Smart grid cyber-physical systems (SG-CPS) are intelligent platforms that incorporate IoT-enabled multifunctional layers including the physical, perception, communication, cyber, and application layers. It includes supervisory control and data acquisition, wide-area measurement systems, and advanced metering infrastructure for remote data aggregation, monitoring, and control operations. From an environmental perspective, these green technologies support two-way operations, which generate and transmit data over wired and wireless communication systems. However, this critical infrastructure faces data privacy and cybersecurity challenges. Hence, extensive research is required to address data privacy and security gaps to strengthen national grid cybersecurity and reduce economic losses. Therefore, this review highlights cryptographic techniques as significant mechanisms to ensure data confidentiality, integrity, and availability. Accordingly, we investigate IoT-enabled multifunctional layer components and applications, their cybersecurity objectives, requirements, and essential cryptographic standards, protocols, and cyber-attacks. Subsequently, we categorize cryptography techniques for analyzing contemporary lightweight, authenticated, and key agreement protocols. Moreover, we present a comparative analysis of the performance, efficiency, and security features in cryptographic solutions. Finally, we identify challenges related to smart grid, cybersecurity, and cryptographic techniques, along with outlined recommended future research directions. The significance of this study lies in providing valuable insights into the performance and security of cryptographic techniques. It supports researchers who may consider tested cryptographic solutions to advance data privacy and cybersecurity within SG’s multifunctional layers of infrastructure.

The growing demand for computation-intensive artificial intelligence (AI) and machine learning (ML) applications necessitates carbon-neutral edge computing to enhance resource efficiency, reduce energy consumption, and promote sustainability in Industrial Internet of Things (IIoT) systems. However, reducing service latency and energy consumption while ensuring execution accuracy and a predictable carbon footprint and its associated cost remains a critical research challenge. Existing works in the literature experience significant challenges for task offloading due to a lack of edge collaboration and ineffective management of Carbon Emission Rights (CER) credits. In this paper, we have developed an optimization framework leveraging Mixed Integer Linear Programming (MILP), namely GRELMON, to jointly minimize service latency and energy consumption while maximizing task accuracy in carbon-neutral collaborative edge and cloud computing for IIoT environments. Moreover, a carbon emission forecasting model using a hybrid deep learning approach is also developed to prevent unnecessary CER purchases. The experimental results demonstrate that GRELMON outperforms state-of-the-art methods by reducing latency and energy consumption while improving the accuracy of the execution of ML tasks.

The Internet of Medical Things (IoMT) has emerged as a transformative technology platform in the healthcare sector, enabling real-time monitoring and intelligent decision-making through connected devices. However, prioritizing and offloading the massive volume of computational tasks generated by IoMT devices while minimizing latency and energy consumption poses significant challenges. Existing approaches often overlook dynamic real-time factors such as task urgency and data freshness, as well as the integration of local task processing via Device-to-Device (D2D) communication with offloading to Mobile Edge Computing (MEC) servers. In this paper, we develop a priority- and Age of Information (AoI)-Aware task offloading framework for latency and energy optimization in healthcare IoT systems, namely PRALEIT, exploiting Mixed Integer Linear Programming (MILP) problem. The developed PRALEIT system introduced probabilistic classification of IoMT tasks based on vital signs and AoI value by leveraging a Bayesian classifier. The experimental results depict that the PRALEIT system significantly reduces task execution delay and energy consumption compared to state-of-the-art models, ensuring reliable and sustainable healthcare services.

To meet the demand of the world’s largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols—that demand considerably high computational power and memory—can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts.

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers ofAnt Colony,Whale,Sine-Cosine, andHenry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

Efficient deployment of a service function chain (SFC) on virtual network functions (VNFs) and their mapping to virtual machines (VMs) in 5G networks is highly important to reduce user application service latency as well as to minimize the cost of VM migration. Existing works in the literature have either considered service latency or migration cost while deploying an SFC on VMs. In this paper, the problem of dynamically mapping VNFs running a SFC to different virtual machines in a 5G network has been formulated as a multi-objective linear Programming (MOLP) problem. The developed optimization model, namely Trade-Lcm, minimizes user application service latency while reducing the cost of migrating virtual network functions associated with an SFC. The numerical performance analysis results demonstrate a significant improvement in minimizing service latency of user applications and cost of VM migration as high as 30% and 10%, respectively, compared to the state-of-the-art work.

Mobile Device Cloud (MDC) is a promising and lucrative cloud environment that uses the idle resources of nearby mobile devices to improve the performance of compute-intensive applications. By computing code on nearby devices instead of a distant master cloud, the MDC system can improve the performance of real-time applications. It is challenging for the MDC system to efficiently use the resources because of cost and QoE. While allocating high-quality resources to mobile applications might be effective in decreasing the latency, it will increase the cost, and the allocation of low-quality resources will increase execution latency, diminishing the quality of experience (QoE) of the users. The works in the literature didn't come up with methods of trading off these two conflicting objectives in a fair way. In this paper, we present an optimization framework, namely QCMDC, for allocating tasks in an MDC environment, which brings a trade-off between maximizing users' QoE and minimizing the execution cost of the worker devices. Empirical evaluations have been carried out in Python and the results demonstrate significant performance improvement in terms of QoE and execution cost compared to other state-of-the-art works.

