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Shallow Water Inspection & Monitoring Robot (SWIM-R) is designed to quickly and safely inspect oil and gas pipelines in extremely shallow waters. Divers clean and inspect pipeline joints. However, diving operations are slow in shallow waters as diving support ships cannot access shallow depths. Remotely operated vehicles (ROVs) that can perform cleaning and inspection are typically suited for deeper regions and are too large for smaller boats that navigate in shallow areas. To resolve this challenge, two SWIM-R vehicles and a companion Autonomous Surface Vehicle (ASV) were developed as a multi-robot system to minimize the reliance on divers for pipeline inspection. A unique mission architecture is presented that avails three operating modes depending on the depth; direct control from the shore, relayed control via the ASV, and direct control from a small zodiac. The mission architecture includes two ROVs; a Cleaning SWIM-R fitted with a water-jet nozzle to clean marine growth from the surface to be inspected, and an Inspection SWIM-R fitted with a neutrally-buoyant multi-functional robotic arm to inspect the surface and crawling tracks to traverse the seafloor. This multi-robot system was field tested, which proved its efficacy in inspecting oil and gas assets in shallow waters.

Smart cities aim to use technology and data to efficiently utilize city resources, improving residents’ quality of life and advancing sustainability. Efficient management of these resources, with the Internet of Things (IoT) playing a crucial role, is essential for enhancing smart cities’ functionality and sustainability. As the number of users grows, providing efficient and timely services becomes increasingly challenging and intelligent management of resources is key to delivering effective IoT services. This paper introduces a novel approach, the Ensemble Fog Layered Service Management (EFLSM) model, designed to manage and optimize resource allocation and usage within a smart city’s fog computing layer. This model addresses challenges such as unnecessary application migrations, and efficient context sharing and fog nodes migration issues. The proposed EFLSM model operates in three stages: Firstly, it assigns service requests to the appropriate fog nodes based upon the available computing resources and context history through a multi-channel queuing model; secondly, it ensures efficient context sharing and migration to address context instance shortages; and thirdly, it guides service request migration to select fog nodes using ensemble machine learning predictions. Experimental results from a hardware test-bed illustrate the deployment of the EFLSM model and demonstrate its superior performance over existing methods like SMCQ, greedy strategies, and random allocation.

Background: Given its short procedure time and low morbidity, there is enthusiasm to perform sleeve gastrectomy (SG) in an outpatient setting. However, most relevant studies include an overnight stay at a medical facility (≤ 24-h). Hence, we investigated the feasibility and safety of a same-day discharge (SDD) protocol for laparoscopic SG. Methods: In a prospective pilot study (02/01/2021–02/28/2022), all patients planned for SG were screened for eligibility. Patients met the inclusion criteria if they were ≤ 65 years old, without major comorbidity, and lived close to the hospital. Postoperatively, patients who met discharge criteria were sent home directly from the recovery room. Patients were called the same night and the next morning. Feasibility was defined as discharge on the day of surgery without emergency department (ED) visit or readmission within 24-h. Secondary outcomes, including 90-day morbidity, were compared to patients who met inclusion criteria but chose a same-day admission (SDA) approach during the same study period. Descriptive statistics are displayed as count (percentage) and median (interquartile range). Results: A total of 320 patients were planned for SG during the study period, 229 of whom met eligibility criteria and underwent SG with 56 agreeing to SDD-SG while 173 opted for SDA-SG. Baseline characteristics were all similar between both groups except for obstructive sleep apnea being more prevalent in SDA-SG group (38.2% vs. 16.1%; P< 0.001). Operative characteristics including procedure time were similar between both groups. Successful SDD-SG was achieved in 54(96%) of patients with a median of 6.0(1.0) hours of stay in the recovery room. Ninety-day morbidity was similar between SDD-SG and SDA-SG groups (1.8% vs. 6.9%, respectively; P= 0.196). Conclusion: A SDD protocol for laparoscopic SG was feasible and safe in selected patients. Larger studies that evaluate patient reported outcomes and include bypass-type procedures may be needed to guide safe use of ambulatory bariatric surgery.

Triple Data Encryption Standard (also known as 3DES) is a symmetric encryption algorithm. European Traffic Management System popularly uses 3DES for authentication and encryption. However, as per a draft published by NIST in 2018, 3DES is officially being retired and not suggested to use for new applications. Several attacks were imposed on 3DES, and the biggest threat to 3DES is meet in the middle attack. Therefore for long term security, it is essential to enhance the security of such algorithms. This paper proposed a new cipher DeCrypt inspired by 3DES, an improved version of the 3DES algorithm, and secured against meet in the middle attack. As per the experiment performed, DeCrypt cipher is 61 per cent faster than 3DES, providing long-term and better security against sweet-32 attacks than other symmetric algorithms. The proposed algorithm is also faster than 3DES due to reduced encryption and decryption time.

The promise of automated-driving cars causes the automotive and consumer electronics (CE) sector to rethink not only what it means to drive, but also the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions between humans and vehicles, which ultimately points to enhance human abilities, such as hearing, visual surveillance, or emotion awareness, as a part of safety concern. The voice-based interactions (speech recognition and stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans’ stress emotions where the environmental noise is inevitable and varies with the location of every passing vehicle. Furthermore, the computational load introduces challenges in their implementation in automated vehicles. In this direction, we propose a front-end processing framework, in particular to stress emotion detection cases (such as anger, sad, fear, and happy) in different nonstationary noisy environments, such as car, airport, traffic, and train. This article encompasses three interrelated issues: 1) analysis, modification, and synthesis of noisy speech emotion in modulation domain in real-time background noise, 2) extracting set of Mel-frequency cepstral coefficients features from noisy speech stimuli for speech emotion recognition, and 3) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture. The experimental results show that favorable performance in state-of-the-art stress monitoring yields high levels of consumer satisfaction for security in vehicle comparison to traditional frameworks.

