TSLBS: A Time-Sensitive and Load Balanced Scheduling Approach to Wireless Sensor Actor Networks

Existing works on scheduling in Wireless Sensor Actor Networks (WSANs) are mostly concerned with energy savings and ignore time constraints and thus increase the make-span of the network. Moreover, these algorithms usually do not consider balance of workloads on the actor nodes and hence, sometimes some of the actors are busy when some others are idle. These problem causes the actors are not utilized properly and the actors’ lifetime is reduced. In this paper we take both time awareness and balance of workloads on the actor in WSANs into account and propose a convex optimization model (TAMMs) to minimize make-span. We also propose a protocol called LIBP to improve load balancing that allocates tasks to actors according to their measured capabilities in such a way to enhance balances of workloads on the actors. Finally, by combination of TAMMs and LIBP, a time-sensitive and load balanced scheduling approach (TSLBS) is proposed. TSLBS considers both local and global tasks and the distribution requirements of WSANs (i.e. WSANs with hybrid architecture). The results of simulations on typical scenarios shows that TSLBs is more efficient in terms of both the make-span and load balancing compared to stochastic task scheduling algorithm (STSA). We also show that TSLBs performs significantly better than STSA in terms of actor’s lifetime.     

Information technology and e-business marketing strategy

The advancement of information technology and its benefits for organizations have made the field of e-business marketing competitive and successful although there have been failures as well. Failure happens due to the use of constant strategies while the e-business environment is dynamic and in such a context, strategy formulation and implementation is a challenge for e-business organization. In this research a questionnaire was designed by reviewing the literature and interviewing experts. Analyzing the collected data, the e-business key strategy factors such as technology, information systems, risk management and compliance were extracted. In the next stage, these key factors were presented as the e-business marketing strategy model. The results suggested that dissimilar dimensions of e-business strategy factors may not be equally conducive to different marketing strategies. In particular, risk management has an insignificant relationship with any of the marketing strategies; while high level of compliance is positively associated only with a focus strategy. IT governance is positively associated with price leadership and differentiation strategies but linked negatively with focus strategy.     

Cyberloafing in the workplace: mitigation tactics and their impact on individuals’ behavior

With the Internet permeating every aspect of daily life, organizations of all types are increasingly concerned about the degree to which their employees are cyberloafing by shirking their work responsibilities to surf the Internet, check e-mail, or send text messages. Although technological interventions against cyberloafing have been shown to be effective, they might be perceived by employees as an invasion to their privacy, and are expected to have repercussions on employee behavior and loyalty. The main objectives of this study are to (1) examine how the introduction of such technological interventions might affect employees’ emotions and fairness perceptions, and (2) understand the effect of the interventions on behavioral outcomes, i.e., employees’ intentions to cyberloaf and their loyalty to the company. We developed a justice-based framework that we empirically test using a field experiment composed of field surveys complemented with hypothetical scenarios describing new organizational initiatives to curb employees’ cyberloafing. Our findings suggest that technological interventions, although associated with perceptions of unfairness, are effective at controlling cyberloafing, albeit at the expense of employee loyalty. On the other hand, contrary to prior findings, we find that fairness perceptions of technological interventions, although reinforcing employee loyalty, are ineffective at curbing cyberloafing. These findings are especially enlightening in that they contradict a common belief that perceived fairness encourages employees, as a sign of their appreciation for this fairness, to curb their misuse of IT. The findings also help managers fine-tune their cyberloafing policies to achieve a long-lasting remedy to their employees’ cyberloafing while maintaining a necessary level of employee loyalty.     

The role of dimensions of perceived risk in adoption of corporate internet banking by customers in Iran

During the recent years, one of the issues considered by the banks in the field of internet banking is the adoption of corporate internet banking (CIB) by corporate clients. The present article tried to examine the factors affecting adoption of CIB by corporate clients based on the perceived risk theory. The research hypotheses were tested using confirmatory factor analysis and the results showed that there was a significant relationship between all the risks and intent to adopt CIB by the corporate clients of the banks. The major factors decreasing the intent to adopt internet banking include performance, privacy, security, financial, time, and social risks respectively. Based on the findings of the present study and similar studies, a comparison was made between the importance of the dimensions of perceived risk in personal and CIB and suggestions were made for decreasing the effects of these significant risks for the corporate clients to adopt CIB.     

Predictor Feedback Stabilization of Stochastic Linear Delayed Systems with Both Additive and Multiplicative Noises

In this paper we investigate memory control of stochastic linear delayed systems with both additive and multiplicative noises. A new formula is first presented to obtain the prediction vector from the system dynamics and then it is used for feedback to reduce the input delay in the original delayed system. To ensure the stability of closed‐loop system, some matrix inequality conditions are given that in the case of feasibility provide the stabilizing gain of the predictor controller. The proposed method is applied to stochastic quarter‐car model of an active suspension system to show the effectiveness of the approach.     

Parameter identification of linear multi-delay systems via a hybrid of block-pulse functions and Taylor’s polynomials

In this paper, an efficient and effective procedure is successfully developed for parameter identification of linear time-invariant multi-delay systems. The proposed framework is based on a hybrid of block-pulse functions and Taylor’s polynomials. Two upper error bounds corresponding to hybrid functions are established. The excellent properties of these functions together with the associated operational matrices of integration and delay are utilised to transform the original problem into a system of linear algebraic equations. The least squares method is then implemented for estimation of the unknown parameters. Several numerical experiments are investigated to demonstrate the usefulness and effectiveness of the proposed procedure. Easy implementation, simple operations and accurate solutions are the main features of the suggested approximation scheme.     

Size-dependent hygro–thermo–electro–mechanical vibration analysis of functionally graded piezoelectric nanobeams resting on Winkler–Pasternak foundation undergoing preload and magnetic field

Microsystem Technologies - In this study, nonlocal beam theory is utilized for vibration analysis of hygro–electro–thermo–mechanical of functionally graded material (FGM) nanobeam...     

Balancing exploration and exploitation in memetic algorithms: A learning automata approach

One of the problems with traditional genetic algorithms (GAs) is premature convergence, which makes them incapable of finding good solutions to the problem. The memetic algorithm (MA) is an extension of the GA. It uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, we introduce a new algorithm based on learning automata (LAs) and an MA, and we refer to it as LA‐MA. This algorithm is composed of 2 parts: a genetic section and a memetic section. Evolution is performed in the genetic section, and local search is performed in the memetic section. The basic idea of LA‐MA is to use LAs during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. For this purpose, we present a criterion for the estimation of success of the local search at each generation. This criterion is used to calculate the probability of applying the local search to each chromosome. We show that in practice, the proposed probabilistic measure can be estimated reliably. On the basis of the relationship between the genetic section and the memetic section, 3 versions of LA‐MA are introduced. LLA‐MA behaves according to the Lamarckian learning model, BLA‐MA behaves according to the Baldwinian learning model, and HLA‐MA behaves according to both the Baldwinian and Lamarckian learning models. To evaluate the efficiency of these algorithms, they have been used to solve the graph isomorphism problem. The results of computer experimentations have shown that all the proposed algorithms outperform the existing algorithms in terms of quality of solution and rate of convergence.