Energy Constrained Reliable Routing Optimized Cluster Head Protocol for Multihop under Water Acoustic Sensor Networks

Underwater acoustic sensor networks (UWASNs) is an emerging technology, comprising of sensor nodes and unattended automated vehicles (AUVs), all working in a collaboration to sense various phenomenon, process digital information, store processed data and communicate among each other and base stations. Acoustic propagation in water is characterized by high and variable delays, fading effect, Doppler spread and multi path which in turn lead to a limited bandwidth and high error rates. Also, battery life and storage capacity of nodes is limited. So there is a need of suitable routing protocol that takes all these limitations into consideration and makes communication in underwater networks viable. This is the first attempt to analyze the performance of ad-hoc routing protocols in underwater acoustic network environment. We used performance metrics like packet delivery ratio, average end-to-end delay, throughput, routing overhead and energy consumption of the sensor nodes. AODV, DSDV, DSR and OLSR are compared for their performance at different traffic conditions, number of nodes and depths. By analyzing our simulation results, we found that among the discussed protocols, AODV may be used for denser networks but with less traffic and DSDV is suitable for higher traffic conditions with optimal number of nodes.

The properties of underwater acoustic channel are quite different from those of terrestrial wireless sensor networks, which brings many challenges to design an efficient media access control (MAC) protocol for underwater acoustic sensor networks (UWASNs). In order to overcome the long propagation delay and the funneling effect of UWASNs, a MAC protocol, named path-oriented code assignment (POCA) CDMA MAC (POCA-CDMA-MAC) protocol, is proposed in the paper. In the proposed protocol, a round-robin method and the CDMA technology are used to make the sink receive packets from multiple neighbors at the same time. Unlike other CDMA-based protocols using the transmitter-oriented code assignment (TOCA) or the receiver-oriented code assignment (ROCA), the POCA is used in the CDMA technology. Since the number of the path is much less than the number of nodes, the length of the spreading code used in systems with POCA is greatly shorter than those of the spreading code used in systems with TOCA or ROCA. Simulation results show that compared to existing MAC protocols for UWASNs, the proposed MAC protocol achieves a higher throughput, and a lower end-to-end delay.

Attack Simulation Model and Channel Statistics in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks are widely used for underwater information collection, environmental monitoring and assisted navigation. For energy limited underwater sensor nodes, the design of energy efficient protocols is a key problem in underwater acoustic sensor networks. In this paper, we propose two novel cluster formulation protocols for the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) in order to achieve further energy savings. The proposed cluster formulation protocols can significantly reduce the transmission collision probability during the cluster formulation process for underwater environments. Simulation results indicate that the proposed protocols outperform the standard LEACH protocol in terms of the network lifetime and the number of alive nodes. The proposed cluster formulation protocols can also be used with other modified LEACH protocols.

UASN (Underwater Acoustic Sensor Network) has intrinsic impediments, since it is utilized and utilizes acoustic signs to impart in the sea-going world. Examples include long delays in propagation, limited bandwidth, high transmitting energy costs, very high attenuation in the signal, expensive implementation and battery replacement etc. The UASN routing schemes must therefore take account of these features to achieve balance energy, prevent void hole and boost network life. One of the significant issue in routing is the presence of void node. A void node is a node that does not have any forwarder node. The presence of void may cause the bundle conveyance in the steering time which prompts information misfortune. The gap during steering influences the network execution regarding proliferation delay, vitality utilization and network lifetime, and so forth. So with the objective to remove the void node in the networks, this work presents an energy efficient optimal path routing for void avoidance in underwater acoustic sensor networks. This work uses the concept of gray wolf optimization algorithm to calculate the fitness function and that fitness function is used to select the best forwarder node in the networks. This work only consider the vertical directions which further reduces the end to end delay. The proposed work has been simulated on MATLAB and performances are evaluated in terms of broadcast copies of data, energy tax, and packet delivery ratio, number of dead nodes, network lifetime and delay.

Like wireless sensor networks, lifetime of sensors is the main constraint for performance of underwater acoustic sensor networks (UASNs). Most previous works on UASNs did not consider dynamics of networks, i.e., as time goes by, in practice, part of sensors may be malfunctioned, deplete their battery power, or get lost due to violent underwater environment changes. Therefore, this paper considers a UASN in ocean and proposes a sleep scheduling scheme in which sensor nodes and autonomous underwater vehicles in this network can dynamically choose to sleep or work to adapt to the environmental change. The concerned problem is to dynamically determine a sufficient number of active nodes in the UASN at different times, such that the targets required to be detected are covered. A special static scenario of the problem has been shown to be NP-complete. Hence, this paper proposes an improved multi-population harmony search algorithm to solve this dynamic problem. By simulation, the proposed algorithm shows high performance in terms of extending network lifetime, robustness, and computing time.

