The Internet of Things (IoT) is transforming major industries, including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations, such as the amalgamation of software-defined networks (SDNs) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. The existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners' devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: 1) convolutional module (CNM); 2) randomization module; and 3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g., 91%-96%) with high model quality even under low privacy budgets (e.g., ε = 0.5).

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

E-commerce transactions rely on end-to-end protocols to provide security guarantees when messages are sent through intermediaries. If autonomous entities with different security requirements are to collaborate, protocols providing end-to-end security must be synthesized at runtime. However, such an approach is predicated on finding security primitives and ways of expressing interdependencies between semantics and security. The proposed approach defines fine-grained security properties by combining basic properties and enforcing them in a predefined order. The use of standard security properties allows autonomous entities to reason about security requirements. Furthermore, the novel propagation rules defined allow end-to-end security properties to be derived from entity security requirements. By using proven schemes designed to enforce such requirements, this approach allows end-to-end security protocols similar to the SET purchase protocol to be created at runtime.

Semantic models help in achieving semantic interoperability among sources of data and applications. The necessity to efficiently manage these types of objects has increased the number of specialized repositories, usually referred to as semantic databases. An increasing number of project initiatives have been recorded that choose to formalize application knowledge using ontologies and semantic data representation. Due to the various sensitivities of data, suitable access control mechanisms pertaining to the semantic repository should be put in place to ensure that only authorized users can obtain access to the information in its entirety. In fact, deciding what can be made available to the user without revealing confidential information is made even more difficult because the user may be able to apply logic and reasoning to infer confidential information from the knowledge being provided. In this paper, we design an authorization security model enforced on a semantic model's entities (concepts) and also propagate on their individuals in the OWL database through an inference policy engine. We provide TBox access control for the construction of a TBox family and propagate this based on the construction of concept taxonomies. We also provide ABox label-based access control for facts in the domain knowledge and report experiments to evaluate the effects of access control on reasoning and modularization.

The performance of TCP over multi-hop wireless networks has been studied in extensive simulation experiments in the past decade, but few theoretical models have yet been introduced to capture the TCP behaviour over contention based MAC protocols, like IEEE 802.11. In this paper, a mathematical model is presented for a single TCP flow over a multi-hop wireless network with arbitrary number of hops, denoted by H in this paper. A stochastic modelling technique is used to formulate the performance of the TCP flow, based on its interaction with the underlying wireless medium. This model is derived for linear topology with static nodes. A statistical formula for the ratio of concurrent transmissions to the total is also introduced for a TCP flow over H wireless hops. The proposed mathematical model has been validated with simulation results indicating that the average deviation is less than 5%.

Web Services technology provided several advantages over other technologies, however it still has serious limitations, including high latency and high protocol overhead. To improve performance, SOAP network traffic needs to be substantially reduced. This paper presents a novel approach, called similarity-based SOAP multicast protocol (SMP), to address the issue of latency. SMP reduces network traffic by aggregating syntactically similar SOAP messages to form a "compact SMP message";. The addresses of clients are encoded as strings in the SMP message header. The similarity of SOAP messages is measured in pairs and is based both on the message template and on the values of each XML tag in the messages. Each XML node in a SOAP message is indexed with an identifier and its position in the SOAP message. Only the indexed form of a SOAP message is sent to clients. Intermediary routers along the paths from server to clients parse the content of each SMP message passing through them and perform necessary operations to forward it to neighbouring routers. Experiments show that SMP can achieve up to 70% reduction in network traffic compared to traditional SOAP unicast.

Web services, which are built on SOAP as the transport protocol, have emerged in recent years as a promising technology to enable interoperability between distributed applications. As such, SOAP may be used to handle a large number of transactions; and consequently it is desirable to have SOAP perform efficiently in high traffic environments. This paper presents eSMP, an extension of our previous work on a similarity-based SOAP multicast protocol (SMP). eSMP offers improvements over SMP in terms of traffic size by using its own routing protocol instead of the conventional shortest path algorithm to route messages to paths that will minimize the number of bytes transmitted in the network. From extensive experiments, it is shown that eSMP achieves a minimum of 25 percent reduction in total network traffic than SMP with a trade-off of 10 percent increase in average response time. Compared to unicast, bandwidth consumptions can by reduced by up to 80 percent when using eSMP and 70 percent when using SMP. Therefore, eSMP is suitable for applications where bandwidth requirement is critical but not time.

