NSEEAR: A energy adaptive routing protocol for heterogeneous wireless sensor networks

Wireless sensor networks congestion occurs easily due to its centralized traffic pattern. Normally, mono-sync wireless sensor network experiences multiple traffic flow congestion in the dense environment of the network, which leads to excess energy consumption and severe packet loss. To overcome these issues, a congestion detection and alleviation mechanism using cluster based heuristic optimized hierarchical routing protocol is proposed in this paper. Here, congestion detection and alleviation utilize the features of sensor nodes. The congestion is categorized into two types: (i) node level congestion and (ii) link level congestion. The node level congestion is detected by assessing the buffer utilization and the interval amid the consecutive data packets. The link level congestion is considered with computing link usage utilizing back-off step of round robin carrier sense multi-access with collision avoidance. Congestion detection and alleviation reactively affected node/link through cuckoo hosted rider search multi-hop routing algorithm. It has two phases: the cluster head selection and multi-path routing. Cluster head selection is performed through Taylor multiple random forest kernel fuzzy C-means clustering algorithm and multi-path routing is performed through cuckoo hosted rider search multi-hop routing algorithm. The simulation of the proposed method is done in network simulator tool. Here, the performance metrics, like packet delivery ratio, delay, energy consumption, packet drop, overhead, network lifetime and throughput are calculated. The experimental outcomes of the proposed technique shows 11.6%, 18.4% and 28.1% lower delay, 78.2%, 65.4% and 52.6% higher packet delivery ratio, and 29.2%, 37.4% and 40.8% lower packet drop compared with the existing methods, like congestion detection and alleviation using multi-attribute decision-making in optimization-based hybrid congestion alleviation routing protocol in wireless sensor networks, congestion detection and alleviation using hybrid K-means with greedy best first search algorithms in packet rate reduction utilizing adaptive weight firefly algorithm ant colony optimization based routing protocol in wireless sensor networks and congestion detection and alleviation using multi-input time on task optimization algorithm for altered gravitational search algorithm routing protocol in wireless sensor networks.

The advancement of smart city infrastructure depends heavily on the deployment of wireless sensor networks (WSNs) to monitor environmental conditions, traffic, healthcare systems, and public safety in real time. However, the limited energy resources of sensor nodes pose a critical challenge to network longevity, data reliability, and service continuity. This research presents the design and evaluation of an adaptive energy-efficient routing protocol that dynamically integrates residual energy levels, spatial topology awareness, and traffic load adaptability into routing and cluster-head selection processes. Using simulation-based experiments, the proposed protocol was compared with benchmark schemes including LEACH, TEEN, and SEP under varying traffic and deployment conditions. Results reveal that the proposed approach significantly delays first-node-death, increases overall network stability, and sustains higher Packet Delivery Ratio (PDR)s across different traffic loads, while simultaneously improving throughput and reducing end-to-end delay. Statistical analysis confirms that these improvements are systematic and not incidental, validating the hypothesis that energy-efficient routing in WSNs requires a holistic and adaptive design. The findings emphasize the potential of such protocols in enabling cost-effective, reliable, and scalable WSN deployments for smart cities. Practical recommendations derived from this research highlight the importance of incorporating adaptive cluster-head selection, multipath routing, and traffic-awareness into network design, while encouraging pilot projects and integration with renewable energy and edge computing to further optimize performance. Collectively, the study provides a pathway for transforming WSNs into robust, energy-aware communication backbones that underpin sustainable urban intelligence systems.

Wireless sensor network is used to monitor and record physical conditions of the environment or any other physical object and organizing the collected data at a central location called as base station. Sensor nodes in sensor network are spatially dispersed. In this paper a new protocol REBM (Residual energy based multipath routing protocol) has been created based on residual energies of nodes and it selects an optimal path using residual energy of nodes. Comparison of REBM with Energy aware routing(EAR) protocol has also been done and by results it is found that overall performance of REBM is better than EAR protocol.

