In this paper, a new RF energy harvesting (RF-EH) policy is proposed for Internet of Things (IoT) networks using cooperative communication approach. The proposed policy is evaluated in three devices network which are source, relay, and destination. Such a network model can be adopted in most IoT networks. Three scenarios are studied, where the relay has RFEH capability with limited battery in all scenarios. However, the source has unlimited energy in the first scenario, but the limited energy in the second and third ones. In addition, in the third scenario source has RF-EH capability. New analyses based on discrete phase-type distribution (D-PHD) model as well as a simple analytical method (SAM) are presented to derive performance metrics which are network effective lifetime, average delay of source/relay packets, and source packets overflow. The effect of energy harvesting on such metrics are investigated to find the optimum relay policy. Results show that the proposed policy can maximize network effective lifetime in comparison with time switching (TS) and power splitting (PS)-based protocols.

Much work has been done in the area of cognitive radio networks to formulate the inherent tradeoff among different metrics, such as sensing and throughput. Primary user (PU) traffic behavior can affect the mentioned tradeoff and must be considered in analyses, especially when cognitive users coexist with the dynamic-traffic PU. In this case, PU-returns to the spectrum during secondary users' (SUs) transmission are more likely and, hence, such kinds of interference are inevitable. Furthermore, how to model the PU traffic is important. Note that there are two kinds of interference between SU and PU in cognitive radio networks, one due to sensing error and the other due to primary user reoccupancy. In this paper, a new metric called energy per successful transmission time is defined to address both kinds of inference by formulating SUs' successful transmission time as well as energy consumption. Furthermore, collision probability and average packet delay are formulated considering these interference metrics and the primary user traffic is modeled as an alternating phase type renewal process by which many traffic behaviors, such as long range dependency and self-similarity can be modeled. Finally, some numerical examples are given and the corresponding curves for different metrics are discussed.

In order to improve the accuracy of the identification of Primary Users (PU) in Cognitive Radio Networks (CRNs), Cooperative Spectrum Sensing (CSS) has been introduced. However, there are various challenges in the implementation of CSS which should be properly addressed. One of the most challenging issues which should be considered is the energy consumption for CSS. In this paper, the purpose is to solve such an issue through the effective management of sensors for CSS. In order to do so, a sensor selection algorithm for CSS is proposed for a CRSN including sensors with various detection capabilities. The proposed algorithm selects the appropriate sensors satisfying the desired exactitude of CSS while their energy constraints are carefully considered. The simulation results confirm the benefits of the proposed algorithm in term of energy efficiency compared to other state-of-the-art methods.

In this paper, we study the asymptotic throughput capacity of a static multi-channel multi-interface infrastructure wireless mesh network (InfWMN) wherein each infrastructure node has m interfaces and c channels of unequal bandwidth are available. First, an upper bound on the InfWMN per-user capacity is established. Then, the feasible lower bound is derived by construction. We prove that both lower and upper bounds are tight. We limit our analysis for more practical case of$\mathrm{m} \le \mathrm{c}$. However, for the asymptotic upper bound, our analysis can be used for the general case in which there is no constraint on m and c. Our study shows that in such a network with Ncrandomly distributed mesh clients, Nrregularly placed mesh routers, and Nggateways, the asymptotic per-client throughput capacity has different bounds, which depend on the ratio between the total available bandwidth for the network and the sum of m first greatest data rates of c available channels, i.e.,$\sum \limits_{\mathrm{j} = 1}^\mathrm{C} \mathrm{w}_\mathrm{j} / \sum \limits_{\mathrm{j} = 1}^\mathrm{m} \mathrm{w}_\mathrm{j}$. The results of this paper are more general compared to the existing published researches. In addition, in the case that$\mathrm{w}_\mathrm{i} = \mathrm{W}/\mathrm{c} \forall \mathrm{i},\mathrm{}1 \le \mathrm{i} \le \mathrm{c}$, our results reduce to the previously reported studies. This implies that our study is comprehensive compared to the formerly published researches.

In cognitive radio networks (CRNs) unlicensed Secondary Users (SU) are allowed to access the resources of licensed Primary Users (PU), if they do not make harmful interference to them. In Cooperative Spectrum Sensing (CSS), SUs sense the spectrum independently and send their sensing information to a fusion center combining the received information and making the final decision about the PU presence. The reliability of decision that FC makes is improved by CSS. However, the various requirements of SUs such as battery constraints are not usually considered during CSS process. Therefore, in this paper an energy based user selection algorithm is proposed in which the participants in CSS are selected based on their energy constraints. Our proposed algorithm prevents from early finishing the battery life of SUs by balanced distribution of sensing process among all of them. Numerical results confirm the improved performance of the proposed algorithm in comparison with conventional ones.

In this paper, we address the issue of coverage in a hybrid wireless sensor network with regard to energy constrains. Since battery resources of sensors is limited, it is important to design energy-efficient algorithms. Limiting the moving distance of mobile sensors and using the minimum number of working sensor nodes are good ways of conserving energy resources in sensors. In our algorithm, maximum coverage is obtained subject to energy constrains. To obtain this goal, we use minimum number of working sensor nodes in the area and limit the moving distance of each mobile sensor. Simulation results show our algorithm provides high degree of coverage in an area with less number of working sensor nodes compared with related works.

This paper presents a queuing analysis for mixed voice and data in mobile cellular radio networks that is germane to GSM/GPRS. The work considers a class that consists of a finite number of open sessions and illustrates the quality of service (QoS) on such systems. We start with a general model description in which an analytical model provides the mean delay and the number of data users that are continuously backlogged. A computational algorithm of this is then formulated which significantly improves facility. Both of these works are novel and topical. For this work, complete sharing scheduling for both real and non real-time services is considered. The real-time service is modelled as circuit switched and non real-time service as packet switched. The results are corroborated using the simulation package OPNET. QoS in terms of mean delay and the access policy for a new data user joining the network is also discussed.