Continuous-monitoring applications are an important class of wireless sensor network (WSN) applications. These applications require periodic refreshed data information at the sink nodes. To date, this entails the need for the sensor nodes to continuously transmit in a periodic fashion to the sink nodes, which may lead to excessive energy consumption. In this paper, we show that continuous monitoring does not necessarily imply continuous reporting. Instead, we demonstrate that we can achieve continuous monitoring using an event-driven reporting approach. Building on this idea, we propose two new mechanisms that enable energy conservation in continuous-monitoring WSNs. The first mechanism can augment any existing protocol, whereas the second mechanism is conceived for cluster-based WSNs. With both mechanisms, sensor nodes only transmit information whenever they sense relevant data. To evaluate the efficiency of our proposals, the basic unscheduled transmission model and three well-known cluster-based protocols are used as baseline examples. In particular, new analytical models for conventional cluster-based systems and for our approach-enabled systems are complemented by simulations to present a quantified perspective of the potential benefits of the proposed reporting technique. We prove that significant energy conservation can be achieved using our reporting approach.

In this paper, a novel joint connection-level and packet-level analytical model for the performance evaluation of cognitive radio networks (CRNs) under VoIP traffic is proposed and developed. The proposed teletraffic model captures the most relevant features of both CRNs (i.e., unreliable spectrum sensing, primary users' activity detection) and VoIP traffic (i.e., ON/OFF activity processes for individual VoIP sessions, periodic packet generation of individual VoIP sessions in the ON state). In addition, our mathematical model captures relevant aspects of multiuser VoIP-based networks such as the use of packet buffering, adaptive modulation coding, proportional fair sharing scheduling for secondary users with different link qualities, and call admission control (CAC). Our mathematical analysis is based on the time-scale decomposition technique and employs conventional discrete time Markov chain tools. However, the most important feature of our mathematical formulation is the introduction of the so called cross-level analysis methodology used here to link packet-level and connection-level analyses. This proposed methodology allows to use time-scale decomposition technique when it is no longer possible to perform the connection level analysis in an independent manner from the packet level analysis (i.e., when connection level statistics depend on packet level parameters).

Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) have been used for monitoring animals but when their habitats have difficult access and are areas of a large expanse, remote monitoring by classic techniques becomes a difficult task. The use of traditional WSNs requires a restrictive number of hops in a multi-hoping routing scheme, traveling long distances to the sink node where data is stored by nodes and UAVs are used to collect data by visiting each node. However, the use of UAVs is not straightforward since the energy balance between the WSN and UAV has to be carefully calibrated. Building on this, we propose two data collection schemes in clustered based WSNs: (1) WSN oriented and (2) UAV oriented. In the former, nodes within each cluster member (CM), send information to their cluster head (CH) and for recollection, the UAV visits all CHs. As the UAV visits many CHs the flight time is increased. In the latter, all CHs send data from their CMs to a sink node, hence, the UAV only visits this node, reducing the flying time but with a higher system energy cost. To find the most suitable scheme for different monitoring conditions in terms of the average energy consumption and the buffer capacity of the system, we develop a mathematical model that considers both the dynamics of the WSN along with the UAV.

The volume of data transmitted over networks has significantly increased in recent years. For that reason, safeguarding the privacy, authenticity, and confidentiality of specific data is imperative, necessitating a type of encryption; symmetric encryption, known for its computational efficiency, is ideal for securing extensive datasets. A principal component within symmetric key algorithms is the substitution box (S-box), which creates confusion between plaintext and ciphertext, enhancing the security of the process. This paper proposes a fashion method to create chaotic S-boxes using the Rössler attractor as a chaotic process and the Rijndael S-box as a permutation box. The proposed S-boxes are evaluated with bijectivity, non-linearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear approximation probability (LAP), and differential uniformity (DU). The analyses show that the proposed method helps generate a high-resistance S-box to well-known attacks and high efficiency, executing in short computational time.

In this paper, a novel prioritization mechanism for random access strategies in cellular networks is proposed. The proposed prioritized random access strategy sets different retransmission probabilities to users, depending on their priorities. First, a mathematical analysis method for simultaneously evaluating throughput and access delay, considering an infinite population model and different retransmission policies, is developed. Most of the previous related research has been done, considering both finite population and saturation conditions where all the nodes in the system always have a packet ready to be transmitted, and the transmission queue of each station is assumed to be always nonempty. This assumption is a good approximation for a local area network working at full capacity. However, in a cellular system, the assumptions of a finite population and every node in a cell always having a packet to transmit are not very realistic. Here, the analytical results consider a Poisson arrival process-which is more suitable for the traffic model in a cellular system-for the users in the cells. Then, considering slotted ALOHA as a random access strategy and the developed analysis method, the case where two different types of service exist (low-and high-priority users) is mathematically analyzed. With the proposed prioritized random access strategy, the average access delay achieved for the high priority users is always lower than that for the low-priority users. Additionally, a mathematical expression for the throughput is derived with and without service differentiation. Finally, two approaches for finding the optimum retransmission probabilities are developed; in the first approach, the number of backlogged packets is required, whereas a simpler and efficient alternative method requires only the knowledge of the mean new packet arrival rate. For the performance analysis, four of the most common backoff algorithms are considered: (1) Uniform (UB); (2) Binary Exponential (BEB); (3) Negative Exponential (XB); and (4) Geometric (GB).

Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.