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Given the existence of techniques that disrupt conventional RF communication channels, the demand for innovative alternatives to electromagnetic-based communications is clear. Covert communication, which claims to conceals the communication channel, has been explored using bio-inspired sounds in aquatic environments, but its application in terrestrial areas is largely unexplored. This work develops a mathematical analysis of a wireless sensor network that operates stealthily in outdoor environments by using birdsong audio signals from local birds for covert communication. Stored bird sounds are modified to insert sensor data while altering the sound minimally, both in characteristics and random silence/song patterns. This paper introduces a technique that modifies a fourth-level coefficient detail with a wavelet transform, then applies an inverse transform to achieve imperceptible audio modifications. The mathematical analysis includes a statistical study of the On/Off periods of different birds’ songs and a Markov chain capturing the system’s main dynamics. We derive the system throughput to highlight the potential of using birdsong as a covert communication medium in terrestrial environments. Additionally, we compare the performance of the sound-based network to the RF-based network to identify the proposed system’s capabilities.

Wireless sensor networks are composed of many nodes distributed in a region of interest to monitor different environments and physical variables. In many cases, access to nodes is not easy or feasible. As such, the system lifetime is a primary design parameter to consider in the design of these networks. In this regard, for some applications, it is preferable to extend the system lifetime by actively reducing the number of packet transmissions and, thus, the number of reports. The system administrator can be aware of such reporting reduction to distinguish this final phase from a malfunction of the system or even an attack. Given this, we explore different mathematical functions that drastically reduce the number of packet transmissions when the residual energy in the system is low but still allow for an adequate number of transmissions. Indeed, in previous works, where the negative exponential distribution is used, the system reaches the point of zero transmissions extremely fast. Hence, we propose different dampening functions with different decreasing rates that present oscillations to allow for packet transmissions even at the end of the system lifetime. We compare the system performance under these mathematical functions, which, to the best of our knowledge, have never been used before, to find the most adequate transmission scheme for packet transmissions and system lifetime. We develop an analytical model based on a discrete-time Markov chain to show that a moderately decreasing function provides the best results. We also develop a discrete event simulator to validate the analytical results.

In this study, we analyze the CSMA Non-Persistent protocol with a finite number of nodes, providing more accurate results for applications like wireless sensor networks. The finite model addresses scenarios where the node count is moderate, capturing realistic system dynamics. Our analysis reveals a dependency on the node count, impacting system throughput. As the node count increases, throughput behavior aligns with Kleinrock’s infinite model. We derive a complex closed-form throughput expression for a finite quantity of nodes in the system, solved numerically, and offer an approximate expression for specific conditions. These insights advance understanding of low-contention network performance, especially in scenarios where the infinite model becomes inadequate.

In modern and future communication systems, we expect peaks of traffic that largely exceed the capacity of the system, since they are originally designed to support normal traffic loads. Such peaks can be caused by emergency events and cultural or sporting gatherings, among others. Indeed, implementing more channels than the ones required in normal traffic conditions would entail higher costs and energy consumption. As such, when a traffic peak arrives, the system performance is greatly affected. To this end, we propose the use of mobile channels that assist cellular systems to increase the capacity of the network for a certain period. In this paper, we derive the blocking probability of a UAV (Unmanned Aerial Vehicle)-assisted cellular system to temporarily increase the capacity of the communication network in case of a traffic overload. The analysis presented in this work allows a careful design of future communication systems requiring fewer channels, that can serve users in normal traffic load conditions while using UAVs to maintain an adequate blocking probability when the traffic load increases. To this end, we develop the ErlangU formula, similar to the ErlangB formula for a conventional voice service cellular system.

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network’s overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.

