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The paper aims to address wireless sensor network (WSN) security by proposing new cluster-based mobile key management scheme (CMKMS). The growing demands of WSNs in variety of real-time and mission-critical applications increase the challenges in terms of energy efficiency, security and mobility. The security is important to avoid malicious attacks and improve the energy efficiency, while mobility helps to improve the reachability of network. The CMKMS algorithm focuses on the management and maintenance of keys under cluster-based mobile WSN network.

The paper studied the related work in the area of key management and compared the different key management algorithms according to the technique used. The comparative study shows that the available key management algorithms are efficient in terms of security, but they are not scaleable enough to the changing conditions of network and do not work efficiently under node mobility. The research uses cluster-based approach for improving scalability, where cluster head (CH) acts as a key manager. The work developed by considering both static and mobile CH.

The CMKMS considers two phases, first for key maintenance which establishes the two private keys, home key for own cluster and foreign key when node moves from one cluster to another. The second phase maintains the keys when CH moves from one cluster to another. The proposed algorithm improves the efficiency of key management algorithm in terms of security, mobility, energy efficiency and scalability of network. The simulation of scheme in different realistic situation shows that proposed solution shows less computational overheads, energy consumption and delay as compared with state-of-art solution.

The research is validated using computer-based simulation, which limits the testing of research by considering external environmental conditions. Therefore, research can be further test using WSN test bed.

The research address the issues in key management, developed the scaleable, and node mobility supportable key management algorithms, for addressing the demands of growing WSN applications.

This paper accomplish the security of WSN using scaleable and mobility-supported key management algorithms.

This paper examines the design and evaluation of a TV on Demand (TVoD) system, consisting of a globally accessible storage architecture where all TV content broadcast over a period of time is made available for streaming. The proposed architecture consists of idle Internet Service Provider (ISP) servers that can be rented and released dynamically as the client load dictates. This paper examines issues of resource management and content placement within this Video Content Distribution Network (VCDN). The existing placement algorithm is computationally expensive and in some cases, infeasible to execute within any reasonable length of time. This work proposes a number of new placement heuristics each of which attempts intelligently to reduce the search space so that only the best proxies are considered for replica placement. An extensive evaluation of these placement algorithms is carried out to identify a good placement algorithm without being computationally expensive.

The purpose of this study is to contribute an actor-network-theory (ANT) perspective of how algorithms are used and whether this reflects positively on organisational practices and related perceptions of organisation performance, by both customers and employees. Furthermore, the organisation’s view of adopting algorithms to manage their performance is explored.

The current study is a study set in this virtual space, as it seeks to use data available online from different stakeholders of the case organisation. Online “virtual space” studies have become more common over the past few years. Primary and secondary data are shared online, for instance on relevant rating websites for customers (such as TrustPilot) and employees (such as Indeed), which serve as a reflection of the organisational performance and the communication of their values. This type of study has become more prevalent, either as a study undertaken online fully (Hine, 2007) or partially (for instance Baxter et al., 2019), which provides a different perspective to traditionally carried out face-to-face interactions.

The current study has contributed insights from a global case organisation in the logistics sector originating in the Middle East (ME). The case organisation clearly indicates that its algorithm, in line with particular presentations and discussions on online platforms, is focussed on analysing limited factors, which are prediction of accurate delivery timeslot, preferred and precise location for delivery and speed of delivery overall. This reflects an inherent bias of the delivery industry, which the case organisation operates in, and may focus attention on delivering on those objectives, with a detrimental effect on the organisation’s broader organisational mindset. This refers to long-term values and sustainability-related objectives.

Findings will reflect on whether algorithms can be used to align a multi-national organisation to achieve its desired performance and related desirable results. This will be one of the main contributions of the current study, which engages with a multi-national organisation in the logistics sector that is based in the ME region.

This work examines the intersection between traditional human resource management and the novel employment arrangements of the expanding gig economy. While there is a substantial multidisciplinary literature on the digital platform labor phenomenon, it has been largely centered on the experiences of gig workers. As digital labor platforms continue to grow and specialize, more managers, executives, and human resource practitioners will need to make decisions about whether and how to utilize gig workers. Here the authors explore and interrogate the unique features of human resource management (HRM) activities in the context of digital labor platforms. The authors discuss challenges and opportunities regarding (1) HRM in organizations that outsource labor needs to external labor platforms, (2) HRM functions within digital labor platform firms, and (3) HRM policies and practices for organizations that develop their own spin-off digital labor platform. To foster a more nuanced understanding of work in the gig economy, the authors identify common themes across these contexts, highlight knowledge gaps, offer recommendations for future research, and outline pathways for collecting empirical data on HRM in the gig economy.

