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An evolutionary routing protocol for load balancing and QoS enhancement in IoT enabled heterogeneous WSNs
Abstract
Energy awareness is a key concern of recent advances in the Internet of Things (IoT) enabled wireless sensor networks (WSNs), and many optimization approaches to reduce energy consumption have been proposed. The most widely used routing technique for achieving energy efficiency in WSNs is the clustering hierarchy. Data transmission over long distances has a negative impact on network efficiency in terms of stability period, network lifetime, and QoS due to the selection of inadequate Cluster Heads (CHs). This paper describes a new Evolutionary Gateway-based Load-Balanced Routing (E-GLBR) algorithm for efficiently selecting appropriate CHs. The proposed algorithm is based on the genetic algorithm optimization method with a new fitness function that takes into account four major parameters to achieve the following goals: improving the CH selection process, reducing energy transmission range, increasing network stability and lifetime, and improving WSN coverage. A comparison simulation with the most recent related methods in MATLAB simulator is performed to evaluate the performance of our proposed algorithm. The simulation findings demonstrate that applying the developed evolutionary approach reduces the network’s energy consumption rate and increases the wireless network throughput. In various network scenarios, our suggested approach surpasses all other examined methods, extending the network coverage and prolonging the stability periods of Evolutionary Routing Protocol (ERP), Energy-Efficient Weighted Clustering (EEWC), Distance Incorporated Modified Stable Election Protocol (D-MSEP), and Energy dependent cluster formation (EDCF) by 55%, 43%, 26%, and 12% respectively.
... The use of SDNs ranges from civic, medical care, and ecological to military use [1]. However, the development and administration of networked sensors have faced numerous difficulties due to the specialized attention to the special characteristics of networks of sensors, such as power constraints, strict capacity requirements, dynamic structure, high system density, in addition, massive deployments [2]. At all stages of the communication standard system, these difficulties have required consciousness of energy and strong standard implementations. ...
... The dragonfly algorithm is harnessed for deploying sensor nodes and identifying optimal routes to facilitate packet delivery to the Base Station (BS). Benelhouri et al. [49] introduced an Evolutionary Gateway-based Load-Balanced Routing(E-GLBR) algorithm for effective CH selection by integrating a unique fitness function that considers four key parameters such as residual energy, node density, transmission distance, CH's energy consumption. This approach enhanced the CH selection process, minimizing energy transmission range, boosting network stability and longevity, and enhancing the overall coverage of the Wireless Sensor Network (WSN). ...
An essential concern in the modern period is the growing energy consumption in the Internet of Things (IoT) networks, particularly when connecting resource-constrained devices. Prolonging the network’s lifespan under these circumstances poses a significant challenge. Clustering is a prominent strategy to enhance the network’s operational duration. However, poor cluster heads (CH) selection rapidly drains energy from network nodes. In multi-hop clustering systems, CH consumes more power by forwarding the data from other CHs and its data, creating “hot spots” in the network. We propose a novel evolutionary multi-objective clustering protocol (EMCP) for heterogeneous IoT networks to prolong the network lifetime. In the initial stage, the genetic algorithm optimizes the CH selection process by considering the residual energy, communication radius, the distance between the edge node and CH, and the distance between CHs as fitness parameters. The distance between IoT nodes and CHs determines the clustering process. The energy monitoring module also keeps track of the CHs’ power consumption following each data transmission. If the CH’s remaining power drops to a threshold value, the new cluster head is one of the IoT nodes close to the previous cluster head with the highest residual energy. In addition, the simulation is performed with multiple edge servers to solve the hot spot problem. The suggested protocol’s performance is compared against state-of-the-art protocols for various parameters, including network stability, network longevity, the number of alive nodes, residual energy, and throughput. According to the simulation findings, the EMCP protocol achieves 15,347, 19,493, 24,187, 41,833 rounds, and 1,901,246 packets, respectively, for metrics such as network stability, half nodes dead, three-quarter node dead, last node dead, and throughput. Similarly, EMCP-MS has 17,565, 25,585, 30,410, 45,567 rounds, and 1,957,942 packets for the same metrics. Thus, the EMCP protocol has 94.55%, 80.54%, and 26.15% more life than the GAOC, DDMPEAwithANUM, and OptGACHE protocols. EMCP-MS has 75.49%, 56.55%, and 26.51% more longevity than multi-sink versions of the above-mentioned state-of-the-art protocols.
... For cluster-head selection, IZ-SEP considers two parameters: the neighbors' count within the cluster and the node's remaining energy. In [67], E-GLBR is proposed with better-optimized zone creation, communication mode, and CHs election compared with IS-ZEP. The sensing area is divided into four zones with different sizes and coordinates, based on which the nodes communicated to BS either directly, through a predefined gateway or through selected CHs. ...
There is increasing attention, recently, to optimizing energy consumption in IoT-based large-scale networks. Extending the lifetime of battery-powered nodes is a key challenge in such systems and their various application scenarios. This paper proposes a new zone-based and event-driven protocol for saving energy in large-scale heterogeneous WSNs called TESEES (Threshold Enabled Scalable and Energy Efficient Scheme). The proposed protocol is designed to support network scenarios deploying higher levels of heterogeneity with more than three types of sensor nodes (i.e., four, five, and more). TESEES is a reactive version of the proactive SEES protocol, in which we leverage a novel state-of-the-art thresholding model on the zone-based hierarchical deployments of heterogeneous nodes to regulate the data reporting process, avoiding unnecessary frequent data transmission and reducing the amount of energy dissipation of the sensing nodes and the entire system. With this model, we present a general technique for formulating distinct thresholds for network nodes in each established zone. This mechanism allows for individually configuring the nodes with transmission settings tailored to their respective roles, independent of the heterogeneity levels, total node count, or initial energy. This approach ensures that each node operates optimally within the network. In addition, we present an improved hybrid TMCCT (Threshold-based Minimum Cost Cross-layer Transmission) algorithm that operates at the node level and ensures effective data transmission control by considering current sensor values, heterogeneous event thresholds, and previous data records. Instead of periodical data transmission, this hybridization mechanism, integrated with a grid of energy-harvesting relay nodes, keeps the zone member nodes in the energy-saving mode for maximum time and allows for reactive data transmission only when necessary. This results in a reduced data-reporting frequency, less traffic load, minimized energy consumption, and thus a greater extension of the network’s lifetime. Moreover, unlike the traditional cluster-head election in the weighted probability-based protocols, TESEES relies on an efficient mechanism for zone aggregators’ election that runs at the zone level in multiple stages and employs various static and dynamic parameters based on their generated weights of importance. This leads to selecting the best candidate nodes for the aggregation task and, hence, fairly rotating the role among the zones’ alive nodes. The simulation results show significant improvements in the total energy saving, the lifetime extension, and the transmitted data reduction, reaching 29%, 68%, and 26% respectively, compared to the traditional SEES protocol. Also, the average energy consumption per single round has decreased by 36%.
