Simulated annealing and genetic algorithm-based hybrid approach for energy-aware clustered routing in large-range multi-sink wireless sensor networks

Wireless sensor networks (WSNs) are widely applied in monitoring and communication but remain constrained by limited energy resources. Efficient routing protocols are critical to prolong network lifetime and ensure balanced energy consumption among sensor nodes. Traditional cluster-based routing often fails to achieve optimal energy balance, leading to premature node failures and degraded performance. Intelligent optimization algorithms, including meta heuristics such as particle swarm optimization (PSO) and emperor penguin optimization (EPO), have been applied to improve routing efficiency. These methods enhance convergence and adaptability but typically operate as standalone approaches. Limited attention has been given to integrating multi criteria decision making (MCDM) methods with meta heuristics. Without this integration, assigning precise weights to multiple criteria and balancing energy consumption across nodes remains difficult. This paper proposes a novel uneven cluster-based routing protocol that integrates fuzzy constrained nonlinear programming- variable weight analysis-technique for order preference by similarity to ideal solution (FCNP-VWA-TOPSIS) with an improved EPO. The protocol first assigns accurate weights to seven multi criteria using FCNP VWA and selects cluster heads (CHs) with TOPSIS. It then constructs the routing tree using improved EPO guided by the weighted fitness function. Extensive simulations show that the proposed protocol achieves superior energy balance, extending network lifetime by 158.0%, 119.3%, and 113.7% compared to uneven clustering routing (UCR), unequal clustering fuzzy logic intelligent algorithm (UCFIA), and fuzzy multi-criteria clustering and bio-inspired energy-efficient routing (FMCB ER), respectively. By combining MCDM and meta heuristic optimization, the protocol advances cluster-based routing in WSNs, significantly enhancing energy efficiency and reliability for real world applications.

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self‐organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near‐optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.

Wireless Sensor Networks (WSNs) play a necessary position in todays actual world applications. WSNs are a team of autonomous sensor nodes that are geographically distributed to accumulate information and monitor events. WSNs are similarly sense that they rely on wireless connectivity and spontaneous formation of networks so that sensor information can be transported via wireless. Sometimes called dust network, minute sensors as small as dust referring to smart dust. WSNs are spatially distributed two to screen or bodily temperature, sound, pressure, etc. and to cooperatively ignore their data via the network to different locations. Each such sensor network node has typically various parts. Routing in WSNs in one of the important technique, because all node executes its battery power, if may be one node failure it collapse entire networks. Routing in WSNs is in reality difficult due to the intrinsic characteristics that differentiate these networks from different networks. The plan of routing protocols in WSN is affected via number of exigent factors. In this paper, a review on routing protocols for Wireless Sensor Networks (WSNs) and performance of its execution.

Energy efficiency is a critical problem for wireless sensor networks. In past decades, network coding has been proved as a promising technology to reduce the number of transmissions, save energy, and improve network throughput, especially suitable for the wireless network that motivates the emergence of network coding aware routings. However, existing network coding aware routings have the failure decoding problem, because of defective network coding conditions. On the other hand, existing network coding aware routings usually consider the ideal traffic scheduling and neglect nodes’ energy and load which are important to wireless sensor networks. Therefore, it is not appropriate to directly apply existing network coding aware routings in wireless sensor networks. Regarding the problems above problems, this paper proposes traffic-shaped network coding aware routing (TSCAR) for wireless sensor networks. In the TSCAR, a universal network coding condition is presented to solve the false decoding problem. And traffic shaping mechanism is proposed to shape the traffic of different flows to create more coding actions when coding opportunities exist. In addition, network calculus is used to analyze the delay of the TSCAR. Besides, a coding, load, and energy aware routing metrics are proposed to evaluate the discovered paths. Extensive simulation results demonstrate that the TSCAR increases the number of coding opportunities and the proportion of coded packets, improves network throughput, and extends the network lifetime of wireless sensor networks.

