This paper introduces a novel clustering and routing protocol for Internet of Things (IoT) sensor networks aimed at reducing energy consumption and extending the lifespan, a critical requirement for smart cities. The proposed protocol demonstrates significant potential in diverse IoT applications, including smart logistics parks, smart medical centers, smart factory zones, smart universities, and smart cities. By ensuring energy-efficient, long-lasting sensor networks, the proposed protocol supports continuous real-time data collection and monitoring, enabling cities to achieve sustainability goals, enhance urban resilience, and improve operational efficiency. The protocol dynamically divides the sensing area into equal-sized Hexagonal Clusters (HCs), adapting to variations in network size, Sensor Node (SN) count, and their distribution. Each HC is managed by a Cluster Head (CH), selected using the Marine Predators Algorithm (MPA), which excels in optimization tasks. The MPA algorithm determines the optimal CH by evaluating four key cluster fitness function parameters: the SNs’ distance to the sink, the Average Scale of Building Occlusions (ASBO), the SN centrality, and the CH role count. Furthermore, our proposed protocol incorporates an innovative relay cost function for the purpose of data forwarding, where CHs transmit data hop-by-hop to the sink, selecting relay nodes based on their role count, distance from the sink, ASBO, and distance from other CHs. Simulation results validate the protocol’s effectiveness, demonstrating its superior performance in extending network lifespan, increasing throughput, reducing average delay, and conserving energy. The proposed protocol outperforms existing protocols, establishing a new benchmark for energy-efficient clustering and routing in IoT applications.

The Internet of Things (IoT) has impacted sectors like smart cities and healthcare by enabling real-time data collection through Wireless Sensor Networks (WSNs). However, challenges such as limited battery life, connectivity disruptions, and the separate management of clustering and routing hinder performance and energy efficiency. Existing cluster-based IoT-WSN routing protocols often suffer from issues such as random or non-energy-aware Cluster Head (CH) selection, imbalanced energy consumption, imperfect load balancing, and a lack of integration between clustering and routing. These shortcomings reduce overall network efficiency. To address these challenges, we propose the Load-Driven, Location-and Power-aware Equilibrium Optimizer-Based Clustering and Routing Protocol (LDLP-EO-CRP), which integrates clustering and routing to optimize CH selection and balance energy consumption. LDLP-EO-CRP is designed for sustainable smart city applications, including smart waste management, energy-efficient street lighting, air quality monitoring, and intelligent traffic management. By enabling IoT systems to operate reliably over extended periods, LDLP-EO-CRP supports continuous, real-time data collection and monitoring, contributing to citywide sustainability goals and enhancing urban resilience. The proposed protocol divides the sensing area into hexagonal clusters, adapting to changes in network size, node distribution, and density. Each cluster is managed by a CH selected using the EO algorithm, which considers key factors through its fitness function, namely, the remaining energy of sensor nodes, their distances from the sink, loads, and proximities to neighboring nodes. LDLP-EO-CRP incorporates a novel relay-based data forwarding technique, where each CH transfers data hop-by-hop to the sink, based on the loads and proximities of candidate relay nodes. Simulation results demonstrate that LDLP-EO-CRP outperforms existing protocols, extending network lifespan, increasing throughput, reducing delay, and optimizing energy usage.

Data gathering and critical events detection are two essential functionalities for Wireless Sensor Network (WSN). In this paper, we propose double mobile sinks network architecture, where two mobile sink nodes visit the Cluster Heads (CHs) to collect the captured data, which is very energy effective in terms of energy transmission efficiency and reliable compared with the case of having one static sink node. Moreover, the proposed architecture provides a capable scheme for supporting critical and non-critical data, which assures a timely delivery for any critical event to the remote monitoring and decision-making center with minimal interference to the non-critical data. Our proposed architecture shows a superior performance in terms of packets transmission delay, and requires low buffer occupancy for the CHs nodes when compared to related work in the literature. Finally, the paper provides a preliminary hardware design and implementation for the proposed architecture.

