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"The demand for a Wireless Sensor Network (WSN) has increased enormously because of its great ability to supervise the outside world as well as due to its vast range of applications. Since these sensor nodes depend greatly on battery power and being deployed in adverse environments, substituting the battery is a tiresome job. Cluster-based routing techniques are prominent methods to extend the lifetime of wireless sensor networks. In this research, the work on energy efficient clustering approach is considered in two phases. During the cluster head selection phase, cluster heads are chosen which can stabilize the power consumption in sensor networks, by considering both the residual energy and distance of node with respect to sink. Later, during the cluster formation phase, a non-cluster head node will choose a cluster head that lies in close proximity with the center point between the sensor nodes and sink. Also, these non-cluster head nodes should be within the transmission range of the cluster head, as selected by the above method. Initially, the Low Energy Adaptive Clustering Hierarchy (LEACH) which is an eminent protocol for sensor networks is investigated. Furthermore, the same LEACH protocol is enhanced by proposing an effective cluster head election scheme as well as a new cluster formation scheme as mentioned above. Simulation results reveal that the proposed algorithm outperforms the traditional LEACH protocol in prolonging network lifetime."

"The low-cost Wireless Sensor Network (WSN) consists of small battery powered devices called sensors, with limited energy capacity. Once deployed, accessibility to any sensor node for maintenance and battery replacement is not feasible due to the spatial scattering of the nodes. This will lead to an unreliable, limited lifetime and a poor connectivity network. In this paper a novel bio-inspired cluster-based deployment algorithm is proposed for energy optimization of the WSN and ultimately to improve the network lifetime. In the cluster initialization phase, a single cluster is formed with a single cluster head at the center of the sensing terrain. The second phase is for optimum cluster formation surrounding the inner cluster, based on swarming bees and a piping technique. Each cluster member distributes its data to its corresponding cluster head and the cluster head communicates with the base station, which reduces the communication distance of each node. The simulation results show that, when compared with other clustering algorithms, the proposed algorithm can significantly reduce the number of clusters by 38% and improve the network lifetime by a factor of 1/4."

"Wireless Sensor Network (WSN) technology has gained importance in recent years due to its various benefits, practicability and extensive utilization in diverse applications. The innovation helps to make real-time automation, monitoring, detecting and tracking much easier and more effective than previous technologies. However, as well as their benefits and enormous potential, WSNs are vulnerable to cyber-attacks. This paper is a systematic literature review of the security-related threats and vulnerabilities in WSNs. We review the safety of and threats to each WSN communication layer and then highlight the importance of trust and reputation, and the features related to these, to address the safety vulnerabilities. Finally, we highlight the open research areas which need to be addressed in WSNs to increase their flexibility against security threats."

"Wireless sensor network (WSN) adalah sekelompok node sensor yang mengambil data dengan parameter pengukuran tertentu dan kemudian mengirim data secara nirkabel ke node pusat atau server untuk pemrosesan data. Salah satu hal yang paling penting dalam WSN adalah umur dari jaringan. Node sensor dalam WSN dirancang agar memiliki bentuk yang kecil sehingga mudah untuk dipindahkan, diganti dan rendah biaya produksi. Untuk mendukung desain tersebut maka energi dari node sensor dalam WSN biasanya tergantung baterai yang terintegrasi. Sehingga umur dari WSN tergantung pada kapasitas baterai dari masing-masing node sensor. Node sensor harus bekerja seefisien mungkin agar penggunaan energi dapat lebih hemat sehingga umur WSN bisa lebih panjang. Semakin lama umur WSN, maka diharapkan semakin tinggi total data atau semakin banyak data yang dikirimkan. Pada proses transmisi data, node sensor mengkonsumsi lebih banyak energi daripada saat melakukan proses data; Oleh karena itu, WSN perlu menggunakan metode routing untuk menghemat energi pada saat proses transmisi. Salah satu dari banyak cara untuk memperpanjang umur WSN adalah dengan menggunakan konsep pengelompokan node atau disebut dengan metode klaster. Salah satu protokol routing yang menggunakan konsep klaster adalah LEACH. Penelitian ini mengusulkan metode routing DivNCGL yang dimodifikasi berdasarkan LEACH dengan cara mendistribusikan cluster heads (CHs) untuk mencegah terpilihnya CH yang berdekatan. Proses distribusi dilakukan dengan cara membagi luas area sebaran node diawal proses set-up menjadi subregion. Proses pembagian subregion menggunakan nilai probabilitas yang didapatkan dengan metode non-cooperative game berdasarkan sisa node aktif, nilai energi total yang tersisa, dan energi yang dibutuhkan untuk melakukan transmisi. Parameter evalusi dilihat dari umur jaringan, total disipasi energi dan total data terkirim. Perbandingan simulasi akan melihat dari 3 metode yaitu LEACH, LEACH dengan probabilitas non-cooperative game (NCGL), dan DivNCGL. Dari hasil simulasi, umur WSN menggunakan metode DivNCGL meningkat lebih dari 30% dengan disipasi energi yang stabil dibandingkan dengan protokol LEACH. Peningkatan umur WSN akan meningkatkan jumlah data yang dikirimkan dan data yang diterima dengan menggunakan metode DivNCGL meningkat mencapai 70% dibandingkan dengan LEACH.