Recent advancements in sensing and networking technology, such as the Internet of Things (IoT), provide a low-cost monitoring system that serves as the backbone of the smart city. In recent years, various real-time applications have developed to monitor and control physical objects using smart sensors. The use of smart sensors generates a massive amount of data in terms of text, numeric, image and video formats, etc. In IoT, transmitting an enormous amount of data consume more energy. So, clustering is the right option for collecting and exchanging data over the network. Many researchers have used the optimization algorithms to choose cluster head (CH) in the cluster. During the CH rotation, many of the optimization algorithms take more time to converge. To address this problem, this paper proposes a new caledonian crow learning algorithm (NCCLA) to identify the appropriate CH in the cluster. Generally, it has two processes, namely cluster formation and CH selection. The Euclidean distance is used to create a cluster within the network. The NCCLA algorithm is proposed to find the best CH on the cluster. Simulation is conducted on MATLAB R2019a. The efficacy of the proposed NCCLA algorithm is compared to the fuzzy clustering particle swarm optimization (FCPSO) and the Improved Fruit Fly Optimization Algorithm (IFFOA). The performance of NCCLA increases the network lifespan by 10% and increases the data packet delivery by 10%.

Background: Indocyanine green, near infrared, fluorescence angiography (ICG-FA) is increasingly adopted in colorectal surgery for intraoperative tissue perfusion assessment to reduce anastomotic leakage rates. However, the economic impact of this intervention has not been investigated. This study is a cost analysis of the routine use of ICG-FA in colorectal surgery from the hospital payer perspective. Methods: A decision analysis model was developed for colorectal resections considering two scenarios: resection without using ICG-FA and resection with intraoperative ICG-FA for anastomotic perfusion assessment. Incorporated into the model were the costs of ICG agent, fluorescence angiography equipment, surgery, anastomotic leak, and the leak rates with and without ICG-FA. All input data were derived from recent publications. Results: The routine use of ICG-FA for colorectal anastomosis is cost saving when cost analysis is performed using the following base case assumptions: 8.6% leak rate without ICG-FA, odds ratio of 0.46 for reduction of leakage with ICG-FA (4.8% leak rate relative to 8.6% base case), cost of ICG-FA of $250, and incremental cost of leak, not requiring reoperation, of $9,934.50. Conclusion: Using the best available evidence and most conservative base case values, routine use of ICG-FA in colorectal surgery was found to be cost saving. Since the evidence suggests there is a reduction in leak rate, the routine use of ICG-FA is a dominating strategy. However, the overall quality of evidence is low and there is a clear need for prospective, randomized controlled trials.

Internet of Things (IoT) has revolutionized the digital world by connecting billions of electronic devices over the internet. IoT devices play an essential role in the modern era when conventional devices become more autonomous and smart. On the one hand, high‐speed data transfer is a major issue where the 5G‐enabled environment plays an important role. On the other hand, these IoT devices transfer the data by using protocols based on centralized architecture and may cause several security issues for the data. Merging artificial intelligence to 5G wireless systems solves several issues such as autonomous robots, self‐driving vehicles, virtual reality, and engender security problems. Building trust among the network users without trusting third party authorities is the system's primary concern. Blockchain emerged as a key technology based on a distributed ledger to maintain the network's event logs. Blockchain provides a secure, decentralized, and trustless environment for IoT devices. However, integrating IoT and blockchain also has several challenges; for example, major challenge is low throughput. Currently, the ethereum blockchain network can process approximately 12 to 15 transactions per second, while IoT devices require relatively higher throughput. Therefore, blockchains are incapable of providing functionality for a 5G‐enabled IoT based network. The limiting factor of throughput in the blockchain is their network. The slow propagation of transactions and blocks in the P2P network does not allow miners and verifiers to fastly mine and verify new blocks, respectively. Therefore, network scalability is the major issue of IoT based blockchains. In this work, we solved the network scalability issue using blockchain distributed network while to increase the throughput of blockchain, this article uses the Raft consensus algorithm. Another most important issue with IoT networks is privacy. Unfortunately, the blockchain distributed ledgers are public and sensitive information is available on the network for everyone are private, but in such cases, third party editing is not possible without revealing the original contents. To solve privacy issues, we used zkLedger as a solution that is based on zero knowledge‐based cryptography. The proposed framework presents the challenges and solutions of integrating blockchain with 5G‐enabled IoT devices. Major challenges are slow throughput, network scalability, and privacy issues of blockchain that are solved using several new technologies such as Raft consensus algorithm, blockchain distributed network, and zero‐knowledge proofs.

ABSTRACTIn this paper, we present a clustering-based energy aware secure routing protocol that computes trustworthy and energy efficient secure routes for wireless ad hoc networks (WANETs). Clustering in ad hoc networking paradigm is very useful for achieving scalability and robustness as it helps to save the bandwidth, to take simpler routing decisions, thus results in decreased energy dissipation of the network. WANETs are often deployed in such places where recharge or replace of energy sources is difficult. Further, unpredicted mobility and quasi-static nature of nodes and dynamic network topology yield a serious need for battery awareness during routing. In light of this, we first provide a definition of network lifetime based on application-specific criteria. Also a mathematical model for calculating residual battery power has been framed to propose an energy aware routing protocol. Trust-based scheme is used to assure secure end-to-end delivery of packets in the network. Extensive simulation has been carried out to show the efficacy of the proposed protocol to prolong the network lifetime.

Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time.