Background: Metastasis to the axillary lymph nodes is a key indicator of prognosis in breast cancer. Lymph node ratio (LNR) is reported to be superior to the absolute number of nodes involved (pN stage) in classifying patients at high versus low risk of death following breast cancer. Aim of the study: This prospective study investigated the predictive value of the lymph node ratio (LNR) compared with the number of positive lymph nodes (pN), serum C-reactive protein (CRP) levels, hormones status and other prognostic factors in Syrian -breast cancer patients. Patients and methods: 60 breast cancer patients aged between 32 to 77 years old, admitted in National hospital of Jableh (Lattakia-Syria) were selected from December 2015 until December 2016. LNR calculated as the ratio of the number of positive nodes to the total number of nodes excised, LNR had been categorized as low: (0 -0.2), intermediate: (>0.2 - 0.65), and high risk: (>0.65 – 1). Blood samples were collected for CRP levels on admission and measured by immunoturbidimetric determination, levels >5mg/l were considered positive. Estrogen-Receptor (ER), Progesterone Receptor (PR) and human epidermal growth factor receptor 2 (Her-2) expression were assessed. Results: Strong relationship was found between LNR-classification system and increased number of positive lymph nodes in breast cancer patients (P 4 were at high risk (100%) in comparison with patients with pN=4, and this difference was statistically important (P 0.05). Mean level of CRP was 5.38 ±2.94mg/l at low LNR, 7.29±5.21mg/l at intermediate LNR and 7.68±4.55mg/l at high LNR, but the differences were not statically significant (P=0.27). Patients at high risk according to LNR-classification were positive for ER-receptor (n=4, 100%, P=0.248), PR-receptors(n=3, 75%, (P=0.028) and for Her-2 receptor (n=3, 75%, P=0.963). Conclusion: Patients with number of positive lymph nodes more than 4 lymph nodes were at high risk according to LNR-classification system in comparison with patients with 4 lymph nodes or less as a predictive number of lymph nodes. LNR- classification was more interesting than pNstaging in comparison with level of serum CRP, but similar to other classic risk factor such as Grade, Stage, tumor size and age. LNR-classification system was interesting in positive PR-receptor expression patients as an added value of LNR for predicting future response to hormone therapy.

The internet of underwater things (IoUTs) is regarded as one of the most technologies in smart oceans that Underwater acoustic sensor networks (UASNs) have recently emerged to support the concept of IoUT. Network lifetime is a vital requirement in UASNs because underwater sensors are supplied with batteries that are difficult to recharge. Relay node placement (RNP) is investigated in recent years to increase the network lifetime that the relay node plays an intermediate node between the critical node and one of its neighbours (i.e., line segment RNP). Due to the tiny feasible region in the line segment RNP, avoiding the redundant relay nodes is a challenging task, especially in the dense UASNs that the distance between nodes is short. In this paper, we design an efficient RNP by defining a threshold distance d<inf>&#x03F5;</inf>. Our objective is to make good tradeoffs between the performance of 3D UASN and complexity in relay-assistance networks. To do this, we introduce a modified difference convex approach (MoDCA). To evaluate our proposed method, we compare it with previous DCA and RIA in terms of the averag number of relay nodes (NoR<inf>a</inf>) and network lifetime through two simulation scenarios.

Underwater acoustic sensor networks (UASNs) have been used for various scenarios, such as marine exploration, and development of marine resources. However, due to the challenging underwater environment, UASNs suffers from high propagation delay, low transmission reliability and energy limitation, which pose significant obstacles to the delivery efficiency of data packets. What's more, the sparse topology, the failure of node or link, and other factors can cause void holes, resulting in the considerable package retransmission and the low network reliability of UASNs. To this end, this paper proposed a reinforcement-learning-based opportunistic routing protocol (ROEVA) to reduce energy consumption as well as to improve the transmission reliability which also addresses the issue of void routing in underwater acoustic sensor networks. To seek optimal routing rules, a reward function based on reinforcement learning is proposed, where factors such as energy, delay, link quality, and depth information are all taken into account for appropriate routing decisions. Before forwarding the data packet, a two-hop availability checking function is defined, which can identify trap nodes and avoid routing holes. In addition, aiming to reduce packet redundancy and collisions, a waiting mechanism derived from opportunistic routing is proposed. According to the calculated Q-values, the waiting mechanism determines the priority list for packet forwarding. Evaluation results demonstrate that the proposed ROEVA protocol outperforms HHVBF, RCAR, QELAR and GEDAR in terms of energy efficiency, packet delivery ratio (PDR), average hop count, and end-to-end delay, which are analyzed by varying the number of nodes from 100 to 500.

