Triangular fuzzy-based spectral clustering for energy-efficient routing in wireless sensor network

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

Energy and congestion-aware load balanced multi-path routing for wireless sensor networks in ambient environments

Extensive research is being done on Wireless sensor Networks (WSNs) in present decade. WSNs consist of large number of sensor nodes which perform sensing of certain phenomena and send the sensed data to the sinks. This operation consumes energy of sensor nodes which effects the lifetime of a network. To prolong the lifetime of WSNs various strategies and routing algorithms have been proposed. Use of mobile sinks has proved effective in this respect. In this paper we are analyzing the impact of multiple mobile sinks with proactive hierarchical clustering protocols LEACH and network reactive hierarchical protocol TEEN. This paper will give simulation based analysis of using multiple mobile sinks in network and their effect on lifetime of a network. Performance analysis will be done on basis of dead nodes, alive nodes, throughput and residual energy.

ABSTRACTWireless sensor networks, or WSNs, are essential for energy monitoring, home automation, the medical industry, computing, and other fields. The two problems with wireless sensor network are the network lifetime and data transmission delay. The cluster head selection (CHS) is a difficult task. Therefore, a novel Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network (HYB‐AO‐AOA‐CHS‐WSN) is proposed in this paper. The proposed HYB‐AO‐AOA integrates Archimedes and Arithmetic optimization algorithm (AO‐AOA). The stimulation of sensor nodes initiates the CHS process. The stimulated sensor nodes are evaluated separately for delay, energy, node degree, intercluster distance, and intracluster distance for determining the cluster heads. Then, the multipath routing is handled by the HYB‐AO‐AOA technique. The proposed HYB‐AO‐AOA‐CHS‐WSN model is implemented in MATLAB. The metrics, like delay, number of alive nodes, throughput, and residual energy is examined. The proposed SRD‐DCGAN approach attains 25.72%, 21.32% and 19% higher throughput, 26.56%, 21.35% and 32.09% lower delay than the existing techniques.

Workflow-based Service-oriented WSN s have recently received a lot of attention from both academia and industry. With well-defined service components, various flexible WSN applications can be developed. However, the key characteristic of WSN s is resource constraints. Sensor nodes in WSN s have limited storage, computation and especially limited energy. As service components in WSN s rely on the data gathered by sensor nodes, they are also resource-constrained. Unfortunately, traditional workflow technologies (i.e., service selection, composition and adaptation) ignore the residual energy of services. This will bring unbalanced energy consumption and furthermore shorten the network lifetime. In order to resolve this issue, we proposed an energy-aware QoS -guaranteed workflow management mechanism. In this mechanism, a new QoS model is first presented to improve the QoS evaluation. Then, based on this QoS model, an efficient service selection schema, which considers both the energy and the QoS of services, is proposed. Furthermore, an adaptation mechanism for balanced energy consumption is also proposed. Experimental evaluations demonstrate the capability of our proposed approach.

Wireless sensor networks are set of thousands or more micro sensor nodes that one of the most important constraints of them is energy consumption of these sensor nodes .Since these nodes have very small dimensions, energy production needs batteries with limited power. It is obvious that the problem of energy reduction was always one of the biggest challenge in these networks. In these networks, routing is data-centric. On the other hand, in these networks it is only important to receive the data packet by the BS. In this paper we propose a new method for routing in wireless sensor networks that instead of using the traditional routing methods attempts to add nodes which have the minimum distances from each other. In our innovative method, we define node arrangement such that always data packets which are sent, arrive to destination through the shortest path which cause to reduce the total energy consumption. Since the sensor nodes which are near to each other have many redundant data, we can aggregate data packet continuously in the network. Simulation results show that our proposed algorithm works very well in spaces with large dimensions (size) and can reduce the total energy consumption more than the best traditional algorithms such as PEGASIS and leach with factor of 34%.

In this paper, a power-aware routing algorithm with base station assistance for multi-hop wireless sensor networks that increases the operational lifetime of networks is proposed. We consider a practical and feasible network. This network consists of a set of sensor nodes connected by wireless links and only a base station to collect the data from these sensor nodes. In addition, each sensor node has two wireless receiving channels, which one receives messages from other nodes and the other receives assistant messages from the base station that has high transmitting power and is energy-rich. This scheme adequately utilizes the powerful resources of the base station to provide information to sensor nodes, so these nodes can reduce power consumption by using the knowledge to make the reasonable route. In contrast to conventional power-aware algorithms, the proposed algorithm is superiority for large scale sensor networks for maximizing the lifetime of this network.

FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks

Network lifetime play a vital role in setting up an energy efficient wireless sensor network. The lifetime of the network can be improved by efficient deployment and scheduling of the sensor nodes inside the network. In this paper, on the basis of mathematically calculated upper bound lifetime of the network, the sensor nodes are efficiently deployed by using Simulated Annealing and Particle Swarm Optimization with pre-specified sensing range of the sensor nodes and on the second fold the sensor nodes are efficiently scheduled by applying the Simulated Annealing and Dempster Shafer Theory. The overall objective of this paper is to find the optimized location and schedule for sensor nodes. The comparative study shows that the simulated annealing and particle swarm optimization algorithms performs better for sensors deployment. Simulated annealing and Dempster Shafer theory achieves the goal to provide the different schedules with high efficiency.

In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications.Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station.But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime.In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network.The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station.GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value.Also, GCR uses a greedy route finding strategy for balancing energy consumption.Experimental results show that GCR achieves significant energy savings and prolong network lifetime.

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.

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).

Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets

Distributed Wireless Sensor Networks (DWSNs) comprised of set of sensor nodes that are geographically distributed in harsh environments. However, a centralized WSN does not consider the network scalability and also leads to high energy consumption. Due to these open issues, distributed computing approach is considered since it resolves scalability issue and several paths are established for data transmission. To achieve higher energy efficiency, scalability and security, in this paper we propose a distributed protocol called Secure Coronas-Based Zone Clustering and Routing (SC-ZCR). The proposed SC-ZCR aim at addressing the number of issues in DWSN and it support for long-term deployment. In SC-ZCR, we will pursue several processes including Zone Clustering, Energy Efficient Routing and Data Encryption and Security Verification. Zone clustering is carried out using Adaptive Neuro-Fuzzy System, where we consider four parameters: node angle, distance between sensor node to the sink node, node residual energy and belief value. Belief value of each sensor node is computed using Principal Component Analysis. Then energy efficient routing is established by Q-Hop Routing Protocol, which finds optimum and shortest path using Whale Optimization Algorithm. For data packets encryption, RC6 is used and then security level of data packets are verified using potential weight factor $$\delta$$, which is computed using key size $$K$$, block size $$B$$, and number of rounds $$R$$. Experiments conducted using NS3.26 simulator and the simulation result show that the proposed SC-ZCR outperforms in terms of Coverage Ratio, Residual Energy, Network Lifetime, Delay, Packet Drop Rate, and Security Strength.

Tracking Heterogeneous Dynamic Sensor Node using Fuzzy Logic to Prolong System lifetime in WSN