Summary Spectrum sharing protocols for cognitive radio networks (CRNs) are often designed based on an exclusive channel occupancy policy. This policy does not allow concurrent cognitive radio (CR) transmissions to take place over the same channel. Unfortunately, this can significantly affect spectrum utilization and network performance. Allowing several secondary users to simultaneously share the same channel naturally improves spectrum utilization and avoids unnecessary blocking of CR transmissions. The key challenge in enabling efficient operation of multichannel CRNs is how to perform efficient power‐controlled media‐access‐control (MAC) protocols. These protocols include both power control and channel assignment. In this paper, we propose a power‐controlled MAC protocol for CRNs based on interference‐channel occupancy model with the objective of maximizing the number of served CR transmissions. It uses a novel power‐controlled channel assignment mechanism that allows several concurrent interference‐limited transmissions to simultaneously proceed over each idle channel in the same neighborhood. We present two variants of the power‐controlled channel assignment mechanism. The first proposed variant is suitable for CRNs with fixed data packet size, whereas the other is suitable for dynamic CRNs with variable packet size. The second variant of our protocol employs a users' scheduling procedure that attempts to fully utilize the available time‐frequency blocks. Simulation results indicate that employing transmission power control along with appropriate channel assignment and users' scheduling in CRNs can significantly improve the spectrum utilization and the overall network performance.

This study considers the Gaussian cognitive interference channel (IC), in which one of the two users operates in cognitive mode. In addition, to transmit its signal, the cognitive sender can cooperatively forward the primary signal to its destination, the primary receiver. Moreover, the transmission over this IC also experiences interference from a third source, which is commonly known as the common interferer source. To reduce the effect of both the primary signal and the common interference signal at the secondary destination, both the cognitive transmitter and its destination may have noisy complete (non‐causal) or partial (causal) knowledge of these signals. For this channel model, the achievable rate region is computed in which the availability of both the primary signal and the common interference signal at the cognitive transmitter and its destination can be (i) noisy non‐causal and (ii) causally estimated. The authors numerical results confirm that knowing both the primary signal and the common interference signal can significantly increase the achievable rate region. Indeed, the obtained achievable rate region is larger than the available bounds in the literature. Furthermore, representative numerical examples are also shown to demonstrate the value of the power allocation at the cognitive source.

Incremental relaying was initially presented so as to adjust for the shortages or limitations related to the usual cooperative methods, specifically, the fixed relaying methods in which the relay node assists the source node in transmitting the signals belonging to the destination independent of the channel status. Matter of fact, the adaptive relaying methods related to the cooperative diversity networks have attracted current researchers’ attention as of being capable of offering an efficient way in assigning the channel expedients when required. In this study, a two‐hop decode‐and‐forward incremental relaying over Rayleigh fading channels is assessed taking into account the existence of multiple non‐identical interferers close to the destination. Interestingly, investigating the non‐identical interferers case instead of identical ones, like what are published in the literature, is extremely challenging as it refers to a more generalised case. Interestingly, the performance of the system is degraded due to co‐channel interference. To this end, tight closed‐form expressions of the bit error rate and outage probability are ultimately obtained. Analytical and simulation results are introduced to consolidate the assumptions considered.

In this research, two novel cluster head selection protocols are proposed, namely, Energy and Distance Based Cluster Head Selection (EDB‐CHS) and EDB‐CHS with Balanced Objective Function (EDB‐CHS‐BOF). They mainly aim at balancing energy consumption amongst all sensor nodes, prohibiting their precocious death, and extending the network lifetime. More specifically, in EDB‐CHS protocol, a new cluster shape is proposed and consequently a tight closed‐form expression for the optimal number of cluster heads (CHs) in the network is derived. Additionally, an efficacious CHS algorithm is presented, which is an expression of threshold probability that takes into account the residual energy of sensor nodes, how far they are located from the Base Station (BS), as well as the node's optimal probability of being a CH. On the other hand, the EDB‐CHS‐BOF protocol is proposed mainly to address the matter of having long‐distance communications as a result of obtaining adjacent CHs. In EDB‐CHS‐BOF protocol, a new threshold probability for each sensor node to be a CH in any round is proposed. Not only to this extent, but rather a balanced objective function is proposed to ensure a well‐proportioned distribution of CHs over the network. Simulation results demonstrate the superiority of the proposed protocols over other directly related works in terms of network lifetime and total data delivery.

