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In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore‐exploit behavioural intelligence of Physarum to survive against the attractive‐adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize EN's workload‐environment with Physarum's nutritive‐environment, where the high volume of workloads at the backbone network corresponds to a poor‐nutrient environment. The expert system explores EN to find out how to manage the workloads as Physarum handles its survivability, and exploits the users' workload patterns by grouping the highly communicating users together to redesign the network structure as Physarum's intelligence to exploit energy from rich‐ and poor‐nutrient food sources through redesigned tubular structures. We define two factors, such as nutrient‐intensity and chemo‐attractant to aid the redesign process. EN evolves through a set of redesigned clusters with an objective function to maximize its sustainability for a given set of explored workloads by minimizing the workloads through the backbone. EN evolution terminates when there is no change in the backbone utilization, resembling the organism's stay in a dormant state until it experiences a favourable environment. Our experimental results on an EN with a higher volume of workloads at the backbone producing 14.26 kWh energy consumption demonstrated that the developed expert system reduced the energy consumption to 11.27 kWh, thus enhanced the sustainability from 21% to 61%. [ABSTRACT FROM AUTHOR]

We have developed a fuzzy logic framework to simulate self-awareness in wireless sensor networks (WSNs) that envisages data transmission losses due to abnormalities in the operating environment. Fuzzy logic is selected to model the awareness, because it requires less data to encode, and occupies less memory, making it suitable for sensor nodes with limited resources. We view WSN as a multi-layered infrastructure, where a central-server in the top-layer manages the gateways in the intermediate-layer, and the gateways are aggregating the raw sensed data from the bottom layer’s sensors. The self-awareness is simulated using Mamdani Fuzzy inference system (FIS) that utilizes a custom-defined membership functions of the selected parameters, such as temperature, humidity, wind speed, and battery residual-level to guide WSN through its data transmissions. The input and output parameters are defined with three linguistic variables (low, medium and high) and a predetermined set of rules is applied to the selected FIS to determine the impact level. The impact is mapped into packet error rate and the gateway then decides whether to carry out the data transmissions while being aware of the selected uncertainties and their impact. The experimental results on a multivariate dataset integrating real-time meteorological data with randomly generated battery voltage revealed that the proposed FIS framework demonstrated 38% energy savings by non-executing unworthy communications against normal transmissions. [ABSTRACT FROM AUTHOR]

We have developed an expert system comprising a self‐aware framework for resource‐efficient and accurate data transmission within a low‐power lossy sensor network (LLN) deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision‐making under dynamic environmental conditions and employs a multi‐criteria decision‐making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM‐RPL with a standard RPL and a fuzzy‐based RPL, where the results show that our framework is found to be better with gains of 13, 25 and 13%, respectively. [ABSTRACT FROM AUTHOR]

Nowadays every product we purchase has a tag informing its users how much an automobile or an appliance will cost to run and how much energy a food product will provide on consumption, for example. With the advent of Internet, purchasing Apps becomes habitual, as Apps facilitate easy way of trading and generate money according to App online stores. Currently, Apps provide label that contains a combination of developer info, memory size, category, operating system compatibility, user ratings and reviews, and privacy policy. However, the product label information is asymmetrical and it varies with the developer. It is challenging to develop common standards explicit to users, as their needs are dynamic; moreover, the App undergoes a series of state changes during execution, according to the usage pattern. We propose a quantification label framework to assess the mobile/web App using a set of quantifiers, such as functionality, degree and domain of connectivity, battery consumption, and its vulnerability index for generating a set of standard labels. The corresponding labels in the label frame are: features, popularity, energy consumption and security. Further, we utilized principal component analysis (PCA), a statistical technique to assist the customers in selecting the prominent features from the dataset and to compare related apps, which analyzes the quantifiers in the given datasets and distinguish them according to the variances. The experimental results in quantifying a real time traffic data of an internet based App validated the proposed label by revealing the key features to the users; moreover, PCA carried out on four sets of traffic data enabled the selection of prominent data, and the results also revealed the possible embedding of the framework within an App for dynamic monitoring. [ABSTRACT FROM AUTHOR]

Abstract: The bio‐organisms are resilient in nature, where resilience is viewed as a sign of intelligence for understanding and reacting to the unforeseen changes in order to retain their normal functionalities. The resilient behavior within the livings is developed through an evolutionary cycle. Here, we want to provide an enterprise network (EN) with a resilient behavior by embedding intelligent formulations to guide EN through uncertain workload, which is not considered during early design stage. We formulated the resilient management problem within EN as an optimization problem, with an objective function is to maximize the resilience. We developed a bio‐inspired tool comprising of Genetic Algorithm (GA), Molecular Assembly (MA) and Ant Colony Optimization (ACO). Our experimental results on an EN of 100 nodes, with a heavy backbone traffic of 7.6 TB demonstrated that GA gains the maximum resilience of 50%, whereas MA gains 46%, ACO gains 31.54%, and the original EN had 20% resilience. However, with light workload, MA and GA performed closely with a maximum resilience of 64% and 62%, respectively, and ACO showed a moderate resilience with a maximum of 32%. [ABSTRACT FROM AUTHOR]

One of the core challenges in enterprise networks (EN) is how to select and manage the data storage system (DSS). In this paper, we have explored the storage architectures, such as a directattached storage and consolidated servers, and centralized storage architectures, such as a storage area network (SAN), network attached storage (NAS) and hybrid storage system (HSS) for EN. We have formulated the selection and management of DSS as an optimization problem, where our framework generates alterative architectures serving DSS while sustaining or enhancing EN performance. A cost model is proposed to select DSS in a cost-effective manner. The extensive simulations have been carried out utilizing the co-simulation environment, which facilitates a fine-grain outcome in performance measurement of EN and DSS using: average response time, packet loss rate, throughput, average server's utilization and service's QoS threshold. The simulation results of EN comprising of 1750 clients and 105 servers illustrate a trade-off of 77% increased in DSS's throughput utilizing HSS, 36% with SAN and 23% with NAS. However, a slight increase in response time of 2% is observed with SAN due to its complex network infrastructure in comparison with other centralized storage systems. [ABSTRACT FROM AUTHOR]