... Data-data identitas dapat diletakkan secara tidak kasat mata di lingkungan sekitar kita. Jaringan sensor [5] akan memungkinkan orang untuk berinteraksi dengan dunia nyata dari jarak jauh. Teknologi identifikasi yang disebutkan di sini mencakup objek dan identifikasi lokasi. ...
Di era Industri 4.0, sistem pemantauan mesin berbasis IoT mutlak diperlukan sehingga mesin bisa bekerja dengan cerdas dan kegagalan mesin bisa dideteksi dari awal. Pemantauan industri sangat diperlukan untuk mengetahui kondisi mesin yang selalu berubah secara drastis dan dapat mempengaruhi kinerja produksi. Pemantauan mesin di Industri bermanfaat untuk memenuhi target produksi yang tinggi, mengurangi konsumsi daya listrik, meningkatkan kualitas dan manajemen industri secara global. Tujuan pada penelitian ini adalah merancang sebuah sistem pemantauan mesin berbasis PLC dan sistem pencatatan berbasis raspberry pi yang terhubung menggunakan aplikasi Whatsapp. Sebuah grup whatsapp yang berisi beberapa orang pengguna akan menerima notifikasi kondisi mesin melalui grup. Hal ini sangat bermanfaat apabila terjadi keadaan darurat di mesin, maka semua pengguna di grup tersebut bisa langsung mengetahuinya. Berdasarkan hasil penelitian, sistem ini mampu merespon pembacaan PLC dengan rata-rata 1.84 detik dan penerimaan pesan di whatsapp dengan rata-rata 4.43 detik. Hal ini memungkinkan untuk memantau kondisi mesin dengan cepat dan tepat
... EC can be minimized by reducing the node's wake-up time, reducing packet collisions and retransmissions, and controlling overhead. So, it's important to use a method of DT that keeps these things to a minimum [11] [12]. ...
... The rapid progression of Wireless Sensor Networks (WSNs) technology has extended its applications in various fields, significantly augmenting the demand for real-time image transmission and compression [1][2][3][4]. Comprising of numerous low-cost, low-power sensor nodes with computational and communicational abilities, WSNs have found widespread use in areas such as environmental monitoring, security surveillance, smart transportation, and healthcare [5][6][7][8][9][10]. Real-time image transmission in WSNs carries substantial importance as it delivers timely and accurate information to users, aiding in precise decisionmaking [11][12][13][14][15][16]. ...
Breast Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—D cancer remains one of the most common cancers among women globally, necessitating early detection to improve prognosis and survival rates. Recent advancements in machine learning (ML) have shown promise in enhancing the accuracy and efficiency of breast cancer and lesion detection. This review paper discusses the current methodologies, challenges, and future directions of ML applications in the detection and diagnosis of breast cancer. We analyze various ML algorithms used for analyzing mammograms, ultrasound, and MRI data, evaluating their effectiveness and applicability in clinical settings.
Prolonged network operations are crucial for any Wireless Sensor Network (WSN) based applications such as health care, military, industrial, etc. The fixed energy and the restrained communication range of the sensor nodes (SNs) make it challenging to achieve a longer life of network operations. Generally, the data collection and direct message transmissions to the base station (BS) consume most of the SNs' energy resulting in a shorter network lifetime. Reduction in the energy expended on these data transmissions leads to an improvement in the WSNs' network lifetime. Clustering approaches are employed to mitigate this issue. The cluster head (CH) in each cluster collects the data from the member nodes and transmits it to the BS. Proper cluster head node selection is vital in achieving enhanced network lifetime. Therefore, in this work, a new clustering technique is proposed that probabilistically selects the best CHs for each cluster by considering the residual energy of each SN. The suggested technique has been compared with the existing scheme and the experimental outcomes show the proposed technique has improved the network lifetime by an average of 8.32% and 14.23% in terms of the first node dead and the last node dead respectively.
A key function of wireless sensor networks (WSN) is data collection. Due to the hot spot issue and the limited energy supply, developing data gathering techniques is complicated. The WSN faces three main challenges: security, data routing, and processing a lot of data. Since compressive sensing can achieve simultaneous sampling and compression, it is widely used in signal processing technique. Due to resource limitations and computational limitations, WSN security solutions are different from those in traditional networks. Compressive sensing (CS) and Elliptical curve Diffie-Hellman key exchange are used to solve these problems. The measurement matrix is configured to be as a public key that is understood by both the sensor node and the base station in order to achieve high safety and efficiency for data gathering in wireless sensor networks. Security and effective data collecting are the main study goals. A prime-numbered address strategy for TPID (tree path identifier) routing and cluster head selection is used. Comparison between seven types of CS algorithms is introduced over different data sparsity levels. The network parameters is being tested are Network life time, throughput, residual node energy and total energy dissipated. The results revels that the compression system can reduce the size of the transmitted data and consequently the energy consumption while still maintains the data security.
In this paper, we examine routing protocols with the shortest path in sensor networks. In doing this, we propose a genetic algorithm (GA)-based Ad Hoc On-Demand Multipath Distance Vector routing protocol (GA-AOMDV). We utilize a fitness function that optimizes routes based on the energy consumption in their nodes. We compare this algorithm with other existing ad hoc routing protocols including LEACH-GA, GA-AODV, AODV, DSR, EPAR, EBAR_BFS. Results prove that our protocol enhances the network performance in terms of packet delivery ratio, throughput, round trip time and energy consumption. GA-AOMDV protocol achieves average gain that is 7 to 22% over other protocols. Therefore, our protocol extends the network lifetime for data communications.
