An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

Effect of Mental Health Problems on Academic Performance among University Students in Pakistan

Scientific interest in student’s mental health experiences has been increasing in the last years due to their influence on students’learning processes,academic performance,and recently,the suicidal news of a student at a private university due to her mental health condition in Lahore Punjab,Pakistan,captured public attention.That incident also shifted researchers and scholars’attention on the much underexplored and tabooed sphere of the broader public health domain.Hence,the current study aimed to explore the relationship between mental health problems and Pakistani university students’academic performance.Participants were 540 senior semester students within the age range of 20–35 years taken from public and private institutes of Pakistan’s major cities,including Lahore,Islamabad,and Peshawar.Descriptive and inferential statistics was employed for data analysis.Pearson Product Moment Correlation,Hierarchical Regression analysis,and Independent sample t-test were used for data analysis in inferential statistics.The results of the study reveal that there is a strong positive association between mental health and improvement in academic performance.Mental health problems negatively affect the academic performance of university students.The findings of the current study were beneficial for shedding light on the ignorant area within the broader public health domain;the results are also helpful in raising awareness for not only students but also the parents and university administration to plan and design effective intervention strategies to provide proper mental health that resultantly promote academic excellence.

Power Control and Routing Selection for Throughput Maximization in Energy Harvesting Cognitive Radio Networks

This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes)harvest energy from the environment and use the energy exclusively for transmitting data.The SU nodes(i.e.,relay nodes)on the path,store and forward the received data to the destination node.We consider a real world scenario where the EH-SU node has only local causal knowledge,i.e.,at any time,each EH-SU node only has knowledge of its own EH process,channel state and currently received data.In order to study the power and routing issues,an optimization problem that maximizes path throughput considering quality of service(QoS)and available energy constraints is proposed.To solve this optimization problem,we propose a hybrid game theory routing and power control algorithm(HGRPC).The EH-SU nodes on the same path cooperate with each other,but EH-SU nodes on the different paths compete with each other.By selecting the best next hop node,we find the best strategy that can maximize throughput.In addition,we have established four steps to achieve routing,i.e.,route discovery,route selection,route reply,and route maintenance.Compared with the direct transmission,HGRPC has advantages in longer distances and higher hop counts.The algorithm generates more energy,reduces energy consumption and increases predictable residual energy.In particular,the time complexity of HGRPC is analyzed and its convergence is proved.In simulation experiments,the performance(i.e.,throughput and bit error rate(BER))of HGRPC is evaluated.Finally,experimental results show that HGRPC has higher throughput,longer network life,less latency,and lower energy consumption.

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources.In order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G networks.The optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the CRN-NOMA.Under the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission power.First,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization solution.Secondly,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to solve.At the same time,the convergence and time complexity of the algorithm are verified.Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput.In terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.