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A wireless sensor network (WSN) is a group of sensor nodes that take data with specific measurement parameters and then send the data wirelessly to a central node or server for data processing. One of the most critical things in WSN is the age of the network. The sensor nodes in WSN are designed to be compact, so they are easy to move or replace, with low production costs. To support the compact design of sensor nodes, the energy from the sensor nodes in the WSN usually depends on the integrated battery. So the lifespan of the WSN depends on the battery capacity of each sensor node. The sensor node must work as efficiently as possible so that energy use can be more efficient so that the WSN life can be longer. The longer the lifespan of the WSN, it is expected for higher total data sent. In the data transmission process, the sensor node consumes more energy than when processing data; Therefore, WSN needs to use a routing method to save energy during the transmission process. One of the many ways to extend the life of the WSN is to use the concept of grouping nodes or the so-called cluster method. One of the routing protocols that use the cluster concept is LEACH. This study proposes the DivNCGL routing method, a modified method based on LEACH by distributing cluster heads (CHs) to prevent the selection of adjacent CH. The distribution process is carried out by dividing the distribution area of ​​the nodes at the beginning of the setup process into subregions. The subregion division process uses the probability value obtained by the non-cooperative game method based on the remaining active nodes, the total remaining energy, and the energy required for transmission. Parameter evaluation is seen from network lifetime, total energy dissipation, and total data sent. The simulation comparison will look at 3 methods, namely LEACH, LEACH with probability non-cooperative game (NCGL), and DivNCGL. From the simulation results, the lifespan of WSN using the DivNCGL method increased up to 30% with stable energy dissipation compared to the LEACH protocol. The increasing lifespan of the WSN also increases the amount of data transmitted. Using the DivNCGL method, the received data increased up to 70% compared to LEACH."

"Wireless Sensor Network (WSN) adalah jaringan yang terdiri dari kumpulan node sensor berukuran kecil yang berfungsi untuk memantau dan mengirimkan data ke base station (BS). WSN menawarkan berbagai keunggulan seperti biaya yang rendah dan instalasi yang mudah. Namun, keterbatasan energi pada setiap node sensor menyebabkan masa operasionalnya menjadi terbatas. Keterbatasan ini semakin signifikan pada aplikasi di lingkungan yang sulit dijangkau karena penggantian atau pengisian ulang energi tidak dapat dilakukan dengan mudah. Oleh karena itu, dibutuhkan efisiensi energi untuk memastikan WSN dapat beroperasi secara optimal dalam jangka waktu yang lama. Penelitian ini mengusulkan protokol clustering dan routing menggunakan algoritma tuna swarm optimization dan hybrid multi-hop routing (TSO-HMR) untuk mengoptimalkan efisiensi energi dan memperpanjang network lifetime. Algoritma tuna swarm optimization (TSO) digunakan untuk mengoptimalkan klaster jaringan dengan mengevaluasi rata-rata dan standar deviasi jarak antara node sensor dan pusat klasternya sehingga jarak komunikasi dalam klaster berkurang dan konsumsi energi menjadi lebih efisien. Sementara itu, pemilihan cluster head (CH) dilakukan secara adaptif dengan mengevaluasi energi residu node sensor, jarak ke base station (BS), dan jarak node sensor dalam klaster. Skema hybrid multi-hop routing dirancang untuk memilih jalur komunikasi yang optimal untuk mengurangi energi yang dikonsumsi dalam proses komunikasi antara CH dan BS dengan CH dapat mengirimkan data langsung ke BS atau melalui CH lain yang bertindak sebagai relay dengan memenuhi persyaratan seperi energi residu tersisa, jarak antara relay dengan BS dan CH, serta jarak antara CH dengan BS. Hasil simulasi menunjukkan bahwa TSO-HMR meningkatkan efisiensi energi secara signifikan, yaitu dengan meningkatnya network lifetime sebesar 94.1% dibandingkan penelitian sebelumnya, yaitu tuna swarm optimization and fuzzy logic control (TSFC). Penelitian ini menunjukkan potensi TSO-HMR sebagai solusi yang andal untuk mengatasi keterbatasan energi dalam WSN, khususnya pada aplikasi yang membutuhkan kinerja jaringan dengan masa operasional yang panjang.