This article presents the design, analyses and implementation of the novel routing protocol for energy optimization based on LEACH for WSN. Network Lifetime is the major problem in various routing protocols used in WSN. In order to overcome that problem, our proposed routing protocol is developed, which is a combination of Micro Genetic algorithm with LEACH protocol. Our proposed µGA-LEACH protocol, strengthen the cluster head (CH) selection and also reduce the energy consumption of the network when compared to existing protocols. This paper shows the improvement of network lifetime and energy consumption with the optimal CH selection based on a micro genetic algorithm and also compared the results with an existing hierarchical routing protocol like LEACH, LEACH-C, LEACH GA and GADA LEACH routing protocol with various packet sizes, and initial energy.
Autonomous Wireless Sensor Networks (WSNs) form the basis of several Internet of Things (IoT) applications. Efficiency in routing data is a major concern in these networks since sensor nodes are energy-constrained and mostly unaware of the optimal routes regarding a set of QoS metrics. In this paper, the energy consumption and data delivery reliability are deemed the most important factors to the adequate overall network and application operation. Nevertheless , to enhance the chances of data packets to arrive at destinations, in general, energy consumption and reliability have to compromise. Although many routing techniques exist for WSNs, it is hard to determine, in general, if they really can find good solutions considering such trade-off. In light of this, we present a multi-objective integer problem (MOIP) to the routing problem in WSNs to optimize both energy consumption and delivery reliability. Moreover, to solve the proposed MOIP, we introduce an efficient evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The solution method is capable of constructing Pareto set approximations in a low computational time that can be used to better evaluate the solutions provided by routing protocols prior to deployment. The results of experiments with medium-sized and large-scale scenarios based on real case studies indicated the clear compromise between energy efficiency and delivery ratio. Moreover, by the results achieved by NSGA-II, the scalability does not affect the performance of the routing in optimizing the two objectives.
The restricted battery of the sensors has always been a bottleneck for the WSNs. To withstand the network life for a elongated period, the network can be partitioned into clusters. To boost the network routine, a three-level heterogeneous clustering procedure is proposed in this paper. The three levels of heterogeneity split the sensors into three diverse groups using their energies. The proposed protocol picks the most competent nodes as cluster head (CH) by utilizing the threshold and energy factors. The competent picking of CH helps in uplifting the performance of the whole network routine and improves the functioning of the network. The proposed scheme is simulated using the MATLAB simulator and the results are contrasted with the conventional approaches to illustrate the advantages of proposed protocol over other techniques.
This paper presents an evolutionary algorithm that simulates simplified scenarios of the diffusion of an infectious disease within a given population. The proposed evolutionary epidemic diffusion (EED) computational model has a limited number of variables and parameters, but is still able to simulate a variety of configurations that have a good adherence to real-world cases. The use of two space distances and the calculation of spatial 2-dimensional entropy are also examined. Several simulations demonstrate the feasibility of the EED for testing distinct social, logistic and economy risks. The performance of the system dynamics is assessed by several variables and indices. The global information is efficiently condensed and visualized by means of multidimensional scaling.
Wireless sensor networks are widely used in monitoring and managing environmental factors like air quality, humidity, temperature, and pressure. The recent works show that clustering is an effective technique for increasing energy efficiency, traffic load balancing, prolonging the lifetime of the network and scalability of the sensor network. In this paper, a new energy-efficient clustering technique has been proposed based on a genetic algorithm with the newly defined objective function. The proposed clustering method modifies the steady-state phase of the LEACH protocol in a heterogeneous environment. The proposed objective function considers three main clustering parameters such as compactness, separation, and number of cluster heads for optimization. The simulation result shows that the proposed protocol is more effective in improving the performance of wireless sensor networks as compared to other state-of-the-art methods, namely SEP, IHCR, and ERP.
Wireless Sensor Networks are considered to be among the most rapidly evolving technological domains thanks to the numerous benefits that their usage provides. As a result, from their first appearance until the present day, Wireless Sensor Networks have had a continuously growing range of applications. The purpose of this article is to provide an up-to-date presentation of both traditional and most recent applications of Wireless Sensor Networks and hopefully not only enable the comprehension of this scientific area but also facilitate the perception of novel applications. In order to achieve this goal, the main categories of applications of Wireless Sensor Networks are identified, and characteristic examples of them are studied. Their particular characteristics are explained, while their pros and cons are denoted. Next, a discussion on certain considerations that are related with each one of these specific categories takes place. Finally, concluding remarks are drawn.
Event critical applications demand blanket coverage. On the other hand, nodes closer to the base station are exploited as they have to spend additional energy in relaying data of far away nodes. This brings in the idea of implementing blanket coverage in heterogeneous wireless sensor networks. I-DEEC improvises distributed energy efficient clustering (DEEC) by deploying network nodes in two layers. Layer 1 strategically tessellate hexagons to deploy nodes as normal or super nodes based on distance from the base station, considering the high data requirement within hop distance around the base station. Layer 2 randomly deploys advanced nodes with condition that no two advanced nodes sense the same area. Further, it uses the sum of the ratio of node’s distance to the base station along with residual energy ratio to calculate the possibility of a node to be selected as a cluster head, followed by the selection of the optimal percentage high possibility nodes as cluster heads. I-DEEC provisions blanket coverage by extending the stability period by reducing the ratio between initial energy of different types of nodes. I-DEEC revamps DEEC protocol in terms of network lifetime, percentage area coverage, throughput, and residual energy.
The nodes in wireless sensor networks are communicating wirelessly and their energies are depleted in the communication among themselves or with the Base Station (BS). In order to save their energies various heterogeneous routing protocols have been discovered. In this paper, we have proposed DMSEP (Distance incorporated Modified Stable Election Protocol) which is an improved version of MSEP (Modified Stable Election Protocol) as it incorporates the distance factor in probabilistic formula of cluster head selection in each cluster. The inclusion of distance factor helps in avoidance of cluster head selection farther from the BS. This leads to saving of huge amount of energy leading to the escalated stability period by 32% and 173.38% as compared to the MSEP protocol. In addition to this the network lifetime is enhanced by 56.20% as compared to MSEP. The proposed protocol is best suitable for large area applications to disseminate the information from the network.
