Mobile Sink based Routing Protocol (MSRP) for Prolonging Network Lifetime in Clustered Wireless Sensor Network

Dissemination of gathered data to sink is one of the main aspects in wireless sensor networks. Gathered data can be sent to the sink either directly or through multi-hop path. For an application like continuous monitoring of data, data have to be reported continuously to sink. As a result, node that form a part of static path towards sink, especially nodes near the sink may dissipate energy rapidly resulting in hotspot problem. Recent studies reveal the use of mobile sink as an efficient method for collecting data, as it avoids the hotspot problem that happens with static sink. While using mobile sink, we need to take care in maintaining dynamic paths and delay constraints. In this paper, we have used the concept of both mobile sink and static sink. Additionally we have introduced a timer and distance based approach for determining a better path. Here, we distribute the load among different CHs and use dynamic shorter paths, as opposed to usual fixed longer paths. Extensive simulations are carried out in Castalia-3.2 for evaluation of our approach. Our results show improved energy efficiency and network lifetime as compared to using static sink and mobile sink separately. The proposed system also reduces end to end delay by which applications like continuous monitoring can be enhanced.

In wireless sensor networks (WSNs), sensor nodes near static sink will have more traffic load to forward and the network lifetime will get largely reduced. This problem is referred to as the hotspot problem. Recently, adopting sink mobility has been considered as a good strategy to overcome the hotspot problem. Despite its many advantages, due to the dynamic network topology caused by sink mobility, data transmission to the mobile sink is a challenging task. To achieve efficient data dissemination, nodes need to reconstruct their routes toward the latest location of the mobile sink, which weakens the energy conservation aim. In this paper, we proposed an energy-efficient cluster-based dynamic routes adjustment approach (EECDRA) which aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sinks. The network is divided into several equal clusters and cluster heads are selected within each cluster. We also set some communication rules that manage routes reconstruction process accordingly requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sinks. Simulation results show that the mobile sinks for reducing reconstruction of route have improved the energy efficiency and prolonged lifetime of wireless sensor network.

Large scale Wireless Sensor Networks (WSNs) often utilize multiple mobile sink nodes to improve the network lifetime and scalability. However, most of the studies conducted in this context, consider unlimited buffer capacity for the sink nodes. But, this model cannot truly describe the behavior of WSNs and causes congestion in the sink nodes. To solve this problem, in this paper, we use limited buffer capacity for each mobile sink node in WSNs and present a two-level Fuzzy Logic Controller (FLC)-based dynamic clustering scheme and congestion prevention. In this scheme, sink nodes try to predict current load based on their loads in previous rounds by using ARIMA method and based on it, the first FLC selects the nearest uncongested sink node from multiple available mobile sink nodes. Then, the second FLC applies the output of the first FLC to select appropriate nodes as cluster heads to mitigate the energy consumption in the network. Extensive simulation results indicate the effectiveness of the proposed fuzzy logic-based solution in reducing congestion in the mobile sink nodes and improving load balancing in them which these result in the network lifetime improvement and decreasing the number of retransmissions.

In this paper we investigate the benefits of a heterogeneous architecture for wireless sensor networks composed of a few resource rich mobile nodes and a large number of simple static nodes. These mobile nodes can either act as mobile relays or mobile sinks. To investigate the performance of these two options and the trade-offs associated with these two options, we first consider a finite network. We then compute the lifetime for different routing algorithms for three cases (i) when the network is all static (ii) when there is one mobile sink and (iii) when there is one mobile relay. We find that using the mobile node as a sink results in the maximum improvement in lifetime. We contend however that in hostile terrains, it might not always be possible for the sink to be mobile. We then investigate the performance of a large dense network with one mobile relay and show that the improvement in network lifetime over an all static network is upper bounded by a factor of four. Also, the proof implies that the mobile relay needs to stay only within a two hop radius of the sink. We then construct a joint mobility and routing algorithm which comes close to the upper bound. However this algorithm requires all the nodes in the network to be aware of the location of the mobile node. We then proposed an alternative algorithm, which achieves the same performance, but requires only a limited number of nodes in the network to be aware of the location of the mobile. We finally compare the performance of the mobile relay and mobile sink and show that for a densely deployed sensor field of radius R hops, we require O(R) mobile relays to achieve the same performance as the mobile sink.