Linear topologies arise naturally in the context of Internet-of-Things (IoT) applications for smart cities, where the infrastructure itself commonly has a linear or semi-linear structure. This is the case of buildings, public transportation systems, road infrastructure, and utility distribution networks. Given the prevalence of this type of topologies, several Medium Access Control (MAC) protocols have been designed to take advantage of their particular properties. Unfortunately, most of them do not scale well as the node density and the distance in hops to the sink increases. The result is that packets generated many hops away from the sink tend to experience unacceptable high end-to-end delay and low delivery probabilities. This paper introduces HP-MAC, a synchronized duty-cycled MAC protocol for Linear Sensor Networks (LSNs) that assigns transmission priorities to nodes to avoid collisions, through the implementation of distributed elections based on hash functions. HP-MAC also implements a packet queuing scheme that acts as a mechanism to control the amount of network resources allocated to data flows generated at different distances to the sink. This way, packets can reach their destination with loss probability and end-to-end delay that do not depend on their distance to the sink. We use a Discrete-Time Markov Chain (DTMC) to model the performance of the proposed protocol. Numerical solutions of this model show that HP-MAC outperforms state-of-the-art representatives in terms of throughput, end-to-end delay, power consumption, and packet loss probability. These results are validated through extensive discrete-event simulations.

Peer-to-Peer (P2P) networks have emerged as potential solutions to issues that cause inefficient download times in networks because they can use the resources in the entire network, allowing nodes to act both as servers and clients simultaneously. Commonly, P2P networks use radio frequency (RF) to communicate among nodes; however, light fidelity (LiFi) has been developed as an alternative to wireless fidelity (WiFi) systems due to some advantages such as great speed (up to 1 Tbps), a high level of security, and less saturated channels in unlicensed bandwidth, making it ideal for RF-sensitive environments and networks with static nodes, since LiFi systems require a high node alignment level to enable efficient communication. In this article, we develop a mathematical model that captures the dynamics of LiFi- and WiFi-based P2P networks in static environments to allow for adequate LiFi links in managed conditions where the content is distributed from a single source node to the rest of the peers through different architectures. For the services considered in our work, we compare the performance of P2P networks using WiFi 7 and LiFi 2.0 technologies to provide clear quantitative numerical results that allow for adequate selection between these systems. Also, we validate our mathematical results through extensive simulations.

Peer-to-peer (P2P) networks are distributed systems with a communication model in which no central authority governs the behavior of individual peers. These networks currently account for a considerable percentage of all bandwidth worldwide. However, this communication model also has a clear disadvantage: it has a multitude of vulnerabilities and security threats. The nature of the P2P philosophy itself means that there is no centralized server responsible for uploading, storing, and verifying the authenticity of the shared files and packets. A direct consequence of this is that P2P networks are a good choice for hackers for the spread of malicious software or malware in general since there is no mechanism to control what content is shared. In this paper, we present a mathematical model for P2P networks to study the effect of two different attacks on these systems, namely, malware and denial of service. To analyze the behavior of the cyber attacks and identify important weaknesses, we develop different Markov chains that reflect the main dynamics of the system and the attacks. Specifically, our model considers the case in which a certain number of nodes are infected with a cyber worm that is spread throughout the network as the file is shared among peers. This allows observation of the final number of infected peers when an initial number (we evaluate the system for from 1 to 14 initial nodes) of malicious nodes infect the system. For the DoS attack, our model considers the portion of peers that are unable to communicate and the average attack duration to study the performance degradation of such an attack. A two-pronged approach was used to study the impact of the attacks on P2P networks; the first focused only on the P2P network, and the second focused on the attacks and the network.

Time-Stable Geocast is a group communication pattern where the destination is composed of a set of nodes that, at any time during a predefined time interval, have been located within a given geographic destination region. Time-Stable Geocast is well suited to supporting Smart City and Intelligent Transportation Systems (ITS) applications such as road safety, crowdsensing, entertainment, and business applications that can benefit from disseminating position and time-based abiding information. In this paper, we present GeoTemporal-cast, an opportunistic time-stable geocast routing protocol for Delay-Tolerant Vehicular Ad Hoc Networks (DT-VANETs) that, unlike previous proposals, does not assume that either data messages are generated inside the geographic destination region or that an underlying routing protocol will deliver the packets there. GeoTemporal-cast computes shortest-path trees over the street-layout graph that are rooted at a connected component that includes all the nodes representing intersections located inside the destination region. By traveling along the edges of these trees, data messages can reach the destination region from all the streets with access to the destination region. We evaluate the performance of GeoTemporal-cast using detailed simulations in NS-3 that consider city-wide realistic scenarios involving up to 9,080 vehicles. Our results show that GeoTemporal-cast outperforms a set of time-stable protocols for Delay-Tolerant Networks, including Time-Stable Geographically-Restricted Epidemic and variations of Spray & Wait, in terms of delivery ratio, delivery delay, and overhead.