The applications of Wireless Sensor Network is increasing rapidly in almost every domain. So, the limited node’s battery life in the network should be utilized efficiently. Various approaches have been proposed earlier to lessen the usage of energy in the network and to enhance the network lifespan. In this paper we are proposing an approach for efficient cluster head selection namely Energy Dependent Cluster Formation in Heterogeneous Wireless Sensor Network (EDCF) to enhance the lifespan of network. The simulation of the proposed EDCF technique is performed in MATLAB simulator and to measure its performance the comparison is performed with various existing protocols. The proposed EDCF protocol has shown the enhancement in the lifespan of the network as compared to the previous clustering approaches.
The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user-friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing techniques to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation technique in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, cluster-head (CH) has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long-run operation of WSN. In this paper, genetic algorithm (GA)-based threshold-sensitive energy-efficient routing protocol (TERP) is proposed to prolong network lifetime. Multi-hop communication between CHs and base station (BS) is utilized using GA to achieve optimal link cost for load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TERP named stable TERP (STERP) so as to extend the stability period (time interval from initial time to the death of first node) of the network. In STERP, energy-aware heuristics is applied for CH selection in order to improve the stability period. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.
Wireless sensor networks (WSNs) has recently drawn lots of attention due to its application in multiple domains. The sensors have limited power sources and in many applications they cannot be recharged or replaced due to hostile nature of the environment. Finding near optimal solutions for the energy problem is still an issue in WSNs. A new era is opened with algorithms inspired by nature to solve optimization problems. In this paper, we propose genetic algorithm based approaches for clustering and routing in WSNs. The objective of this mechanism is to prolong lifetime of a sensor and increase the quality of service. We perform extensive simulations of the proposed algorithms and compare the simulation results with that of the existing algorithms. The results demonstrate that the proposed algorithms outperform the existing algorithms in terms of various performance metrics including energy consumption and number of packets received by the base station.
Nowadays, the energy efficiency in wireless sensor networks (WSN) has become a challenging issue. Indeed, due to the sustainable development and deployment of such networks in our daily lives, energy efficiency is asked lto be significantly improved in order to achieve green networking. Greedy behaviours are one of the most malicious and insidious Denial of Service attacks, that threaten the green behaviour of wireless network. In such an attack, a greedy node attempts to improve its bandwidth at the expense of other nodes by not respecting its access network procedure, resulting in an overall increase of the energy consumption in the network. In this paper, we are interested in evaluating the energy efficiency of WSN in presence of a greedy behaviour. For this effect, we consider ZigBee networks using the non slotted IEEE 802.15.4 procedure. A time Petri net based modelling that characterizes the behaviour of both greedy and sane nodes, is first presented. Then, we simulate the model under TiNA platform by considering different scenarios in order to assess the energy efficiency of the network as well as other performance parameters. Hence, we confront the obtained values to discuss the impact of the greedy attack on the energy efficiency as well as a strategy to detect such a non green behaviour. Finally, we propose countermeasures to thwart the impact of the latter on the energy efficiency of the network.
In this paper, we propose a 3-level heterogeneous network model for WSNs to enhance the network lifetime, which is characterized by a single parameter. Depending upon the value of the model parameter, it can describe 1-level, 2-level, and 3-level heterogeneity. Our heterogeneous network model also helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function. We compute the network lifetime by implementing SEP protocol for our network model. The SEP implementation for the existing 1-level, 2-level, and 3-level heterogeneous network models are denoted as SEP-1, SEP-2, and SEP-3, respectively, and for our proposed 3-level heterogeneous network model, the SEP implementations are denoted as hetSEP-1, hetSEP-2, and hetSEP-3, respectively. In SEP-3 and hetSEP-3 the values of the network lifetime increase by 164.18% and 192.80%, respectively for increasing 100% in network energies as compared to the original implementation of SEP.
This paper describes the application of a Wireless Traffic Monitoring and Tracking system in the Spanish city of Granada, as an approach for addressing important tasks in the field of Smart Traffic. To this end, several nodes of the so-called MOBYWIT system have been deployed at important urban points. They collect real-time vehicles’ movement information based on Bluetooth signals detection. The gathered data have been processed in several ways, showing some of the applications that the system has, such as the composition of Origin/Destination matrices, the computation of accurate displacement times, or the estimation of real traffic in short terms by means of Time Series Forecast. The obtained results validate the system and proves its value as a tool for the urban traffic flow monitoring, analysis and prediction, which could be used as a part of an intelligent transportation system.
In this paper, we propose a 3-level heterogeneous network model for WSNs to enhance the network lifetime, which is characterized by a single parameter. Depending upon the value of the model parameter, it can describe 1-level, 2-level, and 3-level heterogeneity. Our heterogeneous network model also helps to select cluster heads and their respective cluster members by using weighted election probability and threshold function. We compute the network lifetime by implementing DEEC protocol for our network model. The DEEC implementation for the existing 1-level, 2-level, and 3-level heterogeneous network models are denoted as DEEC-1, DEEC-2, and DEEC-3, respectively, and for our proposed 3-level heterogeneous network model, the DEEC implementations are denoted as hetDEEC-1, hetDEEC-2, and hetDEEC-3, respectively. The network lifetime in DEEC-3 and hetDEEC-3 increases by 154.17% and 182.67%, respectively by increasing the total network energy 100% with respect to the original DEEC.
In heterogeneous wireless sensor network the routing protocol are used to determine the efficiency of sensor networks. Routing protocols provide an optimal data transmission route from sensor nodes to sink node to save energy of nodes. Clustering mechanism is one of the popular routing mechanisms used in WSN for optimizing the problem in sensor nodes. To check the efficiency of different clustering scheme against different level of energy for sensor nodes, this paper represents three cluster based routing protocols; Low Energy Adaptive Clustering Hierarchy (LEACH), Deterministic Cluster Head Selection in LEACH(DCHS-LEACH) in single energy level nodes, Stable Election Protocol (SEP) in two energy level nodes. We proposed a two energy level based Modified Stable Election Protocol (M-SEP). Later part we adopt a new approach to define the lifetime of sensor network using four new matrices FND(First Node Dies),SND(Some Node Dies), HND(Half Node Dies) and LND(Last Node Dies). Simulating all the matrices results reveal that M-SEP protocol performs 55%, 22.5%, and 40% respectively longer than LEACH, DCHS-LEACH, SEP. It also shows that M-SEP increases the stability period and packet transmission rate as compare with other routing protocol.