Mobile clients often need to operate while disconnected from the network due to limited battery life and network coverage. Hoarding supports this by fetching frequently accessed data into clients' local caches prior to disconnection. Existing work on hoarding have focused on improving data accessibility for individual mobile clients. However, due to storage limitations, mobile clients may not be able to hoard every data object they need. This leads to cache misses and disruption to clients' operations. In this paper, a new concept called cooperative hoarding is introduced to reduce the risks of cache misses for mobile clients. Cooperative hoarding takes advantage of group mobility behaviour, combined with peer cooperation in ad-hoc mode, to improve hoard performance. Two cooperative hoarding approaches are proposed that take into account access frequency, connection probability, and cache size of mobile clients so that hoarding can be performed cooperatively. Simulation results show that the proposed methods significantly improve cache hit ratio and provides better support for disconnected operations compared to existing schemes.

In this paper, we introduce the concept of cooperative hoarding to reduce the risks of cache misses for mobile clients. Cooperative hoarding takes advantage of group mobility behaviour, combined with peer cooperation in ad-hoc mode, to improve hoard performance. Two cooperative hoarding approaches that take into account clients' access frequencies, connection probabilities and cache size when performing hoarding are proposed. Test results show that the proposed methods significantly improve cache hit ratio and reduce query costs compared to existing approaches.

Traditional cache replacement policies rely on the temporal locality of users' access pattern to improve cache performance. These policies, however, are not ideal in supporting mobile clients. As mobile clients can move freely from one location to another, their access pattern not only exhibits temporal locality, but also exhibits spatial locality. In order to ensure efficient cache utilisation, it is important to take into consideration the location and movement direction of mobile clients when performing cache replacement. In this paper. we propose a mobility-aware cache replacement policy, called MARS, suitable for wireless environments. MARS takes into account important factors (e.g. client access rate, access probability, update probability and client location) in order to improve the effectiveness of onboard caching for mobile clients. Test results show that MARS consistently outperforms existing cache replacement policies and significantly improves mobile clients' cache hit ratio.

Caching is a commonly used technique for reducing access latency and improving scalability. However, the static nature of existing network caching techniques makes them unsuitable for wireless environments. As mobile clients move from one location to another, the performance of these caches deteriorates. To combat this problem, object relocation strategies can be used, where objects are dynamically relocated to locations near the moving clients. Existing work on object relocation have focused on achieving relocation transparency. Little attention has been given to the network overhead introduced by the relocation. In this paper, we propose a low overhead object relocation strategy suitable for wireless environments. Object lists are passed between nodes prior to relocation to ensure only the nearest copy of each object is relocated. We have developed detailed analytical models of the proposed strategy and a number of other strategies to facilitate comparison. Analytical and simulation results show the proposed strategy effectively reduce the effect of mobility on the performance of network caches. The relocation overhead of the proposed method is also significantly lower compared to existing schemes.

Recent advances in wireless communication and global positioning technologies have led to increasing interest in location dependent information services. As mobile users move between locations utilising such services, their access patterns not only exhibit temporal locality, but also spatial locality. Traditional cache replacement policies were designed to deal with temporal locality, as a result, they are inefficient location dependent services. In this paper a mobility-aware algorithm called MARS+ is proposed to detect regular client movement patterns and provides information to improve cache performance. Test results show that MARS+ improves clients' cache hit ratio by more than 16% compared to existing policies.

Caching is one technique that reduces costs and improves performance in mobile environments. It also increases availability during temporary, involuntary disconnections. However, our focus is on voluntary, client initiated disconnections, where hoarding can be used to predict data requirements. Existing hoarding approaches ignore conflicts arising out of write sharing and are thus unable to deal with them. However, since conflicts are detrimental to bandwidth utilisation, for scenarios with high write sharing, hoarding techniques need to provide support for sharing in a manner that reduces or avoids conflicts. We propose a hoarding approach for disconnected write operations that focuses on reducing the likelihood of conflicts, arising from write sharing, in a highly concurrent environment. Data that clients might need when disconnected is predicted based on the notion of semantic similarity. To avoid/reduce conflicts, data are first clustered based on their update probabilities. The hoard tree is then created based on the clusters and semantic similarity between data. Simulations show an increase in the cache hit-rate along with an reduction in the total number of conflicts.

Object caching is often used to improve the performance of mobile applications, but the gain is often lessened by the additional load of maintaining consistency between an original object and its cached copy. This paper aims at reducing the consistency maintenance work and proposes a protocol that distinguishes between two classes of consistency (i.e. weak and strong) and treats them differently. Strong consistency is used for data that needs to be consistent all the time, whereas weak consistency is for cases when stale data can be tolerated or only specific updates are relevant to the application. Consistency is maintained by using strict and permissive read/write time locks that enable data sharing for a fixed time period and support concurrency control. A notification protocol for propagating updates to clients is also proposed. Performance tests have shown that switching from strong to weak consistency reduces the number of aborts due to conflicting operations by almost half even with high read/write sharing.