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Wireless Sensor Network (WSN) consists of numerous small-scale sensor nodes deployed to monitor environmental parameters and transmit data to a base station (BS). Despite their advantages, including cost-effectiveness and ease of deployment, WSNs face a critical limitation in the form of constrained energy resources at each sensor node, which significantly affects the operational lifetime of the network. This limitation becomes particularly challenging in inaccessible environments, where replacing or recharging batteries is impractical. Consequently, optimizing energy consumption is paramount to ensure prolonged and reliable network operation. This study introduces a novel clustering and routing protocol, termed tuna swarm optimization and hybrid multi-hop routing (TSO-HMR), designed to enhance energy efficiency and extend network lifetime. The tuna swarm optimization (TSO) algorithm is employed to optimize cluster formation by minimizing the average and standard deviation of distances between nodes and their respective cluster centers. This approach improves the compactness of cluster structures, reducing intra-cluster communication distances and energy consumption. Furthermore, the selection of cluster heads (CHs) is performed adaptively using a fitness function that evaluates three critical parameters: the residual energy of sensor nodes, their distances to the BS, and the intra-cluster distances. To further reduce energy consumption during data transmission, a hybrid multi-hop routing scheme is incorporated. This scheme enables CHs to transmit data either directly to the BS or through other CHs acting as relays. The relay selection process is governed by multiple criteria, including residual energy, the distance between the relay and both the BS and transmitting CH, as well as the distance between the transmitting CH and the BS. This dual-mode routing mechanism ensures optimal energy utilization across the network. Simulation results demonstrate that the TSO-HMR protocol achieves substantial improvements in energy efficiency, with a 94.1% increase in network lifetime compared to the baseline protocol, tuna swarm optimization and fuzzy logic control (TSFC). This research highlights the potential of TSO-HMR as a robust solution for addressing energy constraints in WSNs, particularly in scenarios demanding prolonged operational performance. "