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.

In a mobile crowdsourcing system (MCS), a platform outsources sensing tasks to numerous mobile worker devices. The collected data are analyzed and the processed information is shared among many other interested users. The platform pays the workers for the sensing data and earns money from the users receiving processed information services. Distributing the sensing workloads among the potential workers so as to maintain the required data quality and to make a reasonable amount of profit is a challenging problem for such a platform. In this paper, we develop a workload allocation policy that makes a reasonable tradeoff between worker utilities and platform profit. It quantifies the utility (i.e., the quality of sensed data) of a worker as a function of worker mobility, current location, and past sensing records. The workload allocation problem is formulated as a multiobjective nonlinear programming (MONLP) problem which aims to make the desired tradeoff between worker utilities and platform profit. The allocation problem is shown to be NP-hard and thus we develop two greedy algorithms with relaxed constraints to achieve polynomial time solutions. Performance of the proposed workload allocation policy is evaluated in a distributed computation environment using MATLAB. The results show its effectiveness compared to state-of-the-art methods in terms of platform profit, quality of sensing data, and request service satisfaction.

The key challenges of high-throughput data delivery in multi-radio multi-channel Wireless Mesh Networks (WMNs) are fluctuating channel conditions, dynamic traffic flows, co-channel interferences, congestion, etc. In this paper, we first formulate a mixed integer non-linear programming (MINLP) optimization framework that chooses, at each router, a number of outgoing link-channel pairs and allocates power(s) on those so that the routers' total outgoing flow rate is maximized while the interference and the congestion are kept at minimum level. Due to the NP-hardness of this optimal solution, we then develop a greedy heuristic method that separates the joint problem into two sub-problems, greedily chooses the high-performing link-channel pairs and heuristically goes either for increasing power levels on the best link-channel pairs or utilizing more pairs at minimum power. Finally, our simulation results show that the proposed system outperforms the state-of-the-art works in terms of throughput, delay and fairness.

Summary Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.

Summary In a mobilead hocnetwork, the data packet may fail to be delivered for various reasons mostly for route failure, congestion, and battery energy drain. Hence, providing reliable and timely data delivery in this network in an energy‐efficient way is challenging. Although there exist several solutions to solve these problems, they can handle either route failure or congestion or energy‐efficient routing. Hence, to cope up with all the problems simultaneously, we propose a route failure and congestion‐aware energy‐efficient cross‐layer design that spans the transport and network layer. In the transport layer, we introduce the concept of local packet buffering during link failure and congestion. As a result, the packet dropping rate of the network and energy consumption decreases. In the network layer, a routing protocol is proposed for selecting the energy‐efficient path for data transmission. It uses the buffering mechanism in case of route maintenance. In addition, we employ a multilevel congestion detection and control mechanism at the source and intermediate nodes that can judiciously take the most appropriate decision for congestion control in the network proactively. The simulation results showed that the proposed cross‐layer design provided better performance as compared with the state‐of‐the‐art protocols. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents a hybridization of Multilayer Neural Network-based Bangla phoneme recognition method for Automatic Speech Recognition (ASR) incorporating dynamic parameters. The method consists of four stages: at first stage, a multilayer neural network (MLN) converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities. Phoneme probabilities from the first stage are inserted into second stage MLN for obtaining more accurate phoneme probabilities with reduced context where the third stage computes dynamic (velocity (A) and acceleration (AA)) parameters from the phoneme probabilities by using three point linear regressions (LRs). Finally, the phoneme probabilities, dynamic parameters, A and AA, and the input MFCCs, combined as hybrid features, are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.

This paper is a contribution to the inception of multiple tokens in solving distributed mutual exclusion problem. The proposed algorithm is based on the token ring approach and allows simultaneous existence of multiple tokens in the logical ring of the network. Each competing process generates a unique token and sends it as request to enter the critical section that travels along the ring. The process can only enter the critical section if it gets back its own token. The algorithm also handles the coincident existence of multiple critical sections (if any) in the system. The algorithm eliminates the idle time message passing, increases overall throughput and provides fault-tolerance. We discuss the impact of process failures and loss of tokens and propose corresponding recovery methods. The results of simulation show that the proposed algorithm overcomes the key limitations of the major token ring algorithms.

Sensor nodes report the sensed data packets to the sink and depending on the application these packets may have diverse attributes: time-critical (TC) and non time-critical (NTC). In such a heterogeneous traffic environment, designing a data dissemination framework that can achieve both the reliability and delay guarantee while preserving the energy efficiency, namely multi-constraint QoS (MCQoS), is a challenging problem. This paper proposes a new aggregate routing model and a localized algorithm (DARA) that implements the model. DARA is designed for multi-sink multipath location aware network architecture. Delay-differentiated multi-speed packet forwarding and in-node packet scheduling mechanisms are also incorporated with DARA. The simulation results demonstrate that DARA effectively improves the reliability, delay guarantee and energy efficiency.