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WSNs are now widely used for information gathering and transmission using WSN. Due to its low cost and simple communication, this type of network is widely used in many applications. Although hierarchical routing protocols may handle a variety of applications, choosing a Cluster Head (CH) and balancing network overload are difficult problems. This recommended strategy provides LEACH Protocol based on Novel Trust Management with Cryptographic RSA algorithm (NTM-LEACH-RSA) to extend the lifetime of network and to consume less energy. Here the proposed methodology includes two aspects for improving Security in WSN. Using the suggested NTM-LEACH technique, cluster formation and cluster head election are carried out in the first phase. Here, the threshold function value, the distance and density between nearby nodes, and the trust value are used to elect the cluster head. Based on the energy domain and the distance domain, the threshold function value is estimated in this case. The RSA cryptography technique is employed in the second phase to protect data transmission and guarantee data integrity. By using simulation tools, the proposed NTM-LEACH-RSA methodology's performance analysis is estimated. In comparison to other algorithms currently in use, it also offers higher performance results.

Abstract For hierarchical wireless sensor network (WSN), the clustered routing protocol can effectively deal with large-scale application requirements, thereby, how to efficiently elect the secure cluster heads becomes very critical. Unfortunately, many current studies only focus on improving security while neglecting energy efficiency and transmission performance. In this paper, a lightweight trust management scheme (LTMS) is proposed based on binomial distribution for defending against the internal attacks. Simultaneously, distance domain, energy domain, security domain and environment domain are considered and introduced to propose a multidimensional secure clustered routing (MSCR) scheme by using dynamic dimension weight in hierarchical WSNs. The simulation results show that LTMS can effectively prevent a malicious node from being elected as a cluster head, and MSCR can achieve a balance between security, transmission performance and energy efficiency under the requirements of environmental applications.

Medical sensor nodes are used in pervasive healthcare applications like remote patient monitoring, elderly care to collect patients vital signs for identifying medical emergency. These resource restricted sensor nodes are prone to various malicious attacks, data faults and data losses. Presence of faulty data, data loss in collected patient data may lead to incorrect analysis of patient condition, which decreases the reliability of pervasive healthcare system. The aim of this work is to alert the caregiver and raise the alarm only when the patient enters into medical emergency situation. The proposed scheme also reduces the false alarms and alerts caused by data fault and misbehaving sensor nodes. To achieve this, we introduce a context aware trust management scheme for data fault detection, data reconstruction and event detection in pervasive healthcare systems. It employs heuristic functions, data correlation and contextual information based algorithms to identify the data faults and events. It also reconstructs the data faults and data loss for identifying patient condition. Performance of this approach is evaluated with the help of real data samples collected by medical sensor network prototype of remote patient monitoring application. The experimental results show that the proposed trust scheme outperforms state-of-the-art techniques and achieves good detection accuracy in data fault detection and event detection. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, social network has become an important application in the network life. However, due to the rapid increase in the number of users, the influx of a variety of bad information is brought up as well as the existence of malicious users. Therefore, it is emergent to design a valid management scheme for user’s authentication to ensure the normal operation of social networks. Node trust evaluation is an effective method to deal with typical network attacks in wireless sensor networks. In order to solve the problem of quantification and uncertainty of trust, a novel trust management scheme based on Dempster-Shafer evidence theory for malicious nodes detection is proposed in this paper. Firstly, by taking into account spatiotemporal correlation of the data collected by sensor nodes in adjacent area, the trust degree can be estimated. Secondly, according to the D-S theory, the trust model is established to count the number of interactive behavior of trust, distrust or uncertainty, further to evaluate the direct trust value and indirect trust value. Then, a flexible synthesis method is adopted to calculate the overall trust to identify the malicious nodes. The simulation results show that the proposed scheme has obvious advantages over the traditional methods in the identification of malicious node and data fusion accuracy, and can obtain good scalability. [ABSTRACT FROM AUTHOR]

The Internet is populated with billions of electronic gadgets that have become a part of our fabric. Internet of Things is gaining popularity in at most all applications from smart buildings, intelligent transportation, healthcare and defence. IoT is a many vision one paradigm technology. One of the many visions of IoT is to make 'Things' social. Social Internet of Things (SIoT) is a young paradigm that integrates IoT and Social networking principles where Things are not only autonomous and smarter but also socially conscious. The authors propose a trust management scheme to facilitate trustworthy automatic decision making based on behaviour of objects. The authors use SIoT Trust metrics namely direct trust, centrality, community interest, Cooperativeness, Service Score to compute Trustworthiness among objects. The Expected trust and periodic trust updates evidently identify the presence of 'on off' selective forwarding attacks. The authors demonstrate the advantages of the proposed scheme with other existing trust management schemes in the literature. [ABSTRACT FROM AUTHOR]

This paper considers a vehicle edge computing system that uses nonOrthogonal multiple access technologies for task uploading and data packet downloading to solve the problems of insufficient computing power on the vehicle side, significant task processing delay, high energy consumption, and lack of wireless resources. The study looks into how to develop, compute offloading, and content caching queues under the vehicle edge computer network, with the help of NOMA, to decrease the total energy consumption at the MEC end. Regardless of the fact that there have been a lot of studies on computational offloading and content caching, the energy consumption of both is rarely investigated and dealt with simultaneously. The simulation findings demonstrate that, compared to typical orthogonal multiple access methods, the nonOrthogonal multiple access offload cache approach in the vehicle edge computing system may considerably lower the system energy consumption. [ABSTRACT FROM AUTHOR]

Information security is important for the Internet of Things (IoT), the security of front-end information is especially critical. With this consideration, the integrity and authenticity of sensed information directly impacts the results of back-end big data and cloud computing. The front end of the IoT faces many security threats. In these security threats, internal attacks cannot be defended by traditional security schemes, such as encryption/decryption, authentication, and so on. Our contribution in this paper is that a DirichletDistribution-based Trust Management Scheme (DDTMS) in IoT is proposed to defend against the internal attacks. The novelty of our scheme can be summed up in two aspects. The first aspect considers the actual physical channel to extend the node behaviors from success and failure to success, failure, and uncertainty, meanwhile, the corresponding behaviors are weighted by using s, wf, wu>, in order to limit the measurement of each behavior by custom. In the second aspect, we introduce a third-party recommendation to calculate the trust value more acurrately. The simulated results demonstrate that DDTMS is better than the other two reputation models (Beta distribution and Gaussian distribution),and can more accurately describe the reputation changes to detect the malicious node quickly. [ABSTRACT FROM AUTHOR]