Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals , the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time . We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.

Abstract Spectrum-sensing as a service has been proposed and studied by many researchers over the past decade as a promising approach to support the viability of cognitive radio networks (CRNs). A spectrum-sensing service provider (SSP) provides information about spectrum occupancy to its clients that is generally more accurate than what clients can learn on their own. Two approaches are used by SSPs in their operation, the dedicated sensing infrastructure approach (sensor-aided CRN) and the crowdsensing approach. In this work, we assume a hybrid model where a dedicated sensing infrastructure is used along with crowdsensing. We study the tradeoff between sensing time paid by cognitive users to the SSP and their achievable transmission time. Our objective is to maximize the minimum achievable transmission time for any cognitive user in the network by carefully selecting the channels to be used. Two algorithms are proposed, one is based on the hill-climbing search algorithm (abbreviated HCA) and the other is a less optimal but faster greedy selection algorithm (abbreviated GSA). Results show that both HCA and GSA are within 3% of the optimal solution. Results also confirm that GSA is faster than HCA, while HCA outperforms GSA.

In this paper, we study the channel allocation problem in cognitive radio wireless mesh networks (CR-WMNs). We aim at finding an allocation strategy that guarantees quality of service (QoS) (link reliability), maximizes network coverage, and alleviates the need for a common control channel to coordinate the communication process. The allocation of a particular channel to a mesh client (MC) is considered feasible if the MC can establish connectivity with the backbone network in both the upstream and the downstream directions, and has the signal-to-interference-plus-noise ratio (SINR) of the uplink and the downlink with its parent mesh router (MR) within a predetermined threshold. A receiver-based channel allocation (RBA) model that achieves the aforementioned objectives is proposed (channel assignment under this model can be proven to be NP-hard). We then formulate a mixed integer linear program, of the channel allocation problem under the proposed model, and compare its performance to that of two other baseline models, namely, transmitter-based and all-tunable channel allocation strategies. The results prove the superiority of the proposed model. We also developed a heuristic algorithm, which is shown to be an accurate algorithm.

Cognitive radio networks (CRNs) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. A well-known property of CRNs is the potential heterogeneity in channel availability among secondary users. Therefore, multicast throughput in CRNs may suffer from significant degradation because of this property since a link-level broadcast of a frame may only reach a small subset of destinations that are able to receive on the same channel. This may necessitate multiple sequential transmissions of the same frame by the source on different channels to guarantee delivery to all receivers in the destination set. In case of high data generation rate, delivery delay will be high due to the repeated transmissions by the source. In this paper, we propose an assistance strategy to reduce the effect of the channel heterogeneity property on the multicast throughput in cognitive radio wireless mesh networks (CR-WMNs). This assistance strategy is composed of two main activities: first, allowing multicast receivers to assist the source in delivering the data, and second, allowing the transmission of coded packets so that multicast receivers belonging to different multicast groups can decode and extract their data concurrently. Results show that the proposed assistance paradigm reduces multicast time and increases throughput significantly.

Cognitive radio networks (CRN) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. In this work, we consider the multicast routing and channel allocation problem in cognitive radio mesh networks. Due to the potential heterogeneity in channel availability among mesh routers (MRs) and the frequency switching latency, end-to-end delay and throughput degradation could be subject to a significant increase. We propose an on-demand multicast routing and channel allocation algorithm that takes channel heterogeneity and switching latency into consideration. The algorithm aims at reducing the end-to-end delay, and at the same time reducing the degradation of throughput using a dynamic programming approach.

In this work, we consider the multicast problem in a single cell in a cognitive mesh network. Due to the potential heterogeneity in channel availability among the members of a multicast group(s), the total multicast time could be longer due to transmitting the multicast data over multiple channels. We propose, in this work, an assisted multicast strategy with the objective of minimizing the total multicast time. This assistance is composed of two main activities, first, allowing the receivers in a multicast group to forward the data they have received to other members of the multicast group(s), and second, allowing the transmission of coded (bitwise XORed) packets so that receivers belonging to different multicast groups can decode and extract their data concurrently. We show, in this paper, that the proposed assistance paradigm achieves a considerable reduction in the total multicast time, which in turn increases the system throughput.

