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Physical layer wireless communications are more and more essential, and are vulnerable to malicious users owing to the nature of broadcast channels. Herein, we consider a centralized multi-channel cognitive radio network in the presence of eavesdroppers (EVEs). In the network, the secondary base station (SBS) shares currently free primary channels to simultaneously communicate with secondary users (SUs), while passive eavesdroppers attempt to overhear data in the secondary communications. Each limited-battery SU is equipped with two antennas (one for transmitting signals, and other for receiving signals) and is powered by a solar energy harvester. Meanwhile, the SBS equipped with multiple antennas can operate in full-duplex (FD) transmission mode (simultaneously transmit and receive signals) or in half-duplex (HD) transmission mode (transmit and receive signals in turn during each half of a time slot) with the SUs. In this paper, we propose a novel scheme to maximize the secondary system security of the multi-channel cognitive system in the presence of multiple passive EVEs, in which the EVEs are able to overhear the data of the SBS−SU transmissions on all the primary channels. The problem of decision making is formulated as the framework of a partially observable Markov decision process (POMDP), and an optimal solution is achieved by adopting value iteration−based dynamic programming. Specifically, in each time slot, the SBS allocates optimal channel and optimal action (i.e. either stay silent or employ HD/FD transmission modes with optimal transmission power) for each SU in order to obtain maximum long-term secrecy rate for the secondary system.

This paper studies an optimal transmit power decision policy for energy-efficient data transmissions between a sensor node (i.e., the source) and a cluster head (i.e., the destination) in cluster-based wireless sensor networks in the presence of a full-duplex (FD) active eavesdropper. In this network, the source is powered by a wireless energy harvester, while the destination is constantly supplied by the traditional electrical energy. The eavesdropper is capable of FD transmitting and receiving and hence opportunistically launches jamming attacks against the destination while eavesdropping, which affects not just the legitimate transmissions but the eavesdropper itself. The destination can also work in the FD mode to simultaneously receive information signals and send an artificial noise to interfere with the eavesdropper. Therefore, we investigate an optimal power allocation policy for the source in order to maximize the secrecy transmission rate against an FD eavesdropper. In addition, we study the problem of decision making in two different scenarios. First, the legitimate nodes are assumed to have prior information about the arrival of harvested energy and about the eavesdropper’s jamming attack model. The problem is formulated as the framework of a partially observable Markov decision process and is solved with value iteration–based dynamic programming. Second, the legitimate nodes do not know the dynamics of the environment in advance, so the problem becomes a standard Markov decision process. Hence, we propose an actor-critic learning framework to find the solution from practical interactions with the environment. Finally, we verify the performance of the proposed schemes by simulations.

In this paper, we investigate a security mode decision policy for a cognitive radio network (CRN) powered by a non-radio frequency energy harvester. In such a network, a cognitive user (CU), which has a finite battery capacity, senses the presence of the primary user and tries to access the time-slotted primary channel opportunistically to transmit data. However, communication can be vulnerable to sudden attacks that are carried out by hidden eavesdroppers. Therefore, we propose an energy-efficient data encryption scheme for CRNs to increase the effective security level under energy limitation constraints. The operation mode decision policy is formulated as a framework of a partially observable Markov decision process. In this approach, based on the sensing results and the remaining energy at the beginning of each time slot, CUs can decide to stay silent to save energy, or become active and encrypt data using opportune private-key encryption methods considering the effect of the current action on the future reward to maximize the effective security. Finally, we evaluate the performance of the proposed scheme by using numerical simulation results.

Due to the highly dynamic nature of cognitive vehicular networks, several sporadic links induce a long delay in the network. The selection of a stable route is therefore one of the key design factors to improve overall network stability, thereby reducing the end-to-end delay. Software-defined networking (SDN) is a novel approach that improves network intelligence. To this end, we propose a novel SDN-based routing protocol for cognitive vehicular networks that finds a stable route between source and destination. As this is a cognitive routing protocol, spectrum sensing is therefore the primary task of this algorithm to improve network stability by keeping primary user activity safe. We apply a belief propagation algorithm for channel selection. This is an SDN-based vehicular communications scheme where two nodes can only communicate when they have consensus about a common idle channel. The protocol has two phases: the registering phase and the route prediction phase. The SDN main controller (MC) is responsible for a global view of the network, whereas several local controllers (LCs) are responsible for localized global views of the network. This layering of controllers into two kinds (MC and LCs) improves the network performance in terms of end-to-end delay, high delivery ratio, and low overhead. We prove this in our simulation results by comparing our proposed scheme with two existing schemes (one with, and another without, SDN).

In this paper, the problem of quality improvement for sensing models in various applications is considered. A choice of sensing model strongly influences the design of wireless sensor networks and the performance of protocols, such as traffic aggregation, broadcasting, energy-efficient routing, target or barrier coverage, and so on. For these reasons, it is useful to investigate the nature of the sensing ability and the manner in which it depends on the characteristics of wireless sensor networks. This paper investigates the impact of experiment design on the evaluation of sensing quality function. This paper provides an approach to a statistically efficient estimation of sensing model parameters.

