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In this paper, a novel interference free dual-hop device-to-device (D2D) aided cooperative relaying strategy (CRS) based on spatial modulation (SM) (termed D2D-CRS-SM) is proposed. In D2D-CRS-SM, two cellular users (e.g., a near user (NU) and a relay-aided far user (FU)) and a pair of D2D transmitter (D1)-receivers (D2) are served in two time-slots. Two different scenarios are investigated considering information reception criteria at the NU. Irrespective of the scenarios, the base station (BS) exploits SM to map information bits into two sets: modulation bits and antenna index, in phase-1. In the first scenario, the BS maps FU information as the modulation bits and NU information as antenna index, whereas modulation bits correspond to NU information and the antenna index carries FU’s information in scenario-2. The iterative-maximum ratio combining (i-MRC) technique is then used by NU and D1 to de-map their desired information bits. During phase-2, D1 also exploits SM to forward FU’s information received from BS and its own information bits to the D2D receiver D2. Then, FU and D2 retrieve their desired information by using i-MRC. Due to exploiting SM in both phases, interference free information reception is possible at each receiving node without allocating any fixed transmit power. The performance of D2D-CRS-SM is studied in terms of bit-error rate and spectral efficiency considering M-ary phase shift keying and quadrature amplitude modulation. Finally, the efficiency of D2D-CRS-SM is demonstrated via the Monte Carlo simulation.

With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.

In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim’s location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.

Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks’ challenges handled by SDN-based vehicular networks.

In the age of the Internet of Things, connected devices are changing the delivery system in the healthcare communication environment. With the integration of IoT in healthcare, there is a huge potential for improvement of the quality, safety, and efficiency of health care in addition to promising technological, economical, and social prospects. Nevertheless, this integration comes with security risks such as data breach that might be caused by credential-stealing malware. In addition, the patient valuable data can be disclosed when the perspective devices are compromised since they are connected to the internet. Hence, security has become an essential part of today’s computing world regarding the ubiquitous nature of the IoT entities in general and IoT-based healthcare in particular. In this paper, research on the algorithm for anonymizing sensitive information about health data set exchanged in the IoT environment using a wireless communication system has been presented. To preserve the security and privacy, during the data session from the users interacting online, the algorithm defines records that cannot be revealed by providing protection to user’s privacy. Moreover, the proposed algorithm includes a secure encryption process that enables health data anonymity. Furthermore, we have provided an analysis using mathematical functions to valid the algorithm’s anonymity function. The results show that the anonymization algorithm guarantees safety features for the considered IoT system applied in context of the healthcare communication systems.