The dynamic nature of wireless sensor networks (WSNs) and numerous possible cluster configurations make searching for an optimal network structure on-the-fly an open challenge. To address this problem, we propose a genetic algorithm-based, self-organizing network clustering (GASONeC) method that provides a framework to dynamically optimize wireless sensor node clusters. In GASONeC, the residual energy, the expected energy expenditure, the distance to the base station, and the number of nodes in the vicinity are employed in search for an optimal, dynamic network structure. Balancing these factors is the key of organizing nodes into appropriate clusters and designating a surrogate node as cluster head. Compared to the state-of-the-art methods, GASONeC greatly extends the network life and the improvement up to 43.44 %. The node density greatly affects the network longevity. Due to the increased distance between nodes, the network life is usually shortened. In addition, when the base station is placed far from the sensor field, it is preferred that more clusters are formed to conserve energy. The overall average time of GASONeC is 0.58 s with a standard deviation of 0.05.
Latest advancements in micro-electro-mechanical-system (MEMS) and wireless communication technology, opens the way for the growth in applications of wireless sensor networks (WSNs). Wireless sensor network is comprised of huge number of small and cheap devices known as sensor nodes. The sensor nodes communicate together by many wireless strategies and these communication strategies are administered by routing protocols. Performance of sensor networks largely depends on the routing protocols, which are application based. Keeping this in mind, we have carried out extensive survey on WSN routing protocols. Based on structure of network, routing protocols in WSN can be broadly classified into three categories: flat routing, hierarchical or cluster based routing, and location based routing. Due to certain advantages, clustering is flattering as an active stem in routing technology. In this paper, authors have been reported a comprehensive survey on cluster based routing protocols in wireless sensor networks. We outline the merits and limitations of the clustering schemes in WSNs, and propose a taxonomy of cluster based routing methods. Finally, we summarize and conclude the paper with some future directions.
The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energy-constrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energy-saving problem. This research covers several areas going from physical layer optimisation to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.
We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.
Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Clustering techniques are largely used for these purposes. In this paper, we propose and evaluate a clustering technique called a Developed Distributed Energy-Efficient Clustering scheme for heterogeneous wireless sensor networks. This technique is based on changing dynamically and with more efficiency the cluster head election probability. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) by about 30% and than the Distributed Energy-Efficient Clustering (DEEC) by about 15% in terms of network lifetime and first node dies.
In this paper, an optimized cluster head (CH) selection method based on genetic algorithm (NCOGA) is proposed which uses the adaptive crossover and binary tournament selection methods to prolong the lifetime of a heterogeneous wireless sensor network (WSN). The novelty of the proposed algorithms is the integration of multiple parameters for the CH selection in a heterogeneous WSN. NCOGA formulates fitness parameters by integrating multiple parameters like the residual energy, initial energy, distance to the sink, number of neighbors surrounded by a node, load balancing factor, and communicating mode decider (CMD). The parameters for load balancing and CMD are utilized to discover out the best candidate to be selected as a relay CH and for deciding the mode of communication (single or multi-hop) of CH. Further, these parameters are useful in avoiding hot-spot problem in the network. The working of the NCOGA starts based on the criteria “consider only those nodes which have energy higher than the pre-defined threshold energy”. This criterion of nodes selection makes the NCOGA more efficient and quickly convergent. Extensive computer simulations are conducted to determine the effectiveness of the NCOGA. Simulation results reveal that the proposed NCOGA outperforms the state-of-the-art optimization algorithms based on GA in terms of several performance metrics, specifically, stability period, residual energy, network lifetime, and throughput.
In cluster-based sensor networks, at each cluster, sensor nodes send the collected data to a cluster head which aggregates and forwards them to a sink node. Data transmission from a cluster head to the sink node can be done in a multi-hop fashion through other cluster heads. Hence, two problems need to be addressed in this regard: Selection of cluster heads, and optimal multi-hop routing. In previous studies, these two problems have been solved separately in two independent phases. This paper proposes a novel approach to solve them simultaneously in order to increase the network lifetime. In the proposed scheme, the cluster head’s role in transmitting the inter-cluster traffic is considered during the cluster head selection process. In other words, cluster heads are selected in a way which reduces the energy consumption for transmitting data from a cluster head to the sink node. To achieve this goal, the genetic algorithm is used in two levels. The first-level genetic algorithm selects the cluster heads while the second-level one considers multi-hop routing among them. Simulation of the proposed method and comparison of its results with three previously proposed schemes which solve the problems separately indicate the superiority of the proposed optimization scheme in improving the lifetime of the network.
In this work, an improved version of the gateway based multi-hop routing protocol was studied. The MGEAR protocol is mainly used for prolonging network lifetime in homogeneous wireless sensor network. Herein, the proposed approach aims to prolong network lifetime and enhance the throughput of this protocol in the case of heterogeneous wireless sensor networks (HWSNs). In the MGEAR, the network is divided into several fields; sensor nodes in the first field communicate directly with the base station. Sensors in the center of the network send their data to the gateway which perform data fusion and spread to the base station. The rest of nodes are divided into two equal regions, in each region sensor nodes are grouped into clusters with a leading node as cluster-head. The central point of our approach is the selection of cluster-heads which is based on a ratio between the residual energy of each sensor node and the average energy of the region which it belongs. In order to equalize the load and prolong the lifetime of sensors, the cluster-head election probability is computed in each round according to the residual energy of each sensor node. Finally, the simulation results showed that this model had higher throughput and increased the lifetime by 130%, 151%, 167%, 171%, and 215% compared to HCR, ERP, ModLEACH, D-MSEP and DDEEC protocols respectively in the case of 2-level heterogeneity. In case of 3-level heterogeneity, the network lifetime is increased by 123%, 150%, 163% and 218% compared to HCP, ModLEACH, hetSEP and hetDEEC.