Effect of the number of positive lymph nodes and lymph node ratio on prognosis of patients after resection of pancreatic adenocarcinoma

Online Fountain Codes (OFCs) adapt their encoding strategy to decoder feedback, making them attractive for Underwater Acoustic Sensor Networks (UASNs) with stringent delay and energy constraints. Yet, conventional OFCs perform poorly under the harsh conditions of UASNs, where high error rates, long propagation delays, and limited feedback severely restrict efficiency. This work identifies two key challenges: balancing recovery efficiency with intermediate decoding, and reducing encoding and decoding complexity under sparse feedback. To address them, we propose a Dual-Perception Fountain Code framework based on Deep Reinforcement Learning (DRL-OFC). A DRL agent learns optimized degree distributions to reduce redundant packets and improve decoding, while a feedback-aware strategy adapts transmissions to UASNs conditions. Simulations show that DRL-OFC achieves lower overhead, stronger intermediate recovery, fewer coded packets, and better energy efficiency than existing OFC schemes, confirming its suitability for resource-constrained underwater networks.

The Underwater Acoustic Sensor Network (UASN) plays a dynamic role in various application extents like surveillance, military, terrestrial networks and industrial application. In UASN, each Underwater Sensor Node (USN) is deployed in many places. The multiple paths are always used for transmitting data from USN to the Sink Node (SN). Each sensor constitutes a set of small devices and a small battery. If data is received from multiple nodes at the same time then, there is a chance for a collision and also it will bring down network lifetime. In recent years, only a few research works are done in fuzzy based routing in UASN. For increasing the network’s lifetime, there is always a need for an optimum routing path in UASN with improved parameters related to propagation delay, energy and throughput. The present paper proposes the Fuzzy Logic Depth based Energy-Aware Routing (FLDEAR) path in UASN. A static USN was utilized in the proposed system. Each USN is assigned a Priority Number (PN) by the SN. The PN is used for avoiding collision in the proposed model. The simulation is conducted employing Aqua-Sim (AS) which is based on Network Simulator-2(NS2). The simulation results illustrate that this system outperforms the existing routing protocol such as protocols of Energy Efficient Cooperative Opportunistic Routing (EECOR), Fuzzy Depth Based Routing (FDBR) and Depth Based Routing (DBR) in terms of improved parameters related to energy, propagation delay and throughput. Finally, the lifetime of UASN is improved by 70–80% by the implementation of the proposed FLDEAR protocol.

To reduce the energy cost of underwater acoustic sensor networks (UWSNs), the duty cycle (i.e., periodic wake-up and sleep) concept has been used in several medium access control (MAC) protocols. Although these protocols are energy efficient, they sacrifice bandwidth utilization, which leads to lower transmission rate. In order to solve this problem, asynchronous duty cycle with network-coding Asynchronous Duty Cycle with Network-Coding MAC (ADCNC-MAC) is proposed. It contains initialization of the MAC protocol phase and data transmission phase. In the first phase, we use an asynchronous duty cycle to find a rendezvous time for exchanging data. A strategy to select network coder nodes is presented to confirm the number of network coder nodes and distribution in the network coder layer. In the data transmission phase, the network coder nodes transmit using the proposed network-coding-based algorithm and a higher volume of packet will be transmitted to the Sink with the same number of transmissions. Simulation results show that ADCNC-MAC achieves higher power efficiency, improves packet delivery ratio (PDR), and network throughput.

UWASN (UnderWater Acoustic Sensor Network) is a kind of WSN (Wireless Sensor Network) consisting of underwater acoustic sensor nodes, which can be deployed for real-time warship monitoring, oceanographic data collection, environmental monitoring, disaster prevention, etc. The design of a reliable data transfer protocol for UWASN is challenging [1] owing to the specific characteristics of acoustic channels: high error rates, high energy consumption, limited bandwidth, low transmission speed, and long and unstable packet delivery delay.

This paper proposes a whole cycle analysis method of supply chain disruption based on underwater acoustic sensor networks (UASNs). Firstly, the UASNs are deployed to monitor offshore disruptions (such as oil leakage), and the monitoring data of underwater disruptions can be obtained. Based on the monitoring data, scenario inference and Bayesian network forecasting are applied to reversely infer the upstream causes and probabilities of the disruption, which will provide decision-making basis for the effective prevention of such disruptions in the future. At the same time, based on the monitoring data via UASNs, the DA-NET model of the petroleum supply chain (PSC) system is built and the impact of the disruption on PSC operation is analyzed quantitatively, such as cost increase or time delay. The results show that with disruption monitoring via UASNs, the proposed method can not only infer the causes of a disruption, but also quantitatively determine the impact of the disruption on the operation of PSC, which has certain guiding significance for the enterprise managers of the PSC to prevent such disruptions in advance and to deal with them afterwards.