Cognitive radio (CR) plays an important and promising role in solving wireless spectrum scarcity problems by exploiting wireless spectrum bands that have been underutilised thus far. CR is a type of wireless communication in which wireless devices use environmental sensing to detect and change transmission parameters. The authors propose the probabilistic and deterministic path detection (PDPS) Protocol for CR networks in this study. PDPS aims to increase the probability of finding the path of best quality between any source and any destination. The performance of the proposed protocol is evaluated by conducting comprehensive simulation scenarios and experiments using a special simulator, which was built using Java. The simulation results highlight the benefits of the proposed path selection technique in comparison to state‐of‐the‐art protocols. Comparisons are carried out using several performance metrics; namely throughput, stability and end‐to‐end delay.

In this article, we propose an Adaptive Energy- aware Fixed-clustering Based Data Dissemination Approach (AEFA) for WSNs. The main target of this work is to minimize and balance energy consumption among all nodes participating in these networks. This approach proposes a new way for selecting the initial cluster heads to build balanced size and evenly distributed clusters. Besides, a new way for circulating the cluster head job, depending on the energy and location information of the nodes in each cluster, is presented. The simulation results show the superiority of our approach over those directly connected works.

In this article, a new reactive routing protocol is proposed which is considered as a major extension of Ad-hoc On-demand Distance Vector (AODV) routing protocol, namely, Mobility Aware and Dual Phase Ad-hoc On-demand Distance Vector with Adaptive Hello Messages. It primarily concentrates on building routes considering nodes' speeds and direction of motion with respect to source nodes, thereby establishing more stable routes and reducing the route breakages. Moreover, we proposed an adaptive control packet announcement mechanism, which is directly connected to the periodic hello messages approach, with nodes' speeds, yielding a significant reduction in the amount of control overhead and congestion over the network. Our protocol is evaluated in terms of packet delivery ratio, control overhead, end-to-end delay, and energy consumption.

In this paper, we propose a new energy–aware and density-based clustering and routing algorithms for gathering data in wireless sensor networks. It basically aims at distributing the energy consumption between nodes in the network. In our proposed protocol, we introduce a topology that partitions the network field into equally-sized layers where each layer is divided into equal clusters. Additionally, we introduce a density-based algorithm that takes into consideration the number of nodes in a cluster. Finally, we propose an energy aware relaying algorithm that uses free space channel propagation model among nodes and selects the relay nodes based on their energy levels and locations among others. The performance of our proposed protocol is assessed through carrying out numerous MATLAB experiments. Simulation results showed that our proposed protocol has a remarkable improvement against other related works in term of network lifetime.

In this article, a new cluster head selection algorithm is proposed which is considered as a major modification of Centralized Energy Efficient Distance (CEED) algorithm, namely, Modified CEED (MOD-CEED). Actually, MOD-CEED balances the energy consumption between all sensor nodes by distributing the workload among them. More specifically, we compute the optimal number of cluster heads in the network based on the energy dissipation model of LEACH algorithm. Furthermore, we introduce an efficient cluster head selection technique by taking into consideration the residual energy of the sensor node and the distance between the sensor node and the Base Station. Our proposed algorithm is evaluated in terms of network lifetime using MATLAB simulator version 8.5. Simulation results summarize that MOD-CEED contributes efficiently in extending the network lifetime. Moreover, it has demonstrated its superiority over CEED and another relevant algorithm, namely, LEACH-DT.

Wireless sensor networks have been used in several applications to perform remote monitoring for a certain Area of Interest. In this work, we propose to utilize an Unmanned Aerial Vehicle (UAV) to improve the ground network connectivity, act as a data mule that collects the captured data and off-load the ground sensor nodes, who have normally limited storage capacities. We have investigated the effect of several parameters that affect the communication between the UAV and the ground sensors such as the UAV height, communication channel weather conditions, and nodes' noise factor, and evaluated the network performance measured by the average packet loss rate, end-to-end delay, and throughput. We found out that when the nodes' transceivers have a relatively high noise factor (above 20), it is recommended to keep the UAV altitude around 150 meters or below. We also noticed that the performance degrades drastically when having severe weather conditions, which requires additional mechanisms for improving the transmission performance such as the usage of error correction codes and Multiple Input Multiple Output antennas.