A genetic algorithm based distance-aware routing protocol for wireless sensor networks

The unique nature of wireless sensor networks (WSNs) poses new design challenges for development of applications, protocols and algorithms. In recent years, a substantial amount of research effort has been directed towards overcoming the underlying challenges to minimize energy consumption by the sensor nodes. The exchange of routing control packets has a significant impact on energy consumption from the small irreplaceable and non rechargeable batteries of the sensor nodes. In this paper, we propose a cross-layer adaptive routing (CLAR) protocol for WSNs, which aims to minimize the use of routing control packets in WSNs; thereby minimizing energy consumption which in turn prolongs network-lifetime. We also develop analytical model to derive energy consumption for route establishment and present simulation results, which reveal better performance and improved energy efficiency in WSNs when using the proposed cross-layer adaptive routing protocol.

Wireless Sensor Networks (WSNs) are energy-constrained. However, recent advances in ambient energy harvesting technology have made it possible for sensor nodes to harvest energy from ambient environment and serve as a supplement or the only energy source. And most of current routing protocols are specially designed for battery-powered WSNs, may not be suitable for energy harvesting WSNs (EH-WSNs), especially for those entirely powered by harvesting energy. In this paper, we propose an Adaptive Energy Harvesting Aware Clustering (AEHAC) routing protocol for EH-WSNs, which takes node energy state into cluster head election algorithm and can adjust its parameter according to the network deploying environment. We analyze and evaluate the routing performance in terms of two metrics available node number and network throughput. Simulation results show that AEHAC maintains available nodes about 15% and networks throughput 19% more than traditional LEACH.

The technological advancements have led to the revolution in sensing technology. Wireless Sensor Network (WSN) has been one of the important researched areas which have attracted attention of various researchers. The battery constraints have led to the development of energy efficient routing protocols. Past studies ignore the importance of distance factor for the selection of Cluster Head (CH), which led to inefficient energy consumption in the network. In this chapter, Distance based Enhance Threshold Sensitive Stable Election Protocol (DETSSEP) has been proposed in which CH selection is based on networks average energy, nodes remaining energy and distance between nodes and Base Station (BS). Dual hop communication is used between distant CHs and BS to achieve uniform energy consumption in the network. It is observed through the simulation analysis that DETSSEP outperforms Enhance Threshold Sensitive Stable Election Protocol (ETSSEP) in various performance matrices viz. stability period, throughput, lifetime and remaining energy of the network.

In this paper we present a low complexity cooperative diversity clustering protocol for heterogeneous wireless sensor networks. Here we analyze the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are clustered in a co-operative fashion to prolong the effective network lifetime. In contrast to these approaches, we propose HACC, a heterogeneous-aware cooperative clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to its residual energy. We then propose a simple modification in the clustering algorithm to exploit virtual MIMO based cooperative transmission. In place of selecting a single cluster head at network layer, we proposed multiple cluster heads in each cluster to obtain a full diversity gain over long distance communication without sacrificing transmission data rate, and established virtual MIMO based cooperative transmission. We treated the cooperative cluster head selection method as a Facility Location Problem (FLP) to gain better energy utilization. Finally, the simulation results demonstrate that our cooperative clustering approach is more effective in prolonging the time interval before the death of the first node (we refer to as stability period) compared to the one obtained using current clustering protocols, and observe that this proposal can achieve higher order diversity gain with improved spectral efficiency when the distance to Base Station is over a given threshold.

In wireless sensor networks, power is the most essential resource because each sensor node has limited batteries. Many kinds of existing clustering protocols have been developed to balance and maximize lifetime of the sensor nodes in wireless sensor networks. These protocols select cluster heads periodically, and they considered only `How can we select cluster heads energy-efficiently?' or `What is the best selection of cluster heads?' without considering energy-efficient period of the cluster heads replacement. Unnecessary head selection may dissipate limited battery power of the entire sensor networks. In this paper, we present T-LEACH, which is a threshold-based cluster head replacement scheme for clustering protocols of wireless sensor networks. T-LEACH minimizes the number of cluster head selection by using threshold of residual energy. Reducing the amount of head selection and replacement cost, the lifetime of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that T-LEACH outperformed LEACH in terms of balancing energy consumption and network lifetime.