ABSTRACTRecent advancements in information technology have led to the widespread adoption of the Internet of Things (IoT) across various applications. Wireless sensor networks (WSNs), consisting of low‐cost, compact sensors, are crucial for IoT systems, enabling data collection for tasks like surveillance and tracking. A major challenge in WSNs is achieving energy efficiency while extending network lifetime (NLT), necessitating effective clustering and routing strategies. Numerous existing methodologies for energy‐efficient clustering and routing exhibit potential; however, they are hindered by constraints including inadequate adaptability to fluctuating network conditions, suboptimal selection of s cluster heads (CHs), and uneven energy consumption, resulting in diminished network longevity and efficacy. These issues require novel strategies to improve overall performance. To tackle this issues, this research presents a novel hybrid technique combining fuzzy logic with barnacles mating optimization (FL‐BMO) to identify the most optimal CHs by evaluating critical criteria like average sink distance, average intracluster distance, residual energy, and CH balance factor. The FL‐BMO methodology utilizes fuzzy logic to address uncertainties in sensor data, and the BMO algorithm, modeled after barnacle mating patterns, offers a resilient and adaptable optimization process, markedly enhancing energy efficiency and network longevity. In addition, an innovative natural‐inspired hybrid cross‐layer sunflower optimization routing (NiHCLR‐SFO) technique has been introduced that entails optimal routing path selection. This approach balances exploration and exploitation during a route selection process, integrating multiple layers of the network functionality which eventually results in improved routing efficiency and network throughput. Such a hybrid approach has been implemented in MATLAB. The proposed method is compared with fuzzy reinforcement learning based data gathering (FRLDG), neuro‐fuzzy‐emperor penguin optimization (NF‐EPO), bio‐inspired cross‐layer routing (BiHCLR), and fuzzy rule‐based energy‐efficient clustering and immune‐inspired routing (FEEC‐IIR) protocols. From these comparisons, it was observed that the method propagates definite NLT gains reaching 39.74%, 32.92%, 15.95%, and 4.8076%, respectively. The proposed method outperforms the existing approaches (FRLDG, NF‐EPO, FEEC‐IIR, and BiHCLR) across several performance parameters: 99% packet delivery ratio (PDR), 2.8 ms of end‐to‐end delay time (E2ED), 1 Mbps of throughput, 30 mJ of energy consumption, 6000 rounds NLT, 2% bit error rate (BER), 1.25 buffer occupancy ratio, and 0.5% of packet loss ratio (PLR).

In the Internet of Things (IoT), a wireless sensor network (WSN) is deployed for collecting the interesting data of an application field. Sensor nodes in an IoT WSN are usually with the heterogeneous property. Some nodes have more power (energy) and additional functionality (e.g., data aggregation) than others. Cluster-based routing is usually used in WSNs for data transmissions due to efficiently routing consideration. Based on cluster-based routing, the cluster heads (CHs) act as the sensed data forwarding role. Once one or more CHs fail, the faulty CHs cannot forward the sensed data of their serving sensor nodes. As a result, the sink node (gateway) has not sufficient sensed data of the IoT application field. This will deeply affect the information processing of the IoT applications. We utilize the virtual CH formation and flow graph modeling to efficiently tolerate the failures of CHs. First, the available resources of all failure-free CHs are logically organized as a virtual CH to be the common backup of all faulty CHs. Then, the flow graph modeling is used to achieve fault tolerance with the minimum total energy consumption among all failure-free CHs. Finally, we perform extensive experiments to demonstrate the effectiveness of our approach in the fault-tolerant routing of the IoT WSNs.

