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Blockchain has gained the attention of scholars and industry practitioners due to its immutability, transparency, and operational features, which can improve overall supply chain efficiency. However, a holistic review of blockchain-based supply chains through the lens of digital trust has remained elusive, making participants reluctant to share information due to growing attacks on digital systems and fraud. Therefore, this study examines digital trust in using blockchain technology for supply chain operations by following a five-stage review process consisting of a systematic literature review protocol. The study performs a bibliometric and morphological analysis of 123 articles published between 2012 and 2023 to explore the current state and provide future research directions to develop safe, secure, reliable, and transparent blockchain-based supply chains. Further, our analysis reveals five characteristics of digital trust: transparency, cybersecurity, data protection, accountability, reliability and provenance, and regulatory compliance, which are essential to ensuring digital trust for supply chain sustainability, four research pathways, and keyword combinations for future research with other industry 4.0 technologies. Although blockchain applications for secure and trusted environments have been recognized, very little attention has been given to the detailed discussion on digitally trusted blockchain-based supply chains. The present study contributes to the literature by synthesizing the available literature on blockchain-based supply chains from the perspective of digital trust, thereby analyzing the current state and providing future opportunities for researchers and practitioners working in industry sectors and developing blockchain-based supply chains. [ABSTRACT FROM AUTHOR]

This study aimed to identify the cause-and-effect relationship among the factors influencing a resilient supply chain and to quantify their importance using a cause and effect (Ishikawa) diagram and Multi-Criteria Decision Making (MCDM) tool respectively. The results showed that redesigning the network and creating a supply chain buffer were the most important criteria/pillars, and the most important factors were creating redundancy and robustness. The study also found that approximately 80% and 20% of resilience can be achieved by developing absorption and reactive capabilities, respectively. The importance weight provided by this study can be used by industry professionals to estimate achieving supply chain resilience, while academicians can use it as the foundation for future researches. Further, researches may be carried out by developing a digital twin to understand potential impact of disruptions and resilience strategies. Blockchain implementation precisely smart contracts may be studied, since it appears to be one among best solutions to create transparency, coordination and control mechanism in the supply chain system. The novelty of this study lies in the weighted Ishikawa diagram that quantifies the contribution of each factor in percentage terms, providing specific insights into the factors influencing supply chain resilience. [ABSTRACT FROM AUTHOR]

Cloud computing and its derivatives, such as fog and edge computing, have propelled the IoT era, integrating AI and deep learning for process automation. Despite transformative growth in healthcare, education, and automation domains, challenges persist, particularly in addressing the impact of multi-hopping public networks on data upload time, affecting response time, failure rates, and security. Existing scheduling algorithms, designed for multiple parameters like deadline, priority, rate of arrival, and arrival pattern, can minimize execution time for high-priority applications. However, the difficulty lies in simultaneously minimizing overall application execution time while mitigating resource depletion issues for low-priority applications. This paper introduces a cloud-fog-based computing architecture to tackle fog node resource starvation, incorporating joint probability, loss probability, and maximum entropy concepts. The proposed model utilizes a probabilistic application scheduling algorithm, considering priority and deadline and employing expected loss probability for task offloading. Additionally, a second algorithm focuses on resource starvation, optimizing task sequence for minimal response time and improved quality of service in a multi-Queueing fog system. The paper demonstrates that the proposed model outperforms state-of-the-art models, achieving a 3.43-5.71% quality of service improvement and a 99.75-267.68 msec reduction in response time through efficient resource allocation. [ABSTRACT FROM AUTHOR]