Reversible data hiding (RDH) algorithms are concerned with the recovery of the original cover image upon the extraction of the secret data. In some RDH algorithms, embedding and extraction are performed through modifying the histogram of prediction errors that are computed using certain predictors. However, the embedding capacity and image quality in these algorithms are dependent on the characteristics of a single predictor. In this paper, we propose the idea of employing multiple predictors in order to increase the embedding capacity. The idea of multiple predictors is integrated in the modification of prediction errors algorithm without adding any overhead regarding the predictor that is used for embedding at each pixel in the image. The selection of the accurate predictor depends on the polarity of the prediction errors from all predictors. Experimental results proved the efficiency of the proposed algorithm in terms of increasing the embedding capacity and producing competitive quantitative and qualitative quality of the stego image when compared with recent algorithms.

One of the crucial and inherent issues in a practical iris recognition system is the occlusion that happens due to eyelids and eyelashes. This occlusion increases the complexity and degrades the performance of matching and feature extraction processes. Generally, two types of approaches have been proposed to solve this issue. The first approach requires generating an iris mask that indicates which part of the iris is useful and which others are occluded. However, in the second approach, a fixed region of interest (ROI) within the iris area is selected to avoid the regions of occlusion. In this paper, we experimentally study both approaches but due to the latter characteristic, which is its ability to simplify the matching and feature extraction processes, it has been adopted in our techniques used, specifically for iris segmentation, iris normalization, and feature extraction. Accordingly, for matching and feature extraction, the lower side of the pupillary region (i.e. the innermost 25% of the lower half of the iris ring) is found to be the best ROI. This small area of iris is almost free of eyelids and eyelashes and it contains abundant texture information. Interestingly, this selection of small area helps us in proposing a simple yet efficient technique for feature extraction, called mean-based feature extraction technique (MB-FET). This technique is based on analyzing the local intensity variations. The proposed technique achieves a lower processing burden than other traditional methods such as Fourier or wavelet decompositions (e.g. Gabor wavelet). In most traditional techniques, many parameters (e.g. five parameters for 2D-Gabor filter) must be optimally determined in advance to achieve an accurate feature extraction process. Unfortunately, these parameters may not match various variations in image capturing conditions (e.g. variations in illumination due to change in image capturing distance). Moreover, the basic functions of the traditional methods are fixed in advance (off-line) and do not necessarily match the texture of all irises in the database. However, for our proposed technique MB-FET, there is no need to determine in advance any parameter or basic function. MB-FET dynamically adapts its parameter (only one parameter) with intensity variations. The proposed technique generates a binary iris code, hence a simple and fast matching process is done using the Hamming distance. The experimental results using the CASIA iris database show that the proposed technique achieves promising results for a robust and reliable iris recognition.

IEEE 802.22 wireless regional area network (WRAN) is a cognitive radio-based network. WRANs are intended to be deployed by different service providers and designed to opportunistically utilize the unused TV bands. WRANs have to self-coexist with other overlapped WRANs in a distributed manner. Therefore, every service provider tries to acquire a band free of interference from others to satisfy a required quality of service. This self-coexistence problem is one of the major challenges in WRAN. In this paper, we propose a Markov-based distributed approach for mitigating this problem. We model the problem as an absorbing discrete-time Markov chain. In this model, if two or more overlapped WRANs select the same band, then each one should either stay or switch to another band according to a certain switching probability. This process continues until each one of the WRANs finds an interference-free band. In this case, the Markov chain reaches the absorbing state. This model is employed to find the optimal switching probability, which in turn minimizes the time required to reach the absorbing state. The switching probability is numerically found as a function of the number of overlapped WRANs and available bands. Extensive simulation has been conducted to validate our numerical results.