"k Berbahasa Indonesia/Berbahasa Lain (Selain Bahasa Inggris): Wireless Sensor Networks (WSN) terdiri dari node sensor yang tersebar dalam area monitoring untuk mengumpulkan dan mengirimkan data. Namun, keterbatasan energi pada node sensor menjadi tantangan utama dalam memperpanjang network lifetime. Ketergantungan pada baterai sebagai sumber daya menyebabkan node sensor mudah mengalami kehabisan energi, yang pada akhirnya memengaruhi kinerja jaringan. Oleh karena itu, diperlukan pendekatan yang efektif untuk mengurangi konsumsi energi dan menyeimbangkan distribusi beban dalam jaringan sensor. Penelitian ini mengusulkan protokol Single-Hop Clustering Routing berbasis Tuna Swarm Optimization (TSO) dan Gravitational Search Algorithm (GSA). Algoritma TSO digunakan untuk membentuk cluster secara optimal dengan meminimalkan jarak komunikasi antar node dalam cluster, sedangkan GSA diterapkan untuk memilih cluster head (CH) berdasarkan faktor-faktor seperti residual energy, jarak ke base station (BS), dan jarak intracluster. Hasil simulasi menunjukkan bahwa algoritma TSO-GSA mampu meningkatkan efisiensi energi dan memperpanjang lifetime jaringan secara signifikan dibandingkan protokol referensi TSO-FC. Pada skenario utama dengan 100 node dalam area 150 × 150 m², TSO-GSA mencatat peningkatan lifetime jaringan sebesar 24.03% pada LND (1615 round dibandingkan 1302 round oleh TSO-FC). Namun, sebagai trade-off, FND pada TSO-GSA terjadi lebih awal, yaitu pada round ke-444 dibandingkan round ke-902 pada TSO-FC. Di sisi lain, protokol ini mampu mempertahankan energi residu yang lebih tinggi, mencapai peningkatan sebesar 17,22% pada round ke-800 dibandingkan TSO-FC. Selain itu, algoritma TSO-GSA menunjukkan adaptabilitas yang baik terhadap perubahan kompleksitas jaringan, dengan peningkatan LND sebesar 26,7% dalam lingkungan dengan 200 node. Dalam hal performa komunikasi, protokol ini juga meningkatkan jumlah total data yang diterima BS sebesar 12,5% dibandingkan TSO-FC. Dengan keunggulan-keunggulan tersebut, penelitian ini menyimpulkan bahwa protokol TSO-GSA dapat menjadi solusi efektif untuk mengoptimalkan efisiensi energi dan memperpanjang lifetime jaringan dalam WSN.

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Wireless Sensor Networks (WSN) consist of sensor nodes distributed in a monitoring area to collect and transmit data. However, the limited energy of sensor nodes is a major challenge in extending network lifetime. Dependence on batteries as a power source causes sensor nodes to easily run out of energy, which ultimately affects network performance. Therefore, an effective approach is needed to reduce energy consumption and balance load distribution in sensor networks. This research proposes a Single-Hop Clustering Routing protocol based on Tuna Swarm Optimization (TSO) and Gravitational Search Algorithm (GSA). The TSO algorithm is used to form clusters optimally by minimizing the communication distance between nodes in the cluster, while GSA is applied to select the cluster head (CH) based on factors such as residual energy, distance to the base station (BS), and intracluster distance. Simulation results show that the TSO-GSA algorithm is able to significantly improve energy efficiency and extend network lifetime compared to the reference protocol TSO-FC. In the main scenario with 100 nodes in a 150 × 150 m² area, TSO-GSA recorded a 24.03% increase in network lifetime on LND (1615 rounds compared to 1302 rounds by TSO-FC). However, as a trade-off, FND in TSO-GSA occurs earlier, at 444th round compared to 902nd round in TSO-FC. On the other hand, this protocol is able to maintain higher residual energy, achieving an improvement of 17.22% in the 800th round compared to TSO-FC. Moreover, the TSO-GSA algorithm shows good adaptability to changes in network complexity, with a 26.7% improvement in LND in a 200-node environment. In terms of communication performance, the protocol also increases the total amount of data received by the BS by 12.5% compared to TSO-FC. With these advantages, this study concludes that the TSO-GSA protocol can be an effective solution to optimize energy efficiency and extend network lifetime in WSNs. "

"WSN merupakan jaringan sensor berskala besar dengan sumber daya yang terbatas. WSN digunakan pada aplikasi yang bersifat spesifik seperti untuk militer, survei, industri sampai pada pemakaian umum. Masalah yang dihadapi WSN adalah keterbatasan sumber daya dan jaringan yang selalu berubah sehingga membutuhkan algoritma yang tepat."

"This book offers an essential guide to Wireless Sensor Networks, IoT Security, Image Processing, Secure Information Systems, and Data Encryption. In addition, it introduces students and aspiring practitioners to the subject of destination marketing in a structured manner. It is chiefly intended for researcher students in the areas of Wireless Sensor Networks and Secure Data Communication (including image encryption, and intrusion detection systems), academics at universities and colleges, IT professionals, policymakers and legislators.Given its content, the book can be used as a reference text for both undergraduate and graduate studies, in courses on Wireless Sensor Networks, Secure Image Processing, and Data Encryption applications. The book is written in plain and easy-to-follow language and explains each main concept the first time it appears, helping readers with no prior background in the field. As such, it is a “must-read” guide to the subject matter."