The aim of this research is to propose a buffer sizing and buffer controlling algorithm (BSCA) as a heuristic algorithm for calculating project buffer and feeding buffers as well as dynamic controlling of buffer consumption in different phases of a wind power plant project in order to achieve a more realistic project duration.

The BSCA algorithm has two main phases of planning and buffer sizing and construction and buffer consumption. Project buffer and feeding buffers are determined in the planning and buffer sizing phase, and their consumption is controlled in the construction and buffer consumption phase. The heuristic algorithm was coded and run in MATLAB software. The sensitivity analysis was conducted to show the BSCA influence on project implementation. Then, to evaluate the BSCA algorithm, inputs from this project were run through several algorithms recently presented by researchers. Finally, the data of 20 projects previously accomplished by the company were applied to compare the proposed algorithm.

The results show that BSCA heuristic algorithm outperformed the other algorithms as it shortened the projects' durations. The average project completion time using the BSCA algorithm was reduced by about 15% compared to the previous average project completion time.

The proposed BSCA algorithm determines both the project buffer and feeding buffers and simultaneously controls their consumption in a dynamic way.

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.

In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.

The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.

The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

The assessment and management of prisoners on hunger strikes in a custodial setting is complex. There is limited clinical guidance available for psychiatrists to draw upon in such cases. The purpose of this paper is to develop a management algorithm through expert elicitation to inform the psychiatric care of prisoners on a hunger strike.

A Delphi method was used to elicit views from Irish forensic psychiatrists, a legal expert and an expert in ethics using a structured questionnaire. Themes were extracted from the results of the questionnaire to propose a management algorithm. A consensus was reached on management considerations.

Five consultant forensic psychiatrists, a legal expert and an expert on psychiatric ethics (n=7) consented to participation, with a subsequent response rate of 71.4 per cent. Consensus was achieved on a proposed management algorithm. Assessment for mental disorder, capacity to refuse food and motivation for food refusal are seen as key psychiatric tasks. The need to work closely with the prison general practitioner and the value of multidisciplinary working and legal advice are described. Relevant aspects of law included mental health, criminal law (insanity) and capacity legislation.

This study outlines a management algorithm for the psychiatric assessment and management of prisoners on a hunger strike, a subject about which there is limited guidance to date. Although written from an Irish perspective, this study outlines key considerations for psychiatrists in keeping with international guidance and therefore may be generalisable to other jurisdictions.

Electricity utilization at electricity peak hour may differ from every single administration region, for example, mechanical region, business territory and residential zone. This paper introduces a demand-side load management (DSM) strategy, which is one of the utilization of smart grid (SG) that is fit for controlling loads inside the residential working so that the client fulfillment is augmented at least expense.

In this paper, a heuristic algorithms-based energy management controller is intended for a residential region in a SG. Here, Antlion Optimization technique is used for DSM techniques such as load shifting, peak clipping, and valley filling in the residential sectors for 24 h with the help of stochastic function to determine the detection of random distribution of the load.

This proposed algorithm offered the greatest fulfillment and least expense caused by the consumers when compared to the traditional cost by taking the individual consumer preferences for the loads and the ideal time scheduling for the load, which is obtained from the rebuilding trap.

Simulation results demonstrate that the comparison of the cost incurred by the users obtained by the DSM techniques is satisfiable.

The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment.

Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management.

The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics.

This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed.

Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging paradigm of pervasive computing. The optimal management of diverse networking resources is a challenging problem. This paper aims to present a context‐aware policy mechanism with related end‐to‐end (E2E) evaluation algorithm for adaptive connectivity management in multi‐access wireless networks.

A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as for the maintenance of on‐going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network quality of service conditions. The decision of the best access networks to be used is made on the basis of an E2E evaluation process. The decision can be made in both master–slave and peer‐to‐peer modes, according to the decision matrixes generated in both ends. The paper focuses on the policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision‐making algorithm in the adaptiv management of heterogeneous networking resources.

The proposed policy mechanism is for the adaptive decision of connection selection in channel establishment and vertical handoff between heterogeneous access networks. A policy is represented as a four‐tuple, including the direction and the class of traffic, requirement expression, and concrete evaluation items. Three steps are involved in the evaluation process, namely policy traverse, decision matrix calculation, and decision‐making.

The policy mechanism can be easily extended to include adaptive selection of multiple user devices in addition to multiple connections.