Network fragmentation is a potential problem in wireless sensor networks (WSNs) due to many reasons like, node failures or environmental conditions (obstacles) that prevent connected deployments. One approach to cope with this problem is to have a mobile sink node (MS) patrol the network field and collect the data from all the fragments across the network. In this paper, we use a dynamic programming (DP) approach to determine the mobility trajectory of the MS within each fragment such that the energy consumption at the sensor nodes within the fragment is minimized. Moreover, we study the problem of finding the shortest route (cycle) that the MS should take in its journey between fragments in order to reduce a fragment's inter-visit time. For this purpose, we propose an Integer Linear Programming (ILP) formulation to find the optimal route. As finding the optimal route is NP-hard, we also propose a heuristic approach to find a near optimal solution.

Network fragmentation is a potential problem in wireless sensor networks (WSNs) due to many reasons like, node failures or environmental conditions (obstacles) that prevent connected deployments. One approach to cope with this problem is to have a mobile sink node (MS) patrol the network field and collect the data from all the fragments across the network. In this paper, we use a dynamic programming (DP) approach to determine the mobility trajectory of the MS within each fragment such that the energy consumption at the sensor nodes within the fragment is minimized. Moreover, we study the problem of finding the shortest route (cycle) that the MS should take in its journey between fragments in order to reduce a fragment's inter-visit time. For this purpose, we propose an Integer Linear Programming (ILP) formulation to find the optimal route. As finding the optimal route is NP-hard, we also propose a heuristic approach to find a near optimal solution.

Repairing connectivity and achieving a certain level of fault tolerance are two important research challenges in wireless sensor networks that have, in many papers in the literature, been jointly studied. Most of the proposals that aim at restoring network connectivity deal with the network as a general graph of N nodes with the edge cost being the number of nodes needed to establish connectivity between the two ends of the edge. This assumption ignores the topological properties of the network, especially the overlap between sensors' communication ranges, and the node-failure pattern that caused the disconnection. In this paper, we try to exploit these properties to minimize the number of additional nodes needed to repair the connectivity.

Exploiting mobility to enhance the performance of wireless sensor networks (WSNs), in terms of connectivity, coverage, and lifetime elongation, has recently been the focus of several research studies. Mobility was adopted in two different ways; either using a network of mobile sensor nodes or deploying a few supplementary special mobile elements, usually referred to as mobile agents to help enhance connectivity and coverage. Different modes of operation (roles) were assigned to mobile agents including being a data relay, data collector, and data sink. In this paper we use a closed queueing network to model mobility and then evaluate data latency under all those roles. The proposed model provides powerful means to understand the effect of different parameters, like velocity and number of mobile agents as well as their movement strategy, on data latency.

Due to the wide range of applications in sensors and Wireless Sensor Networks (WSN), research in this area has recently received increasing attention. WSNs rely on network connectivity to deliver data to a base station through multihop communication. However, connectivity may not be always achievable for a number of reasons. In this paper, we study the problem of data delivery in disconnected WSNs. A special class of disconnected sensor networks called "Fragmented wireless sensor networks (FWSN)" is considered. A FWSN consists of several groups of connected sensors that we call "fragments". To achieve connectivity between these fragments, mobile agents move in the network and act as data relays between fragments, in order to eventually deliver data to the base station. The main contribution of this paper is the modeling of the movement of these mobile relay nodes as a closed queueing network to obtain steady state results of the distribution of the mobile relays in the network. Building on these results, we derive the distributions of the fragment-to-fragment, and fragment-to-sink delays. Comparing these analytical results to results from the TOSSIM simulator, it is shown that this model accurately captures the system behavior, and can be used to predict data delivery delays.