An Improvement of Fuzzy Logic Based Clustering Combined for Mobile Sink Algorithm

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.

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.

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.

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.

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.

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.

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

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.

In WSN, sensors near the sink have to relay the data of the nodes away from the sink and as a result they drain their energy very quickly. It result in network partitioning and can significantly limit the network lifetime. This problem is termed as hotspot problem. Recently, formation of hot spot or energy hole near the sink has emerged as a critical issue for data gathering in WSN. In this paper, we address hotspot problem and purposed Mobile Sink based Routing Protocol (MSRP) for Prolonging Network Lifetime in Clustered Wireless Sensor Network. In MSRP, mobile sink moves in the clustered WSN to collect sensed data from the CHs within its vicinity. During data gathering mobile sink also maintains information about the residual energy of the CHs. Mobile sink based on the residual energy of CHs move to the CHs having higher energy. Consequently, the hotspot problem is minimized as the immediate neighbor of the sink is high energy node and it changes because of regular sink movement. It results in a balanced use of WSN energy and improves network life time. To evaluate the performance of the proposed strategy intensive simulation are carried out using OMNet-4.0. Performance of the proposed strategy is compared to the static sink and multiple sinks strategies, using metrics such as energy per packet, and throughput. The simulation results demonstrate that MSRP is effective in prolonging the network lifetime as well as in improving throughput than static sink and multiple sink strategies.

Wireless sensor networks with the mobile sink can help to prevent the hot-spot problem and improve the network lifetime. However, in practice, the trajectories of the sink can not ideally change due to the obstacle of the environment or the requirement of the application. The constraint on trajectory of the mobile sink causes the different connection time between the mobile sink and the sensor node, which might result in the imbalance depletion of energy. We propose here an efficient data gathering policy to jointly consider the constrained mobility of the sink and the different connection time at the sensor node. The policy increases the network lifetime in wireless sensor networks with path-constrained mobile sink by optimizing the assignment sensor node. In addition, we also propose the policy to cope with the overlapped connection time of the sink and sensor node due to the movement of the sink. The simulation results demonstrate the effectiveness of the proposed policy: by carefully considering the trajectory of the sink and effectively assigning the sensor node, the network lifetime is increased.

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP‐MS) technique for IoT supported WSN. The EACMRP‐MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP‐MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type‐II fuzzy logic (T2FL) technique is used for the optimal selection of multi‐hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP‐MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

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).

Using a single fixed sink in wireless sensor networks (WSNs) creates the hotspot problem. Recently, the mobile sink technique is considered as a good solution to the hotspot problem. Mobile sinks travel physically in the network to collect data directly from special nodes, such as cluster heads (CHs). Find the optimal path for the mobile sink is of utmost importance in order to achieve energy efficiency. The ant colony optimization (ACO) algorithm has been founded to be a good solution to meet this objective. Whereas the traditional ACO algorithm and its versions do not restrict the spread of ants which consumes a lot of energy and makes the convergence speed slow. In this paper, we propose an Intelligent ACO-based Routing Protocol with multiple mobile sinks support for wireless sensor networks (named IARP). This proposed protocol divides the WSN into clusters where each one is managed by a CH. The CH selection is conceived in such a way to use the minimum control overhead and also avoid the greedy re-clustering process. It considers not only pheromone but also remaining energy and hop count heuristic parameters. As well, the scope of ants search is restricted to save a lot of energy. Additionally, we have conceived a fault-tolerant algorithm to resume the normal functionality of the network after the failure of critical nodes i.e. CHs. The proposed protocol is tested by using simulations. The results show that the proposed IARP can significantly improve the performance of WSNs compared to other benchmark protocols.

Efficient energy consumption is a major problem in Wireless Sensor Network. The sink can be static or dynamic. In case of static sink, the node that is placed near the sink is utilized more than other nodes in the network due to frequent packet forwarding. This causes the node near the sink to die very soon, also further communication between the sink and other nodes in the network is disconnected. When dynamic or mobile sinks are used, the energy consumption is distributed among other nodes in the network, which increases the lifetime of the network. In this paper to know the latest position of the sink, we introduce Dynamic Hexagonal Grid Routing Protocol (DHGRP). In the first phase, the protocol divides the network by several hexagonal virtual grids to share the current position of the sink among the nodes. In the second phase, the dynamic path is selected, in case congestion occurs during data transmission or if the mobile sink is moved to a new location. The performance is evaluated by comparing with the existing ring routing protocol, Query-Driven Virtual Grid-based Data Dissemination, and Grid-Cycle Routing Protocol. The results show that the proposed DHGRP protocol has better performance in terms of energy consumption, delay, network lifetime, throughput and Packet Delivery Ratio.