Although IEEE 802.11 Wireless LAN (WLAN) is of a great interest nowadays, it lacks the efficient capability of supporting real-time streaming which is due to mainly the contention nature of wireless media. In this paper, we extend our earlier work concerning improving video traffic over wireless networks through effectively studying the dependencies between video frames and their implications on the overall network performance. In other words, we propose very efficient and novel algorithms that aim to minimize the cost of possible losses by intelligently and selectively discard frames based on their contribution to picture quality, namely, partial and intelligent-partial frame discarding policies considering the dependencies between video frames. The performance metrics that are employed to evaluate the performance of the proposed algorithms include the rate of non-decodable frames and peak signal-to-noise ratio (PSNR). Our results are promising and show significant improvements in the perceived video quality over what is relevant in the current literature.

Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.

Adaptive contrast enhancement (ACE) is a popular method for image contrast enhancement. In this method, enhancement is achieved by adding an amplified version of the high-frequency content of the image to its low-frequency content. The rationale behind that is supported by the fact that the human visual system is sensitive to discontinuities in images, which represent the high-frequency content of the image. Thus, emphasizing this content is expected to improve the perceived contrast. In this paper, a fuzzy ACE (FACE)-based enhancement method, FACE, is proposed. In this method, the contrast gain values are computed using a fuzzy inference system (FIS) whose parameters are entirely derived from the image local statistics. To the best of our knowledge, the computation of the ACE gain values using a FIS has never been addressed before. Experimental results have proved the capability of FACE in enhancing the image contrast with less noise amplification and overenhancement artifacts.

The convolutional coding is a very popular channel coding technique for the major reason of mitigating the probability of having many retransmissions because of difficult (noisy) communication channels. Sequential decoding is a type of convolutional codes that becomes of interest in wireless communication since it affords a decoding time that can be adaptive to channel state. In this study, the authors propose an improved queuing model, using discrete-time semi-Markov chain, which represents a modified packet retransmission policy over previously proposed queuing model. The authors queuing model mainly describes the behaviour of the buffer, which belongs to intermediate hops, when sequential decoding is implemented and concerns also about packets being transmitted over erroneous channels. The authors aim after conducting queuing analysis to find a real mathematical form for the average buffer occupancy as a network performance metric. Although the improved queuing model when incorporating the new policy for retransmission is very complicated, we are finally able to derive an expression for that performance metric considering practical assumptions. They further illustrate how the modified queuing model has a better impact on the end-to-end delay of messages, being transmitted without any extra buffering requirements needed, than the other relevant proposed queuing model. They conduct a simulation study using computer programming with the same assumptions used for queuing analysis to validate their analytical explanations and results. Furthermore, they validate the correctness of their closed-form expression through comparing its results with those obtained from expression related to a queuing model which discarded employing any retransmission policy.

Wireless networks are spreading very fast compared to wired-based networks because of its ease in installation, lower cost, reduced dependence on infrastructure and support for emerging mobile and sensing applications. Unfortunately, wireless channels are less efficient in carrying much of data. Moreover, they are characterised by having lower signal-to-noise ratio, resulting in more corrupted packets. Consequently, the performance of such networks may be severely affected due to invoking transmission control protocol (TCP) congestion algorithms whenever a packet is lost. The authors propose a novel queuing model that utilises Fano decoding and automatic repeat request (ARQ) to reduce the drawbacks of TCP in wireless networks. The proposed model describes how received packets are corrected based on Fano decoding mechanism and how the retransmission of corrupted packets is performed. The major aim of proposing this queuing model, when ARQ is totally incorporated, is to find a generic form expression for the average system capacity. The proposed model is validated through simulation in which the results show perfect agreement with those of the analytical model. The authors do not stop to this extent, but rather verify the correctness of the results through comparing them with those obtained in the previous work, where the mechanism of ARQ was completely neglected.

Fano decoding algorithms are packet error detection and correction mechanisms that are mainly used to ensure a reliable end-to-end delivery over noisy channels. The popularity of using Fano decoding gets high in many network environments because of its lower storage requirements and having a decoding complexity that is a channel dependent. In this paper, we propose a new queuing study over the systems of intermediate hops that implement Fano decoders that are bounded by upper and lower threshold decoding limits. These threshold limits make our study to be general so that it can be adapted in any network environment especially the networks that are affected much in noise. There is no packet retransmission included in our queuing study for those packets that needs time above upper threshold limit. They are considered to be lost. We aim in this queuing study to have an expression about the average number of packets in the decoder's buffer that are waiting to get service.