"Epilepsy" is a common neurological brain disorder that may affect the human being at any stage of life. Electroencephalogram (EEG) signal is the most important tool for the early detection of epileptic seizures in several applications of epilepsy diagnosis. Recent research has carried out various possibilities of predicting and analyzing epileptic seizures by using conventional methods which employed EEG signal processing for feature extraction and further classified by either one of deep learning/ machine learning-based methods. So, there is a requirement to find a suitable and more reliable method to detect and classify EEG signals as epileptic and non epileptic. This paper proposes a non-conventional method in which EEG time series signals are transformed into multiple recurrence plots. Transfer learning methods are used to extract features from these recurrence plots and relevant features are selected by using principal component analysis thereby reducing the computational complexity. These features are fed into various machine learning classifiers which include Decision Tree, K-Nearest Neighbor, Gaussian, Random Forest, Bagging, and Support Vector Machine. Among these classifiers, SVM gives the best performance with 98.15% sensitivity, 99.61% precision, and 98.21% accuracy. This proves to be a novel approach as it evaluates the performance of different machine learning classifiers in two ways. First, with the extracted features only and second by using feature selection on extracted features. The outcomes confirm that employing feature selection enhances the performance of each classifier, thereby validating the effectiveness of feature extraction using deep learning. The proposed work has the potential to classify the epileptic and non-epileptic EEG signal more efficiently in terms of tradeoffs between sensitivity and accuracy. This is an added advantage as it uses the non-conventional and hybrid approach to classify the raw EEG signal and thus opening the possibility of intervention required to prevent the possibility of upcoming seizure. [ABSTRACT FROM AUTHOR]

In this paper an extreme learning machine (ELM) based approach is presented for the anomaly detection in dynamic wireless sensor network (WSN) under extremely imbalanced class distribution conditions. The imbalanced class distribution and dynamic nature of the network result in the classifier's inferior performance. The proposed approach tackles the imbalanced classification of datasets by dividing the class with the majority of samples into multiple sub-classes. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation (AP). The initial preferences for AP are set to influence the total number of segments such that the number of samples in any segment closely matches with the numbers of minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the ELM. The synthetic datasets required to evaluate the performance of the proposed technique are generated using network simulator version 2 (NS2). The dynamic WSN is simulated for different attacks (considering only one attack at a time) using network simulator version 2 (NS2). During the simulation, all required network data is dumped into trace files. These dumped trace files are later processed through MATLAB to extract the required features in a usable format to generate a proper dataset. This process is repeated for two different WSN configurations, one with lower node density, and the other with higher node density, and the generated datasets for these two WSN configurations are denoted as Dataset-1, and Dataset-2 respectively. The experimental findings support the substantial improvement in performance from previous approaches by the new technique. [ABSTRACT FROM AUTHOR]

The IoT devices with advanced computational powers supported by Artificial Intelligence (AI) in diversified application domains require infrastructures that can deliver resources and computational services based on application needs. Fog computing has emerged as a platform for applications that can provide flexible and shared computational capabilities to such delay-sensitive services along with maintaining network availability, minimum latency, and Quality of Service (QoS) maximization. The proposed model offers the adaptive offloading of the applications while achieving the maximum QoS in a delay-constrained service environment. Moreover, to minimize the resource starvation condition, a probabilistic application task scheduler algorithm is presented, which considers the task's initial priority and deadline. The tasks within waiting queues are assigned new priorities and used to select the most relevant task, reducing the response time and failure rate. Besides, a cost parameter is included in the proposed model that chooses the cost-efficient option for offloading the applications to the available servers. This work aims to maximize the overall QoS and optimize the service cost, which is demonstrated through simulation results. Our proposed model adaptive application offloading for QoS maximization with delay constraint (AAOQM-DC) yields a performance improvement by getting 0.11-2.49 sec. downfall in overall response time, 0.12- 1.38 sec. decrement in network delay, and enhanced QoS by achieving 0.07-5.12% lower failure rate in comparison to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The openness nature of the Ad-hoc sensor networks emerged as a security threat in this network environment that leads to packets drop, network overhead, high range energy consumption, and transmission delay. Previous methods such as Trust-Based Malicious Nodes Detection in AD-hoc (TBMND) and Security based Data-Aware Routing Protocol (SDARP) found that this happens due to malicious nodes in the network. A malicious node in a network degrades its efficiency that also affects the routing decision making and also makes error decisions in route selection. Trust vector model and Trust-based Secure Routing (TBSR) have made attempts to evaluate trust nodes via various techniques, but unfortunately, the accuracy level has not been reached to the required range. In this paper, a trust-based energy-aware routing using GEOSR protocol for Ad-hoc sensor networks is presented for providing an energy-efficient and secured routing. In this trust-based energy-aware routing, initially the sensor nodes are deployed in the ad-hoc network. The clustering is performed based on the estimation of distance among the each nodes and the cluster head (CH) is selected based on the threshold value. After CH selection, the trust evaluation is performed for identify the trust and untrusted nodes present in the network. The untrusted node is considered as malicious node which is detected and blocked. The trusted nodes are forwarded to select the optimal routing path for secured transmission. Golden Eagle Optimized Secure Routing (GEOSR) is introduced for selecting the optimal routing path based on the parameters such as distance delay and energy objective function. Thus, energy-efficient and secured routing were done using the GEOSR protocol. GEOSR protocol was then implemented in the NS-2 simulation tool and then compared with existing techniques. GEOSR protocol shows 95% of residual energy for the prediction of two malicious nodes. Thus GEOSR was suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as one of the most important Internet infrastructures for improving service quality, particularly in real-time applications. Due to the convergence in technologies, the scope of the Internet of things (IoT) has evolved to a new dimension, it expands from data collection to device interconnections, and to pre-processing. This acceleration involves cloud and fog computing layers into the system which plays an integral role in IoT data storage and computing. Due to the diversity present in IoT devices, selection of computation devices and allocation of resources are major challenges to be addressed for efficient utilization of resources. In this paper, we presented the offloading and resource allocation model to address the solution to the above challenge. Firstly, a 5-layered neuro-fuzzy model is introduced to retrieve the fuzzy sets and rules which further passes to the fuzzy inference system to model an orchestration decision system. Additionally, to improve the system performance, we have presented the modified least loaded resource allocation algorithm which is adaptively required to reduce the failure rate of the applications. To showcase the efficacy of the model, 4 healthcare applications (augmented reality, patient pre-monitoring, record analysis, and billing systems) are evaluated with their heterogeneous parameters. The simulation findings show that our suggested model improves system performance by lowering network latency by 2.23–9.68 %, computation delay by 3.40–13.66 %, and system performance by 1.03–11.55%. The simulation results demonstrated the suggested model's resilience in terms of network latency, computation time, and failure rate. [ABSTRACT FROM AUTHOR]