The sensor nodes are independent and work as sender, receiver and intermediate node in Wireless Sensor Network (WSN). The sensor nodes are forming the small network in which the one or more sender is sending data to one destination/s through intermediate nodes or sending information to Base Station (BS). The information collect by base station is further analyzed. The nodes are also static or mobile depends on the situation or conditions. The connection establishment and data sending is possible through routing protocols of WSN. The routing protocols of WSN are not same as traditional wireless routing protocols. One major issue on this network is security. The routing protocols are sending information to destination from particular source and the routing strategy is provides the better way of delivery in the network. The sensor nodes in network are performing through limited battery supply called node energy or power. The attacker are also affected the limited energy resource. In this paper the center of consideration is on the security issues related to WSN routing protocols. The routing in WSN remains a key issue because of that without accurately functioning of routing protocols, the network cleanly will not work the technique it's intended to. Unfortunately, routing also one of the most difficult to protect against attacks malicious activities because of the administration in WSN. The better protection scheme is provides the complete security from attacks and the secure routing is provides the better and trustful network performance.

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user Routing (EPFMR) framework in WSN. EPFMR is deployed in the WSN environment using the instance time. The request time sent for the route discovery is the foremost step designed in the EPFMR framework to reduce the energy consumption rate. The proportional fairness routing in WSN selects the best route path for the packet flow based on the relationship between the periods of requests between different SN. Route path discovered for packet flow also measure energy on multi-user route path using the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR develops node dependent energy-efficient localized route path, improving the throughput. The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing, Throughput, RST, node density and average energy per packet in WSN. The Route Searching Time (RST) is reduced using the Boltzmann Distribution (BD), and as a result, the energy is minimized on multi-user WSN. Finally, GIFM applies an instance time difference-based route searching on WSN to attain an optimal energy minimization system. Experimental analysis shows that the EPFMR framework can reduce the RST by 23.47% and improve the throughput by 6.79% compared with the state-of-the-art works.

Wireless Sensor Networks (WSNs) have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things (IoT) and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

The coding of even-bit marking algorithm (CEBM) is characteristic of its simple coding, low energy consumption for computing and low expenses for hardware. It can be used in the wireless sensor network in the measurement and control system for the condition of stored-grain to compress the data of transmission, lower the energy consumption in communication transmission, and use a small quantity of energy expenses for computing to save a great quantity of energy consumption needed in transmission communication. This settles the problem of energy constraint of the wireless sensor nodes in the measurement and control system. Experimental results show that the compression ratio of transmission data can reach 67% to 73% and the energy consumption in the sensor nodes communication can be reduced by 30% to 70%, which reveals CEBM is of great value in real application.

Now days, wireless technology is one of the center of attention for users and researchers. Wireless network is a network having large number of sensor nodes and hence called as “Wireless Sensor Network (WSN)”. WSN monitors and senses the environment of targeted area. The sensor nodes in WSN transmit data to the base station depending on the application. These sensor nodes communicate with each other and routing is selected on the basis of routing protocols which are application specific. Based on network structure, routing protocols in WSN can be divided into two categories: flat routing, hierarchical or cluster based routing, location based routing. Out of these, hierarchical or cluster based routing is becoming an active branch of routing technology in WSN. To allow base station to receive unaltered or original data, routing protocol should be energy-efficient and secure. To fulfill this, Hierarchical or Cluster base routing protocol for WSN is the most energy-efficient among other routing protocols. Hence, in this paper, we present a survey on different hierarchical clustered routing techniques for WSN. We also present the key management schemes to provide security in WSN. Further we study and compare secure hierarchical routing protocols based on various criteria.

A wireless sensor network is an enormous assortment of sensor nodes with inadequate power supply and self-conscious computational competence. Due to the circumscribed communication range and high density of sensor nodes, packet dispatching in sensor networks is usually performed through multi-hop data transmission. For that reason, routing in wireless sensor networks has been deliberated as an imperative field of research over the past decade. These days, multipath routing techniques is widely used in wireless sensor networks to improve network performance over and done with efficient utilization of accessible network resources. Consequently, the main intention of this review is to present the conception of the various routing approaches and its fundamental experiments, as well as the basic motivations for exploiting these techniques in wireless sensor networks. Routing procedures for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in inadequate periods. The energy constriction of WSNs make energy saving become the most significant objective of various routing algorithms. In this paper, a review of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goalmouth.