Power-aware routing, have been studied to avoid energy-scarce sensors and achieve network lifetime improvement but recently, it was noticed that sink mobility can also be used to improve network lifetime by avoiding excessive transmission overhead at nodes that are close to the location that would be occupied by a static sink. Also in many situations, a static sink may be unfeasible because of deployment or security constraints. In this paper, an energy-efficient method is introduced which can deliver data packets to mobile sink nodes. As can observed from simulation results, the mobile sink directed diffusion (MSDD) offers a magnificent way to upgrade directed diffusion algorithm to support sink mobility with minimum energy overhead and acceptable performance. Furthermore, in the proposed algorithm, no location service or predictable sink trajectory is assumed which is a common requirement in most of mobile sink routing algorithms. Simulation results show that MSDD algorithm can reach the packet delivery rate of over 55% in the worst case with the sink speed of 30 m/s with acceptable energy overhead.

In recent years, the Wireless Sensor Network has attracted more and more researchers for their great potential benefits such as security and environmental monitoring, following and evaluating in agriculture. Many routing protocols are designed for energy resources optimization. However, networks with one fixed sink often suffer from a hot spots problem since nodes near sinks take more energy to forward data during the transmission process. The use of fuzzy logic based clustering combined with mobile sink has been proved to be an effective way to enhance network performance features such as energy efficiency, network lifetime. This paper contributes the improvement of network lifetime by considering the fuzzy logic approach which is used to elect the cluster head based on three descriptors: residual energy, local distance and distance to sink. Simulation result presents that our improvement for fuzzy logic based clustering combined with mobile sink algorithm extends the performance of wireless sensor network and better than some other algorithms.

In wireless sensor networks, sensors at different locations in the field use different energy levels to propagate sensing data back to the sink or base station. This causes unbalanced energy usage among sensors and also lowers the network lifetime. Currently there are several techniques to mitigate this problem, such as deploying multiple sinks, adding more sensors on heavy traffic areas, or managing the size of clusters depending on the distance from sensor to sink. In this paper, we propose a distributed algorithm and protocol called Multipath Energy Balancing (MEB) to mitigate unbalanced energy usage in clustered wireless sensor networks using multi-path and multi-hop, with a transmission power control approach. The network field is divided into regions, where the ratio of inter-region transmission traffic from all cluster head sensors in one region to other cluster head sensors in the two regions in front can be pre-computed and pre-programmed into the sensors to ease sensor deployment. To further prolong network lifetime, we also present a simple heuristic algorithm to procrastinate cluster formation and routing. Simulation results show that MEB can balance energy much better than Energy-efficient Clustering (EC) and Balancing Energy Consumption (BEC) solutions. It also has a longer network lifetime than EC and BEC protocols, especially when the required cluster size is small. Procrastinating cluster formation and routing also can further improve the network lifetime.

A wireless sensor network with a large number of small sensor nodes can be used as an effective tool for gathering data in various situations. The paradigm of data aggregation enables wireless sensor networks to accurately estimate the different parameters of an environment. The aim of this paper is to introduce a new fault tolerant protocol striving to reduce overall power consumption, prolong network lifetime, and support mobile nodes in a wireless sensor network. Our protocol, FTPASC (fault tolerant power aware protocol with static clustering), partitions the network into static clusters and distributes the energy load among high-power sensor nodes. Mobility management is another attractive feature of this protocol that guarantees reliable communications between mobile and sensor nodes. Simulation results show that FTPASC outperforms conventional protocols in terms of network lifetime and power consumption minimization

These Wireless Sensor Networks are widely used in applications such as environmental monitoring, smart cities, and industrial automation, where energy efficiency remains a critical challenge due to limited battery resources of sensor nodes. This paper proposes a Hybrid Energy-Efficient Intelligent model that integrates deep learning, swarm optimization, and reinforcement learning to minimize energy consumption and enhance network lifetime. The model combines a Bidirectional Long Short-Term Memory model for energy prediction, Particle Swarm Optimization for optimal cluster head selection, and an Actor-Critic reinforcement learning model for adaptive routing. The proposed approach used temporal energy patterns to prevent premature node failures, balances energy distribution through optimized clustering, and dynamically selects routing paths to reduce communication overhead. Extensive simulation results demonstrate that the proposed HEEIF framework achieves up to 80% energy reduction, improves network lifetime by 20-25%, and maintain a high packet delivery ratio of 94-96% compared to conventional methods such as LEACH, LSTM-based, and RL-based approaches. Additionally, the framework improves throughput, reduces delay, and enhances load balancing. The results indicate that integrating prediction, optimization, and adaptive learning that allows the scalable and efficient solution for next-generation energy-aware WSNs.