Cloud service providers offer diverse utilities to the consumer on their heterogenous requirements which may fit on different levels of performance measurements such as costs, quality of service, and accuracy, etc. Cloud is enriched with unlimited resources along with many advanced features such as scalability, robustness which increased the cloud demand in recent years. The selection of appropriate resources is a challenging task that can minimize the cost, and maximize the resource utilization and consumer experience. We have proposed the optimized kernel naive bayes cloud service selection model (OKNB) which works on the concept of maximum probability. The cloud resource bundle with maximum probability is chosen as a predicted cloud resource. Our proposed model achieves 88.76%, 88.45%, and 93.65% accuracy on response time, CPU utilization, and memory utilization models which is 3.15 and 8.04% higher than the state of the art models on response time and memory utilization models respectively. The proposed model yields 16.83 and 29.175 s lower waiting time in standard deviation and mean value compare to the GMP-SVM model. [ABSTRACT FROM AUTHOR]

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual's health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. [ABSTRACT FROM AUTHOR]

Sustainable manufacturing (SM) has become an emerging research topic in sustainability due to strict government policies, social awareness, and rapid information and technology changes. Industries are now adopting Industry 4.0 technologies to compete in the global market competition. Very few studies discussed the evolution of SM and the related research themes that evolved in recent years. This study aims to identify the research themes and research transformation evolved in SM in the last twenty-one years. The impact of most productive authors, journals and countries is evaluated by citation analysis, while the co-occurrence of keywords is done to identify the thematic evolution and trending topics in SM. The results of this study provide a clear and detailed picture of various research themes that evolved in SM from 1999 to 2020 i.e., sustainable planning and scheduling, sustainable supply chain, lean and environmental management, sustainable machining, decision making, Industry 4.0 and Lean production systems. The number of publications related to Sustainability and Industry 4.0 has increased in the last 3 years. Cluster analysis shows the evolution of many new trending topics in SM i.e., SM in Industry 4.0. Challenges and enabling factors for the sustainable manufacturing practices have identified and role of decision making in SM implementation is discussed. The content analysis on major research themes in SM is discussed with the help of cluster analysis. Based on the literature analysis a comprehensive research framework for future research work is proposed in Industry 4.0 context. The results obtained from this study will be valuable for academia and practitioners enabling them to be updated about the latest developments in SM research. [ABSTRACT FROM AUTHOR]