"Game theory merupakan algoritma yang mencari solusi permasalahan dengan menganalogikan masalah seperti permainan, kemudian diselesaikan dengan pendekatan matematis. Varian karakter dalam Game theory sangat beragam, dan pada perkembangannya selain diimplementasikan pada bidang sosial ekonomi, ternyata dapat diimplementasikan pada jaringan nirkabel. Pada penelitian ini Game theory dikembangkan dengan diimplementasikan pada jaringan sensor nirkabel sebagai metode optimasi . Jaringan sensor nirkabel sebagai pendukung sistem monitor membutuhkan dukungan jaringan yang andal dan stabil. Kondisi ini sangat membutuhkan ketersediaan energi. Karena keterbatasan ketersediaan energi maka semua proses bisnis jaringan harus dilakukan secara efektif dan efisien. Hal yang dilakukan adalah optimasi dengan pengelolaan jaringan yang baik. Optimasi yang dilakukan pada penelitian ini terkait dengan proses lokalisasi berbasis pengklasteran. Alasan pemilihan metode ini karena pengklasteran umumnya tidak memperhatikan permasalahan gangguan lingkungan terhadap sinyal, sementara lokalisasi adalah penempatan node dengan metode memanfaatkan informasi kekuatan sinyal yang diterima. Lokalisasi yang memanfaatkan informasi pengklasteran berupa konfigurasi sensor node berbasis ketersediaan energi, membantu proses pelacakan sensor node, karena dimulai dengan cluster head dengan tingkat ketersediaan energi yang lebih tinggi dibanding sensor node. Oleh karena itu, pada langkah selanjutnya, node yang dilacak dapat menjadi node referensi untuk node lain yang tidak diketahui. Game theory sebagai algoritma optimasi akan membantu menentukan koalisi anchor node. Pemilihan node sebagai anggota koalisi menggabungkan pendekatan geometris dengan Game theory. Konsep yang diusulkan ini akan divalidasi menggunakan simulator yang dibangun di atas platform Matlab. Akurasi adalah salah satu indikator kinerja lokalisasi, dan Root Mean Square Error (RMSE) dipilih sebagai parameter pengukuran untuk menunjukkan tingkat akurasi. Hasil simulasi menunjukkan bahwa jumlah sensor node mati dapat ditunda sekitar 1000 siklus jika dilakukan lokalisasi dengan Game theory. Hasil eksperimen menunjukkan bahwa kinerja jaringan cenderung meningkat setelah proses lokalisasi berbasis pengklasteran. Hal ini diindikasikan dengan peningkatan jumlah paket data yang dikirim dan masa hidup sensor node yang lebih lama. Hasil simulasi memvalidasi bahwa pada skenario pengujian pengiriman data terjadi peningkatan paket data yang dikirim sekitar 20%. Lokalisasi sendiri dalam implementasinya dapat mengalami kendala berupa interferensi . Interferensi sinyal radio dapat mengurangi akurasi hasil lokalisasi target node. Kondisi ini dapat mempengaruhi kualitas informasi yang diambil oleh sensor node terutama ketika jaringan mendukung sistem penginderaan jauh atau pemantauan yang nilai informasi sangat krusial. Gangguan ini timbul karena transmisi terjadi secara simultan pada suatu kanal transmisi, atau karena letak sensor yang kurang tepat. Oleh karena itu perlu dijadwalkan transmisi selama lokalisasi dan mengatur posisi anchor node. Pengaturan jadwal transmisi direpresentasikan dalam komputasi probabilitas transmisi node menggunakan pendekatan Game theory dengan mempertimbangkan energi sisa. Probabilitas transmisi untuk menghindari interferensi membentuk skema penjadwalan transmisi node. Komunikasi data yang lancar akan membuat kualitas proses lokalisasi baik. Hal ini ditandai dengan nilai RMSE yang rendah. Berdasarkan hasil eksperimen, probabilitas keberhasilan transmisi meningkat sekitar 20%. Terdapat korelasi yang kuat antara realibilitas dan parameter throughput.

