPA) scheme based on the genetic algorithm to provide better QoS for the CR by selecting a best channel in terms of the quality, the power and the PU activity. The CSPA deals with the problem of channel switchings and it provides better QoS to the CR user. Simulation results prove that CSPA outperforms the existing schemes in terms of channel switchings, average service time, power and throughput.

Cognitive radio (CR) is a novel methodology that facilitates unlicensed users to share a licensed spectrum without interfering with licensed users. This intriguing approach is exploited in the Long Term Evolution-Advanced (LTE-A) network for performance improvement. Although LTE-A is the foremost mobile communication standard, future underutilization of the spectrum needs to be addressed. Therefore, dynamic spectrum access is explored in this study. The performance of CR in LTE-A can significantly be enhanced by employing predictive modeling. The neural network-based channel state and idle time (CSIT) predictor is proposed in this article as a learning scheme for CR in LTE-A. This predictive-based learning is helpful in two ways: sensing only those channels that are predicted to be idle and selecting the channels for CR transmission that have the largest predicted idle time. The performance gains by exploiting CSIT prediction in CR LTE-A network are evaluated in terms of spectrum utilization, sensing energy, channel switching rate, packet loss ratio, and average instantaneous throughput. The results illustrate that significant performance is achieved by employing CSIT prediction in LTE-A network.

This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.

The cognitive radio network (CRN) is a promising solution to the problem of spectrum scarcity. To achieve efficient spectrum utilization, cognitive radio requires a robust spectrum sensing and spectrum sharing scheme. Therefore, spectrum sharing scheme plays a key role in achieving the optimal utilization of the available spectrum. The spectrum sharing in CRN is more challenging than traditional wireless network. The main factors besides throughput and fairness which need to be addressed in spectrum sharing of CRN are primary user (PU) activity, transmission power, and variations in the radio environment. In this article, we propose fair, efficient, and poweroptimized (FEPO) spectrum sharing scheme that will incorporate all critical factors mentioned above to maximize the spectrum utilization. Simulation results show that FEPO scheme outperforms in terms of transmission power by reducing the number of retransmissions and guarantees required level of throughput and fairness. Moreover, periodic monitoring helps to reduce the number of collisions with PUs.