In the past decade, the cloud environment has emerged as a resource-enriched technology for computational modeling. Most cloud data centers are deployed far from the device layer, which may cause higher service delivery time. Therefore, Fog servers are implemented to retain the commitment of minimum service delivery time. The computational resources (processing unit, memory, storage) and network bandwidth are to be appropriately scheduled to make an efficient system. For delay and resource-constrained devices, the essential issues include minimum service latency and improved system performance by offloading non-sensitive devices to the cloud layer. In this study, an adaptive bandwidth adjustment technique is proposed considering the sensitivity of the applications and load on the multiple queues at the access point based on which, a compelling dynamic bandwidth ratio is calculated. Further, blocking probability of task and task dropping probability is used as task offloading probability to overcome the dropping ratio of type-II applications. The random early detection (RED) algorithm is implemented in queueing management to maximize the system performance. Extensive simulation results highlighted that the proposed model significantly improves the system performance in terms of network delay, queue delay, and overall response time over traditional algorithms. [ABSTRACT FROM AUTHOR]

In the era of technology, compact and low-powered IoT devices have become an integral part of the daily routines of various sectors such as healthcare, education, industries, defense, and agriculture, etc. The IoT has increased the flow of enormous data from one network to another and extended the dependability of the public networks. In health applications, this dependency can cause excessive packets to be lost and prolonged waiting, which can cause sensitive applications to suffer. The authors suggested fog computing as a potential middle layer in the delay-aware computational system in existing works. There are many issues with the cloud-based computation model, such as location awareness, long-distance communication path, and unmanageable network bandwidth. These parameters can be solved using a fog computing model, which helps achieve ultra-low latency and minimum service delivery time. However, task offloading and scheduling are significant open research issues concerning resource management and allocation in a cloud-fog system. This paper presents a delay-aware application offloading focusing on reducing the response time and maximizing the system performance. Our proposed model developed a multiclass open queueing model that maintains the traffic on different queues, aiming to achieve maximum system performance. The simulation results demonstrate that our proposed model DAAO (Delay-aware application offloading) yields a performance improvement of 14.30% service rate and minimized failure rate by 2.0%. The simulation results supported the robustness of the proposed model in terms of minimum response time and maximum success rate. Our proposed model achieves a better tradeoff than existing work in terms of offloading and minimum failure rate. [ABSTRACT FROM AUTHOR]

In the current scenario path loss, coverage and capacity by operating network under fading condition is research challenge. In this paper, signal strength of long term evolution network operating at 1.8 GHz is recorded for analysis. During the measurement, the range of received signal strength varies in between − 72 and − 104 dBm for basement data transmission. The variation of received signal at 12 distinct points in the basement was marked sincerely. Based on this received signal the link budget is formulated and presented. The capacity of a network on given link budget is calculated and verified. The data recorded for such environment using spectrum analyzer and simulating with Matlab is presented. The behaviour of signal referred to a value of path loss exponent i.e. 4.55. The path loss exponent provides the real coverage under urban area in fading condition. Using different models of prediction, real path loss under fading is calculated and based upon this path loss value a model is proposed for next generation networks. [ABSTRACT FROM AUTHOR]

The paper focuses on the seamless transmission of the medical data between two points of e-health ecosystem. To successfully transmit the data one need to have a reliable network with required minimum signal strength and distortion. Based on system performance and its stability in accordance with the network, there are certain and quick variations in the network that could lead to distortion of data. The variation of signal strength during observation ranges in between − 90 and − 185 dBm. Transmission of signals from the basement to other location needs minimum threshold signal − 147 dBm for transfer of data with suitable speed. During analysis it was observed that some of the networks are not able to respond in deep fading environment, i.e., for basement signal transmission due to their installation criterion. The medical data transmitted from the basement need suitable selection of network in the given area. The above parameters are applied to the few hospitals in Greater Noida. UP (India) where the readings were taken at their respective basements to represent the signal strength with its fading parameters. Normally the basement fading tends to 25–35 dB in case of reception. The selection criteria in this paper is based on signal strength measurement, coverage analysis and regression analysis, prevail, that Worldwide Interoperability for Microwave Access is the best suited network for transmission of medical data from one place to another place with minimum cost. [ABSTRACT FROM AUTHOR]