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Game theory is an algorithm that overcomes problems by analogizing games and then solving them with a mathematical approach. Variants of characters in Game theory are very diverse, and in their development, apart from being implemented in the socio-economic field, it can actually be implemented in wireless networks. In this study, Game theory was developed and implemented on a wireless sensor network as an optimization method. Wireless sensor network as a monitoring system support requires reliable and stable network support. This condition really requires the availability of energy. Due to the limited energy availability, all network business processes must be carried out effectively and efficiently. What is done is optimization with good network management. The optimization carried out in this study is related to the clustering-based localization process. The reason for choosing this method is that clustering generally does not pay attention to environmental interference problems to the signal. Localization is the placement of nodes by utilizing the received signal strength information. Localization that utilizes clustering information in sensor node configurations based on energy availability helps the sensor node tracking process. This is because it starts with the cluster head with a higher energy availability level than the sensor node. Therefore, the tracked node can be a reference node for other unknown nodes in the next step. Game theory as an optimization algorithm will help determine the anchor node coalition. The selection of nodes as coalition members combines a geometric approach with Game theory. This proposed concept will be validated using a simulator built on the Matlab platform. Accuracy is an indicator of localization performance, and Root Mean Square Error (RMSE) was chosen as a measurement parameter to indicate the level of accuracy. The simulation results show that the number of dead sensor nodes can be delayed by approximate 1000 cycles if localization is carried out with Game theory. The experiment results show that network performance tends to increase after the clustering-based localization process. This is indicated by an increase in the number of data packets sent and a longer sensor node lifetime. The simulation results validate that there is an increase in data packets sent by about 20% in the data delivery test scenario. Localization itself in its implementation can experience obstacles in the form of interference. Radio signal interference can reduce the accuracy of the target node localization results. This condition can affect the quality of information retrieved by sensor nodes. This happens especially when the network supports remote sensing or monitoring systems where information is valuable. This disturbance arises because the transmission coincides on a transmission channel or the sensor is not located correctly. Therefore, it is necessary to schedule transmissions during localization and set anchor node positions. The transmission schedule arrangement is represented in the computation of the node transmission probability using a Game theory approach by considering the residual energy. The transmission probability of avoiding interference forms a node transmission scheduling scheme. Smooth data communication will make the quality of the localization process good. A low RMSE value characterizes it. Our experiments show that the probability of successful transmission increases by 20%, as shown by the graph. "

"Efisiensi energi dan stream data mining pada Wireless Sensor Networks (WSN) adalah masalah yang sangat menarik untuk dibahas. Teknologi Routing Protocol dan Resource-Aware dapat dilakukan untuk meningkatkan efisiensi energi. Dalam penelitian ini peneliti mencoba untuk menggabungkan teknologi Routing Protocol menggunakan routing Distance Vector dan Resource-Aware (RA) framework pada Wireless Sensor Networks heterogen dengan menggabungkan sun-SPOT dan platform Imote2 Wireless Sensor Networks. RA melakukan proses pemantauan sumber daya dari memori, baterai, dan beban CPU lebih optimal dan efisien. Proses ini menggunakan Light-Weight Clustering (LWC) dan Light Weight Frequent Item (LWF). Hasil yang diperoleh bahwa dengan mengadaptasi Resource-Aware dalam Wireless Sensor Networks, masa pakai wireless sensor meningkatkan sampai ± 16,62%.

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AbstractEfficiency energy and stream data mining on Wireless Sensor Networks (WSNs) are a very interesting issue to be discussed. Routing protocols technology and resource-aware can be done to improve energy efficiency. In this paper we try to merge routing protocol technology using routing Distance Vector and Resource-Aware (RA) framework on heterogeneity wireless sensor networks by combining sun-SPOT and Imote2 platform wireless sensor networks. RA perform resource monitoring process of the battery, memory and CPU load more optimally and efficiently. The process uses Light-Weight Clustering (LWC) and Light Weight Frequent Item (LWF). The results obtained that by adapting Resource-Aware in wireless sensor networks, the lifetime of wireless sensor improve up to ± 16.62%."