Software-defined wireless sensor networking is an emerging networking architecture envisioned to play
a critical role in the looming internet of things paradigm. Since energy is a scarce resource in wireless
sensor networks, many energy-efficient routing algorithms were proposed to enhance the network life-
time. However, most of these algorithms lack network stability and reliability in the presence of dead
nodes. This paper presents ESRA: Energy Soaring-based Routing Algorithm for IoT Applications in
Software-Defined Wireless Sensor Networks, specifically for monitoring environment to address this
shortcoming. The proposed ESRA algorithm efficiently selects the network cluster heads to be considered
for solving the controller placement problem, intending to achieve network reliability and stability and
enhance the network lifetime. The selection of controllers among the cluster heads is formulated as an
NP-hard problem, considering the residual energy of the cluster heads, their spatial distance to the sink,
and their load or density. To tackle this NP-hard problem, genetic algorithm is adopted to optimize the
network lifetime, throughput, latency, and network reliability in the presence of different percentages
of dead nodes. Simulation results showed that ESRA outperforms other three state-of-the art algorithms
in terms of network lifetime and throughput by 15%, 20%, and 25%, in terms of energy savings by 10%,
20%, and 25%, and in terms of delay by 10%, 15%, and 20%. We also applied the proposed scheme on real
networks adopted from the internet topology zoo, which showed promising results compared to other existing works.
The advancement of the Internet of Things (IoT) technologies will play a significant role in the growth of smart cities and industrial applications. Wireless Sensor Network (WSN) is one of the emerging technology utilized for sensing and data transferring processes in IoT-based applications. However, heterogeneous faults like hardware, software, and time-based faults are the major determinants that affect the network stability of IoT based WSN (IWSN) model. In this paper, a novel Energy-Efficient Heterogeneous Fault Management scheme has been proposed to manage these heterogeneous faults in IWSN. Efficient heterogeneous fault detection in the proposed scheme can be achieved by using three novel diagnosis algorithms. The new Tuned Support Vector Machine classifier facilitates to classify the heterogeneous faults where the tuning parameters of the proposed classifier will be optimized through Hierarchy based Grasshopper Optimization Algorithm. Finally, the performance results evident that the diagnosis accuracy of the proposed scheme acquires 99% and the false alarm rate sustains below 1.5% during a higher fault probability rate. The diagnosis accuracy rate is enhanced up to 17% as compared with existing techniques.
The sensors employed in the wireless sensor network are battery-powered which are prepared with a limited energy supply. Most of the applications in the field of wireless sensor network primarily focus on enhancing the network life by incorporating different techniques. The clustering algorithms have proved to be one of the most competent solutions for improving the performance of wireless sensor network. The clustering based approaches in the wireless sensor network manage the network functioning to handle the limited energy in the best possible way to uplift the network lifetime. The surveys in any area can help in providing comprehensive and quick information about that area. Keeping this viewpoint in mind, the intensive study about the existing clustering protocols is presented in this paper. In this survey, the clustering approaches are classified into four categories: homogeneous network-based, heterogeneous network-based, fuzzy logic based and heuristic based protocols. The classification has been performed on the basis of their network organization and techniques utilized to manage the clustering procedures. The various parameters of performance, features, and clustering methodologies are considered for the comparison of all the four categories of protocols to evaluate their competency. The objective, key features, shortcomings, and benefits of different clustering techniques are examined in this survey to provide a deeper insight of the clustering area to the researchers, which can help them in their new journey of research in this domain.
The most crucial design constraint on the Internet of Things (IoT) enabled wireless sensor networks (WSNs) is energy dissipation. The inefficient data collection by the resource constraint sensors becomes a major roadblock in energy preservation to achieve network longevity. The energy of nodes has to be utilized in an efficient manner which helps in increasing the longevity of WSNs. Clustering is a technique that can utilize the energy of the sensors efficiently by maintaining load balancing among the sensors for increasing the lifetime and scalability of the networks. In this paper, the energy consumption of the network is improved by considering the Genetic Algorithm evolutionary computing technique. The proposed OptiGACHS protocol describes the improved cluster head (CH) selection procedure by incorporating criteria of distance, density, energy, and heterogeneous node’s capability for developing fitness function. The OptiGACHS protocol operates with single, multiple static, and multiple movable sinks to have an impartial comparative examination. Multiple movable sinks are proposed to shorten transmission distance between the sink and CH and also pact with the hot-spot problem. A deployment strategy for nodes is also discussed for energy and distance optimization during network operation. It is observed from simulations that the proposed OptiGACHS protocol outperforms existing protocols.
Energy conservation is the primary task in Wireless Sensor Networks (WSNs) as these tiny sensor nodes are the backbone of today’s Internet of Things (IoT) applications. These nodes rely exclusively on battery power to maneuver in hazardous environments. So, there is a requirement to study and design efficient, robust communication protocols to handle the challenges of the WSNs to make the network operational for a long time. Although traditional technologies solve many issues in WSNs, it may not derive an accurate mathematical model for predicting system behavior. So, some challenging tasks like routing, data fusion, localization, and object tracking are handled by low complexity mathematical models to define system behavior. In this paper, an effort has been made to provide a big outlook to the current “researchers” on machine learning techniques that have been employed to handle various issues in WSNs, and special attention has been given to routing problems.
Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.
Wireless Sensor Networks (WSNs) have left an indelible mark on the lives of all by aiding in various sectors such as agriculture, education, manufacturing, monitoring of the environment, etc. Nevertheless, because of the wireless existence, the sensor node batteries cannot be replaced when deployed in a remote or unattended area. Several researches are therefore documented to extend the node's survival time. While cluster-based routing has contributed significantly to address this issue, there is still room for improvement in the choice of the cluster head (CH) by integrating critical parameters. Furthermore, primarily the focus had been on either the selection of CH or the data transmission among the nodes. The meta-heuristic methods are the promising approach to acquire the optimal network performance. In this paper, the 'CH selection' and 'sink mobility-based data transmission', both are optimized through a hybrid approach that consider the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm respectively for each task. The robust behavior of GA helps in the optimized the CH selection, whereas, PSO helps in finding the optimized route for sink mobility. It is observed through the simulation analysis and results statistics that the proposed GAPSO-H (GA and PSO based hybrid) method outperform the state-of-art algorithms at various levels of performance metrics.
