Unmanned Aerial Vehicles (UAVs) play a significant role in wireless communication because of their high maneuverability and the advantage of forming Line-of-Sight (LoS) links with ground users. In this research, we investigate the trajectory design and resource scheduling problem of UAV relay communication optimization scenarios, considering heterogeneous tasks and QoS (Quality of Service) awareness. First, we model the optimization scenario and transform the problem-solving into a Markov Decision Process (MDP). Next, we propose R-DQN, a robust DQN (Deep Q-Network) algorithm tailored for heterogeneous tasks and QoS-aware UAV relay communication optimization scenarios. R-DQN introduces corresponding mechanisms in the training process, network structure, and sampling method to improve the effective exploration capability of DQN, making it more robust and suitable for the dynamic constrained optimization scenario tackled. Simulations and experimental results show that the proposed R-DQN algorithm has better convergence and global optimization abilities than other algorithms, such as Dueling DQN, Noisy DQN, and DDQN.

With the increasing integration of location-based services (LBSs) into various societal domains, location privacy preservation has emerged as a pivotal concern. In most continuous LBS privacy protection approaches, the user needs to send a query to an untrusted location service provider (LSP) to request the corresponding query results, and these results are discarded immediately after being used. This leads to similar queries in the future having to be sent to the LSP again, which increases the risk of privacy leakage when facing the LSP. To solve these issues, caching techniques are typically used to provide answers to users’ future queries. However, minimizing the interaction with the LSP is a challenge. Here, we propose a trajectory privacy-preserving scheme based on a transition matrix and caching (TMC) scheme for continuous LBS in the Industrial Internet of Things (IIoT). It employs multilevel caching to reduce the risk of exposing sensitive information to untrusted entities. We designed a transition matrix to predict the user’s next query location and simplify the computation complexity. We designed a cloaking set generation algorithm by considering transition entropy, location prediction, data freshness, and cache contribution degree to enhance user location privacy and improve the cache hit rate. The security analysis demonstrates how the TMC scheme resists attacks from both internal and external entities and ensures robustness in trajectory privacy. The experimental results show that the proposed TMC scheme can provide a higher level of privacy protection and lower system overhead compared to several previous schemes.

Existing schemes in the realm of mobile healthcare (also, e-Healthcare) based on cloud and IoMT (Internet of Medical Things) do not ensure end-to-end security and are not compliant with HIPAA (Health Insurance Portability and Accountability Act). It is also very difficult often for these schemes to obtain evidence from the cloud in case of security breaches. In addition to these issues, mobile healthcare applications are prone to various types of attacks and formal proof is often unavailable. In this work, we propose our community cloud framework in an IoMT setting that ensures end-to-end security and circumvents many of the existing negative aspects using the Trusted Platform Module (TPM). We provide necessary proofs using BAN logic and Scyther tool. Also, we show that the energy consumption and the costs of communication and computation for our proposed protocol are far less than that of the existing protocols. We have implemented our protocol using Kotlin language in Android Studio ensuring all the required security properties.

As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next generation high-speed delivery networks are essential. On the other hand, once the IoT data are delivered to the cloud, the massive IoT healthcare data are processed and analyzed employing the state-of-the-art analytics tools such as deep machine learning and so forth. However, given the explosion of big data (from various sources in addition to the healthcare data), the delivery network as well the cloud may experience network and computational congestion, respectively. This may impact the realtime analytics of the healthcare data, e.g., critical for in-house patients and senior citizens aging at home. To address this issue, the emerging IoT edge analytics concept can be regarded as a promising solution to process the big healthcare data close to the source. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of Data being generated. In this paper, we propose a deep learning based IoT edge analytics approach to support intelligent healthcare for residential users. The performance of the proposal is validated using computer-based simulation for online training of a real dataset. The reported results of our proposal exhibit encouraging performance in terms of low loss rate, high accuracy, and low execution time to support near real-time actionable decision making on the healthcare data.

The advent of Vehicular Ad Hoc Networks (VANETs) has made communication and information exchange between vehicles possible. This has allowed us to create mobile distributed applications running on VANET and has also brought forward several new challenges in distributed algorithm design. Among these algorithms, here we are interested in the snapshot algorithms, which are used to recover an application from failure. Snapshot algorithms for Mobile Ad hoc Networks (MANETs) have been studied for quite some time and VANET would form the biggest MANET ever implemented. Therefore, designing a reliable snapshot algorithm for VANET is still an open research issue. In this paper, we propose an efficient minimum-process non-intrusive snapshot algorithm for mobile distributed application running on VANET and this is the first work of this kind on this issue.

In this paper, we propose a new Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs) called MMSMAC (Multi-Mode Sensor MAC protocol), which can operate and switch among three modes: synchronous, asynchronous, and hybrid, according to the application requirements. In the synchronous mode, MMSMAC organizes the sensor nodes under even and odd clusters. Each sensor node has its own active/sleep and send/receive periods according to its cluster identifier, which ensures better load balancing among nodes. In the asynchronous mode, sensor nodes communicate freely without the utilization of even and odd clusters. In this mode, we also propose another mechanism to circumvent the hidden host problem. In the hybrid mode, the features of synchronous and asynchronous modes are combined. Simulation results and analysis show that each of the MMSMAC modes shows convincing performance gains and outperforms B-MAC and CSMA/TDMA protocols.

Abstract A heterogeneous distributed sensor network (HDSN) is a type of distributed sensor network where sensors with different deployment groups and different functional types participate at the same time. In other words, the sensors are divided into different deployment groups according to different types of data transmissions, but they cooperate with each other within and out of their respective groups. However, in traditional heterogeneous sensor networks, the classification is based on transmission range, energy level, computation ability, and sensing range. Taking this model into account, we propose a secure group association authentication mechanism using one‐way accumulator which ensures that: before collaborating for a particular task, any pair of nodes in the same deployment group can verify the legitimacy of group association of each other. Secure addition and deletion of sensors are also supported in this approach. In addition, a policy‐based sensor addition procedure is also suggested. For secure handling of disconnected nodes of a group, we use an efficient pairwise key derivation scheme to resist any adversary's attempt. Along with proposing our mechanism, we also discuss the characteristics of HDSN, its scopes, applicability, future, and challenges. The efficiency of our security management approach is also demonstrated with performance evaluation and analysis. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we present an enhanced heterogeneous tree based key management scheme for security of Wireless Sensor Networks (WSNs). Our scheme combines efficiently different key management techniques in each architecture level and also it has a dynamic key renewal process. Here, whenever a node is compromised, key renewal is done by one way hash functions and simple XOR operations. This combination gives the scheme good performance in terms of key storage overhead as well as resilience to the node capture attack. We compare our scheme with other heterogeneous schemes and show that our scheme gives better performance.

Heterogeneous applications could be assimilated within the same wireless sensor network with the aid of modern motes that have multiple sensor boards on a single radio board. Different types of data generated from such types of motes might have different transmission characteristics in terms of prio