We present the greedy optimal algorithm for contention resolution (GOAL-CR), a greedy algorithm that solves a variant of the standard contention resolution problem where a set of nodes want to access a shared resource only once, and the objective is to minimize the time it takes for all the nodes to access the resource successfully. These assumptions hold, for instance, in event reporting applications or in the cluster formation phase of wireless sensor networks. We formally prove that the GOAL-CR computes access policies that minimize the expected contention resolution time. We also show, numerically, that the performance of the greedy policies is close to that of a protocol with complete information about the exact number of nodes that have not yet accessed the resource; this latter assumption is hard to fulfill in practice but allows the derivation of a lower bound for the problem. In addition, we show how to adapt the algorithm to scenarios where there is uncertainty in the initial number of nodes and to scenarios where nodes have very limited memory. Finally, we use simulations to show the robustness of the GOAL-CR against asynchronous starts.

Abstract In this paper, dynamic and anticipated resource leasing strategies to address the problem of leased resource availability are investigated. Then, network performance analysis is developed, and the performance evaluations of different leasing strategies (namely permanent, dynamic, and anticipated leasing strategies) are provided for some network scenarios. The developed mathematical model additionally considers non-homogeneous bandwidth requirement, where different primary users (PUs) and secondary users (SUs) may require a number of channels. The tradeoff between spectrum leasing cost and system Erlang capacity (defined as the maximum offered traffic for which all the QoS requirements are guaranteed) is also investigated. Specifically, the maximum Erlang capacity is obtained by optimizing the number of reserved channels for different traffic scenarios considering a maximum allowed number of simultaneously rented channels. Also, the minimum achievable cost per Erlang is obtained by considering a fractional number of leased channels. Additionally, the cost per Erlang of capacity as function of both the utilization factor of the primary resources and the maximum allowed number of simultaneously rented channels is evaluated. Numerical results demonstrate that system performance strongly depends on the value of the mean secondary service time relative to the mean primary service time. Also, numerical results show that the cost of leasing a spectrum band is drastically reduced (compared to the permanent leasing strategy) when dynamic spectrum leasing strategies are employed.

Abstract In this paper, the Erlang capacity achieved by the separate or joint use of several resource management mechanisms commonly considered in the literature (spectrum aggregation, spectrum adaptation, call buffering, channel reservation, selective interruption, and preemptive prioritization) to mitigate the effects of secondary call interruptions in cognitive radio networks (CRNs) is evaluated and compared. Heterogeneous traffic is considered, and service differentiation between real-time and elastic (data) traffic is done in terms of their different delay tolerance characteristics. The aim of our investigation is to identify the most relevant resource management mechanisms to improve the performance of the considered networks. As such, both the individual and joint effect of each resource management mechanisms on system performance are evaluated with the objective of comparing the gains in capacity achieved by each resource management mechanism studied in this work. For this purpose, the different resource management mechanisms studied are carefully combined and, for each resulting strategy, optimization of its configuration is presented to maximize the achievable Erlang capacity. For the performance evaluation of the considered strategies in heterogeneous traffic CRNs, a general teletraffic analysis is developed. Numerical results show that spectrum adaptation and call buffering are the mechanisms that best exploit the elasticity of delay-tolerant traffic in heterogeneous traffic CRNs and, therefore, most significantly improve system performance.