Wireless Sensor Networks (WSNs) are emerging as they demands for various applications, for example, military surveillance, home automation, vehicle tracking, environmental monitoring, wildlife tracking, health monitoring, and scientific exploration. Usually, sensor nodes operate with limited battery capacity. Using conventional batteries, it is not always efficient to design long-lasting sensor networks. Moreover, the replacement of the batteries is too challenging to operate in harsh environmental conditions. Therefore, to overcome, one such technique is to recharge the battery of sensor nodes using an energy harvesting system. On the other hand, some of the existing energy harvesting WSNs still lacking the intelligent strategy for judiciously utilizing both the energy management and harvesting system. The review work we present is categorized into energy management and renewable energy harvesting techniques. In energy management techniques, we discuss various methods to save energy consumption of the energy harvesting sensor networks. Notably, we study their protocol design strategies for energy-saving and essential strategies such as prediction for maximizing the energy harvesting of the sensor nodes. We also summarize their shortcomings and ability to deal with the energy harvesting system. In renewable energy harvesting schemes, we present various energy harvesting mechanisms such as solar, wind and others. We also discuss the different energy harvesting mechanisms, especially their protocol design strategies for maximizing energy harvesting, and summarize their merits and demerits. The work also discusses various challenging issues for energy harvesting WSNs followed by future research directions, and some recent applications.
This paper presents a generic model of the energy aware, secure sensing and computing system composed of clusters comprised of static and mobile wireless sensors. The architecture of the modelled system is based on the paradigms of edge and fog computing. The data collected by all sensing devices (edge devices) located in each cluster is preprocessed and stored at the edge closures and routed over the wireless network to a base station, i.e. a gateway of a cluster. The local aggregation, analysis and essential computing tasks are performed by the clusters’ gateways. Finally, the results of these operations are sent through the backbone network to the cloud data center for data fusion, correlation and further computing based on the data gathered from the all sensing clusters. The proposed edge and fog implementation can significantly offload of the cloud data centers and improve the security aspects in data processing. We point out that due to limited computing and energy resources of edge devices effective deployment of sensors and power management are vital design issues that need to be boosted in order to carry out a substantial amount of computation, increase the lifespan of a sensing system and ensure high quality monitoring. We overview various approaches to deployment of sensing devices in a workspace and discuss the issues related to the energy aware and secure communication. The results of the evaluation of the performance of the selected energy conservation techniques through simulation and experiments conducted in testbed networks are presented and discussed.
A constraint oriented wireless sensor network (WSN) faces a challenging task of handling different issues i.e., routing mechanism, coverage of the underlined sensing fields for as long as possible, energy efficiency, reliable transmission of packets, congestion controls etc. Load balancing or routing protocols, which are specifically designed for the resource limited sensor networks, resolve some of these issues. However, most of these mechanisms were either applications specific or overlay complex. Moreover, these techniques underestimated an important aspect of WSNs i.e., sensor nodes criticality or vulnerability factor that is defined as the importance of a node from the network connectivity perspective. In this paper, an efficient load balancing and performance optimization scheme (LBS) is presented to address these issues such as throughput, long term connectivity, end to end delay and energy efficiency in resource limited WSNs. The proposed LBS technique conflates a sensor node criticality factor, residual energy and hop-count to form an optimized load balancing strategy. Initially, the proposed LBS routes most of the load or traffic on the shortest paths by assigning a highest weight-age factor, 80%, to the hop count metric and lower weight-age to both criticality and residual energy metrics i.e., 20%. Load balancing strategy is revised if one or more nodes deplete 50% of their on-board batteries. Simulation results verify that the proposed LBS scheme outperforms the existing field proven approaches i.e., shortest path, vulnerability aware routing, energy based spreading and multiple path based load balancing schemes.
Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Due to some external or internal factors, WSN may change dynamically and therefore it requires depreciating dispensable redesign of the network. The traditional WSN approaches have been explicitly programmed which make the networks hard to respond dynamically. To overcome such scenarios, machine learning (ML) techniques can be applied to react accordingly. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The survey of the ML techniques for WSNs is presented in [1], covering period of 2002 – 2013. In this survey, we present various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018. In addition, we also discuss ML algorithms for synchronization, congestion control, mobile sink scheduling and energy harvesting. Finally, we present a statistical analysis of the survey, the reasons for selection of a particular ML techniques to address a issue in WSNs followed by some discussion on the open issues.
Wireless sensor networks have been employed widely in various fields, including military, health care, and manufacturing applications. However, the sensor nodes are limited in terms of their energy supply, storage capability, and computational power. Thus, in order to improve the energy efficiency and prolong the network life cycle, we present a genetic algorithm-based energy-efficient clustering and routing approach GECR. We add the optimal solution obtained in the previous network round to the initial population for the current round, thereby improving the search efficiency. In addition, the clustering and routing scheme are combined into a single chromosome to calculate the total energy consumption. We construct the fitness function directly based on the total energy consumption thereby improving the energy efficiency. Moreover, load balancing is considered when constructing the fitness function. Thus, the energy consumption among the nodes can be balanced. The experimental results demonstrated that the GECR performed better than other five methods. The GECR achieved the best load balancing with the lowest variances in the loads on the cluster heads under different scenarios. In addition, the GECR was the most energy-efficient with the lowest average energy consumed by the cluster heads and the lowest energy consumed by all the nodes.
A wireless sensor network (WSN) consists of a group of energy-constrained sensor nodes with the ability of both sensing and communication, which can be deployed in a field of interest (FoI) for detecting or monitoring some special events, and then forwarding the aggregated data to the designated data center through sink nodes or gateways. In this case, whether the WSN can keep the FoI under strict surveillance and whether the WSN can gather and forward the desired information are two of the most fundamental problems in wireless sensor networks that need to be solved. Therefore, preserving network connectivity while maximizing coverage by using the limited number of energy constrained nodes is the most critical problem for the deployment of WSNs. In this survey article, we classify and summarize the state-of-the-art algorithms and techniques that address the connectivity-coverage issues in the wireless sensor networks.
The Internet of Things (IoT) is heavily affecting our daily lives in many domains, ranging from tiny wearable devices to large industrial systems. Consequently, a wide variety of IoT applications have been developed and deployed using different IoT frameworks. An IoT framework is a set of guiding rules, protocols, and standards which simplify the implementation of IoT applications. The success of these applications mainly depends on the ecosystem characteristics of the IoT framework, with the emphasis on the security mechanisms employed in it, where issues related to security and privacy are pivotal. In this paper, we survey the security of the main IoT frameworks, a total of 8 frameworks are considered. For each framework, we clarify the proposed architecture, the essentials of developing third-party smart apps, the compatible hardware, and the security features. Comparing security architectures shows that the same standards used for securing communications, whereas different methodologies followed for providing other security properties.