Nowadays signature attacks are termed as very big problem because it leads to software vulnerability.Malware writers confuse their malicious code to malicious code detectors such as Signature-based detection. However,it fails to detect new malware. This research article addresses the signature based intrusion detection from IntrusionDetection (IDS) systems. The proposed hybrid techniques for Generation of Signature are done using GeneticAlgorithm (GA) and Simulated Annealing (SA) approaches. For this, signature-set in execution statements are selected by using simulated annealing and genetic algorithm, which produce the optimal solution of selection. Then thegenerated signatures are matched with IDS by using the two pattern matching techniques, namely (i). Finite stateautomaton based search for Single Pattern matching technique and (ii) Rabin Karp string search algorithm for multiple pattern matching technique. These techniques are used to match the signature as in an effective manner. In addition tothis the Fuzzy Logic classification is used to find the degrees of truth of vulnerability for classification. The aim ofthe proposed work is to improve the final resultant accuracy in compared to existing techniques. The proposed RabinKarp- fuzzy logic system returns the higher performance metrics namely precision is 88% and Recall is 80% and inopen source dataset it contains 30 vulnerabilities this proposed worked well in detecting 28 vulnerabilities/ defect, theaccuracy of this proposed is 94.27%.

A Wireless Sensor Network (WSN) consists of a large number of small, low-cost and low-power wireless sensing nodes. WSNs can gather information about the environment automatically and unattended and are suitable for many applications.The typical characteristic of WSNs is that they are energy and bandwidth constrained. Hence, routing protocols and algorithms for WSN must aim to conserve these two scarce resources. WSNs are also highly application-specific. This mean, firstly, that there is a tight bound between the application layer and the different protocol layers. Secondly, there are some WSN target applications that require certain protocol functionality that is not mandatory for other WSNs. In other words, both the general challenges and the specific application challenges must be addressed.This thesis aims to address routing in WSNs both from a general and an application specific perspective. Among the general energy-and bandwidth related topics the work in this thesis focuses on aggregation and routing-efficiency. Among the application-related topics the work focuses on localization and interoperability.The main contributions are:• A method for letting the routing protocol contribute in node localization.• A method for increasing the energy and bandwidth utilization with passive clustering.• A method for increasing the energy and bandwidth utilization using multiple sinks.• A data-aggregation scheme for WSNs that interoperates with external networks via a standardized interface.• A hybrid routing mechanism that are able to operate in high-interference scenarios.• Lessons learned from a real-world test campaign of a surveillance WSN.

Due to open network nature of wireless sensor networks make them highly vulnerable to a variety of security attacks and easy target for adversaries, which may capture these nodes, analyze and easily insert fake route information. Wireless sensor network is an emerging, cost effective and unsupervised solution for collecting this information from the physical world and sending this information back to centralized authority for further processing. GRPW (Geographic Routing in connected wireless sensor networks based on Multiple Sinks) is one of the basic routing protocols used for Supporting Mobile Sinks in Wireless Sensor Networks. GRPW, a geographical routing protocol for wireless sensor networks, is based on an architecture partitioned by logical levels, on the other hand based on a multipoint relaying flooding technique to reduce the number of topology broadcast. GRPW-MuS uses periodic HELLO packets to neighbor detection. The wormhole attack can form a serious threat in wireless sensor networks, especially against many wireless sensor networks routing protocols and location-based wireless security systems. Here, a trust model to handle this attack in GRPW is provided called GRPW-MuS-s. Using OMNET++ simulation and the MiXiM framework, results show that GRPW-MuS-s protocol only has very small false positives for wormhole detection during the neighbor discovery process (less than GRPW). The average energy usage at each node for GRPW-MuS-s protocol during the neighbor discovery and route discovery is very low than GRPW-MuS, which is much lower than the available energy at each node. The cost analysis shows that GRPW-MuS-s protocol only needs small memory usage at each node, which is suitable for the sensor network.