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Efficient clustering and routing is a main challenge in a wireless sensor network (WSN). To achieve better quality-of-service (QoS) performance, this work introduces k-medoids with improved artificial-bee-colony (K-IABC)-based energy-efficient clustering and the cross-layer-based Harris-hawks-optimization-algorithm (CL-HHO) routing protocol for WSN. To overcome the power-asymmetry problem in wireless sensor networks, a cross-layer-based optimal-routing solution is proposed. The goal of cross-layer routing algorithms is to decrease network-transmission delay and power consumption. This algorithm which was used to evaluate and select the effective path route and data transfer was implemented using MATLAB, and the results were compared to some existing techniques. The proposed CL-HHO performs well in packet-loss ratio (PLR), throughput, end-to-end delay (E2E), jitter, network lifetime (NLT) and buffer occupancy. These results are then validated by comparing them to traditional routing strategies such as hierarchical energy-efficient data gathering (HEED), energy-efficient-clustering routing protocol (EECRP), Grey wolf optimization (GWO), and cross-layer-based Ant-Lion optimization (CL-ALO). Compared to the HEED, EECRP, GWO, and CL-ALO algorithms, the proposed CL-HHO outperforms them.

Conserving energy of sensor nodes and ensuring balanced workloads among them are fundamental concerns in Wireless Sensor Network (WSN) design. Clustering strategies offer a promising avenue to minimize node energy consumption, thereby prolonging network lifespan. Nevertheless, numerous multi-hop routing protocols using clustering technique face the challenge of nodes nearer to the Base Station (BS) depleting their energy faster due to forwarding data from the entire network leading to premature node failure and network partitioning known as ‘hotspot problem’. The paper introduces an Energy-Efficient Mega-Cluster-Based Routing (EEMCR) protocol specially designed for expansive coverage area. The primary principle behind designing this protocol is to eliminate the hotspot problem and restrict the transmission range of nodes to the threshold distance defined by the radio energy model, thereby enhancing the overall network lifespan. The protocol adopts a centralized approach employing fixed clustering wherein the BS partitions the network into square-shaped clusters. The cluster size is determined by the threshold transmission range of the sensor radio energy model, guaranteeing that all network communication stays within this threshold distance. Four such clusters form a mega-cluster with a Mega-Cluster-Head (MCH) elected among the four Cluster Heads (CHs). The MCH role is evenly distributed among nodes of all four clusters in subsequent rounds for uniform distribution of its overhead. Implementing data aggregation at two levels (CH level as well as MCH level) leads to reduced data traffic and energy consumption throughout the network. Moreover, data collection by two data mules based on odd–even round number ensures balanced data traffic and energy distribution across the network. Analysis indicates that the proposed protocol effectively mitigates the hot-spot problem and reduces data transmission overhead of sensor nodes. In simulation, the proposed protocol on an average improves network life by 34.5%, 23.5%, 14.5% and 5.5% as compared to existing protocols FCEEC, DBSCAN, LPGCR and FBECS respectively for deployment of nodes between 600 to 1200. Also, approximately 46%, 32%, 21% and 14% of lesser sensor nodes are dead for proposed protocol in respective rounds as compared to existing protocols FCEEC, DBSCAN, LPGCR and FBECS respectively. Comparative evaluations demonstrate improved network lifetime when compared to equivalent recent routing protocols.

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

In clustered cognitive radio sensor networks (CRSNs), availability of free channels, spectrum sensing and energy utilization during clustering and cluster head (CH) selection is essential for fairness of time and event-driven data traffic. The existing multi-hop routing protocols in CRSNs generally adopt a perfect spectrum sensing which is not same in the practical spectrum sensing of nodes in real networks. High imbalance in residual energy between the selected CHs negatively impacts the delivery of data packets. Hence, hybrid mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi-hop clustering routing protocol (HMABFOA) is proposed as an imperfect spectrum sensing approach for achieving better utilization of downlink energy harvesting and sustain maximized degree of energy between the nodes in the network. This HMABFOA scheme reduces the negative impact of imperfect spectrum sensing for extended network lifetime which sustains the capabilities of the network surveillance. It helped in constructing a distributed cluster with multi-hop routing selection between clusters depending on a energy level function that explores and exploits the factors associated with CHs selection. The merits of Mexican axolotl optimization algorithm (MAOA) is used for better CH selection and cluster formation with energy stability is sustained in the network. Further bitterling fish optimization (BFOA) algorithm is used for optimized multi-hop route between the clusters with minimal energy consumption and maximized spectrum sensing that proves better channels availability. The simulation results guaranteed maximized network lifetime of 24.38%, spectrum utilization rate of 24.58%, and minimized energy utilization of 25.62%, better than the baseline approaches.

Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.

Due to resource shortages and device diversity, energy efficiency and scalability issues are critical in the Internet of Things (IoT) space. Managing edge resources consistently to encourage resource sharing among devices is complex, given IoT’s device heterogeneity and dynamic environmental conditions. In response to these challenges, our research presents a suite of intelligent techniques tailored for optimizing resources in IoT devices. Our solution’s core component is a thorough full-stack system architecture made to flexibly handle a diverse range of IoT devices, each of which operates under resource limitations. This paradigm centers on the deployment of multiple edge servers, strategically positioned to cater to the unique requirements of IoT devices, which exhibit compatibility with heterogeneity, high performance, and adaptive intelligence. To realize this vision, we create a clustered environment within the realm of heterogeneous IoT devices. We employ an African vulture’s optimization algorithm (AVOA), approach to establish connections between Cluster Head (CH) nodes. Following this crucial step, we meticulously select edge nodes situated in close proximity to the data source for transmission, reducing energy consumption and latency. Our proposed Multi-Edge-IoT system sets a new standard for efficiency within the IoT ecosystem, outperforming existing approaches in key metrics such as energy consumption, latency, communication overhead, and packet loss rate. It represents a significant stride towards the harmonious and resource-efficient operation of IoT devices in an increasingly interconnected world.

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme.

The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols.

The power utilization has verified as a major problem in wireless sensor networks (WSNs). Many researchers have provided efficient solutions for power utilization. Clustering is one of them that helps in topology control and ensures efficient power utilization. However, the clustering method should be effective that could obtain the best clusters. Comprehensive evolution of clustering protocols for the lifetime of sensor nodes is a unique approach to the total enhancement of the lifetime of WSNs. There are many conflicting factors that affect the efficiency of clustering, i.e. distance between CH and the base station, distance form node to cluster head (CH), maximum residual energy of CHs, etc. The coordination among these factors has a capability to insure the optimal power utilization by reducing power consumption and load balancing among the nodes and CHs. In this paper, we have considered total sixteen such factors and made coordination among them to select the best CHs. Multiple attribute decision-making methods are used to choose best set of CHs from the available alternatives that can fulfill the condition of coordination efficiently. The experimental results validate that the coordination among these sixteen factors put up one of the best demonstration for choosing best CHs.