Herein, the authors consider harvested energy powered cognitive radio networks (CRNs) in which harvested energy is stored in a rechargeable battery which has finite capacity. In addition, a practical scenario in which the amount of harvested power is finite is taken into account. Cognitive users (CUs) opportunistically access a licensed channel (or primary channel); meanwhile, it should be ensured that their confidential communications are not leaked to an eavesdropper. We investigate an optimal spectrum sensing schedule and the optimal amount of transmission energy for the CUs in each processing time slot. In particular, at the beginning of each time slot, based on the remaining energy in the battery, CU transmitter decides either: 1) to be active to sense the channel and transmit its data if the channel is found vacant or 2) to stay inactive during the current time slot in order to save energy and wait for more incoming energy for use in the next time slots. The decision is based on expected secrecy transmission rate calculated for both cases over subsequent $K$ time slots. The proposed scheme aims to improve long-term secrecy transmission rate of CRNs in comparison with a conventional scheme where the decision for the current time slot is made to maximize current secrecy transmission rate without considering any future reward.

In this paper, we consider data capture of a cognitive radio-based red network in tactical wireless networks. In the blue (friendly) network, an eavesdropping sensor node is deployed to capture data of the red (adversary) network, where the red network adopts cognitive radio for opportunistic data transmission. Since most wireless networks suffer energy constraints, we investigate the data capture (eavesdropping) performance of the blue network in two scenarios of energy management: no energy harvesting and energy harvesting. In both of the energy scenarios, we formulate the problem of maximizing data-capturing performance of blue network in the framework of a partially observable Markov decision process (POMDP) by considering feedback observations of actions. Furthermore, a recursive method is adopted to solve the problem by which the optimal action policy for the eavesdropping sensor nodes of blue network is obtained. Finally, we compare the performances of the proposed scheme with those of different reference methods under the two mentioned energy scenarios. Numerical results show that the proposed POMDP-based scheme can improve the data-capturing performance of the eavesdropping sensor node.

This paper considers the throughput maximization of a secondary user (SU) in a realistic cognitive radio (CR) network where the battery suffers from constant energy leakage. We investigate two different CR scenarios where the primary user (PU) switches between idle and active states in a time-slotted manner. In the first scenario, the SU knows the exact state of the PU at the beginning of each time slot, whereas in the second scenario, the SU attempts to detect state of the PU by spectrum sensing. For both scenarios, we determine the maximum throughput of the SU with consideration of battery leakage of the SU and interference constraint of the PU. The optimal solutions of transmitting power and sensing duration are achieved by using golden section search method and a simplified brute-force search method. Finally, the theoretical analysis is verified through the Monte Carlo simulations.

In this paper, we consider a hybrid underlay-overlay cognitive radio with energy harvesting. In the considered system, secondary user can harvest energy from the primary user's signal as well as from the other ambient sources, such as solar, wind, vibration and so on. Energy is harvested from the primary user's signal when the primary channel is found in busy state. The secondary user either operates in one of the two transmission modes; overlay and underlay in order to maximize the throughput, remains in the sleep mode in order to conserve energy, or harvests energy from the primary channel in order to maximize the remaining energy. To maximize long-term throughput of the system, we propose an access strategy in which the partially observable Markov decision process framework is used to determine action of the secondary user, and energy threshold is used to determine the transmission mode (overlay or underlay) of secondary user. Simulations show that for certain values of the system parameters, the considered system provides 60% improved throughput than overlay-only cognitive radio and 43% enhanced throughput than a hybrid cognitive radio system harvesting energy only from the ambient sources. However, increasing the throughput also increases computational burden on the secondary user, which may increase latency and energy requirements of the system.

The cost for increasing the accuracy of cooperative spectrum sensing in cognitive radio requires a long time for collecting sensing data. Sequential fusion, in which the sensing data reports are sequentially collected, combined, and tested with two thresholds for deciding to wait for the next data report or concluding the presence or absence of the primary signal, is considered to reduce the collecting time. A further reduction in the average sequential report number (ASN) can be achieved by arranging the reports in descending order of data quality where the better sensing data is reported earlier. In this paper, the ASN and the error probability of the ordered sequential fusion are analytically calculated based on the direct method. The results are then adopted to design an optimal method for obtaining the optimal sequential thresholds and truncated point. The optimal truncated point enables one to minimize the ASN by discarding the sensing data with low quality, while the optimal sequential thresholds ensure the maintaining of the sensing performance of sequential fusion be the same level to that of the conventional parallel fusion.

Event detection is a central issue in wireless sensor networks. In this paper, we propose a novel event-detection scheme which utilizes empirical distribution and the combination method of evidence theory. Unlike conventional methods where the sensor uses energy detection to determine the presence or absence of an event, our scheme uses a goodness-of-fit (GOF) test to measure the distance between the observed data and the empirical distributions of both the presence and absence hypotheses. Multiple types of such two-sided GOF tests are combined to create a well-adapted detector using evidence theory. The simulation results show that the proposed detector is more accurate than conventional detectors in different kinds of noisy environments.