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Energy saving to prolong the network life is an important design issue while developing a new routing protocol for wireless sensor network. Clustering is a key technique for this and helps in maximizing the network lifetime and scalability. Most of the routing and data dissemination protocols of WSN assume a homogeneous network architecture, in which all sensors have the same capabilities in terms of battery power, communication, sensing, storage, and processing. Recently, there has been an interest in heterogeneous sensor networks, especially for real deployments. This research paper has proposed a new energy aware clustering protocol (EACP) for heterogeneous wireless sensor networks. Heterogeneity is introduced in EACP by using two types of nodes: normal and advanced. In EACP cluster heads for normal nodes are elected with the help of a probability scheme based on residual and average energy of the normal nodes. This will ensure that only the high residual normal nodes can become the cluster head in a round. Advanced nodes use a separate probability based scheme for cluster head election and they will further act as a gateway for normal cluster heads and transmit their data load to base station when they are not doing the duty of a cluster head. Finally a sleep state is suggested for some sensor nodes during cluster formation phase to save network energy. The performance of EACP is compared with SEP and simulation result shows the better result for stability period, network life and energy saving than SEP.

Using the available but limited resources of sensors more efficiently has been the recent interest in designing the routing protocols for Wireless Sensor Network (WSN). The major concern includes energy, storage and computing resources. Accordingly, we propose an adaptive energy-aware Multi-path routing protocol with load balance (AEMRP-LB). It introduces a concept named direction-angle to overcome the deficiency of broadcast and takes into account the tradeoff between the residual energy and hop count to establish multiple node-disjoint paths. The traffic load is balanced over the selected paths by using a weighted traffic scheduling algorithm considering their transmitting capacity, and then AEMRP-LB uses the advantages of Multi-path Source Routing to report the acquired data as saving the computing and storage resources of the sensors. In the scene with multiple Source-Sink pairs, AEMRP-LB can adaptively adjust the available residual energy of shared nodes so as to make reasonable use of them. The simulation results show that AEMRP-LB outperforms the two comparative schemes, and the sensors consume the energy in a more equitable way which ensures a more graceful degradation of service with time.

QoSNET: An integrated QoS network for routing protocols in large scale wireless sensor networks

The sensor nodes in wireless sensor networks (WSNs) typically have limited energy sources and are battery-operated. Reducing energy- usage, latency, and bandwidth is necessary to extend the network’s lifetime. WSN is often deployed in dynamic environments where nodes in the network can join, leave, or fail at any instant. Dynamic topology changes have the potential to disrupt established routes, necessitating more frequent route discovery and maintenance. Since a huge number of nodes are randomly deployed, a lot of redundant data packets are sent, which increases network traffic and creates delays. Robust and adaptable routing and aggregation techniques are necessary to meet these demands and adapt to shifting network conditions. The proposed survey paper offers a thorough analysis of the data aggregation mechanisms and energy-efficient routing algorithms applied to sensor networks. We have categorized the protocols depending on the network structure, data-gathering strategies, routing methodology, and node mobility. Based on the protocol performance parameters such as energy efficiency, network longevity, latency, routing overhead, packet delivery ratio, network throughput, and residual energy, we have provided a thorough classification and comparative overview of the key protocols. Moreover, we have determined the research gaps in the existing data aggregation techniques, and key areas which could point future researchers in the right direction.

Increasingly, the services operations which perform data sensing and data propagation in a dynamic environment are important tasks of wireless sensor networks. Therefore, increasing the network lifetime is the main contribution of this investigation. We propose a novel power-aware chessboard-based adaptive routing (PCAR) protocol to support immobility management in wireless sensor networks. The paramount design challenge in this work is to scale-down the network energy consumption, thus maximizing the network lifetime. Our PCAR protocol utilizes vector-oriented propagation, power-consideration decision, and multi-path routing protocols to guide the propagating data to its destination. Moreover, the properties of the clusters are combined in the PCAR to form the cluster-plates in a chessboard-based clustered sensor network. The alternate usage of cluster-head nodes and sleep nodes increases energy efficiency. The opportune divide-and-conquer multi-path fusion mechanism slows down and balances energy consumption. Finally, a performance analysis shows that energy efficiency is achieved by the PCAR protocol.