In this paper, a tri-objective optimization problem related to sequencing in multi-model reconfigurable assembly systems (RAS) is addressed. RASs are also designed around product families similar to the more generic reconfigurable manufacturing systems (RMSs). A multi-model assembly line needs reconfiguration after assembling each batch of the products from the family. Further, the changeover of assembly line from one product type to another is also of varying degree of complexity and reconfiguration cost. Two novel indices for commonality and reconfiguration complexity are proposed here to incorporate this feature of RAS. Optimizing the reconfiguration time/cost is taken as the first objective while fulfilling the product due dates and the work load balance among the assembly stations as the second and third objective functions, respectively. This optimization problem is solved using a Multi-objective Self-Organizing Migrating Algorithm (MOSOMA) metaheuristic to find the non-dominated Pareto optimal solutions representing optimum sequencing of products in the family. An approach for identifying the best solution from the multiple Pareto optimal solutions using aggregate fitness is also proposed. A numerical illustration along with computational procedure is presented on a hypothetical RAS model. [ABSTRACT FROM AUTHOR]

In this paper, we take an overall look at key technical challenges in the evolution of the radio access network (RAN) architecture towards Cloud RAN and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of general-purpose processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. In the evolution towards 5G, we propose the use of Cloud-RAN-based multi-connectivity anchoring to address processing bottlenecks in a scalable manner. We identify opportunities for optimization across RAN and other network layers enabled by the distributed edge cloud architecture. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11276-022-02991-1 B Correction to: Wireless Networks (2022) 28:2913-2936 b https://doi.org/10.1007/s11276-022-02991-1. Author Sachin Tripathi affiliation number is incorrectly identified in the original version of the article. [Extracted from the article]

This paper deals with the multi-agent system for distributed computer-aided process planning problem. The proposed architecture consists of three autonomous agents (Global Manager Agent, Design Agent, and Optimization Agent) capable of communicating to each other through eXtensible Markup Language (XML). In contrast to the traditional process planning model, a new paradigm incorporating fixture cost has been conceptualized. Optimization agent is facilitated by the virtues of DNA computing and identifies the optimal/near optimal process plan with the help of available resources. Furthermore, in order to investigate the efficacy of the developed architecture, the manufacturing of a prismatic part having 23 operations has been simulated and solved accordingly. Results obtained reveal the superiority of the proposed architecture. [ABSTRACT FROM AUTHOR]

We propose a general framework for obtaining asymptotic distributional bounds on the stationary backlog $$W^{A_1 + A_2 ,c} $$ in a buffer fed by a combined fluid process A 1 + A 2 and drained at a constant rate c. The fluid process A 1 is an (independent) on–off source with average and peak rates ρ1 and r1 , respectively, and with distribution G for the activity periods. The fluid process A 2 of average rate ρ2 is arbitrary but independent of A 1. These bounds are used to identify subexponential distributions G and fairly general fluid processes A 2 such that the asymptotic equivalence P[ W A1+A2, c >ϰ]∼ P[ W A1, c—ρ2>ϰ] (ϰ → ∞) holds under the stability condition ρ1 + ρ2 < c and the non-triviality condition c – ρ2 < r 1. In these asymptotics the stationary backlog $${W^{A_1 ,c - \rho _2 } }$$ results from feeding source A 1 into a buffer drained at reduced rate c – ρ2. This reduced load asymptotic equivalence extends to a larger class of distributions G a result obtained by Jelenkovic and Lazar [19] in the case when G belongs to the class of regular intermediate varying distributions. [ABSTRACT FROM AUTHOR]

We introduce general starvation and consider cyclic networks with general blocking and starvation (GBS). The mechanism of general blocking allows the server to process a limited number of jobs when the buffer downstream is full, and that of general starvation allows the server to perform a limited number of services in anticipation of jobs that are yet to arrive. The two main goals of this paper are to investigate how the throughput of cyclic GBS networks is affected by varying (1) the total number of jobs J, and (2) the buffer allocation k=(k..., k) subject to a fixed total buffer capacity K=k +... + k. In particular, we obtain sufficient conditions for the throughput to be symmetric in J and to be maximized when J=K/2. We also show that the equal buffer allocation is optimal under the two regimes of light or heavy usage. In order to establish these results, we obtain several intermediate structural properties of the throughput, using duality, reversibility, and concavity, which are of independent interest. [ABSTRACT FROM AUTHOR]

Recently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for the smart product development in the next-generation WSNs environments. [ABSTRACT FROM AUTHOR]