Abstract In this paper, teletraffic performance and channel holding time characterization in mobile cognitive radio cellular networks (CRCNs) under fixed-rate traffic with hard-delay constraints are investigated. To this end, a mathematical model to capture the effect of interruption of ongoing calls of secondary users (SUs) due to the arrival of primary users (PUs) is proposed. The proposed model relies on the use of an independent potential interruption time associated with the instant of possible interruption for each ongoing call in every visited cell. Then, a Poisson process is used to approximate the secondary users’ call interruption process due to the arrival of PUs. Based on this model and considering that unencumbered service time (UST) and cell dwell time (CDT) of SUs are independent generally distributed random variables, analytical formulae for both the probability distributions of channel holding times and inter-cell handoff attempts rate are derived. Also, a novel approximated closed-form mathematical expression for call forced termination probability of SUs is derived under the restriction that the UST is exponentially distributed. Additionally, by considering all the involved time variables exponentially distributed and employing fractional channel reservation to prioritize intra- and inter-cell handoff call attempts over new call requests, a queuing analysis to evaluate the call-level performance of CRCNs in terms of the maximum Erlang capacity is developed. The accuracy of our proposed mathematical models is extensively investigated under a variety of different evaluation scenarios for all the considered call-level performance metrics. Numerical results demonstrate that channel holding time statistics are highly sensitive to both interruption probability of ongoing secondary calls and type of probability distribution functions used to model CDT and UST. From the teletraffic perspective, numerical results reveal that the system Erlang capacity largely depends on the relative value of the mean secondary service time to the mean primary service time and the primary channels’ utilization factor. Also, the obtained results show that there exists a critical utilization factor of the primary resources from which it is no longer possible to guarantee the required quality of service of SUs and, therefore, services with hard-delay constraints cannot be even supported in CRCNs.

In the context of smart cities, there is a general benefit from monitoring close encounters among pedestrians. For instance, for the access control to office buildings, subway, commercial malls, etc., where a high amount of users may be present simultaneously, and keeping a strict record on each individual may be challenging. GPS tracking may not be available in many indoor cases; video surveillance may require expensive deployment (mainly due to the high-quality cameras and face recognition algorithms) and can be restrictive in case of low budget applications; RFID systems can be cumbersome and limited in the detection range. This information can later be used in many different scenarios. For instance, in case of earthquakes, fires, and accidents in general, the administration of the buildings can have a clear record of the people inside for victim searching activities. However, in the pandemic derived from the COVID-19 outbreak, a tracking that allows detecting of pedestrians in close range (a few meters) can be particularly useful to control the virus propagation. Hence, we propose a mobile clustering scheme where only a selected number of pedestrians (Cluster Heads) collect the information of the people around them (Cluster Members) in their trajectory inside the area of interest. Hence, a small number of transmissions are made to a control post, effectively limiting the collision probability and increasing the successful registration of people in close contact. Our proposal shows an increased success packet transmission probability and a reduced collision and idle slot probability, effectively improving the performance of the system compared to the case of direct transmissions from each node.

WSNs are complex systems that are mainly limited by the battery life of the nodes in order to have an adequate performance. During the operation of the system, it is not uncommon to have a portion of nodes with low energy levels while other nodes have high energy levels. Nodes with very low residual energy must reduce their energy consumption since their operational lifetime is almost over. In this paper, we consider cluster-based WSNs for the event detection where there is a high concentration of high energy nodes and low concentration of low energy nodes. Building on this, we propose extending the battery life of low energy nodes in both the cluster formation and the steady-state phases. For the former, energy efficiency is achieved by means of assigning prioritized access to the shared channel to low energy nodes while delaying the contention access of high energy nodes which can support higher number of collisions before energy depletion. For the latter, we consider the duty-cycle of nodes where the sleep and active modes have dwelling times related to their residual energy levels. The system and the impact of the proposed residual energy-based mechanisms are mathematically evaluated using Markovian models.

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Evolution in wireless sensor networks (WSNs) has allowed the introduction of new applications with increased complexity regarding communication protocols, which have to ensure that certain QoS parameters are met. Specifically, mobile applications require the system to respond in a certain manner in order to adequately track the target object. Hybrid algorithms that perform Continuous Monitoring (CntM) and Event-Driven (ED) duties have proven their ability to enhance performance in different environments, where emergency alarms are required. In this paper, several types of environments are studied using mathematical models and simulations, for evaluating the performance of WALTER, a priority-based nonpreemptive hybrid WSN protocol that aims to reduce delay and packet loss probability in time-critical packets. First, randomly distributed events are considered. This environment can be used to model a wide variety of physical phenomena, for which report delay and energy consumption are analyzed by means of Markov models. Then, mobile-only environments are studied for object tracking purposes. Here, some of the parameters that determine the performance of the system are identified. Finally, an environment containing mobile objects and randomly distributed events is considered. It is shown that by assigning high priority to time-critical packets, report delay is reduced and network performance is enhanced.