A Wireless Sensor Network (WSN) is an aggregation of sensor nodes which are remotely deployed in large numbers, operate autonomously in an unattended environment and have limited energy resource. In most of the hierarchical routing protocols, the cluster head (CH) selection is on the basis of random probability equation. There is a scope to reduce the energy dissipation by improving CH selection procedure. The proposed scheme, coined as GADA-LEACH, makes use of evolutionary genetic algorithm for improving CH selection in legacy LEACH routing protocol in sensor networks. The concept of relay node is introduced which acts as an intermediary between CH and base station (BS) to ease the communication between the CH and BS. The simulation results obtained supports that our proposed algorithm is efficient in terms of network lifetime.
The importance and the possibilities of wireless sensor networks (WSNs) are now quite clear, because they can be found in all kinds of applications, from those in our daily life to those in the military. However, the limited energy of sensors, i.e., the lifetime problems of WSN, has attracted many researchers from different disciplines. Several recent studies showed that metaheuristic algorithms provide promising solutions to the lifetime problems. This paper begins with a brief review of the lifetime problems and the basic ideas of metaheuristic algorithms. Then, the detailed descriptions of metaheuristic algorithms for solving the lifetime problems from the perspectives of problems and algorithms are given. Some simple examples for illustrating how metaheuristic algorithms can be used to solve the lifetime problems and their performance are given. Several important open and possible research issues are discussed to provide the future research trends of this area.
In cluster analysis, a fundamental problem is to determine the best estimate of the number of clusters; this is known as the automatic clustering problem. Because of lack of prior domain knowledge, it is difficult to choose an appropriate number of clusters, especially when the data have many dimensions, when clusters differ widely in shape, size, and density, and when overlapping exists among groups. In the late 1990s, the automatic clustering problem gave rise to a new era in cluster analysis with the application of nature-inspired metaheuristics. Since then, researchers have developed several new algorithms in this field. This paper presents an up-to-date review of all major nature-inspired metaheuristic algorithms used thus far for automatic clustering. Also, the main components involved during the formulation of metaheuristics for automatic clustering are presented, such as encoding schemes, validity indices, and proximity measures. A total of 65 automatic clustering approaches are reviewed, which are based on single-solution, single-objective, and multiobjective metaheuristics, whose usage percentages are 3%, 69%, and 28%, respectively. Single-objective clustering algorithms are adequate to efficiently group linearly separable clusters. However, a strong tendency in using multiobjective algorithms is found nowadays to address non-linearly separable problems. Finally, a discussion and some emerging research directions are presented.
Wireless Body Area networks (WBANs) have developed as an effective solution for a wide range of healthcare military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection prototype system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtualized Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.
The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.
Wireless Sensor Networks (WSNs) consist of large number of randomly
deployed energy constrained sensor nodes. Sensor nodes have ability to
sense and send sensed data to Base Station (BS). Sensing as well as
transmitting data towards BS require high energy. In WSNs, saving energy
and extending network lifetime are great challenges. Clustering is a key
technique used to optimize energy consumption in WSNs. In this paper, we
propose a novel clustering based routing technique: Enhanced Developed
Distributed Energy Efficient Clustering scheme (EDDEEC) for
heterogeneous WSNs. Our technique is based on changing dynamically and
with more efficiency the Cluster Head (CH) election probability.
Simulation results show that our proposed protocol achieves longer
lifetime, stability period and more effective messages to BS than
Distributed Energy Efficient Clustering (DEEC), Developed DEEC (DDEEC)
and Enhanced DEEC (EDEEC) in heterogeneous environments.
Wireless sensor network (WSN) is a rapidly evolving technological platform with tremendous and novel applications. Recent advances in WSN have led to many new protocols specifically designed for them where energy awareness (i.e. long lived wireless network) is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. As routing approach with hierarchical structure is realized to successfully provide energy efficient solution, various heuristic clustering algorithms have been proposed. As an attractive WSN routing protocol, LEACH has been widely accepted for its energy efficiency and simplicity. Also, the discipline of meta-heuristics Evolutionary Algorithms (EAs) has been utilized by several researchers to tackle cluster-based routing problem in WSN. These biologically inspired routing mechanisms, e.g., HCR, have proved beneficial in prolonging the WSN lifetime, but unfortunately at the expense of decreasing the stability period of WSN. This is most probably due to the abstract modeling of the EA's clustering fitness function. The aim of this paper is to alleviate the undesirable behavior of the EA when dealing with clustered routing problem in WSN by formulating a new fitness function that incorporates two clustering aspects, viz. cohesion and separation error. Simulation over 20 random heterogeneous WSNs shows that our evolutionary based clustered routing protocol (ERP) always prolongs the network lifetime, preserves more energy as compared to the results obtained using the current heuristics such as LEACH, SEP, and HCR protocols. Additionally, we found that ERP outperforms LEACH and HCR in prolonging the stability period, comparable to SEP performance for heterogeneous networks with 10% extra heterogeneity but requires further heterogeneous-aware modification in the presence of 20% of node heterogeneity.
This paper presents an overview of the potential obstacles and challenges related to research topics such as IoT, DfPL WSNs (Wireless Sensor Networks) in IoT and disaster management using WSNs. This review will analyse key aspects of deploying a DfPL WSN in IoT scenario for disaster management. In an IoT scenario the DfPL WSN is only collecting raw data that is forwarded to the Internet using a Compressed Sensing (CS) IoT framework or other solutions including data compression. Compressed Sensing (CS) refers to a method used to reduce the number of samples collected in an IoT WSN. Thus it is possible to create stand-alone applications that require fewer resources. There is no need to process the data in the WSN as this can be done in the Data Analysis Network, after the data is reconstructed. This will enable a reduced volume of data transmitted and lower power consumption for battery-operated nodes. The detection of people in a disaster scenario who are simply moving and not in the pos-session of a 'tracking device' is revolutionary. The aim here is to build upon our patent-pended technology in order to deliver a robust field-trial ready human detection system for disaster situ-ations.