Wireless Sensor Networks (WSNs) significantly impact reliable data communications in environmental monitoring, health care, and transportation applications. Low-powered and small sensor nodes compose these networks, configured to perform specific tasks like detection, management, prediction, and monitoring. Clustering in WSNs is a reliable method of effectively transferring data within the network through routing. Thus, developing an effective routing algorithm to optimize network functionality is a big concern. Cluster Heads (CHs), selected through a range of metrics, are responsible for data aggregation and transmission to the Base Station (BS). Many clustered routing algorithms have been developed to address various issues like energy efficiency, network lifetime, and hotspot problems. However, several challenges still need to be addressed, including network segmentation, isolated node issues, and data routing failures. This survey offers a comprehensive review of various routing protocols and their different performance metrics. It evaluates the architectural challenges that caused network segmentations and data routing failures in the case of unexpected head node failures during high-stress events, particularly indoor fires. Recommended solutions to the mentioned issues are also explored. A new taxonomy for the CH-selection methods based on the technique used is proposed, along with a comparative review of other CH-selection surveys. Additionally, this survey scrutinizes the challenges and constraints inherent in current fault tolerance routing algorithms, evaluating their efficacy in fire-related incidents. The survey concluded that protocol designers must focus on the development and implementation of fire-adaptive routing protocols, incorporating optimal fault tolerance routing algorithms that adapt seamlessly to unforeseen environmental changes, including fire incidents. Such adaptability is pivotal in preserving network topology and preventing data loss.

Efficient path planning and data gathering protocols for the wireless sensor network

Wireless Sensor Networks (WSN) play a vital role in several real-time applications ranging from military to civilian. Despite the benefits of WSN, energy efficiency becomes a major part of the challenging issue in WSN, which necessitate proper load balancing amongst the clusters and serves a wider monitoring region. The clustering technique for WSN has several benefits: lower delay, higher energy efficiency, and collision avoidance. But clustering protocol has several challenges. In a large-scale network, cluster-based protocols mainly adapt multi-hop routing to save energy, leading to hot spot problems. A hot spot problem becomes a problem where a cluster node nearer to the base station (BS) tends to drain the energy much quicker than other nodes because of the need to implement more transmission. This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems (JSOUCP-MHP) in WSN. The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search, persecution, and jumping skills to attack prey. The presented JSOUCP-MHP technique mainly resolves the hot spot issue for maximizing the network lifespan. The JSOUCP-MHP technique elects a proper set of cluster heads (CHs) using average residual energy (RE) to attain this. In addition, the JSOUCP-MHP technique determines the cluster sizes based on two measures, i.e., RE and distance to BS (DBS), showing the novelty of the work. The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy. The comparison study shows the significance of the JSOUCP-MHP technique over other models.

Energy efficient data collection through hybrid unequal clustering for wireless sensor networks

This paper concerns the definition of an analytical model and distributed protocols for determining the routes of a mobile data collector (sink) traveling through the nodes of a wireless sensor network (WSN). The routes are determined with the overall aim of maximizing the network lifetime. The contribution of our work is twofold. First, we introduce a novel mixed integer linear programming formulation for determining the sink's route and the sojourn time at the different "sink sites." The model takes into account realistic parameters such as the maximum distance the sink can travel between sites, different sink mobility rates, as well as the costs to support and perform data routing. Solutions to the model provide the route of the sink as a sequence of sites and the sojourn times at those sites that induce the maximum network lifetime. We then propose the Greedy Maximum Residual Energy (GMRE) protocol for sink mobility. GMRE is distributed and localized, thus being suitable for wireless sensor networking. In GMRE the sink greedily keeps moving toward those areas in the network where there is the most residual energy, as if "drawn" to them. This heuristic is then compared with a very simple and energy-unaware protocol where the next site in the sink route is chosen randomly and uniformly each time the sink moves. Simulation results show that GMRE leads to improvements in network lifetime that are four times as much as the lifetime when the sink is kept static, while balancing energy consumption throughout the network.

Improving energy efficiency and prolonging network lifetime is a challenging research issue for wireless sensor networks (WSNs). Nowadays, adding mobility technology into WSNs has drawn increasing attention. In this paper, we combine the uneven clustering algorithm with mobile sink strategy and propose our mobile sink based uneven clustering algorithm. First, we study the uneven clustering algorithm with a fixed sink node located at the center of a rectangle network. We analyze the performance of energy consumption and network lifetime, and compare our algorithm with LEACH. Then we use mobile sink node instead of fixed sink node to collect fused data under similar environment. Simulation results show that mobile sink node can efficiently mitigate hot spots near sink node as sink node moves either randomly or along a predetermined fixed path.